Tag Archives: policy

[$] Deferring seccomp decisions to user space

Post Syndicated from corbet original https://lwn.net/Articles/756233/rss

There has been a lot of work in recent years to use BPF to push policy
decisions into the kernel. But sometimes, it seems, what is really wanted
is a way for a BPF program to punt a decision back to user space. That is
the objective behind this patch set giving
the secure
computing (seccomp) mechanism a way to pass complex decisions to
a user-space helper program.

How to rotate your Twitter API key and bearer token automatically with AWS Secrets Manager

Post Syndicated from Apurv Awasthi original https://aws.amazon.com/blogs/security/how-to-rotate-your-twitter-api-key-and-bearer-token-automatically-with-aws-secrets-manager/

Previously, I showed you how to rotate Amazon RDS database credentials automatically with AWS Secrets Manager. In addition to database credentials, AWS Secrets Manager makes it easier to rotate, manage, and retrieve API keys, OAuth tokens, and other secrets throughout their lifecycle. You can configure Secrets Manager to rotate these secrets automatically, which can help you meet your compliance needs. You can also use Secrets Manager to rotate secrets on demand, which can help you respond quickly to security events. In this post, I show you how to store an API key in Secrets Manager and use a custom Lambda function to rotate the key automatically. I’ll use a Twitter API key and bearer token as an example; you can reference this example to rotate other types of API keys.

The instructions are divided into four main phases:

  1. Store a Twitter API key and bearer token in Secrets Manager.
  2. Create a custom Lambda function to rotate the bearer token.
  3. Configure your application to retrieve the bearer token from Secrets Manager.
  4. Configure Secrets Manager to use the custom Lambda function to rotate the bearer token automatically.

For the purpose of this post, I use the placeholder Demo/Twitter_Api_Key to denote the API key, the placeholder Demo/Twitter_bearer_token to denote the bearer token, and placeholder Lambda_Rotate_Bearer_Token to denote the custom Lambda function. Be sure to replace these placeholders with the resource names from your account.

Phase 1: Store a Twitter API key and bearer token in Secrets Manager

Twitter enables developers to register their applications and retrieve an API key, which includes a consumer_key and consumer_secret. Developers use these to generate a bearer token that applications can then use to authenticate and retrieve information from Twitter. At any given point of time, you can use an API key to create only one valid bearer token.

Start by storing the API key in Secrets Manager. Here’s how:

  1. Open the Secrets Manager console and select Store a new secret.
     
    Figure 1: The "Store a new secret" button in the AWS Secrets Manager console

    Figure 1: The “Store a new secret” button in the AWS Secrets Manager console

  2. Select Other type of secrets (because you’re storing an API key).
  3. Input the consumer_key and consumer_secret, and then select Next.
     
    Figure 2: Select the consumer_key and the consumer_secret

    Figure 2: Select the consumer_key and the consumer_secret

  4. Specify values for Secret Name and Description, then select Next. For this example, I use Demo/Twitter_API_Key.
     
    Figure 3: Set values for "Secret Name" and "Description"

    Figure 3: Set values for “Secret Name” and “Description”

  5. On the next screen, keep the default setting, Disable automatic rotation, because you’ll use the same API key to rotate bearer tokens programmatically and automatically. Applications and employees will not retrieve this API key. Select Next.
     
    Figure 4: Keep the default "Disable automatic rotation" setting

    Figure 4: Keep the default “Disable automatic rotation” setting

  6. Review the information on the next screen and, if everything looks correct, select Store. You’ve now successfully stored a Twitter API key in Secrets Manager.

Next, store the bearer token in Secrets Manager. Here’s how:

  1. From the Secrets Manager console, select Store a new secret, select Other type of secrets, input details (access_token, token_type, and ARN of the API key) about the bearer token, and then select Next.
     
    Figure 5: Add details about the bearer token

    Figure 5: Add details about the bearer token

  2. Specify values for Secret Name and Description, and then select Next. For this example, I use Demo/Twitter_bearer_token.
     
    Figure 6: Again set values for "Secret Name" and "Description"

    Figure 6: Again set values for “Secret Name” and “Description”

  3. Keep the default rotation setting, Disable automatic rotation, and then select Next. You’ll enable rotation after you’ve updated the application to use Secrets Manager APIs to retrieve secrets.
  4. Review the information and select Store. You’ve now completed storing the bearer token in Secrets Manager.
    I take note of the sample code provided on the review page. I’ll use this code to update my application to retrieve the bearer token using Secrets Manager APIs.
     
    Figure 7: The sample code you can use in your app

    Figure 7: The sample code you can use in your app

Phase 2: Create a custom Lambda function to rotate the bearer token

While Secrets Manager supports rotating credentials for databases hosted on Amazon RDS natively, it also enables you to meet your unique rotation-related use cases by authoring custom Lambda functions. Now that you’ve stored the API key and bearer token, you’ll create a Lambda function to rotate the bearer token. For this example, I’ll create my Lambda function using Python 3.6.

  1. Open the Lambda console and select Create function.
     
    Figure 8: In the Lambda console, select "Create function"

    Figure 8: In the Lambda console, select “Create function”

  2. Select Author from scratch. For this example, I use the name Lambda_Rotate_Bearer_Token for my Lambda function. I also set the Runtime environment as Python 3.6.
     
    Figure 9: Create a new function from scratch

    Figure 9: Create a new function from scratch

  3. This Lambda function requires permissions to call AWS resources on your behalf. To grant these permissions, select Create a custom role. This opens a console tab.
  4. Select Create a new IAM Role and specify the value for Role Name. For this example, I use Role_Lambda_Rotate_Twitter_Bearer_Token.
     
    Figure 10: For "IAM Role," select "Create a new IAM role"

    Figure 10: For “IAM Role,” select “Create a new IAM role”

  5. Next, to define the IAM permissions, copy and paste the following IAM policy in the View Policy Document text-entry field. Be sure to replace the placeholder ARN-OF-Demo/Twitter_API_Key with the ARN of your secret.
    
{
    "Version": "2012-10-17",
    "Statement": [
        {
            “Sid”: “AllowLambdaToStoreAndRetrieveSecrets”
"Effect": "Allow",
            "Action": [
                "secretsmanager:DescribeSecret",
                "secretsmanager:GetSecretValue",
                "secretsmanager:PutSecretValue",
                "secretsmanager:UpdateSecretVersionStage"
            ],
            "Resource": "*"
        },
        {
            “Sid”: “GenerateARandomStringToExecuteRotation”
            "Effect": "Allow",
            "Action": [
                "secretsmanager:GetRandomPassword"
            ],
            "Resource": "*"
        },
        {
            “Sid”: “AccessToNetworkInterface”
            "Action": [
                "ec2:CreateNetworkInterface",
                "ec2:DeleteNetworkInterface",
                "ec2:DescribeNetworkInterfaces",
                "ec2:DetachNetworkInterface"
            ],
            "Resource": "*",
            "Effect": "Allow"
        },
        {
            “Sid”: “RecordProgressInLogsForEasierDebugging”
            "Effect": "Allow",
            "Action": [
                "logs:CreateLogGroup",
                "logs:CreateLogStream",
                "logs:PutLogEvents"
            ],
            "Resource": "*"
        },
        {
            “Sid”: “GetMasterCredential”
            "Effect": "Allow",
            "Action": [
                "secretsmanager:GetSecretValue"
            ],
            "Resource": ARN-OF-Demo/Twitter_API_Key
        }
    ]
}

    Here’s what it will look like:
     

    Figure 11: The IAM policy pasted in the "View Policy Document" text-entry field

    Figure 11: The IAM policy pasted in the “View Policy Document” text-entry field

  6. Now, select Allow. This brings me back to the Lambda console with the appropriate Role selected.
  7. Select Create function.
     
    Figure 12: Select the "Create function" button in the lower-right corner

    Figure 12: Select the “Create function” button in the lower-right corner

  8. Copy the following Python code and paste it in the Function code section.
    
import base64
import json
import logging
import os

import boto3
from botocore.vendored import requests

logger = logging.getLogger()
logger.setLevel(logging.INFO)

def lambda_handler(event, context):
    """Secrets Manager Twitter Bearer Token Handler

    This handler uses the master-user rotation scheme to rotate a bearer token of a Twitter app.

    The Secret PlaintextString is expected to be a JSON string with the following format:
    {
        'access_token': ,
        'token_type': ,
        'masterarn': 
    }

    Args:
        event (dict): Lambda dictionary of event parameters. These keys must include the following:
            - SecretId: The secret ARN or identifier
            - ClientRequestToken: The ClientRequestToken of the secret version
            - Step: The rotation step (one of createSecret, setSecret, testSecret, or finishSecret)

        context (LambdaContext): The Lambda runtime information

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not properly configured for rotation

        KeyError: If the secret json does not contain the expected keys

    """
    arn = event['SecretId']
    token = event['ClientRequestToken']
    step = event['Step']

    # Setup the client and environment variables
    service_client = boto3.client('secretsmanager', endpoint_url=os.environ['SECRETS_MANAGER_ENDPOINT'])
    oauth2_token_url = os.environ['TWITTER_OAUTH2_TOKEN_URL']
    oauth2_invalid_token_url = os.environ['TWITTER_OAUTH2_INVALID_TOKEN_URL']
    tweet_search_url = os.environ['TWITTER_SEARCH_URL']

    # Make sure the version is staged correctly
    metadata = service_client.describe_secret(SecretId=arn)
    if not metadata['RotationEnabled']:
        logger.error("Secret %s is not enabled for rotation" % arn)
        raise ValueError("Secret %s is not enabled for rotation" % arn)
    versions = metadata['VersionIdsToStages']
    if token not in versions:
        logger.error("Secret version %s has no stage for rotation of secret %s." % (token, arn))
        raise ValueError("Secret version %s has no stage for rotation of secret %s." % (token, arn))
    if "AWSCURRENT" in versions[token]:
        logger.info("Secret version %s already set as AWSCURRENT for secret %s." % (token, arn))
        return
    elif "AWSPENDING" not in versions[token]:
        logger.error("Secret version %s not set as AWSPENDING for rotation of secret %s." % (token, arn))
        raise ValueError("Secret version %s not set as AWSPENDING for rotation of secret %s." % (token, arn))

    # Call the appropriate step
    if step == "createSecret":
        create_secret(service_client, arn, token, oauth2_token_url, oauth2_invalid_token_url)

    elif step == "setSecret":
        set_secret(service_client, arn, token, oauth2_token_url)

    elif step == "testSecret":
        test_secret(service_client, arn, token, tweet_search_url)

    elif step == "finishSecret":
        finish_secret(service_client, arn, token)

    else:
        logger.error("lambda_handler: Invalid step parameter %s for secret %s" % (step, arn))
        raise ValueError("Invalid step parameter %s for secret %s" % (step, arn))


def create_secret(service_client, arn, token, oauth2_token_url, oauth2_invalid_token_url):
    """Get a new bearer token from Twitter 

    This method invalidates existing bearer token for the Twitter app and retrieves a new one from Twitter.
    If a secret version with AWSPENDING stage exists, updates it with the newly retrieved bearer token and if 
    the AWSPENDING stage does not exist, creates a new version of the secret with that stage label. 

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version

        oauth2_token_url (string): The Twitter API endpoint to request a bearer token

        oauth2_invalid_token_url (string): The Twitter API endpoint to invalidate a bearer token

    Raises:
        ValueError: If the current secret is not valid JSON

        KeyError: If the secret json does not contain the expected keys

        ResourceNotFoundException: If the current secret is not found

    """
    # Make sure the current secret exists and try to get the master arn from the secret
    try:
      current_secret_dict = get_secret_dict(service_client, arn, "AWSCURRENT")
      master_arn = current_secret_dict['masterarn']
      logger.info("createSecret: Successfully retrieved secret for %s." % arn)
    except service_client.exceptions.ResourceNotFoundException:
      return
    # create bearer token credentials to be passed as authorization string to Twitter
    bearer_token_credentials = encode_credentials(service_client, master_arn, "AWSCURRENT")
    # get the bearer token from Twitter
    bearer_token_from_twitter = get_bearer_token(bearer_token_credentials,oauth2_token_url)
    # invalidate the current bearer token
    invalidate_bearer_token(oauth2_invalid_token_url,bearer_token_credentials,bearer_token_from_twitter)
    # get a new bearer token from Twitter
    new_bearer_token = get_bearer_token(bearer_token_credentials, oauth2_token_url)
    # if a secret version with AWSPENDING stage exists, update it with the lastest bearer token
    # if the AWSPENDING stage does not exist, then create the version with AWSPENDING stage
    try:
      pending_secret_dict = get_secret_dict(service_client, arn, "AWSPENDING", token)
      pending_secret_dict['access_token'] = new_bearer_token
      service_client.put_secret_value(SecretId=arn, ClientRequestToken=token, SecretString=json.dumps(pending_secret_dict), VersionStages=['AWSPENDING'])
      logger.info("createSecret: Successfully invalidated the bearer token of the secret %s and updated the pending version" % arn)
    except service_client.exceptions.ResourceNotFoundException:
      current_secret_dict['access_token'] = new_bearer_token
      service_client.put_secret_value(SecretId=arn, ClientRequestToken=token, SecretString=json.dumps(current_secret_dict), VersionStages=['AWSPENDING'])
      logger.info("createSecret: Successfully invalidated the bearer token of the secret %s and and created the pending version." % arn)

def set_secret(service_client, arn, token, oauth2_token_url):
    """Validate the pending secret with that in Twitter

    This method checks wether the bearer token in Twitter is the same as the one in the version with AWSPENDING stage.

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version

        oauth2_token_url (string): The Twitter API endopoint to get a bearer token

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not valid JSON or master credentials could not be used to login to DB

        KeyError: If the secret json does not contain the expected keys

    """
    # First get the pending version of the bearer token and compare it with that in Twitter
    pending_secret_dict = get_secret_dict(service_client, arn, "AWSPENDING")
    master_arn = pending_secret_dict['masterarn']
    # create bearer token credentials to be passed as authorization string to Twitter
    bearer_token_credentials = encode_credentials(service_client, master_arn, "AWSCURRENT")
    # get the bearer token from Twitter
    bearer_token_from_twitter = get_bearer_token(bearer_token_credentials, oauth2_token_url)
    # if the bearer tokens are same, invalidate the bearer token in Twitter
    # if not, raise an exception that bearer token in Twitter was changed outside Secrets Manager
    if pending_secret_dict['access_token'] == bearer_token_from_twitter:
      logger.info("createSecret: Successfully verified the bearer token of arn %s" % arn)
    else:
      raise ValueError("The bearer token of the Twitter app was changed outside Secrets Manager. Please check.")
    
def test_secret(service_client, arn, token, tweet_search_url):
    """Test the pending secret by calling a Twitter API

    This method tries to use the bearer token in the secret version with AWSPENDING stage and search for tweets
    with 'aws secrets manager' string. 

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not valid JSON or pending credentials could not be used to login to the database

        KeyError: If the secret json does not contain the expected keys

    """
    # First get the pending version of the bearer token and compare it with that in Twitter
    pending_secret_dict = get_secret_dict(service_client, arn, "AWSPENDING", token)
    # Now verify you can search for tweets using the bearer token
    if verify_bearer_token(pending_secret_dict['access_token'], tweet_search_url):
        logger.info("testSecret: Successfully authorized with the pending secret in %s." % arn)
        return
    else:
        logger.error("testSecret: Unable to authorize with the pending secret of secret ARN %s" % arn)
        raise ValueError("Unable to connect to Twitter with pending secret of secret ARN %s" % arn)

def finish_secret(service_client, arn, token):
    """Finish the rotation by marking the pending secret as current

    This method moves the secret from the AWSPENDING stage to the AWSCURRENT stage.

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

    """
    # First describe the secret to get the current version
    metadata = service_client.describe_secret(SecretId=arn)
    current_version = None
    for version in metadata["VersionIdsToStages"]:
        if "AWSCURRENT" in metadata["VersionIdsToStages"][version]:
            if version == token:
                # The correct version is already marked as current, return
                logger.info("finishSecret: Version %s already marked as AWSCURRENT for %s" % (version, arn))
                return
            current_version = version
            break

    # Finalize by staging the secret version current
    service_client.update_secret_version_stage(SecretId=arn, VersionStage="AWSCURRENT", MoveToVersionId=token, RemoveFromVersionId=current_version)
    logger.info("finishSecret: Successfully set AWSCURRENT stage to version %s for secret %s." % (version, arn))
def encode_credentials(service_client, arn, stage):
  """Encodes the Twitter credentials
  This helper function encodes the Twitter credentials (consumer_key and consumer_secret) 

  Args:
    service_client (client):The secrets manager service client

    arn (string): The secret ARN or other identifier

    stage (stage): The stage identifying the secret version
  
  Returns:
    encoded_credentials (string): base64 encoded authorization string for Twitter

  Raises:
    KeyError: If the secret json does not contain the expected keys
  """
  required_fields = ['consumer_key','consumer_secret']
  master_secret_dict = get_secret_dict(service_client, arn, stage)
  for field in required_fields:
    if field not in master_secret_dict:
      raise KeyError("%s key is missing from the secret JSON" % field)
  encoded_credentials = base64.urlsafe_b64encode(
    '{}:{}'.format(master_secret_dict['consumer_key'], master_secret_dict['consumer_secret']).encode('ascii')).decode('ascii')
  return encoded_credentials
def get_bearer_token(encoded_credentials, oauth2_token_url):
  """Gets a bearer token from Twitter

  This helper function retrieves the current bearer token from Twitter, given a set of credentials.

  Args:
    encoded_credentials (string): Twitter credentials for authentication

    oauth2_token_url (string): REST API endpoint to request a bearer token from Twitter

  Raises:
    KeyError: If the secret json does not contain the expected keys
  """
  headers = {
      'Authorization': 'Basic {}'.format(encoded_credentials),
      'Content-Type': 'application/x-www-form-urlencoded;charset=UTF-8',
  }
  data = 'grant_type=client_credentials'
  response = requests.post(oauth2_token_url, headers=headers, data=data)
  response_data = response.json()
  if response_data['token_type'] == 'bearer':
      bearer_token = response_data['access_token']
      return bearer_token
  else:
      raise RuntimeError('unexpected token type: {}'.format(response_data['token_type']))
def invalidate_bearer_token(oauth2_invalid_token_url, bearer_token_credentials, bearer_token):
    """Invalidates a Bearer Token of a Twitter App

    This helper function invalidates a bearer token of a Twitter app. 
    If successful, it returns the invalidated bearer token, else None

    Args:
        oauth2_invalid_token_url (string): The Twitter API endpoint to invalidate a bearer token

        bearer_token_credentials (string): encoded consumer key and consumer secret to authenticate with Twitter

        bearer_token (string): The bearer token to be invalidated

    Returns:
        invalidated_bearer_token: The invalidated bearer token

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not valid JSON

        KeyError: If the secret json does not contain the expected keys

    """
    headers = {
      'Authorization': 'Basic {}'.format(bearer_token_credentials),
      'Content-Type': 'application/x-www-form-urlencoded;charset=UTF-8',
    }
    data = 'access_token=' + bearer_token
    invalidate_response = requests.post(oauth2_invalid_token_url, headers=headers, data=data)
    invalidate_response_data = invalidate_response.json()
    if invalidate_response_data:
      return
    else:
      raise RuntimeError('Invalidate bearer token request failed')
def verify_bearer_token(bearer_token, tweet_search_url):
  """Verifies access to Twitter APIs using a bearer token

  This helper function verifies that the bearer token is valid by calling Twitter's search/tweets API endpoint

  Args:
      bearer_token (string): The current bearer token for the application

  Returns:
      True or False

  Raises:
      KeyError: If the response of search tweets API call fails
  """
  headers = {
    'Authorization' : 'Bearer {}'.format(bearer_token),
    'Content-Type': 'application/x-www-form-urlencoded;charset=UTF-8',
  }
  search_results = requests.get(tweet_search_url, headers=headers)
  try:
    search_results.json()['statuses']
    return True
  except:
    return False

def get_secret_dict(service_client, arn, stage, token=None):
    """Gets the secret dictionary corresponding for the secret arn, stage, and token

    This helper function gets credentials for the arn and stage passed in and returns the dictionary by parsing the JSON string

    Args:
        service_client (client): The secrets manager service client

        arn (string): The secret ARN or other identifier

        token (string): The ClientRequestToken associated with the secret version, or None if no validation is desired

        stage (string): The stage identifying the secret version

    Returns:
        SecretDictionary: Secret dictionary

    Raises:
        ResourceNotFoundException: If the secret with the specified arn and stage does not exist

        ValueError: If the secret is not valid JSON

    """
    # Only do VersionId validation against the stage if a token is passed in
    if token:
        secret = service_client.get_secret_value(SecretId=arn, VersionId=token, VersionStage=stage)
    else:
        secret = service_client.get_secret_value(SecretId=arn, VersionStage=stage)
    plaintext = secret['SecretString']

    # Parse and return the secret JSON string
    return json.loads(plaintext)

    Here’s what it will look like:
     

    Figure 13: The Python code pasted in the "Function code" section

    Figure 13: The Python code pasted in the “Function code” section

  9. On the same page, provide the following environment variables:

    Here’s what it will look like:
     

    Figure 14: Add the environment variables

    Figure 14: Add the environment variables

    Note: Resources used in this example are in US East (Ohio) region. If you intend to use another AWS Region, change the SECRETS_MANAGER_ENDPOINT set in the Environment variables to the appropriate region.

    You’ve now created a Lambda function that can rotate the bearer token:
     

    Figure 15: The new Lambda function

    Figure 15: The new Lambda function

  10. Before you can configure Secrets Manager to use this Lambda function, you need to update the function policy of the Lambda function. A function policy permits AWS services, such as Secrets Manager, to invoke a Lambda function on behalf of your application. You can attach a Lambda function policy from the AWS Command Line Interface (AWS CLI) or SDK. To attach a function policy, call the add-permission Lambda API from the AWS CLI.
    
>aws lambda add-permission --function-name Lambda_Rotate_Bearer_Token --principal secretsmanager.amazonaws.com --action lambda:InvokeFunction --statement-id SecretsManagerAccess

Phase 3: Configure your application to retrieve the bearer token from Secrets Manager

Now that you’ve stored the bearer token in Secrets Manager, update the application to retrieve the bearer token from Secrets Manager instead of hard-coding this information in a configuration file or source code. For this example, I show you how to configure a Python application to retrieve this secret from Secrets Manager.

  1. Connect to your Amazon EC2 instance via Secure Shell (SSH). Previously, you configured the application to retrieve the bearer token from a configuration file. Below is the source code for my application.
    
import config

def no_secrets_manager_sample()
    
    # Get the bearer token from a config file. 
    Bearer_token = config.bearer_token
    
    # Use the bearer token to authenticate requests to Twitter

  2. Use the sample code from section titled Phase 1 and update the application to retrieve the bearer token from Secrets Manager. The following code sets up the client and retrieves and decrypts the secret Demo/Twitter_bearer_token.
    
# Use this code snippet in your app.
import boto3
from botocore.exceptions import ClientError


def get_secret():
    secret_name = "Demo/Twitter_bearer_token"
    endpoint_url = "https://secretsmanager.us-east-2.amazonaws.com"
    region_name = "us-east-2"

    session = boto3.session.Session()
    client = session.client(
        service_name='secretsmanager',
        region_name=region_name,
        endpoint_url=endpoint_url
    )

    try:
        get_secret_value_response = client.get_secret_value(
            SecretId=secret_name
        )
    except ClientError as e:
        if e.response['Error']['Code'] == 'ResourceNotFoundException':
            print("The requested secret " + secret_name + " was not found")
        elif e.response['Error']['Code'] == 'InvalidRequestException':
            print("The request was invalid due to:", e)
        elif e.response['Error']['Code'] == 'InvalidParameterException':
            print("The request had invalid params:", e)
    else:
        # Decrypted secret using the associated KMS CMK
        # Depending on whether the secret was a string or binary, one of these fields will be populated
        if 'SecretString' in get_secret_value_response:
            secret = get_secret_value_response['SecretString']
        else:
            binary_secret_data = get_secret_value_response['SecretBinary']
            
        # Your code goes here.

  3. Applications require permissions to access Secrets Manager. My application runs on Amazon EC2 and uses an IAM role to get access to AWS services. I’ll attach the following policy to my IAM role, and you should take a similar action with your IAM role. This policy uses the GetSecretValue action to grant my application permissions to read secrets from Secrets Manager. This policy also uses the resource element to limit my application to read only the Demo/Twitter_bearer_token secret from Secrets Manager. Read the AWS Secrets Manager documentation to understand the minimum IAM permissions required to retrieve a secret.
    
{
"Version": "2012-10-17",
"Statement": {
"Sid": "RetrieveBearerToken",
"Effect": "Allow",
"Action": "secretsmanager:GetSecretValue",
"Resource": Input ARN of the secret Demo/Twitter_bearer_token here 
}
}

    Note: To improve the resiliency of your applications, associate your application with two API keys/bearer tokens. This is a higher availability option because you can continue to use one bearer token while Secrets Manager rotates the other token. Read the AWS documentation to learn how AWS Secrets Manager rotates your secrets.

Phase 4: Enable and verify rotation

Now that you’ve stored the secret in Secrets Manager and created a Lambda function to rotate this secret, configure Secrets Manager to rotate the secret Demo/Twitter_bearer_token.

  1. From the Secrets Manager console, go to the list of secrets and choose the secret you created in the first step (in my example, this is named Demo/Twitter_bearer_token).
  2. Scroll to Rotation configuration, and then select Edit rotation.
     
    Figure 16: Select the "Edit rotation" button

    Figure 16: Select the “Edit rotation” button

  3. To enable rotation, select Enable automatic rotation, and then choose how frequently you want Secrets Manager to rotate this secret. For this example, I set the rotation interval to 30 days. I also choose the rotation Lambda function, Lambda_Rotate_Bearer_Token, from the drop-down list.
     
    Figure 17: "Edit rotation configuration" options

    Figure 17: “Edit rotation configuration” options

The banner on the next screen confirms that I have successfully configured rotation and the first rotation is in progress, which enables you to verify that rotation is functioning as expected. Secrets Manager will rotate this credential automatically every 30 days.
 

Figure 18: Confirmation notice

Figure 18: Confirmation notice

Summary

In this post, I showed you how to configure Secrets Manager to manage and rotate an API key and bearer token used by applications to authenticate and retrieve information from Twitter. You can use the steps described in this blog to manage and rotate other API keys, as well.

Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront investment and on-going maintenance costs of operating your own secrets management infrastructure. To get started, open the Secrets Manager console. To learn more, read the Secrets Manager documentation.

If you have comments about this post, submit them in the Comments section below. If you have questions about anything in this post, start a new thread on the Secrets Manager forum or contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Monitoring your Amazon SNS message filtering activity with Amazon CloudWatch

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/monitoring-your-amazon-sns-message-filtering-activity-with-amazon-cloudwatch/

This post is courtesy of Otavio Ferreira, Manager, Amazon SNS, AWS Messaging.

Amazon SNS message filtering provides a set of string and numeric matching operators that allow each subscription to receive only the messages of interest. Hence, SNS message filtering can simplify your pub/sub messaging architecture by offloading the message filtering logic from your subscriber systems, as well as the message routing logic from your publisher systems.

After you set the subscription attribute that defines a filter policy, the subscribing endpoint receives only the messages that carry attributes matching this filter policy. Other messages published to the topic are filtered out for this subscription. In this way, the native integration between SNS and Amazon CloudWatch provides visibility into the number of messages delivered, as well as the number of messages filtered out.

CloudWatch metrics are captured automatically for you. To get started with SNS message filtering, see Filtering Messages with Amazon SNS.

Message Filtering Metrics

The following six CloudWatch metrics are relevant to understanding your SNS message filtering activity:

  • NumberOfMessagesPublished – Inbound traffic to SNS. This metric tracks all the messages that have been published to the topic.
  • NumberOfNotificationsDelivered – Outbound traffic from SNS. This metric tracks all the messages that have been successfully delivered to endpoints subscribed to the topic. A delivery takes place either when the incoming message attributes match a subscription filter policy, or when the subscription has no filter policy at all, which results in a catch-all behavior.
  • NumberOfNotificationsFilteredOut – This metric tracks all the messages that were filtered out because they carried attributes that didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-NoMessageAttributes – This metric tracks all the messages that were filtered out because they didn’t carry any attributes at all and, consequently, didn’t match the subscription filter policy.
  • NumberOfNotificationsFilteredOut-InvalidAttributes – This metric keeps track of messages that were filtered out because they carried invalid or malformed attributes and, thus, didn’t match the subscription filter policy.
  • NumberOfNotificationsFailed – This last metric tracks all the messages that failed to be delivered to subscribing endpoints, regardless of whether a filter policy had been set for the endpoint. This metric is emitted after the message delivery retry policy is exhausted, and SNS stops attempting to deliver the message. At that moment, the subscribing endpoint is likely no longer reachable. For example, the subscribing SQS queue or Lambda function has been deleted by its owner. You may want to closely monitor this metric to address message delivery issues quickly.

Message filtering graphs

Through the AWS Management Console, you can compose graphs to display your SNS message filtering activity. The graph shows the number of messages published, delivered, and filtered out within the timeframe you specify (1h, 3h, 12h, 1d, 3d, 1w, or custom).

SNS message filtering for CloudWatch Metrics

To compose an SNS message filtering graph with CloudWatch:

  1. Open the CloudWatch console.
  2. Choose Metrics, SNS, All Metrics, and Topic Metrics.
  3. Select all metrics to add to the graph, such as:
    • NumberOfMessagesPublished
    • NumberOfNotificationsDelivered
    • NumberOfNotificationsFilteredOut
  4. Choose Graphed metrics.
  5. In the Statistic column, switch from Average to Sum.
  6. Title your graph with a descriptive name, such as “SNS Message Filtering”

After you have your graph set up, you may want to copy the graph link for bookmarking, emailing, or sharing with co-workers. You may also want to add your graph to a CloudWatch dashboard for easy access in the future. Both actions are available to you on the Actions menu, which is found above the graph.

Summary

SNS message filtering defines how SNS topics behave in terms of message delivery. By using CloudWatch metrics, you gain visibility into the number of messages published, delivered, and filtered out. This enables you to validate the operation of filter policies and more easily troubleshoot during development phases.

SNS message filtering can be implemented easily with existing AWS SDKs by applying message and subscription attributes across all SNS supported protocols (Amazon SQS, AWS Lambda, HTTP, SMS, email, and mobile push). CloudWatch metrics for SNS message filtering is available now, in all AWS Regions.

For information about pricing, see the CloudWatch pricing page.

For more information, see:

timeShift(GrafanaBuzz, 1w) Issue 46

Post Syndicated from Blogs on Grafana Labs Blog original https://grafana.com/blog/2018/05/24/timeshiftgrafanabuzz-1w-issue-46/

Welcome to TimeShift The day has finally arrived; GDPR is officially in effect! These new policies are meant to provide more transparency about the data companies collect on users, and how that data is used. I for one am just excited that the onslaught of "We’ve updated our privacy policy" emails arriving in my pummeled inbox is nearing its end.
Grafana Labs is no exception. We encourage you to check out our privacy policy, and if you have any questions, feel free to contact us at [email protected]

The Benefits of Side Projects

Post Syndicated from Bozho original https://techblog.bozho.net/the-benefits-of-side-projects/

Side projects are the things you do at home, after work, for your own “entertainment”, or to satisfy your desire to learn new stuff, in case your workplace doesn’t give you that opportunity (or at least not enough of it). Side projects are also a way to build stuff that you think is valuable but not necessarily “commercialisable”. Many side projects are open-sourced sooner or later and some of them contribute to the pool of tools at other people’s disposal.

I’ve outlined one recommendation about side projects before – do them with technologies that are new to you, so that you learn important things that will keep you better positioned in the software world.

But there are more benefits than that – serendipitous benefits, for example. And I’d like to tell some personal stories about that. I’ll focus on a few examples from my list of side projects to show how, through a sort-of butterfly effect, they helped shape my career.

The computoser project, no matter how cool algorithmic music composition, didn’t manage to have much of a long term impact. But it did teach me something apart from niche musical theory – how to read a bulk of scientific papers (mostly computer science) and understand them without being formally trained in the particular field. We’ll see how that was useful later.

Then there was the “State alerts” project – a website that scraped content from public institutions in my country (legislation, legislation proposals, decisions by regulators, new tenders, etc.), made them searchable, and “subscribable” – so that you get notified when a keyword of interest is mentioned in newly proposed legislation, for example. (I obviously subscribed for “information technologies” and “electronic”).

And that project turned out to have a significant impact on the following years. First, I chose a new technology to write it with – Scala. Which turned out to be of great use when I started working at TomTom, and on the 3rd day I was transferred to a Scala project, which was way cooler and much more complex than the original one I was hired for. It was a bit ironic, as my colleagues had just read that “I don’t like Scala” a few weeks earlier, but nevertheless, that was one of the most interesting projects I’ve worked on, and it went on for two years. Had I not known Scala, I’d probably be gone from TomTom much earlier (as the other project was restructured a few times), and I would not have learned many of the scalability, architecture and AWS lessons that I did learn there.

But the very same project had an even more important follow-up. Because if its “civic hacking” flavour, I was invited to join an informal group of developers (later officiated as an NGO) who create tools that are useful for society (something like MySociety.org). That group gathered regularly, discussed both tools and policies, and at some point we put up a list of policy priorities that we wanted to lobby policy makers. One of them was open source for the government, the other one was open data. As a result of our interaction with an interim government, we donated the official open data portal of my country, functioning to this day.

As a result of that, a few months later we got a proposal from the deputy prime minister’s office to “elect” one of the group for an advisor to the cabinet. And we decided that could be me. So I went for it and became advisor to the deputy prime minister. The job has nothing to do with anything one could imagine, and it was challenging and fascinating. We managed to pass legislation, including one that requires open source for custom projects, eID and open data. And all of that would not have been possible without my little side project.

As for my latest side project, LogSentinel – it became my current startup company. And not without help from the previous two mentioned above – the computer science paper reading was of great use when I was navigating the crypto papers landscape, and from the government job I not only gained invaluable legal knowledge, but I also “got” a co-founder.

Some other side projects died without much fanfare, and that’s fine. But the ones above shaped my “story” in a way that would not have been possible otherwise.

And I agree that such serendipitous chain of events could have happened without side projects – I could’ve gotten these opportunities by meeting someone at a bar (unlikely, but who knows). But we, as software engineers, are capable of tilting chance towards us by utilizing our skills. Side projects are our “extracurricular activities”, and they often lead to unpredictable, but rather positive chains of events. They would rarely be the only factor, but they are certainly great at unlocking potential.

The post The Benefits of Side Projects appeared first on Bozho's tech blog.

The Practical Effects of GDPR at Backblaze

Post Syndicated from Andy Klein original https://www.backblaze.com/blog/the-practical-effects-of-gdpr-at-backblaze/


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.

The post The Practical Effects of GDPR at Backblaze appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

RFC: LWN’s draft updated privacy policy

Post Syndicated from corbet original https://lwn.net/Articles/755089/rss

It is the season for web sites to be updating their privacy policies and
obtaining consent from their users for whatever data they collect. LWN,
being short of staff with the time or interest to work in this area, is
rather late to this game. The first step is an updated
privacy policy, which we’re now putting out for review. Little has changed
from the current version; we still don’t
collect much data, share data with others, or attempt to
monetize what we have in any way. We would like to ask interested readers
to have a look and let us know about any potential problems they see.

Use AWS Glue to run ETL jobs against non-native JDBC data sources

Post Syndicated from Kapil Shardha original https://aws.amazon.com/blogs/big-data/use-aws-glue-to-run-etl-jobs-against-non-native-jdbc-data-sources/

AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easier to prepare and load your data for analytics. You can create and run an ETL job with a few clicks on the AWS Management Console. Just point AWS Glue to your data store. AWS Glue discovers your data and stores the associated metadata (for example, a table definition and schema) in the AWS Glue Data Catalog.

AWS Glue has native connectors to data sources using JDBC drivers, either on AWS or elsewhere, as long as there is IP connectivity. In this post, we demonstrate how to connect to data sources that are not natively supported in AWS Glue today. We walk through connecting to and running ETL jobs against two such data sources, IBM DB2 and SAP Sybase. However, you can use the same process with any other JDBC-accessible database.

AWS Glue data sources

AWS Glue natively supports the following data stores by using the JDBC protocol:

  • Publicly accessible databases
    • Amazon Aurora
    • MariaDB
    • Microsoft SQL Server
    • MySQL
    • Oracle
    • PostgreSQL

For more information, see Adding a Connection to Your Data Store in the AWS Glue Developer Guide.

One of the fastest growing architectures deployed on AWS is the data lake. The ETL processes that are used to ingest, clean, transform, and structure data are critically important for this architecture. Having the flexibility to interoperate with a broader range of database engines allows for a quicker adoption of the data lake architecture.

For data sources that AWS Glue doesn’t natively support, such as IBM DB2, Pivotal Greenplum, SAP Sybase, or any other relational database management system (RDBMS), you can import custom database connectors from Amazon S3 into AWS Glue jobs. In this case, the connection to the data source must be made from the AWS Glue script to extract the data, rather than using AWS Glue connections. To learn more, see Providing Your Own Custom Scripts in the AWS Glue Developer Guide.

Setting up an ETL job for an IBM DB2 data source

The first example demonstrates how to connect the AWS Glue ETL job to an IBM DB2 instance, transform the data from the source, and store it in Apache Parquet format in Amazon S3. To successfully create the ETL job using an external JDBC driver, you must define the following:

  • The S3 location of the job script
  • The S3 location of the temporary directory
  • The S3 location of the JDBC driver
  • The S3 location of the Parquet data (output)
  • The IAM role for the job

By default, AWS Glue suggests bucket names for the scripts and the temporary directory using the following format:

s3://aws-glue-scripts-<ACCOUNT_ID>-<REGION>/<USER>
s3://aws-glue-temporary-<ACCOUNT_ID>-<REGION>/<USER>

For JDBC drivers, you can create a similar location:

s3://aws-glue-jdbc-drivers-<ACCOUNT_ID>-<REGION>/<USER>

Also for the Parquet data (output), you can create a similar location:

s3://aws-glue-data-output-<ACCOUNT_ID>-<REGION>/<USER>

Keep in mind that having the AWS Glue job and S3 buckets in the same AWS Region helps save on cross-Region data transfer fees. For this post, we will work in the US East (Ohio) Region (us-east-2).

Creating the IAM role

The next step is to set up the IAM role that the ETL job will use:

  1. Sign in to the AWS Management Console, and search for IAM:

  1. On the IAM console, choose Roles in the left navigation pane.
  2. Choose Create role. The role type of trusted entity must be an AWS service, specifically AWS Glue.

  1. Choose Next: Permissions.
  2. Search for the AWSGlueServiceRole policy, and select it.

  1. Search again, now for the SecretsManagerReadWrite This policy allows the AWS Glue job to access database credentials that are stored in AWS Secrets Manager.

CAUTION: This policy is open and is being used for testing purposes only. You should create a custom policy to narrow the access just to the secrets that you want to use in the ETL job.

  1. Select this policy, and choose Next: Review.
  2. Give your role a name, for example, GluePermissions, and confirm that both policies were selected.

  1. Choose Create role.

Now that you have created the IAM role, it’s time to upload the JDBC driver to the defined location in Amazon S3. For this example, we will use the DB2 driver, which is available on the IBM Support site.

Storing database credentials

It is a best practice to store database credentials in a safe store. In this case, we use AWS Secrets Manager to securely store credentials. Follow these steps to create those credentials:

  1. Open the console, and search for Secrets Manager.
  2. In the AWS Secrets Manager console, choose Store a new secret.
  3. Under Select a secret type, choose Other type of secrets.
  4. In the Secret key/value, set one row for each of the following parameters:
    • db_username
    • db_password
    • db_url (for example, jdbc:db2://10.10.12.12:50000/SAMPLE)
    • db_table
    • driver_name (ibm.db2.jcc.DB2Driver)
    • output_bucket: (for example, aws-glue-data-output-1234567890-us-east-2/User)
  5. Choose Next.
  6. For Secret name, use DB2_Database_Connection_Info.
  7. Choose Next.
  8. Keep the Disable automatic rotation check box selected.
  9. Choose Next.
  10. Choose Store.

Adding a job in AWS Glue

The next step is to author the AWS Glue job, following these steps:

  1. In the AWS Management Console, search for AWS Glue.

  1. In the navigation pane on the left, choose Jobs under the ETL
  2. Choose Add job.

  1. Fill in the basic Job properties:
  2. Give the job a name (for example, db2-job).
  3. Choose the IAM role that you created previously (GluePermissions).
  4. For This job runs, choose A new script to be authored by you.
  5. For ETL language, choose Python.

  1. In the Script libraries and job parameters section, choose the location of your JDBC driver for Dependent jars path.

  1. Choose Next.
  2. On the Connections page, choose Next
  3. On the summary page, choose Save job and edit script. This creates the job and opens the script editor.

In the editor, replace the existing code with the following script. Important: Line 47 of the script corresponds to the mapping of the fields in the source table to the destination, dropping of the null fields to save space in the Parquet destination, and finally writing to Amazon S3 in Parquet format.

import sys
import boto3
import json
from awsglue.transforms import *
from awsglue.utils import getResolvedOptions
from pyspark.context import SparkContext
from awsglue.context import GlueContext
from awsglue.dynamicframe import DynamicFrame
from awsglue.job import Job


## @params: [JOB_NAME]
args = getResolvedOptions(sys.argv, ['JOB_NAME'])

sc = SparkContext()
glueContext = GlueContext(sc)
spark = glueContext.spark_session
job = Job(glueContext)
job.init(args['JOB_NAME'], args)

# Getting DB credentials from Secrets Manager
client = boto3.client("secretsmanager", region_name="us-east-2")

get_secret_value_response = client.get_secret_value(
        SecretId="DB2_Database_Connection_Info"
)

secret = get_secret_value_response['SecretString']
secret = json.loads(secret)

db_username = secret.get('db_username')
db_password = secret.get('db_password')
db_url = secret.get('db_url')
table_name = secret.get('db_table')
jdbc_driver_name = secret.get('driver_name')
s3_output = "s3://" + secret.get('output_bucket') + "/" + table_name

# Connecting to the source
df = glueContext.read.format("jdbc").option("driver", jdbc_driver_name).option("url", db_url).option("dbtable", table_name).option("user", db_username).option("password", db_password).load()

df.printSchema()
print df.count()

datasource0 = DynamicFrame.fromDF(df, glueContext, "datasource0")

# Defining mapping for the transformation
applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("EMPNO", "string", "EMPNO", "string"), ("FIRSTNME", "string", "FIRSTNME", "string"), ("MIDINIT", "string", "MIDINIT", "string"), ("LASTNAME", "string", "LASTNAME", "string"), ("WORKDEPT", "string", "WORKDEPT", "string"), ("PHONENO", "string", "PHONENO", "string"), ("HIREDATE", "date", "HIREDATE", "date"), ("JOB", "string", "JOB", "string"), ("EDLEVEL", "integer", "EDLEVEL", "integer"), ("SEX", "string", "SEX", "string"), ("BIRTHDATE", "date", "BIRTHDATE", "date"), ("SALARY", "double", "SALARY", "double"), ("BONUS", "double", "BONUS", "double"), ("COMM", "double", "COMM", "double")], transformation_ctx = "applymapping1")
resolvechoice2 = ResolveChoice.apply(frame = applymapping1, choice = "make_struct", transformation_ctx = "resolvechoice2")
dropnullfields3 = DropNullFields.apply(frame = resolvechoice2, transformation_ctx = "dropnullfields3")

# Writing to destination
datasink4 = glueContext.write_dynamic_frame.from_options(frame = dropnullfields3, connection_type = "s3", connection_options = {"path": s3_output}, format = "parquet", transformation_ctx = "datasink4")

job.commit()

  1. Choose Save.
  2. Choose the black X on the right side of the screen to close the editor.

Running the ETL job

Now that you have created the job, the next step is to execute it as follows:

  1. On the Jobs page, select your new job. On the Action menu, choose Run job, and confirm that you want to run the job. Wait a few moments as it finishes the execution.

  1. After the job shows as Succeeded, choose Logs to read the output of the job.

  1. In the output of the job, you will find the result of executing the df.printSchema() and the message with the df.count().

Also, if you go to your output bucket in S3, you will find the Parquet result of the ETL job.

Using AWS Glue, you have created an ETL job that connects to an existing database using an external JDBC driver. It enables you to execute any transformation that you need.

Setting up an ETL job for an SAP Sybase data source

In this section, we describe how to create an AWS Glue ETL job against an SAP Sybase data source. The process mentioned in the previous section works for a Sybase data source with a few changes required in the job:

  1. While creating the job, choose the correct jar for the JDBC dependency.
  2. In the script, change the reference to the secret to be used from AWS Secrets Manager:
get_secret_value_response = client.get_secret_value(
        SecretId="Sybase_Database_Connection_Info"
)

And change the mapping of the fields in line 47, as follows:

applymapping1 = ApplyMapping.apply(frame = datasource0, mappings = [("au_id", "string", "au_id", "string"), ("au_lname", "string", "au_lname", "string"), ("au_fname", "string", "au_fname", "string"), ("phone", "string", "phone", "string"), ("address", "string", "address", "string"), ("city", "string", "city", "string"), ("state", "string", "state", "string"), ("country", "string", "country", "string"), ("postalcode", "string", "postalcode", "string")], transformation_ctx = "applymapping1")

 

After you successfully execute the new ETL job, the output contains the same type of information that was generated with the DB2 data source.

Note that each of these JDBC drivers has its own nuances and different licensing terms that you should be aware of before using them.

Maximizing JDBC read parallelism

Something to keep in mind while working with big data sources is the memory consumption. In some cases, “Out of Memory” errors are generated when all the data is read into a single executor. One approach to optimize this is to rely on the parallelism on read that you can implement with Apache Spark and AWS Glue. To learn more, see the Apache Spark SQL module.

You can use the following options:

  • partitionColumn: The name of an integer column that is used for partitioning.
  • lowerBound: The minimum value of partitionColumn that is used to decide partition stride.
  • upperBound: The maximum value of partitionColumn that is used to decide partition stride.
  • numPartitions: The number of partitions. This, along with lowerBound (inclusive) and upperBound (exclusive), form partition strides for generated WHERE clause expressions used to split the partitionColumn When unset, this defaults to SparkContext.defaultParallelism.
  • Those options specify the parallelism of the table read. lowerBound and upperBound decide the partition stride, but they don’t filter the rows in the table. Therefore, Spark partitions and returns all rows in the table. For example:
df = glueContext.read.format("jdbc").option("driver", jdbc_driver_name).option("url", db_url).option("dbtable", table_name).option("user", db_username).option("password", db_password).option("partitionColumn", <column_name>).option("lowerBound", 1).option("upperBound", 100).option("numPartitions", 2).load()

It’s important to be careful with the number of partitions because too many partitions could also result in Spark crashing your external database systems.

Conclusion

Using the process described in this post, you can connect to and run AWS Glue ETL jobs against any data source that can be reached using a JDBC driver. This includes new generations of common analytical databases like Greenplum and others.

You can improve the query efficiency of these datasets by using partitioning and pushdown predicates. For more information, see Managing Partitions for ETL Output in AWS Glue. This technique opens the door to moving data and feeding data lakes in hybrid environments.

 

Additional Reading

If you found this post useful, be sure to check out Work with partitioned data in AWS Glue.

About the Authors

Kapil Shardha is a Technical Account Manager and supports enterprise customers with their AWS adoption. He has background in infrastructure automation and DevOps.

 

 

 

William Torrealba is an AWS Solutions Architect supporting customers with their AWS adoption. He has background in Application Development, High Available Distributed Systems, Automation, and DevOps.

 

 

 

 

Firefox 60 released

Post Syndicated from ris original https://lwn.net/Articles/754040/rss

Mozilla has released Firefox 60. From the release
notes: “Firefox 60 offers something for everyone and a little
something extra for everyone who deploys Firefox in an enterprise environment. This release includes changes that give you more content and more ways to customize your New Tab/Firefox Home. It also introduces support for the Web Authentication API, which means you can log in to websites in Firefox with USB tokens like YubiKey.
Firefox 60 also brings a new policy engine and Group Policy support for
enterprise deployments. For more info about why and how to use Firefox in
the enterprise, see this blog post.”

How AWS Meets a Physical Separation Requirement with a Logical Separation Approach

Post Syndicated from Min Hyun original https://aws.amazon.com/blogs/security/how-aws-meets-a-physical-separation-requirement-with-a-logical-separation-approach/

We have a new resource available to help you meet a requirement for physically-separated infrastructure using logical separation in the AWS cloud. Our latest guide, Logical Separation: An Evaluation of the U.S. Department of Defense Cloud Security Requirements for Sensitive Workloads outlines how AWS meets the U.S. Department of Defense’s (DoD) stringent physical separation requirement by pioneering a three-pronged logical separation approach that leverages virtualization, encryption, and deploying compute to dedicated hardware.

This guide will help you understand logical separation in the cloud and demonstrates its advantages over a traditional physical separation model. Embracing this approach can help organizations confidently meet or exceed security requirements found in traditional on-premises environments, while also providing increased security control and flexibility.

Logical Separation is the second guide in the AWS Government Handbook Series, which examines cybersecurity policy initiatives and identifies best practices.

If you have questions or want to learn more, contact your account executive or AWS Support.

Ray Ozzie’s Encryption Backdoor

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/ray_ozzies_encr.html

Last month, Wired published a long article about Ray Ozzie and his supposed new scheme for adding a backdoor in encrypted devices. It’s a weird article. It paints Ozzie’s proposal as something that “attains the impossible” and “satisfies both law enforcement and privacy purists,” when (1) it’s barely a proposal, and (2) it’s essentially the same key escrow scheme we’ve been hearing about for decades.

Basically, each device has a unique public/private key pair and a secure processor. The public key goes into the processor and the device, and is used to encrypt whatever user key encrypts the data. The private key is stored in a secure database, available to law enforcement on demand. The only other trick is that for law enforcement to use that key, they have to put the device in some sort of irreversible recovery mode, which means it can never be used again. That’s basically it.

I have no idea why anyone is talking as if this were anything new. Several cryptographers have already explained why this key escrow scheme is no better than any other key escrow scheme. The short answer is (1) we won’t be able to secure that database of backdoor keys, (2) we don’t know how to build the secure coprocessor the scheme requires, and (3) it solves none of the policy problems around the whole system. This is the typical mistake non-cryptographers make when they approach this problem: they think that the hard part is the cryptography to create the backdoor. That’s actually the easy part. The hard part is ensuring that it’s only used by the good guys, and there’s nothing in Ozzie’s proposal that addresses any of that.

I worry that this kind of thing is damaging in the long run. There should be some rule that any backdoor or key escrow proposal be a fully specified proposal, not just some cryptography and hand-waving notions about how it will be used in practice. And before it is analyzed and debated, it should have to satisfy some sort of basic security analysis. Otherwise, we’ll be swatting pseudo-proposals like this one, while those on the other side of this debate become increasingly convinced that it’s possible to design one of these things securely.

Already people are using the National Academies report on backdoors for law enforcement as evidence that engineers are developing workable and secure backdoors. Writing in Lawfare, Alan Z. Rozenshtein claims that the report — and a related New York Times story — “undermine the argument that secure third-party access systems are so implausible that it’s not even worth trying to develop them.” Susan Landau effectively corrects this misconception, but the damage is done.

Here’s the thing: it’s not hard to design and build a backdoor. What’s hard is building the systems — both technical and procedural — around them. Here’s Rob Graham:

He’s only solving the part we already know how to solve. He’s deliberately ignoring the stuff we don’t know how to solve. We know how to make backdoors, we just don’t know how to secure them.

A bunch of us cryptographers have already explained why we don’t think this sort of thing will work in the foreseeable future. We write:

Exceptional access would force Internet system developers to reverse “forward secrecy” design practices that seek to minimize the impact on user privacy when systems are breached. The complexity of today’s Internet environment, with millions of apps and globally connected services, means that new law enforcement requirements are likely to introduce unanticipated, hard to detect security flaws. Beyond these and other technical vulnerabilities, the prospect of globally deployed exceptional access systems raises difficult problems about how such an environment would be governed and how to ensure that such systems would respect human rights and the rule of law.

Finally, Matthew Green:

The reason so few of us are willing to bet on massive-scale key escrow systems is that we’ve thought about it and we don’t think it will work. We’ve looked at the threat model, the usage model, and the quality of hardware and software that exists today. Our informed opinion is that there’s no detection system for key theft, there’s no renewability system, HSMs are terrifically vulnerable (and the companies largely staffed with ex-intelligence employees), and insiders can be suborned. We’re not going to put the data of a few billion people on the line an environment where we believe with high probability that the system will fail.

EDITED TO ADD (5/14): An analysis of the proposal.

Analyze data in Amazon DynamoDB using Amazon SageMaker for real-time prediction

Post Syndicated from YongSeong Lee original https://aws.amazon.com/blogs/big-data/analyze-data-in-amazon-dynamodb-using-amazon-sagemaker-for-real-time-prediction/

Many companies across the globe use Amazon DynamoDB to store and query historical user-interaction data. DynamoDB is a fast NoSQL database used by applications that need consistent, single-digit millisecond latency.

Often, customers want to turn their valuable data in DynamoDB into insights by analyzing a copy of their table stored in Amazon S3. Doing this separates their analytical queries from their low-latency critical paths. This data can be the primary source for understanding customers’ past behavior, predicting future behavior, and generating downstream business value. Customers often turn to DynamoDB because of its great scalability and high availability. After a successful launch, many customers want to use the data in DynamoDB to predict future behaviors or provide personalized recommendations.

DynamoDB is a good fit for low-latency reads and writes, but it’s not practical to scan all data in a DynamoDB database to train a model. In this post, I demonstrate how you can use DynamoDB table data copied to Amazon S3 by AWS Data Pipeline to predict customer behavior. I also demonstrate how you can use this data to provide personalized recommendations for customers using Amazon SageMaker. You can also run ad hoc queries using Amazon Athena against the data. DynamoDB recently released on-demand backups to create full table backups with no performance impact. However, it’s not suitable for our purposes in this post, so I chose AWS Data Pipeline instead to create managed backups are accessible from other services.

To do this, I describe how to read the DynamoDB backup file format in Data Pipeline. I also describe how to convert the objects in S3 to a CSV format that Amazon SageMaker can read. In addition, I show how to schedule regular exports and transformations using Data Pipeline. The sample data used in this post is from Bank Marketing Data Set of UCI.

The solution that I describe provides the following benefits:

  • Separates analytical queries from production traffic on your DynamoDB table, preserving your DynamoDB read capacity units (RCUs) for important production requests
  • Automatically updates your model to get real-time predictions
  • Optimizes for performance (so it doesn’t compete with DynamoDB RCUs after the export) and for cost (using data you already have)
  • Makes it easier for developers of all skill levels to use Amazon SageMaker

All code and data set in this post are available in this .zip file.

Solution architecture

The following diagram shows the overall architecture of the solution.

The steps that data follows through the architecture are as follows:

  1. Data Pipeline regularly copies the full contents of a DynamoDB table as JSON into an S3
  2. Exported JSON files are converted to comma-separated value (CSV) format to use as a data source for Amazon SageMaker.
  3. Amazon SageMaker renews the model artifact and update the endpoint.
  4. The converted CSV is available for ad hoc queries with Amazon Athena.
  5. Data Pipeline controls this flow and repeats the cycle based on the schedule defined by customer requirements.

Building the auto-updating model

This section discusses details about how to read the DynamoDB exported data in Data Pipeline and build automated workflows for real-time prediction with a regularly updated model.

Download sample scripts and data

Before you begin, take the following steps:

  1. Download sample scripts in this .zip file.
  2. Unzip the src.zip file.
  3. Find the automation_script.sh file and edit it for your environment. For example, you need to replace 's3://<your bucket>/<datasource path>/' with your own S3 path to the data source for Amazon ML. In the script, the text enclosed by angle brackets—< and >—should be replaced with your own path.
  4. Upload the json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar file to your S3 path so that the ADD jar command in Apache Hive can refer to it.

For this solution, the banking.csv  should be imported into a DynamoDB table.

Export a DynamoDB table

To export the DynamoDB table to S3, open the Data Pipeline console and choose the Export DynamoDB table to S3 template. In this template, Data Pipeline creates an Amazon EMR cluster and performs an export in the EMRActivity activity. Set proper intervals for backups according to your business requirements.

One core node(m3.xlarge) provides the default capacity for the EMR cluster and should be suitable for the solution in this post. Leave the option to resize the cluster before running enabled in the TableBackupActivity activity to let Data Pipeline scale the cluster to match the table size. The process of converting to CSV format and renewing models happens in this EMR cluster.

For a more in-depth look at how to export data from DynamoDB, see Export Data from DynamoDB in the Data Pipeline documentation.

Add the script to an existing pipeline

After you export your DynamoDB table, you add an additional EMR step to EMRActivity by following these steps:

  1. Open the Data Pipeline console and choose the ID for the pipeline that you want to add the script to.
  2. For Actions, choose Edit.
  3. In the editing console, choose the Activities category and add an EMR step using the custom script downloaded in the previous section, as shown below.

Paste the following command into the new step after the data ­­upload step:

s3://#{myDDBRegion}.elasticmapreduce/libs/script-runner/script-runner.jar,s3://<your bucket name>/automation_script.sh,#{output.directoryPath},#{myDDBRegion}

The element #{output.directoryPath} references the S3 path where the data pipeline exports DynamoDB data as JSON. The path should be passed to the script as an argument.

The bash script has two goals, converting data formats and renewing the Amazon SageMaker model. Subsequent sections discuss the contents of the automation script.

Automation script: Convert JSON data to CSV with Hive

We use Apache Hive to transform the data into a new format. The Hive QL script to create an external table and transform the data is included in the custom script that you added to the Data Pipeline definition.

When you run the Hive scripts, do so with the -e option. Also, define the Hive table with the 'org.openx.data.jsonserde.JsonSerDe' row format to parse and read JSON format. The SQL creates a Hive EXTERNAL table, and it reads the DynamoDB backup data on the S3 path passed to it by Data Pipeline.

Note: You should create the table with the “EXTERNAL” keyword to avoid the backup data being accidentally deleted from S3 if you drop the table.

The full automation script for converting follows. Add your own bucket name and data source path in the highlighted areas.

#!/bin/bash
hive -e "
ADD jar s3://<your bucket name>/json-serde-1.3.6-SNAPSHOT-jar-with-dependencies.jar ; 
DROP TABLE IF EXISTS blog_backup_data ;
CREATE EXTERNAL TABLE blog_backup_data (
 customer_id map<string,string>,
 age map<string,string>, job map<string,string>, 
 marital map<string,string>,education map<string,string>, 
 default map<string,string>, housing map<string,string>,
 loan map<string,string>, contact map<string,string>, 
 month map<string,string>, day_of_week map<string,string>, 
 duration map<string,string>, campaign map<string,string>,
 pdays map<string,string>, previous map<string,string>, 
 poutcome map<string,string>, emp_var_rate map<string,string>, 
 cons_price_idx map<string,string>, cons_conf_idx map<string,string>,
 euribor3m map<string,string>, nr_employed map<string,string>, 
 y map<string,string> ) 
ROW FORMAT SERDE 'org.openx.data.jsonserde.JsonSerDe' 
LOCATION '$1/';

INSERT OVERWRITE DIRECTORY 's3://<your bucket name>/<datasource path>/' 
SELECT concat( customer_id['s'],',', 
 age['n'],',', job['s'],',', 
 marital['s'],',', education['s'],',', default['s'],',', 
 housing['s'],',', loan['s'],',', contact['s'],',', 
 month['s'],',', day_of_week['s'],',', duration['n'],',', 
 campaign['n'],',',pdays['n'],',',previous['n'],',', 
 poutcome['s'],',', emp_var_rate['n'],',', cons_price_idx['n'],',',
 cons_conf_idx['n'],',', euribor3m['n'],',', nr_employed['n'],',', y['n'] ) 
FROM blog_backup_data
WHERE customer_id['s'] > 0 ;

After creating an external table, you need to read data. You then use the INSERT OVERWRITE DIRECTORY ~ SELECT command to write CSV data to the S3 path that you designated as the data source for Amazon SageMaker.

Depending on your requirements, you can eliminate or process the columns in the SELECT clause in this step to optimize data analysis. For example, you might remove some columns that have unpredictable correlations with the target value because keeping the wrong columns might expose your model to “overfitting” during the training. In this post, customer_id  columns is removed. Overfitting can make your prediction weak. More information about overfitting can be found in the topic Model Fit: Underfitting vs. Overfitting in the Amazon ML documentation.

Automation script: Renew the Amazon SageMaker model

After the CSV data is replaced and ready to use, create a new model artifact for Amazon SageMaker with the updated dataset on S3.  For renewing model artifact, you must create a new training job.  Training jobs can be run using the AWS SDK ( for example, Amazon SageMaker boto3 ) or the Amazon SageMaker Python SDK that can be installed with “pip install sagemaker” command as well as the AWS CLI for Amazon SageMaker described in this post.

In addition, consider how to smoothly renew your existing model without service impact, because your model is called by applications in real time. To do this, you need to create a new endpoint configuration first and update a current endpoint with the endpoint configuration that is just created.

#!/bin/bash
## Define variable 
REGION=$2
DTTIME=`date +%Y-%m-%d-%H-%M-%S`
ROLE="<your AmazonSageMaker-ExecutionRole>" 


# Select containers image based on region.  
case "$REGION" in
"us-west-2" )
    IMAGE="174872318107.dkr.ecr.us-west-2.amazonaws.com/linear-learner:latest"
    ;;
"us-east-1" )
    IMAGE="382416733822.dkr.ecr.us-east-1.amazonaws.com/linear-learner:latest" 
    ;;
"us-east-2" )
    IMAGE="404615174143.dkr.ecr.us-east-2.amazonaws.com/linear-learner:latest" 
    ;;
"eu-west-1" )
    IMAGE="438346466558.dkr.ecr.eu-west-1.amazonaws.com/linear-learner:latest" 
    ;;
 *)
    echo "Invalid Region Name"
    exit 1 ;  
esac

# Start training job and creating model artifact 
TRAINING_JOB_NAME=TRAIN-${DTTIME} 
S3OUTPUT="s3://<your bucket name>/model/" 
INSTANCETYPE="ml.m4.xlarge"
INSTANCECOUNT=1
VOLUMESIZE=5 
aws sagemaker create-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --algorithm-specification TrainingImage=${IMAGE},TrainingInputMode=File --role-arn ${ROLE}  --input-data-config '[{ "ChannelName": "train", "DataSource": { "S3DataSource": { "S3DataType": "S3Prefix", "S3Uri": "s3://<your bucket name>/<datasource path>/", "S3DataDistributionType": "FullyReplicated" } }, "ContentType": "text/csv", "CompressionType": "None" , "RecordWrapperType": "None"  }]'  --output-data-config S3OutputPath=${S3OUTPUT} --resource-config  InstanceType=${INSTANCETYPE},InstanceCount=${INSTANCECOUNT},VolumeSizeInGB=${VOLUMESIZE} --stopping-condition MaxRuntimeInSeconds=120 --hyper-parameters feature_dim=20,predictor_type=binary_classifier  

# Wait until job completed 
aws sagemaker wait training-job-completed-or-stopped --training-job-name ${TRAINING_JOB_NAME}  --region ${REGION}

# Get newly created model artifact and create model
MODELARTIFACT=`aws sagemaker describe-training-job --training-job-name ${TRAINING_JOB_NAME} --region ${REGION}  --query 'ModelArtifacts.S3ModelArtifacts' --output text `
MODELNAME=MODEL-${DTTIME}
aws sagemaker create-model --region ${REGION} --model-name ${MODELNAME}  --primary-container Image=${IMAGE},ModelDataUrl=${MODELARTIFACT}  --execution-role-arn ${ROLE}

# create a new endpoint configuration 
CONFIGNAME=CONFIG-${DTTIME}
aws sagemaker  create-endpoint-config --region ${REGION} --endpoint-config-name ${CONFIGNAME}  --production-variants  VariantName=Users,ModelName=${MODELNAME},InitialInstanceCount=1,InstanceType=ml.m4.xlarge

# create or update the endpoint
STATUS=`aws sagemaker describe-endpoint --endpoint-name  ServiceEndpoint --query 'EndpointStatus' --output text --region ${REGION} `
if [[ $STATUS -ne "InService" ]] ;
then
    aws sagemaker  create-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}    
else
    aws sagemaker  update-endpoint --endpoint-name  ServiceEndpoint  --endpoint-config-name ${CONFIGNAME} --region ${REGION}
fi

Grant permission

Before you execute the script, you must grant proper permission to Data Pipeline. Data Pipeline uses the DataPipelineDefaultResourceRole role by default. I added the following policy to DataPipelineDefaultResourceRole to allow Data Pipeline to create, delete, and update the Amazon SageMaker model and data source in the script.

{
 "Version": "2012-10-17",
 "Statement": [
 {
 "Effect": "Allow",
 "Action": [
 "sagemaker:CreateTrainingJob",
 "sagemaker:DescribeTrainingJob",
 "sagemaker:CreateModel",
 "sagemaker:CreateEndpointConfig",
 "sagemaker:DescribeEndpoint",
 "sagemaker:CreateEndpoint",
 "sagemaker:UpdateEndpoint",
 "iam:PassRole"
 ],
 "Resource": "*"
 }
 ]
}

Use real-time prediction

After you deploy a model into production using Amazon SageMaker hosting services, your client applications use this API to get inferences from the model hosted at the specified endpoint. This approach is useful for interactive web, mobile, or desktop applications.

Following, I provide a simple Python code example that queries against Amazon SageMaker endpoint URL with its name (“ServiceEndpoint”) and then uses them for real-time prediction.

=== Python sample for real-time prediction ===

#!/usr/bin/env python
import boto3
import json 

client = boto3.client('sagemaker-runtime', region_name ='<your region>' )
new_customer_info = '34,10,2,4,1,2,1,1,6,3,190,1,3,4,3,-1.7,94.055,-39.8,0.715,4991.6'
response = client.invoke_endpoint(
    EndpointName='ServiceEndpoint',
    Body=new_customer_info, 
    ContentType='text/csv'
)
result = json.loads(response['Body'].read().decode())
print(result)
--- output(response) ---
{u'predictions': [{u'score': 0.7528127431869507, u'predicted_label': 1.0}]}

Solution summary

The solution takes the following steps:

  1. Data Pipeline exports DynamoDB table data into S3. The original JSON data should be kept to recover the table in the rare event that this is needed. Data Pipeline then converts JSON to CSV so that Amazon SageMaker can read the data.Note: You should select only meaningful attributes when you convert CSV. For example, if you judge that the “campaign” attribute is not correlated, you can eliminate this attribute from the CSV.
  2. Train the Amazon SageMaker model with the new data source.
  3. When a new customer comes to your site, you can judge how likely it is for this customer to subscribe to your new product based on “predictedScores” provided by Amazon SageMaker.
  4. If the new user subscribes your new product, your application must update the attribute “y” to the value 1 (for yes). This updated data is provided for the next model renewal as a new data source. It serves to improve the accuracy of your prediction. With each new entry, your application can become smarter and deliver better predictions.

Running ad hoc queries using Amazon Athena

Amazon Athena is a serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using standard SQL. Athena is useful for examining data and collecting statistics or informative summaries about data. You can also use the powerful analytic functions of Presto, as described in the topic Aggregate Functions of Presto in the Presto documentation.

With the Data Pipeline scheduled activity, recent CSV data is always located in S3 so that you can run ad hoc queries against the data using Amazon Athena. I show this with example SQL statements following. For an in-depth description of this process, see the post Interactive SQL Queries for Data in Amazon S3 on the AWS News Blog. 

Creating an Amazon Athena table and running it

Simply, you can create an EXTERNAL table for the CSV data on S3 in Amazon Athena Management Console.

=== Table Creation ===
CREATE EXTERNAL TABLE datasource (
 age int, 
 job string, 
 marital string , 
 education string, 
 default string, 
 housing string, 
 loan string, 
 contact string, 
 month string, 
 day_of_week string, 
 duration int, 
 campaign int, 
 pdays int , 
 previous int , 
 poutcome string, 
 emp_var_rate double, 
 cons_price_idx double,
 cons_conf_idx double, 
 euribor3m double, 
 nr_employed double, 
 y int 
)
ROW FORMAT DELIMITED 
FIELDS TERMINATED BY ',' ESCAPED BY '\\' LINES TERMINATED BY '\n' 
LOCATION 's3://<your bucket name>/<datasource path>/';

The following query calculates the correlation coefficient between the target attribute and other attributes using Amazon Athena.

=== Sample Query ===

SELECT corr(age,y) AS correlation_age_and_target, 
 corr(duration,y) AS correlation_duration_and_target, 
 corr(campaign,y) AS correlation_campaign_and_target,
 corr(contact,y) AS correlation_contact_and_target
FROM ( SELECT age , duration , campaign , y , 
 CASE WHEN contact = 'telephone' THEN 1 ELSE 0 END AS contact 
 FROM datasource 
 ) datasource ;

Conclusion

In this post, I introduce an example of how to analyze data in DynamoDB by using table data in Amazon S3 to optimize DynamoDB table read capacity. You can then use the analyzed data as a new data source to train an Amazon SageMaker model for accurate real-time prediction. In addition, you can run ad hoc queries against the data on S3 using Amazon Athena. I also present how to automate these procedures by using Data Pipeline.

You can adapt this example to your specific use case at hand, and hopefully this post helps you accelerate your development. You can find more examples and use cases for Amazon SageMaker in the video AWS 2017: Introducing Amazon SageMaker on the AWS website.

 

Additional Reading

If you found this post useful, be sure to check out Serving Real-Time Machine Learning Predictions on Amazon EMR and Analyzing Data in S3 using Amazon Athena.

 

About the Author

Yong Seong Lee is a Cloud Support Engineer for AWS Big Data Services. He is interested in every technology related to data/databases and helping customers who have difficulties in using AWS services. His motto is “Enjoy life, be curious and have maximum experience.”

 

 

IoT Analytics Now Generally Available

Post Syndicated from Randall Hunt original https://aws.amazon.com/blogs/aws/iot-analytics-now-generally-available/

Today, I’m pleased to announce that, as of April 24th 2018, the AWS IoT Analytics service is generally available. Customers can use IoT Analytics to clean, process, encrich, store, and analyze their connected device data at scale. AWS IoT Analytics is now available in US East (N. Virginia), US West (Oregon), US East (Ohio), and EU (Ireland). In November of last year, my colleague Tara Walker wrote an excellent post that walks through some of the features of the AWS IoT Analytics service and Ben Kehoe (an AWS Community Hero and Research Scientist at iRobot) spoke at AWS Re:Invent about replacing iRobot’s existing “rube goldberg machine” for forwarding data into an elasticsearch cluster with AWS IoT Analytics.

Iterating on customer feedback received during the service preview the AWS IoT Analytics team has added a number of new features including the ability to ingest data from external souces using the BatchPutMessage API, the ability to set a data retention policy on stored data, the ability to reprocess existing data, preview pipeline results, and preview messages from channels with the SampleChannelData API.

Let’s cover the core concepts of IoT Analytics and then walk through an example.

AWS IoT Analytics Concepts

AWS IoT Analytics can be broken down into a few simple concepts. For data preparation customers have: Channels, Pipelines, and Data Stores. For analyzing data customers have: Datasets and Notebooks.

Data Preparation

  • Channels are the entry point into IoT Analytics and they collect data from an existing IoT Core MQTT topic or from external sources that send messages to the channel using the Ingestion API. Channels are elastically scalable and consume messages in Binary or JSON format. Channels also immutably store raw device data for easily reprocessing using different logic if your needs change.
  • Pipelines consume messages from channels and allow you to process messages with steps, called activities, such as filtering on attributes, transforming the content of the message by adding or remvoing fields, invoking lambda functions for complex transformations and adding data from external data sources, or even enriching the messages with data from IoT Core. Pipelines output their data to a Data Store.
  • Data Stores are a queryable IoT-optimized data storage solution for the output of your pipelines. Data stores support custom retention periods to optimize costs. When a customer queries a Data Store the result is put into a Dataset.

Data Analytics

  • Datasets are similar to a view in a SQL database. Customers create a dataset by running a query against a data store. Data sets can be generated manually or on a recurring schedule.
  • Notebooks are Amazon SageMaker hosted Jupyter notebooks that let customers analyze their data with custom code and even build or train ML models on the data. IoT Analytics offers several notebook templates with pre-authored models for common IoT use cases such as Predictive Maintenance, Anomaly Detection, Fleet Segmentation, and Forecasting.

Additionally, you can use IoT analytics as a data source for Amazon QuickSight for easy visualizations of your data. You can find pricing information for each of these services on the AWS IoT Analytics Pricing Page.

IoT Analytics Walkthrough

While this walkthrough uses the console everything shown here is equally easy to do with the CLI. When we first navigate to the console we have a helpful guide telling us to build a channel, pipeline, and a data store:

Our first step is to create a channel. I already have some data into an MQTT channel with IoT core so I’ll select that channel. First we’ll name the channel and select a retention period.

Now, I’ll select my IoT Core topic and grab the data. I can also post messages directly into the channel with the PutMessages APIs.

Now that I have a channel my next step is to create a pipeline. To do this I’ll select “Create a pipeline from this channel” from the “Actions” drop down.

Now, I’ll walk through the pipeline wizard giving my pipeline a name and a source.

I’ll select which of the message attributes the pipeline should expect. This can draw from the channel with the sampling API and guess at which attributes are needed or I could upload a specification in JSON.

Next I define the pipeline activities. If I’m dealing with binary data I need a lambda function to first deserialize the message into JSON so the other filter functions can operate on it. I can create filters, calculate attributes based on other attributes, and I can also enrich the message with metadata from IoT core registry.

For now I just want to filter out some messages and make a small transform with a Lambda function.

Finally, I choose or create a data store to output the results of my pipeline.

Now that I have a data store, I can create a view of that data by creating a data set.

I’ll just select all the data from the data store for this dataset but I could also select individual attributes as needed.

I have a data set! I can adjust the cron expression in the schedule to re-run this as frequently or infrequently as I wish.

If I want to create a model from my data I can create a SageMaker powered Jupyter notebook. There are a few templates that are great starting points like anomaly detection or output forecasting.

Here you can see an example of the anomaly detection notebook.

Finally, if I want to create simple visualizations of my data I can use QuickSight to bring in an IoT Analytics data set.

Let Us Know

I’m excited to see what customers build with AWS IoT Analytics. My colleagues on the IoT teams are eager to hear your feedback about the service so please let us know in the comments or on Twitter what features you want to see.

– Randall

Security updates for Tuesday

Post Syndicated from ris original https://lwn.net/Articles/753257/rss

Security updates have been issued by Fedora (cups-filters, ghostscript, glusterfs, PackageKit, qpdf, and xen), Mageia (anki, libofx, ming, sox, webkit2, and xdg-user-dirs), Oracle (corosync, java-1.7.0-openjdk, and pcs), Red Hat (java-1.7.0-openjdk), Scientific Linux (corosync, firefox, gcc, glibc, golang, java-1.7.0-openjdk, java-1.8.0-openjdk, kernel, krb5, librelp, libvncserver, libvorbis, ntp, openssh, openssl, PackageKit, patch, pcs, policycoreutils, qemu-kvm, and xdg-user-dirs), Slackware (libwmf and mozilla), and Ubuntu (apache2, ghostscript, mysql-5.7, wavpack, and webkit2gtk).