The moment we saw Sarah Spencer‘s knitted Stargazing tapestry, we knew we needed to know more. A couple of emails later, and here’s Sarah with a guest blog post telling you all you need to know about her hacking adventure with a 1980s knitting machine and a Raspberry Pi.
Printing a scarf on a Brother KM950i knitting machine from the 1980’s. To do this I have a Brother Motor arm to push the carriage back and forth and a homemade colour changer that automatically selects the colour on the left (the white and purple device with the LED).
Here’s Sarah…
Raspberry Pi: what’s there not to like? It’s powerful, compact, and oh so affordable! I used one as a portable media box attached to a pico projector for years. Setting one up as a media box is one of the most popular uses for them, but there’s so much more you can do.
Cue a 1980s Brother domestic knitting machine. Yep, you read that right. A knitting machine – to knit jumpers, hats, scarves, you name it. They don’t make domestic knitting machines any more, so a machine from the 1980s is about as modern as you can get. It comes with an onboard scanner to scan knitting patterns and a floppy drive port to back up your scans to an old floppy disk. Aah, the eighties – what a time to be alive!
Building a networked knitting machine
But this is an article about Raspberry Pi, right? So what does a 30-year-old knitting machine have to do with that? Well, I hacked my domestic knitting machine and turned it into a network printer with the help of a Raspberry Pi. By using a floppy drive emulator written in Python and a web interface, I can send an image to the Raspberry Pi over the network, preview it in a knitting grid, and tell it to send the knitting pattern to the knitting machine via the floppy drive port.
OctoKnit
I call this set-up OctoKnit in honour of a more famous and widely used tool, OctoPrint for 3D printers, another popular application for Raspberry Pi.
I’ve made the OctoKnit web interface open source. You can find it on GitHub.
This project has been in the works for several years, and there’s been a few modifications to the knitting machine over that time. With the addition of a motor arm and an automatic colour changer, my knitting is getting very close to being hands-free. Here’s a photo of the knitting machine today, although the Raspberry Pi is hiding behind the machine in this shot:
I’ve specialised in knitting multicolour work using a double-layered technique called double Jacquard, which requires two beds of needles. Hence the reason the machine has doubled in size from when I first started.
Knitting for Etsy
I made a thing that can make things, so I need to make something with it, right? Here are a few custom orders I’ve completed through my Etsy store:
Stargazing
However, none of my previous works quite compares to my latest piece, Stargazing: a knitted tapestry. Knitted in seven panels stitched together by hand, the pattern on the Raspberry Pi is 21 times bigger than the memory available on the vintage knitting machine, so it’s knitted in 21 separate but seamless file transfers. It took over 100 hours of work and weighs 15kg.
Stargazing is a celestial map of the night sky, featuring all 88 constellations across both Northern and Southern hemispheres. The line through the center is the Earth’s equator, projected out into space, with the sun, moon and planets of our solar system featured along it. The grey cloud is a representation of our galaxy, the Milky Way.
Happy 6pm, Fri 31st Aug 2018 The tapestry is installed and the planets in the sky have now aligned with those in the knitting
When I first picked up a Raspberry Pi and turned it over in my hand, marvelling at the computing power in such a small, affordable unit, I never imagined in my wildest dreams what I’d end up doing with it.
One of the most common enquiries I receive at Pi Towers is “How can I get my hands on a Raspberry Pi Oracle Weather Station?” Now the answer is: “Why not build your own version using our guide?”
Tadaaaa! The BYO weather station fully assembled.
Our Oracle Weather Station
In 2016 we sent out nearly 1000 Raspberry Pi Oracle Weather Station kits to schools from around the world who had applied to be part of our weather station programme. In the original kit was a special HAT that allows the Pi to collect weather data with a set of sensors.
The original Raspberry Pi Oracle Weather Station HAT
We designed the HAT to enable students to create their own weather stations and mount them at their schools. As part of the programme, we also provide an ever-growing range of supporting resources. We’ve seen Oracle Weather Stations in great locations with a huge differences in climate, and they’ve even recorded the effects of a solar eclipse.
Our new BYO weather station guide
We only had a single batch of HATs made, and unfortunately we’ve given nearly* all the Weather Station kits away. Not only are the kits really popular, we also receive lots of questions about how to add extra sensors or how to take more precise measurements of a particular weather phenomenon. So today, to satisfy your demand for a hackable weather station, we’re launching our Build your own weather station guide!
Fun with meteorological experiments!
Our guide suggests the use of many of the sensors from the Oracle Weather Station kit, so can build a station that’s as close as possible to the original. As you know, the Raspberry Pi is incredibly versatile, and we’ve made it easy to hack the design in case you want to use different sensors.
Many other tutorials for Pi-powered weather stations don’t explain how the various sensors work or how to store your data. Ours goes into more detail. It shows you how to put together a breadboard prototype, it describes how to write Python code to take readings in different ways, and it guides you through recording these readings in a database.
There’s also a section on how to make your station weatherproof. And in case you want to move past the breadboard stage, we also help you with that. The guide shows you how to solder together all the components, similar to the original Oracle Weather Station HAT.
Who should try this build
We think this is a great project to tackle at home, at a STEM club, Scout group, or CoderDojo, and we’re sure that many of you will be chomping at the bit to get started. Before you do, please note that we’ve designed the build to be as straight-forward as possible, but it’s still fairly advanced both in terms of electronics and programming. You should read through the whole guide before purchasing any components.
The sensors and components we’re suggesting balance cost, accuracy, and easy of use. Depending on what you want to use your station for, you may wish to use different components. Similarly, the final soldered design in the guide may not be the most elegant, but we think it is achievable for someone with modest soldering experience and basic equipment.
You can build a functioning weather station without soldering with our guide, but the build will be more durable if you do solder it. If you’ve never tried soldering before, that’s OK: we have a Getting started with soldering resource plus video tutorial that will walk you through how it works step by step.
For those of you who are more experienced makers, there are plenty of different ways to put the final build together. We always like to hear about alternative builds, so please post your designs in the Weather Station forum.
Our plans for the guide
Our next step is publishing supplementary guides for adding extra functionality to your weather station. We’d love to hear which enhancements you would most like to see! Our current ideas under development include adding a webcam, making a tweeting weather station, adding a light/UV meter, and incorporating a lightning sensor. Let us know which of these is your favourite, or suggest your own amazing ideas in the comments!
*We do have a very small number of kits reserved for interesting projects or locations: a particularly cool experiment, a novel idea for how the Oracle Weather Station could be used, or places with specific weather phenomena. If have such a project in mind, please send a brief outline to [email protected], and we’ll consider how we might be able to help you.
Last year, we released Amazon Connect, a cloud-based contact center service that enables any business to deliver better customer service at low cost. This service is built based on the same technology that empowers Amazon customer service associates. Using this system, associates have millions of conversations with customers when they inquire about their shipping or order information. Because we made it available as an AWS service, you can now enable your contact center agents to make or receive calls in a matter of minutes. You can do this without having to provision any kind of hardware. 2
There are several advantages of building your contact center in the AWS Cloud, as described in our documentation. In addition, customers can extend Amazon Connect capabilities by using AWS products and the breadth of AWS services. In this blog post, we focus on how to get analytics out of the rich set of data published by Amazon Connect. We make use of an Amazon Connect data stream and create an end-to-end workflow to offer an analytical solution that can be customized based on need.
Solution overview
The following diagram illustrates the solution.
In this solution, Amazon Connect exports its contact trace records (CTRs) using Amazon Kinesis. CTRs are data streams in JSON format, and each has information about individual contacts. For example, this information might include the start and end time of a call, which agent handled the call, which queue the user chose, queue wait times, number of holds, and so on. You can enable this feature by reviewing our documentation.
In this architecture, we use Kinesis Firehose to capture Amazon Connect CTRs as raw data in an Amazon S3 bucket. We don’t use the recent feature added by Kinesis Firehose to save the data in S3 as Apache Parquet format. We use AWS Glue functionality to automatically detect the schema on the fly from an Amazon Connect data stream.
The primary reason for this approach is that it allows us to use attributes and enables an Amazon Connect administrator to dynamically add more fields as needed. Also by converting data to parquet in batch (every couple of hours) compression can be higher. However, if your requirement is to ingest the data in Parquet format on realtime, we recoment using Kinesis Firehose recently launched feature. You can review this blog post for further information.
By default, Firehose puts these records in time-series format. To make it easy for AWS Glue crawlers to capture information from new records, we use AWS Lambda to move all new records to a single S3 prefix called flatfiles. Our Lambda function is configured using S3 event notification. To comply with AWS Glue and Athena best practices, the Lambda function also converts all column names to lowercase. Finally, we also use the Lambda function to start AWS Glue crawlers. AWS Glue crawlers identify the data schema and update the AWS Glue Data Catalog, which is used by extract, transform, load (ETL) jobs in AWS Glue in the latter half of the workflow.
You can see our approach in the Lambda code following.
from __future__ import print_function
import json
import urllib
import boto3
import os
import re
s3 = boto3.resource('s3')
client = boto3.client('s3')
def convertColumntoLowwerCaps(obj):
for key in obj.keys():
new_key = re.sub(r'[\W]+', '', key.lower())
v = obj[key]
if isinstance(v, dict):
if len(v) > 0:
convertColumntoLowwerCaps(v)
if new_key != key:
obj[new_key] = obj[key]
del obj[key]
return obj
def lambda_handler(event, context):
bucket = event['Records'][0]['s3']['bucket']['name']
key = urllib.unquote_plus(event['Records'][0]['s3']['object']['key'].encode('utf8'))
try:
client.download_file(bucket, key, '/tmp/file.json')
with open('/tmp/out.json', 'w') as output, open('/tmp/file.json', 'rb') as file:
i = 0
for line in file:
for object in line.replace("}{","}\n{").split("\n"):
record = json.loads(object,object_hook=convertColumntoLowwerCaps)
if i != 0:
output.write("\n")
output.write(json.dumps(record))
i += 1
newkey = 'flatfiles/' + key.replace("/", "")
client.upload_file('/tmp/out.json', bucket,newkey)
s3.Object(bucket,key).delete()
return "success"
except Exception as e:
print(e)
print('Error coping object {} from bucket {}'.format(key, bucket))
raise e
We trigger AWS Glue crawlers based on events because this approach lets us capture any new data frame that we want to be dynamic in nature. CTR attributes are designed to offer multiple custom options based on a particular call flow. Attributes are essentially key-value pairs in nested JSON format. With the help of event-based AWS Glue crawlers, you can easily identify newer attributes automatically.
We recommend setting up an S3 lifecycle policy on the flatfiles folder that keeps records only for 24 hours. Doing this optimizes AWS Glue ETL jobs to process a subset of files rather than the entire set of records.
After we have data in the flatfiles folder, we use AWS Glue to catalog the data and transform it into Parquet format inside a folder called parquet/ctr/. The AWS Glue job performs the ETL that transforms the data from JSON to Parquet format. We use AWS Glue crawlers to capture any new data frame inside the JSON code that we want to be dynamic in nature. What this means is that when you add new attributes to an Amazon Connect instance, the solution automatically recognizes them and incorporates them in the schema of the results.
After AWS Glue stores the results in Parquet format, you can perform analytics using Amazon Redshift Spectrum, Amazon Athena, or any third-party data warehouse platform. To keep this solution simple, we have used Amazon Athena for analytics. Amazon Athena allows us to query data without having to set up and manage any servers or data warehouse platforms. Additionally, we only pay for the queries that are executed.
Try it out!
You can get started with our sample AWS CloudFormation template. This template creates the components starting from the Kinesis stream and finishes up with S3 buckets, the AWS Glue job, and crawlers. To deploy the template, open the AWS Management Console by clicking the following link.
In the console, specify the following parameters:
BucketName: The name for the bucket to store all the solution files. This name must be unique; if it’s not, template creation fails.
etlJobSchedule: The schedule in cron format indicating how often the AWS Glue job runs. The default value is every hour.
KinesisStreamName: The name of the Kinesis stream to receive data from Amazon Connect. This name must be different from any other Kinesis stream created in your AWS account.
s3interval: The interval in seconds for Kinesis Firehose to save data inside the flatfiles folder on S3. The value must between 60 and 900 seconds.
sampledata: When this parameter is set to true, sample CTR records are used. Doing this lets you try this solution without setting up an Amazon Connect instance. All examples in this walkthrough use this sample data.
Select the “I acknowledge that AWS CloudFormation might create IAM resources.” check box, and then choose Create. After the template finishes creating resources, you can see the stream name on the stack Outputs tab.
If you haven’t created your Amazon Connect instance, you can do so by following the Getting Started Guide. When you are done creating, choose your Amazon Connect instance in the console, which takes you to instance settings. Choose Data streaming to enable streaming for CTR records. Here, you can choose the Kinesis stream (defined in the KinesisStreamName parameter) that was created by the CloudFormation template.
Now it’s time to generate the data by making or receiving calls by using Amazon Connect. You can go to Amazon Connect Cloud Control Panel (CCP) to make or receive calls using a software phone or desktop phone. After a few minutes, we should see data inside the flatfiles folder. To make it easier to try this solution, we provide sample data that you can enable by setting the sampledata parameter to true in your CloudFormation template.
You can navigate to the AWS Glue console by choosing Jobs on the left navigation pane of the console. We can select our job here. In my case, the job created by CloudFormation is called glueJob-i3TULzVtP1W0; yours should be similar. You run the job by choosing Run job for Action.
After that, we wait for the AWS Glue job to run and to finish successfully. We can track the status of the job by checking the History tab.
When the job finishes running, we can check the Database section. There should be a new table created called ctr in Parquet format.
To query the data with Athena, we can select the ctr table, and for Action choose View data.
Doing this takes us to the Athena console. If you run a query, Athena shows a preview of the data.
When we can query the data using Athena, we can visualize it using Amazon QuickSight. Before connecting Amazon QuickSight to Athena, we must make sure to grant Amazon QuickSight access to Athena and the associated S3 buckets in the account. For more information on doing this, see Managing Amazon QuickSight Permissions to AWS Resources in the Amazon QuickSight User Guide. We can then create a new data set in Amazon QuickSight based on the Athena table that was created.
After setting up permissions, we can create a new analysis in Amazon QuickSight by choosing New analysis.
Then we add a new data set.
We choose Athena as the source and give the data source a name (in this case, I named it connectctr).
Choose the name of the database and the table referencing the Parquet results.
Then choose Visualize.
After that, we should see the following screen.
Now we can create some visualizations. First, search for the agent.username column, and drag it to the AutoGraph section.
We can see the agents and the number of calls for each, so we can easily see which agents have taken the largest amount of calls. If we want to see from what queues the calls came for each agent, we can add the queue.arn column to the visual.
After following all these steps, you can use Amazon QuickSight to add different columns from the call records and perform different types of visualizations. You can build dashboards that continuously monitor your connect instance. You can share those dashboards with others in your organization who might need to see this data.
Conclusion
In this post, you see how you can use services like AWS Lambda, AWS Glue, and Amazon Athena to process Amazon Connect call records. The post also demonstrates how to use AWS Lambda to preprocess files in Amazon S3 and transform them into a format that recognized by AWS Glue crawlers. Finally, the post shows how to used Amazon QuickSight to perform visualizations.
You can use the provided template to analyze your own contact center instance. Or you can take the CloudFormation template and modify it to process other data streams that can be ingested using Amazon Kinesis or stored on Amazon S3.
Luis Caro is a Big Data Consultant for AWS Professional Services. He works with our customers to provide guidance and technical assistance on big data projects, helping them improving the value of their solutions when using AWS.
Peter Dalbhanjan is a Solutions Architect for AWS based in Herndon, VA. Peter has a keen interest in evangelizing AWS solutions and has written multiple blog posts that focus on simplifying complex use cases. At AWS, Peter helps with designing and architecting variety of customer workloads.
Today, at the AWS Summit in Tokyo we announced a number of updates and new features for Amazon SageMaker. Starting today, SageMaker is available in Asia Pacific (Tokyo)! SageMaker also now supports CloudFormation. A new machine learning framework, Chainer, is now available in the SageMaker Python SDK, in addition to MXNet and Tensorflow. Finally, support for running Chainer models on several devices was added to AWS Greengrass Machine Learning.
Amazon SageMaker Chainer Estimator
Chainer is a popular, flexible, and intuitive deep learning framework. Chainer networks work on a “Define-by-Run” scheme, where the network topology is defined dynamically via forward computation. This is in contrast to many other frameworks which work on a “Define-and-Run” scheme where the topology of the network is defined separately from the data. A lot of developers enjoy the Chainer scheme since it allows them to write their networks with native python constructs and tools.
Luckily, using Chainer with SageMaker is just as easy as using a TensorFlow or MXNet estimator. In fact, it might even be a bit easier since it’s likely you can take your existing scripts and use them to train on SageMaker with very few modifications. With TensorFlow or MXNet users have to implement a train function with a particular signature. With Chainer your scripts can be a little bit more portable as you can simply read from a few environment variables like SM_MODEL_DIR, SM_NUM_GPUS, and others. We can wrap our existing script in a if __name__ == '__main__': guard and invoke it locally or on sagemaker.
import argparse
import os
if __name__ =='__main__':
parser = argparse.ArgumentParser()
# hyperparameters sent by the client are passed as command-line arguments to the script.
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--batch-size', type=int, default=64)
parser.add_argument('--learning-rate', type=float, default=0.05)
# Data, model, and output directories
parser.add_argument('--output-data-dir', type=str, default=os.environ['SM_OUTPUT_DATA_DIR'])
parser.add_argument('--model-dir', type=str, default=os.environ['SM_MODEL_DIR'])
parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])
args, _ = parser.parse_known_args()
# ... load from args.train and args.test, train a model, write model to args.model_dir.
Then, we can run that script locally or use the SageMaker Python SDK to launch it on some GPU instances in SageMaker. The hyperparameters will get passed in to the script as CLI commands and the environment variables above will be autopopulated. When we call fit the input channels we pass will be populated in the SM_CHANNEL_* environment variables.
from sagemaker.chainer.estimator import Chainer
# Create my estimator
chainer_estimator = Chainer(
entry_point='example.py',
train_instance_count=1,
train_instance_type='ml.p3.2xlarge',
hyperparameters={'epochs': 10, 'batch-size': 64}
)
# Train my estimator
chainer_estimator.fit({'train': train_input, 'test': test_input})
# Deploy my estimator to a SageMaker Endpoint and get a Predictor
predictor = chainer_estimator.deploy(
instance_type="ml.m4.xlarge",
initial_instance_count=1
)
Now, instead of bringing your own docker container for training and hosting with Chainer, you can just maintain your script. You can see the full sagemaker-chainer-containers on github. One of my favorite features of the new container is built-in chainermn for easy multi-node distribution of your chainer training jobs.
There’s a lot more documentation and information available in both the README and the example notebooks.
AWS GreenGrass ML with Chainer
AWS GreenGrass ML now includes a pre-built Chainer package for all devices powered by Intel Atom, NVIDIA Jetson, TX2, and Raspberry Pi. So, now GreenGrass ML provides pre-built packages for TensorFlow, Apache MXNet, and Chainer! You can train your models on SageMaker then easily deploy it to any GreenGrass-enabled device using GreenGrass ML.
JAWS UG
I want to give a quick shout out to all of our wonderful and inspirational friends in the JAWS UG who attended the AWS Summit in Tokyo today. I’ve very much enjoyed seeing your pictures of the summit. Thanks for making Japan an amazing place for AWS developers! I can’t wait to visit again and meet with all of you.
Amazon QuickSight is a fully managed cloud business intelligence system that gives you Fast & Easy to Use Business Analytics for Big Data. QuickSight makes business analytics available to organizations of all shapes and sizes, with the ability to access data that is stored in your Amazon Redshift data warehouse, your Amazon Relational Database Service (RDS) relational databases, flat files in S3, and (via connectors) data stored in on-premises MySQL, PostgreSQL, and SQL Server databases. QuickSight scales to accommodate tens, hundreds, or thousands of users per organization.
Today we are launching a new, session-based pricing option for QuickSight, along with additional region support and other important new features. Let’s take a look at each one:
Pay-per-Session Pricing Our customers are making great use of QuickSight and take full advantage of the power it gives them to connect to data sources, create reports, and and explore visualizations.
However, not everyone in an organization needs or wants such powerful authoring capabilities. Having access to curated data in dashboards and being able to interact with the data by drilling down, filtering, or slicing-and-dicing is more than adequate for their needs. Subscribing them to a monthly or annual plan can be seen as an unwarranted expense, so a lot of such casual users end up not having access to interactive data or BI.
In order to allow customers to provide all of their users with interactive dashboards and reports, the Enterprise Edition of Amazon QuickSight now allows Reader access to dashboards on a Pay-per-Session basis. QuickSight users are now classified as Admins, Authors, or Readers, with distinct capabilities and prices:
Authors have access to the full power of QuickSight; they can establish database connections, upload new data, create ad hoc visualizations, and publish dashboards, all for $9 per month (Standard Edition) or $18 per month (Enterprise Edition).
Readers can view dashboards, slice and dice data using drill downs, filters and on-screen controls, and download data in CSV format, all within the secure QuickSight environment. Readers pay $0.30 for 30 minutes of access, with a monthly maximum of $5 per reader.
Admins have all authoring capabilities, and can manage users and purchase SPICE capacity in the account. The QuickSight admin now has the ability to set the desired option (Author or Reader) when they invite members of their organization to use QuickSight. They can extend Reader invites to their entire user base without incurring any up-front or monthly costs, paying only for the actual usage.
A New Region QuickSight is now available in the Asia Pacific (Tokyo) Region:
The UI is in English, with a localized version in the works.
Hourly Data Refresh Enterprise Edition SPICE data sets can now be set to refresh as frequently as every hour. In the past, each data set could be refreshed up to 5 times a day. To learn more, read Refreshing Imported Data.
Access to Data in Private VPCs This feature was launched in preview form late last year, and is now available in production form to users of the Enterprise Edition. As I noted at the time, you can use it to implement secure, private communication with data sources that do not have public connectivity, including on-premises data in Teradata or SQL Server, accessed over an AWS Direct Connect link. To learn more, read Working with AWS VPC.
Parameters with On-Screen Controls QuickSight dashboards can now include parameters that are set using on-screen dropdown, text box, numeric slider or date picker controls. The default value for each parameter can be set based on the user name (QuickSight calls this a dynamic default). You could, for example, set an appropriate default based on each user’s office location, department, or sales territory. Here’s an example:
URL Actions for Linked Dashboards You can now connect your QuickSight dashboards to external applications by defining URL actions on visuals. The actions can include parameters, and become available in the Details menu for the visual. URL actions are defined like this:
You can use this feature to link QuickSight dashboards to third party applications (e.g. Salesforce) or to your own internal applications. Read Custom URL Actions to learn how to use this feature.
Dashboard Sharing You can now share QuickSight dashboards across every user in an account.
Larger SPICE Tables The per-data set limit for SPICE tables has been raised from 10 GB to 25 GB.
Upgrade to Enterprise Edition The QuickSight administrator can now upgrade an account from Standard Edition to Enterprise Edition with a click. This enables provisioning of Readers with pay-per-session pricing, private VPC access, row-level security for dashboards and data sets, and hourly refresh of data sets. Enterprise Edition pricing applies after the upgrade.
Available Now Everything I listed above is available now and you can start using it today!
Hey folks, Rob here! It’s the last Thursday of the month, and that means it’s time for a brand-new The MagPi. Issue 70 is all about home automation using your favourite microcomputer, the Raspberry Pi.
Home automation in this month’s The MagPi!
Raspberry Pi home automation
We think home automation is an excellent use of the Raspberry Pi, hiding it around your house and letting it power your lights and doorbells and…fish tanks? We show you how to do all of that, and give you some excellent tips on how to add even more automation to your home in our ten-page cover feature.
Upcycle your life
Our other big feature this issue covers upcycling, the hot trend of taking old electronics and making them better than new with some custom code and a tactically placed Raspberry Pi. For this feature, we had a chat with Martin Mander, upcycler extraordinaire, to find out his top tips for hacking your old hardware.
Upcycling is a lot of fun
But wait, there’s more!
If for some reason you want even more content, you’re in luck! We have some fun tutorials for you to try, like creating a theremin and turning a Babbage into an IoT nanny cam. We also continue our quest to make a video game in C++. Our project showcase is headlined by the Teslonda on page 28, a Honda/Tesla car hybrid that is just wonderful.
We review PiBorg’s latest robot
All this comes with our definitive reviews and the community section where we celebrate you, our amazing community! You’re all good beans
An amazing, and practical, Raspberry Pi project
Get The MagPi 70
Issue 70 is available today from WHSmith, Tesco, Sainsbury’s, and Asda. If you live in the US, head over to your local Barnes & Noble or Micro Center in the next few days for a print copy. You can also get the new issue online from our store, or digitally via our Android and iOS apps. And don’t forget, there’s always the free PDF as well.
New subscription offer!
Want to support the Raspberry Pi Foundation and the magazine? We’ve launched a new way to subscribe to the print version of The MagPi: you can now take out a monthly £4 subscription to the magazine, effectively creating a rolling pre-order system that saves you money on each issue.
You can also take out a twelve-month print subscription and get a Pi Zero W plus case and adapter cables absolutely free! This offer does not currently have an end date.
Backblaze is hiring a Director of Sales. This is a critical role for Backblaze as we continue to grow the team. We need a strong leader who has experience in scaling a sales team and who has an excellent track record for exceeding goals by selling Software as a Service (SaaS) solutions. In addition, this leader will need to be highly motivated, as well as able to create and develop a highly-motivated, success oriented sales team that has fun and enjoys what they do.
The History of Backblaze from our CEO In 2007, after a friend’s computer crash caused her some suffering, we realized that with every photo, video, song, and document going digital, everyone would eventually lose all of their information. Five of us quit our jobs to start a company with the goal of making it easy for people to back up their data.
Like many startups, for a while we worked out of a co-founder’s one-bedroom apartment. Unlike most startups, we made an explicit agreement not to raise funding during the first year. We would then touch base every six months and decide whether to raise or not. We wanted to focus on building the company and the product, not on pitching and slide decks. And critically, we wanted to build a culture that understood money comes from customers, not the magical VC giving tree. Over the course of 5 years we built a profitable, multi-million dollar revenue business — and only then did we raise a VC round.
Fast forward 10 years later and our world looks quite different. You’ll have some fantastic assets to work with:
A brand millions recognize for openness, ease-of-use, and affordability.
A computer backup service that stores over 500 petabytes of data, has recovered over 30 billion files for hundreds of thousands of paying customers — most of whom self-identify as being the people that find and recommend technology products to their friends.
Our B2 service that provides the lowest cost cloud storage on the planet at 1/4th the price Amazon, Google or Microsoft charges. While being a newer product on the market, it already has over 100,000 IT and developers signed up as well as an ecosystem building up around it.
A growing, profitable and cash-flow positive company.
And last, but most definitely not least: a great sales team.
You might be saying, “sounds like you’ve got this under control — why do you need me?” Don’t be misled. We need you. Here’s why:
We have a great team, but we are in the process of expanding and we need to develop a structure that will easily scale and provide the most success to drive revenue.
We just launched our outbound sales efforts and we need someone to help develop that into a fully successful program that’s building a strong pipeline and closing business.
We need someone to work with the marketing department and figure out how to generate more inbound opportunities that the sales team can follow up on and close.
We need someone who will work closely in developing the skills of our current sales team and build a path for career growth and advancement.
We want someone to manage our Customer Success program.
So that’s a bit about us. What are we looking for in you?
Experience: As a sales leader, you will strategically build and drive the territory’s sales pipeline by assembling and leading a skilled team of sales professionals. This leader should be familiar with generating, developing and closing software subscription (SaaS) opportunities. We are looking for a self-starter who can manage a team and make an immediate impact of selling our Backup and Cloud Storage solutions. In this role, the sales leader will work closely with the VP of Sales, marketing staff, and service staff to develop and implement specific strategic plans to achieve and exceed revenue targets, including new business acquisition as well as build out our customer success program.
Leadership: We have an experienced team who’s brought us to where we are today. You need to have the people and management skills to get them excited about working with you. You need to be a strong leader and compassionate about developing and supporting your team.
Data driven and creative: The data has to show something makes sense before we scale it up. However, without creativity, it’s easy to say “the data shows it’s impossible” or to find a local maximum. Whether it’s deciding how to scale the team, figuring out what our outbound sales efforts should look like or putting a plan in place to develop the team for career growth, we’ve seen a bit of creativity get us places a few extra dollars couldn’t.
Jive with our culture: Strong leaders affect culture and the person we hire for this role may well shape, not only fit into, ours. But to shape the culture you have to be accepted by the organism, which means a certain set of shared values. We default to openness with our team, our customers, and everyone if possible. We love initiative — without arrogance or dictatorship. We work to create a place people enjoy showing up to work. That doesn’t mean ping pong tables and foosball (though we do try to have perks & fun), but it means people are friendly, non-political, working to build a good service but also a good place to work.
Do the work: Ideas and strategy are critical, but good execution makes them happen. We’re looking for someone who can help the team execute both from the perspective of being capable of guiding and organizing, but also someone who is hands-on themselves.
Additional Responsibilities needed for this role:
Recruit, coach, mentor, manage and lead a team of sales professionals to achieve yearly sales targets. This includes closing new business and expanding upon existing clientele.
Expand the customer success program to provide the best customer experience possible resulting in upsell opportunities and a high retention rate.
Develop effective sales strategies and deliver compelling product demonstrations and sales pitches.
Acquire and develop the appropriate sales tools to make the team efficient in their daily work flow.
Apply a thorough understanding of the marketplace, industry trends, funding developments, and products to all management activities and strategic sales decisions.
Ensure that sales department operations function smoothly, with the goal of facilitating sales and/or closings; operational responsibilities include accurate pipeline reporting and sales forecasts.
This position will report directly to the VP of Sales and will be staffed in our headquarters in San Mateo, CA.
Requirements:
7 – 10+ years of successful sales leadership experience as measured by sales performance against goals. Experience in developing skill sets and providing career growth and opportunities through advancement of team members.
Background in selling SaaS technologies with a strong track record of success.
Strong presentation and communication skills.
Must be able to travel occasionally nationwide.
BA/BS degree required
Think you want to join us on this adventure? Send an email to jobscontact@backblaze.com with the subject “Director of Sales.” (Recruiters and agencies, please don’t email us.) Include a resume and answer these two questions:
How would you approach evaluating the current sales team and what is your process for developing a growth strategy to scale the team?
What are the goals you would set for yourself in the 3 month and 1-year timeframes?
Thank you for taking the time to read this and I hope that this sounds like the opportunity for which you’ve been waiting.
The adoption of Apache Spark has increased significantly over the past few years, and running Spark-based application pipelines is the new normal. Spark jobs that are in an ETL (extract, transform, and load) pipeline have different requirements—you must handle dependencies in the jobs, maintain order during executions, and run multiple jobs in parallel. In most of these cases, you can use workflow scheduler tools like Apache Oozie, Apache Airflow, and even Cron to fulfill these requirements.
Apache Oozie is a widely used workflow scheduler system for Hadoop-based jobs. However, its limited UI capabilities, lack of integration with other services, and heavy XML dependency might not be suitable for some users. On the other hand, Apache Airflow comes with a lot of neat features, along with powerful UI and monitoring capabilities and integration with several AWS and third-party services. However, with Airflow, you do need to provision and manage the Airflow server. The Cron utility is a powerful job scheduler. But it doesn’t give you much visibility into the job details, and creating a workflow using Cron jobs can be challenging.
What if you have a simple use case, in which you want to run a few Spark jobs in a specific order, but you don’t want to spend time orchestrating those jobs or maintaining a separate application? You can do that today in a serverless fashion using AWS Step Functions. You can create the entire workflow in AWS Step Functions and interact with Spark on Amazon EMR through Apache Livy.
In this post, I walk you through a list of steps to orchestrate a serverless Spark-based ETL pipeline using AWS Step Functions and Apache Livy.
Input data
For the source data for this post, I use the New York City Taxi and Limousine Commission (TLC) trip record data. For a description of the data, see this detailed dictionary of the taxi data. In this example, we’ll work mainly with the following three columns for the Spark jobs.
Column name
Column description
RateCodeID
Represents the rate code in effect at the end of the trip (for example, 1 for standard rate, 2 for JFK airport, 3 for Newark airport, and so on).
FareAmount
Represents the time-and-distance fare calculated by the meter.
TripDistance
Represents the elapsed trip distance in miles reported by the taxi meter.
The trip data is in comma-separated values (CSV) format with the first row as a header. To shorten the Spark execution time, I trimmed the large input data to only 20,000 rows. During the deployment phase, the input file tripdata.csv is stored in Amazon S3 in the <<your-bucket>>/emr-step-functions/input/ folder.
The following image shows a sample of the trip data:
Solution overview
The next few sections describe how Spark jobs are created for this solution, how you can interact with Spark using Apache Livy, and how you can use AWS Step Functions to create orchestrations for these Spark applications.
At a high level, the solution includes the following steps:
Trigger the AWS Step Function state machine by passing the input file path.
The first stage in the state machine triggers an AWS Lambda
The Lambda function interacts with Apache Spark running on Amazon EMR using Apache Livy, and submits a Spark job.
The state machine waits a few seconds before checking the Spark job status.
Based on the job status, the state machine moves to the success or failure state.
Subsequent Spark jobs are submitted using the same approach.
The state machine waits a few seconds for the job to finish.
The job finishes, and the state machine updates with its final status.
Let’s take a look at the Spark application that is used for this solution.
Spark jobs
For this example, I built a Spark jar named spark-taxi.jar. It has two different Spark applications:
MilesPerRateCode – The first job that runs on the Amazon EMR cluster. This job reads the trip data from an input source and computes the total trip distance for each rate code. The output of this job consists of two columns and is stored in Apache Parquet format in the output path.
The following are the expected output columns:
rate_code – Represents the rate code for the trip.
total_distance – Represents the total trip distance for that rate code (for example, sum(trip_distance)).
RateCodeStatus – The second job that runs on the EMR cluster, but only if the first job finishes successfully. This job depends on two different input sets:
csv – The same trip data that is used for the first Spark job.
miles-per-rate – The output of the first job.
This job first reads the tripdata.csv file and aggregates the fare_amount by the rate_code. After this point, you have two different datasets, both aggregated by rate_code. Finally, the job uses the rate_code field to join two datasets and output the entire rate code status in a single CSV file.
The output columns are as follows:
rate_code_id – Represents the rate code type.
total_distance – Derived from first Spark job and represents the total trip distance.
total_fare_amount – A new field that is generated during the second Spark application, representing the total fare amount by the rate code type.
Note that in this case, you don’t need to run two different Spark jobs to generate that output. The goal of setting up the jobs in this way is just to create a dependency between the two jobs and use them within AWS Step Functions.
Both Spark applications take one input argument called rootPath. It’s the S3 location where the Spark job is stored along with input and output data. Here is a sample of the final output:
The next section discusses how you can use Apache Livy to interact with Spark applications that are running on Amazon EMR.
Using Apache Livy to interact with Apache Spark
Apache Livy provides a REST interface to interact with Spark running on an EMR cluster. Livy is included in Amazon EMR release version 5.9.0 and later. In this post, I use Livy to submit Spark jobs and retrieve job status. When Amazon EMR is launched with Livy installed, the EMR master node becomes the endpoint for Livy, and it starts listening on port 8998 by default. Livy provides APIs to interact with Spark.
Let’s look at a couple of examples how you can interact with Spark running on Amazon EMR using Livy.
To list active running jobs, you can execute the following from the EMR master node:
curl localhost:8998/sessions
If you want to do the same from a remote instance, just change localhost to the EMR hostname, as in the following (port 8998 must be open to that remote instance through the security group):
Through Spark submit, you can pass multiple arguments for the Spark job and Spark configuration settings. You can also do that using Livy, by passing the S3 path through the args parameter, as shown following:
For a detailed list of Livy APIs, see the Apache Livy REST API page. This post uses GET /batches and POST /batches.
In the next section, you create a state machine and orchestrate Spark applications using AWS Step Functions.
Using AWS Step Functions to create a Spark job workflow
AWS Step Functions automatically triggers and tracks each step and retries when it encounters errors. So your application executes in order and as expected every time. To create a Spark job workflow using AWS Step Functions, you first create a Lambda state machine using different types of states to create the entire workflow.
First, you use the Task state—a simple state in AWS Step Functions that performs a single unit of work. You also use the Wait state to delay the state machine from continuing for a specified time. Later, you use the Choice state to add branching logic to a state machine.
The following is a quick summary of how to use different states in the state machine to create the Spark ETL pipeline:
Task state – Invokes a Lambda function. The first Task state submits the Spark job on Amazon EMR, and the next Task state is used to retrieve the previous Spark job status.
Wait state – Pauses the state machine until a job completes execution.
Choice state – Each Spark job execution can return a failure, an error, or a success state So, in the state machine, you use the Choice state to create a rule that specifies the next action or step based on the success or failure of the previous step.
Here is one of my Task states, MilesPerRateCode, which simply submits a Spark job:
"MilesPerRate Job": {
"Type": "Task",
"Resource":"arn:aws:lambda:us-east-1:xxxxxx:function:blog-miles-per-rate-job-submit-function",
"ResultPath": "$.jobId",
"Next": "Wait for MilesPerRate job to complete"
}
This Task state configuration specifies the Lambda function to execute. Inside the Lambda function, it submits a Spark job through Livy using Livy’s POST API. Using ResultPath, it tells the state machine where to place the result of the executing task. As discussed in the previous section, Spark submit returns the session ID, which is captured with $.jobId and used in a later state.
The following code section shows the Lambda function, which is used to submit the MilesPerRateCode job. It uses the Python request library to submit a POST against the Livy endpoint hosted on Amazon EMR and passes the required parameters in JSON format through payload. It then parses the response, grabs id from the response, and returns it. The Next field tells the state machine which state to go to next.
Just like in the MilesPerRate job, another state submits the RateCodeStatus job, but it executes only when all previous jobs have completed successfully.
Here is the Task state in the state machine that checks the Spark job status:
Just like other states, the preceding Task executes a Lambda function, captures the result (represented by jobStatus), and passes it to the next state. The following is the Lambda function that checks the Spark job status based on a given session ID:
In the Choice state, it checks the Spark job status value, compares it with a predefined state status, and transitions the state based on the result. For example, if the status is success, move to the next state (RateCodeJobStatus job), and if it is dead, move to the MilesPerRate job failed state.
To set up this entire solution, you need to create a few AWS resources. To make it easier, I have created an AWS CloudFormation template. This template creates all the required AWS resources and configures all the resources that are needed to create a Spark-based ETL pipeline on AWS Step Functions.
This CloudFormation template requires you to pass the following four parameters during initiation.
Parameter
Description
ClusterSubnetID
The subnet where the Amazon EMR cluster is deployed and Lambda is configured to talk to this subnet.
KeyName
The name of the existing EC2 key pair to access the Amazon EMR cluster.
VPCID
The ID of the virtual private cloud (VPC) where the EMR cluster is deployed and Lambda is configured to talk to this VPC.
S3RootPath
The Amazon S3 path where all required files (input file, Spark job, and so on) are stored and the resulting data is written.
IMPORTANT: These templates are designed only to show how you can create a Spark-based ETL pipeline on AWS Step Functions using Apache Livy. They are not intended for production use without modification. And if you try this solution outside of the us-east-1 Region, download the necessary files from s3://aws-data-analytics-blog/emr-step-functions, upload the files to the buckets in your Region, edit the script as appropriate, and then run it.
To launch the CloudFormation stack, choose Launch Stack:
Launching this stack creates the following list of AWS resources.
Logical ID
Resource Type
Description
StepFunctionsStateExecutionRole
IAM role
IAM role to execute the state machine and have a trust relationship with the states service.
SparkETLStateMachine
AWS Step Functions state machine
State machine in AWS Step Functions for the Spark ETL workflow.
LambdaSecurityGroup
Amazon EC2 security group
Security group that is used for the Lambda function to call the Livy API.
RateCodeStatusJobSubmitFunction
AWS Lambda function
Lambda function to submit the RateCodeStatus job.
MilesPerRateJobSubmitFunction
AWS Lambda function
Lambda function to submit the MilesPerRate job.
SparkJobStatusFunction
AWS Lambda function
Lambda function to check the Spark job status.
LambdaStateMachineRole
IAM role
IAM role for all Lambda functions to use the lambda trust relationship.
EMRCluster
Amazon EMR cluster
EMR cluster where Livy is running and where the job is placed.
During the AWS CloudFormation deployment phase, it sets up S3 paths for input and output. Input files are stored in the <<s3-root-path>>/emr-step-functions/input/ path, whereas spark-taxi.jar is copied under <<s3-root-path>>/emr-step-functions/.
The following screenshot shows how the S3 paths are configured after deployment. In this example, I passed a bucket that I created in the AWS account s3://tm-app-demos for the S3 root path.
If the CloudFormation template completed successfully, you will see Spark-ETL-State-Machine in the AWS Step Functions dashboard, as follows:
Choose the Spark-ETL-State-Machine state machine to take a look at this implementation. The AWS CloudFormation template built the entire state machine along with its dependent Lambda functions, which are now ready to be executed.
On the dashboard, choose the newly created state machine, and then choose New execution to initiate the state machine. It asks you to pass input in JSON format. This input goes to the first state MilesPerRate Job, which eventually executes the Lambda function blog-miles-per-rate-job-submit-function.
Pass the S3 root path as input:
{
“rootPath”: “s3://tm-app-demos”
}
Then choose Start Execution:
The rootPath value is the same value that was passed when creating the CloudFormation stack. It can be an S3 bucket location or a bucket with prefixes, but it should be the same value that is used for AWS CloudFormation. This value tells the state machine where it can find the Spark jar and input file, and where it will write output files. After the state machine starts, each state/task is executed based on its definition in the state machine.
At a high level, the following represents the flow of events:
Execute the first Spark job, MilesPerRate.
The Spark job reads the input file from the location <<rootPath>>/emr-step-functions/input/tripdata.csv. If the job finishes successfully, it writes the output data to <<rootPath>>/emr-step-functions/miles-per-rate.
If the Spark job fails, it transitions to the error state MilesPerRate job failed, and the state machine stops. If the Spark job finishes successfully, it transitions to the RateCodeStatus Job state, and the second Spark job is executed.
If the second Spark job fails, it transitions to the error state RateCodeStatus job failed, and the state machine stops with the Failed status.
If this Spark job completes successfully, it writes the final output data to the <<rootPath>>/emr-step-functions/rate-code-status/ It also transitions the RateCodeStatus job finished state, and the state machine ends its execution with the Success status.
This following screenshot shows a successfully completed Spark ETL state machine:
The right side of the state machine diagram shows the details of individual states with their input and output.
When you execute the state machine for the second time, it fails because the S3 path already exists. The state machine turns red and stops at MilePerRate job failed. The following image represents that failed execution of the state machine:
You can also check your Spark application status and logs by going to the Amazon EMR console and viewing the Application history tab:
I hope this walkthrough paints a picture of how you can create a serverless solution for orchestrating Spark jobs on Amazon EMR using AWS Step Functions and Apache Livy. In the next section, I share some ideas for making this solution even more elegant.
Next steps
The goal of this post is to show a simple example that uses AWS Step Functions to create an orchestration for Spark-based jobs in a serverless fashion. To make this solution robust and production ready, you can explore the following options:
In this example, I manually initiated the state machine by passing the rootPath as input. You can instead trigger the state machine automatically. To run the ETL pipeline as soon as the files arrive in your S3 bucket, you can pass the new file path to the state machine. Because CloudWatch Events supports AWS Step Functions as a target, you can create a CloudWatch rule for an S3 event. You can then set AWS Step Functions as a target and pass the new file path to your state machine. You’re all set!
You can also improve this solution by adding an alerting mechanism in case of failures. To do this, create a Lambda function that sends an alert email and assigns that Lambda function to a Fail That way, when any part of your state fails, it triggers an email and notifies the user.
If you want to submit multiple Spark jobs in parallel, you can use the Parallel state type in AWS Step Functions. The Parallel state is used to create parallel branches of execution in your state machine.
With Lambda and AWS Step Functions, you can create a very robust serverless orchestration for your big data workload.
Cleaning up
When you’ve finished testing this solution, remember to clean up all those AWS resources that you created using AWS CloudFormation. Use the AWS CloudFormation console or AWS CLI to delete the stack named Blog-Spark-ETL-Step-Functions.
Summary
In this post, I showed you how to use AWS Step Functions to orchestrate your Spark jobs that are running on Amazon EMR. You used Apache Livy to submit jobs to Spark from a Lambda function and created a workflow for your Spark jobs, maintaining a specific order for job execution and triggering different AWS events based on your job’s outcome. Go ahead—give this solution a try, and share your experience with us!
Tanzir Musabbir is an EMR Specialist Solutions Architect with AWS. He is an early adopter of open source Big Data technologies. At AWS, he works with our customers to provide them architectural guidance for running analytics solutions on Amazon EMR, Amazon Athena & AWS Glue. Tanzir is a big Real Madrid fan and he loves to travel in his free time.
We’re usually averse to buzzwords at HackSpace magazine, but not this month: in issue 7, we’re taking a deep dive into the Internet of Things.
Internet of Things (IoT)
To many people, IoT is a shady term used by companies to sell you something you already own, but this time with WiFi; to us, it’s a way to make our builds smarter, more useful, and more connected. In HackSpace magazine #7, you can join us on a tour of the boards that power IoT projects, marvel at the ways in which other makers are using IoT, and get started with your first IoT project!
Awesome projects
DIY retro computing: this issue, we’re taking our collective hat off to Spencer Owen. He stuck his home-brew computer on Tindie thinking he might make a bit of beer money — now he’s paying the mortgage with his making skills and inviting others to build modules for his machine. And if that tickles your fancy, why not take a crack at our Z80 tutorial? Get out your breadboard, assemble your jumper wires, and prepare to build a real-life computer!
Shameless patriotism: combine Lego, Arduino, and the car of choice for 1960 gold bullion thieves, and you’ve got yourself a groovy weekend project. We proudly present to you one man’s epic quest to add LED lights (controllable via a smartphone!) to his daughter’s LEGO Mini Cooper.
Makerspaces
Patriotism intensifies: for the last 200-odd years, the Black Country has been a hotbed of making. Urban Hax, based in Walsall, is the latest makerspace to show off its riches in the coveted Space of the Month pages. Every space has its own way of doing things, but not every space has a portrait of Rob Halford on the wall. All hail!
Diversity: advice on diversity often boils down to ‘Be nice to people’, which might feel more vague than actionable. This is where we come in to help: it is truly worth making the effort to give people of all backgrounds access to your makerspace, so we take a look at why it’s nice to be nice, and at the ways in which one makerspace has put niceness into practice — with great results.
And there’s more!
We also show you how to easily calculate the size and radius of laser-cut gears, use a bank of LEDs to etch PCBs in your own mini factory, and use chemistry to mess with your lunch menu.
All this plus much, much more waits for you in HackSpace magazine issue 7!
Get your copy of HackSpace magazine
If you like the sound of that, you can find HackSpace magazine in WHSmith, Tesco, Sainsbury’s, and independent newsagents in the UK. If you live in the US, check out your local Barnes & Noble, Fry’s, or Micro Center next week. We’re also shipping to stores in Australia, Hong Kong, Canada, Singapore, Belgium, and Brazil, so be sure to ask your local newsagent whether they’ll be getting HackSpace magazine.
PyCon UK 2018 will take place on Saturday 15 September to Wednesday 19 September in the splendid Cardiff City Hall, just a few miles from the Sony Technology Centre where the vast majority of Raspberry Pis is made. We’re pleased to announce that we’re curating this year’s Education Summit at the conference, where we’ll offer opportunities for young people to learn programming skills, and for educators to undertake professional development!
PyCon UK 2018 is your chance to be welcomed into the wonderful Python community. At the Education Summit, we’ll put on a young coders’ day on the Saturday, and an educators’ day on the Sunday.
Saturday — young coders’ day
On Saturday we’ll be running a CoderDojo full of workshops on Raspberry Pi and micro:bits for young people aged 7 to 17. If they wish, participants will get to make a project and present it to the conference on the main stage, and everyone will be given a free micro:bit to take home!
PyCon UK has been bringing developers and educators together ever since it first started its education track in 2011. This year’s Sunday will be a day of professional development: we’ll give teachers, educators, parents, and coding club leaders the chance to learn from us and from each other to build their programming, computing, and digital making skills.
Professional development for educators
Educators get a special entrance rate for the conference, starting at £48 — get your tickets now. Financial assistance is also available.
Call for proposals
We invite you to send in your proposal for a talk and workshop at the Education Summit! We’re looking for:
25-minute talks for the educators’ day
50-minute workshops for either the young coders’ or the educators’ day
If you have something you’d like to share, such as a professional development session for educators, advice on best practice for teaching programming, a workshop for up-skilling in Python, or a fun physical computing activity for the CoderDojo, then we’d love to hear about it! Please submit your proposalby 15 June.
After the Education Summit, the conference will continue for two days of talks and a final day of development sprints. Feel free to submit your education-related talk to the main conference too if you want to share it with a wider audience! Check out the PyCon UK 2018 website for more information.
GDPR day, May 25, 2018, is nearly here. On that day, will your inbox explode with update notices, opt-in agreements, and offers from lawyers searching for GDPR violators? Perhaps all the companies on earth that are not GDPR ready will just dissolve into dust. More likely, there will be some changes, but business as usual will continue and we’ll all be more aware of data privacy. Let’s go with the last one.
What’s Different With GDPR at Backblaze
The biggest difference you’ll notice is a completely updated Privacy Policy. Last week we sent out a service email announcing the new Privacy Policy. Some people asked what was different. Basically everything. About 95% of the agreement was rewritten. In the agreement, we added in the appropriate provisions required by GDPR, and hopefully did a better job specifying the data we collect from you, why we collect it, and what we are going to do with it.
As a reminder, at Backblaze your data falls into two catagories. The first type of data is the data you store with us — stored data. These are the files and objects you upload and store, and as needed, restore. We do not share this data. We do not process this data, except as requested by you to store and restore the data. We do not analyze this data looking for keywords, tags, images, etc. No one outside of Backblaze has access to this data unless you explicitly shared the data by providing that person access to one or more files.
The second type of data is your account data. Some of your account data is considered personal data. This is the information we collect from you to provide our Personal Backup, Business Backup and B2 Cloud Storage services. Examples include your email address to provide access to your account, or the name of your computer so we can organize your files like they are arranged on your computer to make restoration easier. We have written a number of Help Articles covering the different ways this information is collected and processed. In addition, these help articles outline the various “rights” granted via GDPR. We will continue to add help articles over the coming weeks to assist in making it easy to work with us to understand and exercise your rights.
What’s New With GDPR at Backblaze
The most obvious addition is the Data Processing Addendum (DPA). This covers how we protect the data you store with us, i.e. stored data. As noted above, we don’t do anything with your data, except store it and keep it safe until you need it. Now we have a separate document saying that.
It is important to note the new Data Processing Addendum is now incorporated by reference into our Terms of Service, which everyone agrees to when they sign up for any of our services. Now all of our customers have a shiny new Data Processing Agreement to go along with the updated Privacy Policy. We promise they are not long or complicated, and we encourage you to read them. If you have any questions, stop by our GDPR help section on our website.
Patience, Please
Every company we have dealt with over the last few months is working hard to comply with GDPR. It has been a tough road whether you tried to do it yourself or like Backblaze, hired an EU-based law firm for advice. Over the coming weeks and months as you reach out to discover and assert your rights, please have a little patience. We are all going through a steep learning curve as GDPR gets put into practice. Along the way there are certain to be some growing pains — give us a chance, we all want to get it right.
Regardless, at Backblaze we’ve been diligently protecting our customers’ data for over 11 years and nothing that will happen on May 25th will change that.
Today we’re launching a new partnership between the Scouts and the Raspberry Pi Foundation that will help tens of thousands of young people learn crucial digital skills for life. In this blog post, I want to explain what we’ve got planned, why it matters, and how you can get involved.
This is personal
First, let me tell you why this partnership matters to me. As a child growing up in North Wales in the 1980s, Scouting changed my life. My time with 2nd Rhyl provided me with countless opportunities to grow and develop new skills. It taught me about teamwork and community in ways that continue to shape my decisions today.
As my own kids (now seven and ten) have joined Scouting, I’ve seen the same opportunities opening up for them, and like so many parents, I’ve come back to the movement as a volunteer to support their local section. So this is deeply personal for me, and the same is true for many of my colleagues at the Raspberry Pi Foundation who in different ways have been part of the Scouting movement.
That shouldn’t come as a surprise. Scouting and Raspberry Pi share many of the same values. We are both community-led movements that aim to help young people develop the skills they need for life. We are both powered by an amazing army of volunteers who give their time to support that mission. We both care about inclusiveness, and pride ourselves on combining fun with learning by doing.
Raspberry Pi
Raspberry Pi started life in 2008 as a response to the problem that too many young people were growing up without the skills to create with technology. Our goal is that everyone should be able to harness the power of computing and digital technologies, for work, to solve problems that matter to them, and to express themselves creatively.
In 2012 we launched our first product, the world’s first $35 computer. Just six years on, we have sold over 20 million Raspberry Pi computers and helped kickstart a global movement for digital skills.
The Raspberry Pi Foundation now runs the world’s largest network of volunteer-led computing clubs (Code Clubs and CoderDojos), and creates free educational resources that are used by millions of young people all over the world to learn how to create with digital technologies. And lots of what we are able to achieve is because of partnerships with fantastic organisations that share our goals. For example, through our partnership with the European Space Agency, thousands of young people have written code that has run on two Raspberry Pi computers that Tim Peake took to the International Space Station as part of his Mission Principia.
Digital makers
Today we’re launching the new Digital Maker Staged Activity Badge to help tens of thousands of young people learn how to create with technology through Scouting. Over the past few months, we’ve been working with the Scouts all over the UK to develop and test the new badge requirements, along with guidance, project ideas, and resources that really make them work for Scouting. We know that we need to get two things right: relevance and accessibility.
Relevance is all about making sure that the activities and resources we provide are a really good fit for Scouting and Scouting’s mission to equip young people with skills for life. From the digital compass to nature cameras and the reinvented wide game, we’ve had a lot of fun thinking about ways we can bring to life the crucial role that digital technologies can play in the outdoors and adventure.
We are beyond excited to be launching a new partnership with the Raspberry Pi Foundation, which will help tens of thousands of young people learn digital skills for life.
We also know that there are great opportunities for Scouts to use digital technologies to solve social problems in their communities, reflecting the movement’s commitment to social action. Today we’re launching the first set of project ideas and resources, with many more to follow over the coming weeks and months.
Accessibility is about providing every Scout leader with the confidence, support, and kit to enable them to offer the Digital Maker Staged Activity Badge to their young people. A lot of work and care has gone into designing activities that require very little equipment: for example, activities at Stages 1 and 2 can be completed with a laptop without access to the internet. For the activities that do require kit, we will be working with Scout Stores and districts to make low-cost kit available to buy or loan.
We’re producing accessible instructions, worksheets, and videos to help leaders run sessions with confidence, and we’ll also be planning training for leaders. We will work with our network of Code Clubs and CoderDojos to connect them with local sections to organise joint activities, bringing both kit and expertise along with them.
Get involved
Today’s launch is just the start. We’ll be developing our partnership over the next few years, and we can’t wait for you to join us in getting more young people making things with technology.
Take a look at the brand-new Raspberry Pi resources designed especially for Scouts, to get young people making and creating right away.
Earlier this year on 3 and 4 March, communities around the world held Raspberry Jam events to celebrate Raspberry Pi’s sixth birthday. We sent out special birthday kits to participating Jams — it was amazing to know the kits would end up in the hands of people in parts of the world very far from Raspberry Pi HQ in Cambridge, UK.
The Raspberry Jam Camer team: Damien Doumer, Eyong Etta, Loïc Dessap and Lionel Sichom, aka Lionel Tellem
Preparing for the #PiParty
One birthday kit went to Yaoundé, the capital of Cameroon. There, a team of four students in their twenties — Lionel Sichom (aka Lionel Tellem), Eyong Etta, Loïc Dessap, and Damien Doumer — were organising Yaoundé’s first Jam, called Raspberry Jam Camer, as part of the Raspberry Jam Big Birthday Weekend. The team knew one another through their shared interests and skills in electronics, robotics, and programming. Damien explains in his blog post about the Jam that they planned ahead for several activities for the Jam based on their own projects, so they could be confident of having a few things that would definitely be successful for attendees to do and see.
Show-and-tell at Raspberry Jam Cameroon
Loïc presented a Raspberry Pi–based, Android app–controlled robot arm that he had built, and Lionel coded a small video game using Scratch on Raspberry Pi while the audience watched. Damien demonstrated the possibilities of Windows 10 IoT Core on Raspberry Pi, showing how to install it, how to use it remotely, and what you can do with it, including building a simple application.
Loïc showcases the prototype robot arm he built
There was lots more too, with others discussing their own Pi projects and talking about the possibilities Raspberry Pi offers, including a Pi-controlled drone and car. Cake was a prevailing theme of the Raspberry Jam Big Birthday Weekend around the world, and Raspberry Jam Camer made sure they didn’t miss out.
Yay, birthday cake!!
A big success
Most visitors to the Jam were secondary school students, while others were university students and graduates. The majority were unfamiliar with Raspberry Pi, but all wanted to learn about Raspberry Pi and what they could do with it. Damien comments that the fact most people were new to Raspberry Pi made the event more interactive rather than creating any challenges, because the visitors were all interested in finding out about the little computer. The Jam was an all-round success, and the team was pleased with how it went:
What I liked the most was that we sensitized several people about the Raspberry Pi and what one can be capable of with such a small but powerful device. — Damien Doumer
The Jam team rounded off the event by announcing that this was the start of a Raspberry Pi community in Yaoundé. They hope that they and others will be able to organise more Jams and similar events in the area to spread the word about what people can do with Raspberry Pi, and to help them realise their ideas.
Raspberry Jam Camer gets the thumbs-up
The Raspberry Pi community in Cameroon
In a French-language interview about their Jam, the team behind Raspberry Jam Camer said they’d like programming to become the third official language of Cameroon, after French and English; their aim is to to popularise programming and digital making across Cameroonian society. Neither of these fields is very familiar to most people in Cameroon, but both are very well aligned with the country’s ambitions for development. The team is conscious of the difficulties around the emergence of information and communication technologies in the Cameroonian context; in response, they are seizing the opportunities Raspberry Pi offers to give children and young people access to modern and constantly evolving technology at low cost.
Thanks to Lionel, Eyong, Damien, and Loïc, and to everyone who helped put on a Jam for the Big Birthday Weekend! Remember, anyone can start a Jam at any time — and we provide plenty of resources to get you started. Check out the Guidebook, the Jam branding pack, our specially-made Jam activities online (in multiplelanguages), printable worksheets, and more.
Naturebytes are making their weatherproof Wildlife Cam Case available as a standalone product for the first time, a welcome addition to the Raspberry Pi ecosystem that should take some of the hassle out of your outdoor builds.
Weatherproofing digital making projects
People often use Raspberry Pis and Camera Modules for outdoorprojects, but weatherproofing your set-up can be tricky. You need to keep water — and tiny creatures — out, but you might well need access for wires and cables, whether for power or sensors; if you’re using a camera, it’ll need something clear and cleanable in front of the lens. You can use sealant, but if you need to adjust anything that you’ve applied it to, you’ll have to remove it and redo it. While we’ve seen a few reasonable options available to buy, the choice has never been what you’d call extensive.
The Wildlife Cam Case is ideal for nature camera projects, of course, but it’ll also be useful for anyone who wants to take their Pi outdoors. It has weatherproof lenses that are transparent to visible and IR light, for all your nature observation projects. Its opening is hinged to allow easy access to your hardware, and the case has waterproof access for cables. Inside, there’s a mount for fixing any model of Raspberry Pi and camera, as well as many other components. On top of all that, the case comes with a sturdy nylon strap to make it easy to attach it to a post or a tree.
Order yours now!
At the moment, Naturebytes are producing a limited run of the cases. The first batch of 50 are due to be dispatched next week to arrive just in time for the Bank Holiday weekend in the UK, so get them while they’re hot. It’s the perfect thing for recording a timelapse of exactly how quickly the slugs obliterate your vegetable seedlings, and of lots more heartening things that must surely happen in gardens other than mine.
“Security is hard” is a tautology, especially in the fast-moving world of container orchestration. We have previously covered various aspects of Linux container security through, for example, the Clear Containers implementation or the broader question of Kubernetes and security, but those are mostly concerned with container isolation; they do not address the question of trusting a container’s contents. What is a container running? Who built it and when? Even assuming we have good programmers and solid isolation layers, propagating that good code around a Kubernetes cluster and making strong assertions on the integrity of that supply chain is far from trivial. The 2018 KubeCon + CloudNativeCon Europe event featured some projects that could eventually solve that problem.
Thanks to Susan Ferrell, Senior Technical Writer, for a great blog post on how to use CodeCommit branch-level permissions. —-
AWS CodeCommit users have been asking for a way to restrict commits to some repository branches to just a few people. In this blog post, we’re going to show you how to do that by creating and applying a conditional policy, an AWS Identity and Access Management (IAM) policy that contains a context key.
Why would I do this?
When you create a branch in an AWS CodeCommit repository, the branch is available, by default, to all repository users. Here are some scenarios in which refining access might help you:
You maintain a branch in a repository for production-ready code, and you don’t want to allow changes to this branch except from a select group of people.
You want to limit the number of people who can make changes to the default branch in a repository.
You want to ensure that pull requests cannot be merged to a branch except by an approved group of developers.
We’ll show you how to create a policy in IAM that prevents users from pushing commits to and merging pull requests to a branch named master. You’ll attach that policy to one group or role in IAM, and then test how users in that group are affected when that policy is applied. We’ll explain how it works, so you can create custom policies for your repositories.
What you need to get started
You’ll need to sign in to AWS with sufficient permissions to:
Create and apply policies in IAM.
Create groups in IAM.
Add users to those groups.
Apply policies to those groups.
You can use existing IAM groups, but because you’re going to be changing permissions, you might want to first test this out on groups and users you’ve created specifically for this purpose.
You’ll need a repository in AWS CodeCommit with at least two branches: master and test-branch. For information about how to create repositories, see Create a Repository. For information about how to create branches, see Create a Branch. In this blog post, we’ve named the repository MyDemoRepo. You can use an existing repository with branches of another name, if you prefer.
Let’s get started!
Create two groups in IAM
We’re going to set up two groups in IAM: Developers and Senior_Developers. To start, both groups will have the same managed policy, AWSCodeCommitPowerUsers, applied. Users in each group will have exactly the same permissions to perform actions in IAM.
Figure 1: Two example groups in IAM, with distinct users but the same managed policy applied to each group
In the navigation pane, choose Groups, and then choose Create New Group.
In the Group Name box, type Developers, and then choose Next Step.
In the list of policies, select the check box for AWSCodeCommitPowerUsers, then choose Next Step.
Choose Create Group.
Now, follow these steps to create the Senior_Developers group and attach the AWSCodeCommitPowerUsers managed policy. You now have two empty groups with the same policy attached.
Create users in IAM
Next, add at least one unique user to each group. You can use existing IAM users, but because you’ll be affecting their access to AWS CodeCommit, you might want to create two users just for testing purposes. Let’s go ahead and create Arnav and Mary.
In the navigation pane, choose Users, and then choose Add user.
For the new user, type Arnav_Desai.
Choose Add another user, and then type Mary_Major.
Select the type of access (programmatic access, access to the AWS Management Console, or both). In this blog post, we’ll be testing everything from the console, but if you want to test AWS CodeCommit using the AWS CLI, make sure you include programmatic access and console access.
For Console password type, choose Custom password. Each user is assigned the password that you type in the box. Write these down so you don’t forget them. You’ll need to sign in to the console using each of these accounts.
Choose Next: Permissions.
On the Set permissions page, choose Add user to group. Add Arnav to the Developers group. Add Mary to the Senior_Developers group.
Choose Next: Review to see all of the choices you made up to this point. When you are ready to proceed, choose Create user.
Sign in as Arnav, and then follow these steps to go to the master branch and add a file. Then sign in as Mary and follow the same steps.
On the Dashboard page, from the list of repositories, choose MyDemoRepo.
In the Code view, choose the branch named master.
Choose Add file, and then choose Create file. Type some text or code in the editor.
Provide information to other users about who added this file to the repository and why.
In Author name, type the name of the user (Arnav or Mary).
In Email address, type an email address so that other repository users can contact you about this change.
In Commit message, type a brief description to help you remember why you added this file or any other details you might find helpful.
Type a name for the file.
Choose Commit file.
Now follow the same steps to add a file in a different branch. (In our example repository, that’s the branch named test-branch.) You should be able to add a file to both branches regardless of whether you’re signed in as Arnav or Mary.
Let’s change that.
Create a conditional policy in IAM
You’re going to create a policy in IAM that will deny API actions if certain conditions are met. We want to prevent users with this policy applied from updating a branch named master, but we don’t want to prevent them from viewing the branch, cloning the repository, or creating pull requests that will merge to that branch. For this reason, we want to pick and choose our APIs carefully. Looking at the Permissions Reference, the logical permissions for this are:
GitPush
PutFile
MergePullRequestByFastForward
Now’s the time to think about what else you might want this policy to do. For example, because we don’t want users with this policy to make changes to this branch, we probably don’t want them to be able to delete it either, right? So let’s add one more permission:
DeleteBranch
The branch in which we want to deny these actions is master. The repository in which the branch resides is MyDemoRepo. We’re going to need more than just the repository name, though. We need the repository ARN. Fortunately, that’s easy to find. Just go to the AWS CodeCommit console, choose the repository, and choose Settings. The repository ARN is displayed on the General tab.
Now we’re ready to create a policy. 1. Open the IAM console at https://console.aws.amazon.com/iam/. Make sure you’re signed in with the account that has sufficient permissions to create policies, and not as Arnav or Mary. 2. In the navigation pane, choose Policies, and then choose Create policy. 3. Choose JSON, and then paste in the following:
You’ll notice a few things here. First, change the repository ARN to the ARN for your repository and include the repository name. Second, if you want to restrict access to a branch with a name different from our example, master, change that reference too.
Now let’s talk about this policy and what it does. You might be wondering why we’re using a Git reference (refs/heads) value instead of just the branch name. The answer lies in how Git references things, and how AWS CodeCommit, as a Git-based repository service, implements its APIs. A branch in Git is a simple pointer (reference) to the SHA-1 value of the head commit for that branch.
You might also be wondering about the second part of the condition, the nullification language. This is necessary because of the way git push and git-receive-pack work. Without going into too many technical details, when you attempt to push a change from a local repo to AWS CodeCommit, an initial reference call is made to AWS CodeCommit without any branch information. AWS CodeCommit evaluates that initial call to ensure that:
a) You’re authorized to make calls.
b) A repository exists with the name specified in the initial call. If you left that null out of the policy, users with that policy would be unable to complete any pushes from their local repos to the AWS CodeCommit remote repository at all, regardless of which branch they were trying to push their commits to.
Could you write a policy in such a way that the null is not required? Of course. IAM policy language is flexible. There’s an example of how to do this in the AWS CodeCommit User Guide, if you’re curious. But for the purposes of this blog post, let’s continue with this policy as written.
So what have we essentially said in this policy? We’ve asked IAM to deny the relevant CodeCommit permissions if the request is made to the resource MyDemoRepo and it meets the following condition: the reference is to refs/heads/master. Otherwise, the deny does not apply.
I’m sure you’re wondering if this policy has to be constrained to a specific repository resource like MyDemoRepo. After all, it would be awfully convenient if a single policy could apply to all branches in any repository in an AWS account, particularly since the default branch in any repository is initially the master branch. Good news! Simply replace the ARN with an *, and your policy will affect ALL branches named master in every AWS CodeCommit repository in your AWS account. Make sure that this is really what you want, though. We suggest you start by limiting the scope to just one repository, and then changing things when you’ve tested it and are happy with how it works.
When you’re sure you’ve modified the policy for your environment, choose Review policy to validate it. Give this policy a name, such as DenyChangesToMaster, provide a description of its purpose, and then choose Create policy.
Now that you have a policy, it’s time to apply and test it.
Apply the policy to a group
In theory, you could apply the policy you just created directly to any IAM user, but that really doesn’t scale well. You should apply this policy to a group, if you use IAM groups to manage users, or to a role, if your users assume a role when interacting with AWS resources.
In the IAM console, choose Groups, and then choose Developers.
On the Permissions tab, choose Attach Policy.
Choose DenyChangesToMaster, and then choose Attach policy.
Your groups now have a critical difference: users in the Developers group have an additional policy applied that restricts their actions in the master branch. In other words, Mary can continue to add files, push commits, and merge pull requests in the master branch, but Arnav cannot.
Figure 2: Two example groups in IAM, one with an additional policy applied that will prevent users in this group from making changes to the master branch
Test it out. Sign in as Arnav, and do the following:
On the Dashboard page, from the list of repositories, choose MyDemoRepo.
In the Code view, choose the branch named master.
Choose Add file, and then choose Create file, just as you did before. Provide some text, and then add the file name and your user information.
Choose Commit file.
This time you’ll see an error after choosing Commit file. It’s not a pretty message, but at the very end, you’ll see a telling phrase: “explicit deny”. That’s the policy in action. You, as Arnav, are explicitly denied PutFile, which prevents you from adding a file to the master branch. You’ll see similar results if you try other actions denied by that policy, such as deleting the master branch.
Stay signed in as Arnav, but this time add a file to test-branch. You should be able to add a file without seeing any errors. You can create a branch based on the master branch, add a file to it, and create a pull request that will merge to the master branch, all just as before. However, you cannot perform denied actions on that master branch.
Sign out as Arnav and sign in as Mary. You’ll see that as that IAM user, you can add and edit files in the master branch, merge pull requests to it, and even, although we don’t recommend this, delete it.
Conclusion
You can use conditional statements in policies in IAM to refine how users interact with your AWS CodeCommit repositories. This blog post showed how to use such a policy to prevent users from making changes to a branch named master. There are many other options. We hope this blog post will encourage you to experiment with AWS CodeCommit, IAM policies, and permissions. If you have any questions or suggestions, we’d love to hear from you.
Researchers havedemonstrated the ability to send inaudible commands to voice assistants like Alexa, Siri, and Google Assistant.
Over the last two years, researchers in China and the United States have begun demonstrating that they can send hidden commands that are undetectable to the human ear to Apple’s Siri, Amazon’s Alexa and Google’s Assistant. Inside university labs, the researchers have been able to secretly activate the artificial intelligence systems on smartphones and smart speakers, making them dial phone numbers or open websites. In the wrong hands, the technology could be used to unlock doors, wire money or buy stuff online – simply with music playing over the radio.
A group of students from University of California, Berkeley, and Georgetown University showed in 2016 that they could hide commands in white noise played over loudspeakers and through YouTube videos to get smart devices to turn on airplane mode or open a website.
This month, some of those Berkeley researchers published a research paper that went further, saying they could embed commands directly into recordings of music or spoken text. So while a human listener hears someone talking or an orchestra playing, Amazon’s Echo speaker might hear an instruction to add something to your shopping list.
Earlier this spring, an excited group of STEM educators came together to participate in the first ever Raspberry Pi and Arduino workshop in Puerto Rico.
Their three-day digital making adventure was led by MakerTechPR’s José Rullán and Raspberry Pi Certified Educator Alex Martínez. They ran the event as part of the Robot Makers challenge organized by Yees! and sponsored by Puerto Rico’s Department of Economic Development and Trade to promote entrepreneurial skills within Puerto Rico’s education system.
Over 30 educators attended the workshop, which covered the use of the Raspberry Pi 3 as a computer and digital making resource. The educators received a kit consisting of a Raspberry Pi 3 with an Explorer HAT Pro and an Arduino Uno. At the end of the workshop, the educators were able to keep the kit as a demonstration unit for their classrooms. They were enthusiastic to learn new concepts and immerse themselves in the world of physical computing.
In their first session, the educators were introduced to the Raspberry Pi as an affordable technology for robotic clubs. In their second session, they explored physical computing and the coding languages needed to control the Explorer HAT Pro. They started off coding with Scratch, with which some educators had experience, and ended with controlling the GPIO pins with Python. In the final session, they learned how to develop applications using the powerful combination of Arduino and Raspberry Pi for robotics projects. This gave them a better understanding of how they could engage their students in physical computing.
“The Raspberry Pi ecosystem is the perfect solution in the classroom because to us it is very resourceful and accessible.” – Alex Martínez
Computer science and robotics courses are important for many schools and teachers in Puerto Rico. The simple idea of programming a microcontroller from a $35 computer increases the chances of more students having access to more technology to create things.
Puerto Rico’s education system has faced enormous challenges after Hurricane Maria, including economic collapse and the government’s closure of many schools due to the exodus of families from the island. By attending training like this workshop, educators in Puerto Rico are becoming more experienced in fields like robotics in particular, which are key for 21st-century skills and learning. This, in turn, can lead to more educational opportunities, and hopefully the reopening of more schools on the island.
“We find it imperative that our children be taught STEM disciplines and skills. Our goal is to continue this work of spreading digital making and computer science using the Raspberry Pi around Puerto Rico. We want our children to have the best education possible.” – Alex Martínez
After attending Picademy in 2016, Alex has integrated the Raspberry Pi Foundation’s online resources into his classroom. He has also taught small workshops around the island and in the local Puerto Rican makerspace community. José is an electrical engineer, entrepreneur, educator and hobbyist who enjoys learning to use technology and sharing his knowledge through projects and challenges.
Spencer Ackerman has this interesting story about a guy assigned to crack down on unauthorized White House leaks. It’s necessarily light on technical details, so I thought I’d write up some guesses, either as a guide for future reporters asking questions, or for people who want to better know the risks when leak information.
It should come as no surprise that your work email and phone are already monitored. They can get every email you’ve sent or received, even if you’ve deleted it. They can get every text message you’ve sent or received, the metadata of every phone call sent or received, and so forth.
To a lesser extent, this also applies to your well-known personal phone and email accounts. Law enforcement can get the metadata (which includes text messages) for these things without a warrant. In the above story, the person doing the investigation wasn’t law enforcement, but I’m not sure that’s a significant barrier if they can pass things onto the Secret Service or something.
The danger here isn’t that you used these things to leak, it’s that you’ve used these things to converse with the reporter before you made the decision to leak. That’s what happened in the Reality Winner case: she communicated with The Intercept before she allegedly leaked a printed document to them via postal mail. While it wasn’t conclusive enough to convict her, the innocent emails certainly put the investigators on her trail.
The path to leaking often starts this way: innocent actions before the decision to leak was made that will come back to haunt the person afterwards. That includes emails. That also includes Google searches. That includes websites you visit (like this one). I’m not sure how to solve this, except that if you’ve been in contact with The Intercept, and then you decide to leak, send it to anybody but The Intercept.
By the way, the other thing that caught Reality Winner is the records they had of her accessing files and printing them on a printer. Depending where you work, they may have a record of every file you’ve accessed, every intranet page you visited. Because of the way printers put secret dots on documents, investigators know precisely which printer and time the document leaked to The Intercept was printed.
Photographs suffer the same problem: your camera and phone tag the photographs with GPS coordinates and time the photograph was taken, as well as information about the camera. This accidentally exposed John McAfee’s hiding location when Vice took pictures of him a few years ago. Some people leak by taking pictures of the screen — use a camera without GPS for this (meaning, a really old camera you bought from a pawnshop).
These examples should impress upon you the dangers of not understanding technology. As soon as you do something to evade surveillance you know about, you may get caught by surveillance you don’t know about.
If you nonetheless want to continue forward, the next step may be to get a “burner phone”. You can get an adequate Android “prepaid” phone for cash at the local Walmart, electronics store, or phone store.
There’s some problems with such phones, though. They can often be tracked back to the store that sold them, and the store will have security cameras that record you making the purchase. License plate readers and GPS tracking on your existing phone may also place you at that Walmart.
I don’t know how to resolve these problems. Perhaps the best is grow a beard and on the last day of your vacation, color your hair, take a long bike/metro ride (without your existing phone) to a store many miles away and pick up a phone, then shave and change your color back again. I don’t know — there’s a good chance any lame attempt you or I might think of has already been experienced by law enforcement, so they are likely ahead of you. Maybe ask your local drug dealer where they get their burner phones, and if they can sell you one. Of course, that just means when they get caught for drug dealing, they can reduce their sentence by giving up the middle class person who bought a phone from them.
Lastly, they may age out old security videos, so simply waiting six months before using the phone might work. That means prepaying for an entire year.
Note that I’m not going to link to examples of cheap burner phones on this page. Web browsers will sometimes prefetch some information from links in a webpage, so simply including links in this page can condemn you as having interest in burner phones. You are already in enough trouble for having visited this web page.
Burner phones have GPS. Newer the technology, like the latest Android LTE phones, have pretty accurate GPS that the police can query (without a warrant). If you take the phone home and turn it on, they’ll then be able to trace back the phone to your home. Carrying the phone around with you has the same problem, with the phone’s location correlating with your existing phone (which presumably you also carry) or credit card receipts. Rumors are that Petraeus was partly brought down by tracking locations where he used his credit card, namely, matching the hotel he was in with Internet address information.
Older phones that support 3G or even 2G have poorer GPS capabilities. They’ll still located you to the nearest cell tower, but not as accurately to your exact location.
A better strategy than a burner phone would be a burner laptop computer used with WiFi. You can get a cheap one for $200 at Amazon.com. My favorite are the 11 inch ones with a full sized keyboard and Windows 10. Better yet, get an older laptop for cash from a pawn shop.
You can install chat apps on this like “Signal Desktop”, “Wire Desktop”, or “WhatsApp” that will allow you to securely communicate. Or use “Discord”, which isn’t really encrypted, but it’s popular among gamers so therefore less likely to stand out. You can sit in a bar with free WiFi and a USB headset and talk to reporters without having a phone. If the reporter you want to leak to doesn’t have those apps (either on their own laptop or phone) then you don’t want to talk to them.
Needless to say, don’t cross the streams. Don’t log onto your normal accounts like Facebook. If you create fake Facebook accounts, don’t follow the same things. Better yet, configure your browser to discard all information (especially “cookies”) every time you log off, so you can’t be tracked. Install ad blockers, or use the “Brave” web browser, to remove even more trackers. A common trick among hackers is to change the “theme” to a red background, as a constant subliminal reminder that you using your dangerous computer, and never to do anything that identifies the real you.
Put tape over the camera. I’m not sure it’s a really big danger, but put tape over the camera. If they infect you enough to get your picture, they’ve also infected you enough to record any audio on your computer. Remember that proper encryption is end-to-end (they can’t eavesdrop in transit), but if they hack the ends (your laptop, or the reporter’s) they can still record the audio.
Note that when your burner laptop is in “sleep” mode, it can still be talking to the local wifi. Before taking it home, make sure it’s off. Go into the settings and configure it so that when the lid is closed, the computer is turned completely off.
It goes without saying: don’t use that burner laptop from home. Luckily, free wifi is everyone, so the local cafe, bar, or library can be used.
The next step is to also use a VPN or Tor to mask your Internet address. If there’s an active investigation into the reporter, they’ll get the metadata, the Internet address of the bar/cafe you are coming from. A good VPN provider or especially Tor will stop this. Remember that these providers increase latency, making phone calls a bit harder, but they are a lot safer.
Remember that Ross Ulbricht (owner of dark website market Silk Road) was caught in a library. They’d traced back his Internet address and grabbed his laptop out of his hands. Having it turn off (off off, not sleep off) when the lid is closed is one way to reduce this risk. Configuring your web browser to flush all cookies and passwords on restart is another. If they catch you in mid conversation with your secret contact, though, they’ll at least be able to hear your side of the conversation, and know who you are talking to.
The best measure, though it takes some learning, is “Tails live”. It’s a Linux distribution preconfigured with Tor and various secure chat apps that’ll boot from the USB or SD card. When you turn off the computer, nothing will be saved, so there will be no evidence saved to the disk for investigators to retrieve later.
While we are talking about Tor, it should be noted that many news organizations (NYTimes, Washington Post, The Intercept, etc.) support “SecureDrop” accessed only through Tor for receiving anonymous tips. Burner laptops you use from bars from Tails is the likely your most secure way of doing things.
Summary
The point of this post was not to provide a howto guide, but to discuss many of the technological issues involved. In a story about White House people investigating leaks, I’d like to see something in this technological direction. I’d like to know exactly how they were investigating leaks. Certainly, they were investigating all work computers, accounts, and phones. Where they also able to get to non-work computers, accounts, phones? Did they have law enforcement powers? What could they do about burner phones and laptops?
In any case, if you do want a howto guide, the discussion above should put some fear into you how easily you can inadvertently make a mistake.
If your day has been a little fraught so far, watch this video. It opens with a tableau of methodically laid-out components and then shows them soldered, screwed, and slotted neatly into place. Everything fits perfectly; nothing needs percussive adjustment. Then it shows us glimpses of an AR future just like the one promised in the less dystopian comics and TV programmes of my 1980s childhood. It is all very soothing, and exactly what I needed.
Transform any surface into mixed-reality using Raspberry Pi, a laser projector, and Android Things. Android Experiments – http://experiments.withgoogle.com/android/lantern Lantern project site – http://nordprojects.co/lantern check below to make your own ↓↓↓ Get the code – https://github.com/nordprojects/lantern Build the lamp – https://www.hackster.io/nord-projects/lantern-9f0c28
Creating augmented reality with projection
We’ve seen plenty of Raspberry Pi IoT builds that are smart devices for the home; they add computing power to things like lights, door locks, or toasters to make these objects interact with humans and with their environment in new ways. Nord Projects‘ Lantern takes a different approach. In their words, it:
imagines a future where projections are used to present ambient information, and relevant UI within everyday objects. Point it at a clock to show your appointments, or point to speaker to display the currently playing song. Unlike a screen, when Lantern’s projections are no longer needed, they simply fade away.
Lantern is set up so that you can connect your wireless device to it using Google Nearby. This means there’s no need to create an account before you can dive into augmented reality.
Your own open-source AR lamp
Nord Projects collaborated on Lantern with Google’s Android Things team. They’ve made it fully open-source, so you can find the code on GitHub and also download their parts list, which includes a Pi, an IKEA lamp, an accelerometer, and a laser projector. Build instructions are at hackster.io and on GitHub.
This is a particularly clear tutorial, very well illustrated with photos and GIFs, and once you’ve sourced and 3D-printed all of the components, you shouldn’t need a whole lot of experience to put everything together successfully. Since everything is open-source, though, if you want to adapt it — for example, if you’d like to source a less costly projector than the snazzy one used here — you can do that too.
The instructions walk you through the mechanical build and the wiring, as well as installing Android Things and Nord Projects’ custom software on the Raspberry Pi. Once you’ve set everything up, an accelerometer connected to the Pi’s GPIO pins lets the lamp know which surface it is pointing at. A companion app on your mobile device lets you choose from the mini apps that work on that surface to select the projection you want.
The designers are making several mini apps available for Lantern, including the charmingly named Space Porthole: this uses Processing and your local longitude and latitude to project onto your ceiling the stars you’d see if you punched a hole through to the sky, if it were night time, and clear weather. Wouldn’t you rather look at that than deal with the ant problem in your kitchen or tackle your GitHub notifications?
What would you like to project onto your living environment? Let us know in the comments!
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