Tag Archives: sts

Announcing an update to IAM role trust policy behavior

Post Syndicated from Mark Ryland original https://aws.amazon.com/blogs/security/announcing-an-update-to-iam-role-trust-policy-behavior/

AWS Identity and Access Management (IAM) is changing an aspect of how role trust policy evaluation behaves when a role assumes itself. Previously, roles implicitly trusted themselves from a role trust policy perspective if they had identity-based permissions to assume themselves. After receiving and considering feedback from customers on this topic, AWS is changing role assumption behavior to always require self-referential role trust policy grants. This change improves consistency and visibility with regard to role behavior and privileges. This change allows customers to create and understand role assumption permissions in a single place (the role trust policy) rather than two places (the role trust policy and the role identity policy). It increases the simplicity of role trust permission management: “What you see [in the trust policy] is what you get.”

Therefore, beginning today, for any role that has not used the identity-based behavior since June 30, 2022, a role trust policy must explicitly grant permission to all principals, including the role itself, that need to assume it under the specified conditions. Removal of the role’s implicit self-trust improves consistency and increases visibility into role assumption behavior.

Most AWS customers will not be impacted by the change at all. Only a tiny percentage (approximately 0.0001%) of all roles are involved. Customers whose roles have recently used the previous implicit trust behavior are being notified, beginning today, about those roles, and may continue to use this behavior with those roles until February 15, 2023, to allow time for making the necessary updates to code or configuration. Or, if these customers are confident that the change will not impact them, they can opt out immediately by substituting in new roles, as discussed later in this post.

The first part of this post briefly explains the change in behavior. The middle sections answer practical questions like: “why is this happening?,” “how might this change impact me?,” “which usage scenarios are likely to be impacted?,” and “what should I do next?” The usage scenario section is important because it shows that, based on our analysis, the self-assuming role behavior exhibited by code or human users is very likely to be unnecessary and counterproductive. Finally, for security professionals interested in better understanding the reasons for the old behavior, the rationale for the change, as well as its possible implications, the last section reviews a number of core IAM concepts and digs in to additional details.

What is changing?

Until today, an IAM role implicitly trusted itself. Consider the following role trust policy attached to the role named RoleA in AWS account 123456789012.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": "arn:aws:iam::123456789012:role/RoleB"
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

This role trust policy grants role assumption access to the role named RoleB in the same account. However, if the corresponding identity-based policy for RoleA grants the sts:AssumeRole action with respect to itself, then RoleA could also assume itself. Therefore, there were actually two roles that could assume RoleA: the explicitly permissioned RoleB, and RoleA, which implicitly trusted itself as a byproduct of the IAM ownership model (explained in detail in the final section). Note that the identity-based permission that RoleA must have to assume itself is not required in the case of RoleB, and indeed an identity-based policy associated with RoleB that references other roles is not sufficient to allow RoleB to assume them. The resource-based permission granted by RoleA’s trust policy is both necessary and sufficient to allow RoleB to assume RoleA.

Although earlier we summarized this behavior as “implicit self-trust,” the key point here is that the ability of Role A to assume itself is not actually implicit behavior. The role’s self-referential permission had to be explicit in one place or the other (or both): either in the role’s identity-based policy (perhaps based on broad wildcard permissions), or its trust policy. But unlike the case with other principals and role trust, an IAM administrator would have to look in two different policies to determine whether a role could assume itself.

As of today, for any new role, or any role that has not recently assumed itself while relying on the old behavior, IAM administrators must modify the previously shown role trust policy as follows to allow RoleA to assume itself, regardless of the privileges granted by its identity-based policy:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": [
                    "arn:aws:iam::123456789012:role/RoleB",
                    "arn:aws:iam::123456789012:role/RoleA"
                ]
            },
            "Action": "sts:AssumeRole"
        }
    ]
}

This change makes role trust behavior clearer and more consistent to understand and manage, whether directly by humans or as embodied in code.

How might this change impact me?

As previously noted, most customers will not be impacted by the change at all. For those customers who do use the prior implicit trust grant behavior, AWS will work with you to eliminate your usage prior to February 15, 2023. Here are more details for the two cases of customers who have not used the behavior, and those who have.

If you haven’t used the implicit trust behavior since June 30, 2022

Beginning today, if you have not used the old behavior for a given role at any time since June 30, 2022, you will now experience the new behavior. Those existing roles, as well as any new roles, will need an explicit reference in their own trust policy in order to assume themselves. If you have roles that are used only very occasionally, such as once per quarter for a seldom-run batch process, you should identify those roles and if necessary either remove the dependency on the old behavior or update their role trust policies to include the role itself prior to their next usage (see the second sample policy above for an example).

If you have used the implicit trust behavior since June 30, 2022

If you have a role that has used the implicit trust behavior since June 30, 2022, then you will continue to be able to do so with that role until February 15, 2023. AWS will provide you with notice referencing those roles beginning today through your AWS Health Dashboard and will also send an email with the relevant information to the account owner and security contact. We are allowing time for you to make any necessary changes to your existing processes, code, or configurations to prepare for removal of the implicit trust behavior. If you can’t change your processes or code, you can continue to use the behavior by making a configuration change—namely, by updating the relevant role trust policies to reference the role itself. On the other hand, you can opt out of the old behavior at any time by creating a new role with a different Amazon Resource Name (ARN) with the desired identity-based and trust-policy-based permissions and substituting it for any older role that was identified as using the implicit trust behavior. (The new role will not be allow-listed, because the allow list is based on role ARNs.) You can also modify an existing allow-listed role’s trust policy to explicitly deny access to itself. See the “What should I do next?” section for more information.

Notifications and retirement

As we previously noted, starting today, accounts with existing roles that use the implicit self-assume role assumption behavior will be notified of this change by email and through their AWS Health Dashboard. Those roles have been allow-listed, and so for now their behavior will continue as before. After February 15, 2023, the old behavior will be retired for all roles and all accounts. IAM Documentation has been updated to make clear the new behavior.

After the old behavior is retired from the allow-listed roles and accounts, role sessions that make self-referential role assumption calls will fail with an Access Denied error unless the role’s trust policy explicitly grants the permission directly through a role ARN. Another option is to grant permission indirectly through an ARN to the root principal in the trust policy that acts as a delegation of privilege management, after which permission grants in identity-based policies determine access, similar to the typical cross-account case.

Which usage scenarios are likely to be impacted?

Users often attach an IAM role to an Amazon Elastic Compute Cloud (Amazon EC2) instance, an Amazon Elastic Container Service (Amazon ECS) task, or AWS Lambda function. Attaching a role to one of these runtime environments enables workloads to use short-term session credentials based on that role. For example, when an EC2 instance is launched, AWS automatically creates a role session and assigns it to the instance. An AWS best practice is for the workload to use these credentials to issue AWS API calls without explicitly requesting short-term credentials through sts:AssumeRole calls.

However, examples and code snippets commonly available on internet forums and community knowledge sharing sites might incorrectly suggest that workloads need to call sts:AssumeRole to establish short-term sessions credentials for operation within those environments.

We analyzed AWS Security Token Service (AWS STS) service metadata about role self-assumption in order to understand the use cases and possible impact of the change. What the data shows is that in almost all cases this behavior is occurring due to unnecessarily reassuming the role in an Amazon EC2, Amazon ECS, Amazon Elastic Kubernetes Services (EKS), or Lambda runtime environment already provided by the environment. There are two exceptions, discussed at the end of this section under the headings, “self-assumption with a scoped-down policy” and “assuming a target compute role during development.”

There are many variations on this theme, but overall, most role self-assumption occurs in scenarios where the person or code is unnecessarily reassuming the role that the code was already running as. Although this practice and code style can still work with a configuration change (by adding an explicit self-reference to the role trust policy), the better practice will almost always be to remove this unnecessary behavior or code from your AWS environment going forward. By removing this unnecessary behavior, you save CPU, memory, and network resources.

Common mistakes when using Amazon EKS

Some users of the Amazon EKS service (or possibly their shell scripts) use the command line interface (CLI) command aws eks get-token to obtain an authentication token for use in managing a Kubernetes cluster. The command takes as an optional parameter a role ARN. That parameter allows a user to assume another role other than the one they are currently using before they call get-token. However, the CLI cannot call that API without already having an IAM identity. Some users might believe that they need to specify the role ARN of the role they are already using. We have updated the Amazon EKS documentation to make clear that this is not necessary.

Common mistakes when using AWS Lambda

Another example is the use of an sts:AssumeRole API call from a Lambda function. The function is already running in a preassigned role provided by user configuration within the Lambda service, or else it couldn’t successfully call any authenticated API action, including sts:AssumeRole. However, some Lambda functions call sts:AssumeRole with the target role being the very same role that the Lambda function has already been provided as part of its configuration. This call is unnecessary.

AWS Software Development Kits (SDKs) all have support for running in AWS Lambda environments and automatically using the credentials provided in that environment. We have updated the Lambda documentation to make clear that such STS calls are unnecessary.

Common mistakes when using Amazon ECS

Customers can associate an IAM role with an Amazon ECS task to give the task AWS credentials to interact with other AWS resources.

We detected ECS tasks that call sts:AssumeRole on the same role that was provided to the ECS task. Amazon ECS makes the role’s credentials available inside the compute resources of the ECS task, whether on Amazon EC2 or AWS Fargate, and these credentials can be used to access AWS services or resources as the IAM role associated with the ECS talk, without being called through sts:AssumeRole. AWS handles renewing the credentials available on ECS tasks before the credentials expire. AWS STS role assumption calls are unnecessary, because they simply create a new set of the same temporary role session credentials.

AWS SDKs all have support for running in Amazon ECS environments and automatically using the credentials provided in that ECS environment. We have updated the Amazon ECS documentation to make clear that calling sts:AssumeRole for an ECS task is unnecessary.

Common mistakes when using Amazon EC2

Users can configure an Amazon EC2 instance to contain an instance profile. This instance profile defines the IAM role that Amazon EC2 assigns the compute instance when it is launched and begins to run. The role attached to the EC2 instance enables your code to send signed requests to AWS services. Without this attached role, your code would not be able to access your AWS resources (nor would it be able to call sts:AssumeRole). The Amazon EC2 service handles renewing these temporary role session credentials that are assigned to the instance before they expire.

We have observed that workloads running on EC2 instances call sts:AssumeRole to assume the same role that is already associated with the EC2 instance and use the resulting role-session for communication with AWS services. These role assumption calls are unnecessary, because they simply create a new set of the same temporary role session credentials.

AWS SDKs all have support for running in Amazon EC2 environments and automatically using the credentials provided in that EC2 environment. We have updated the Amazon EC2 documentation to make clear that calling sts:AssumeRole for an EC2 instance with a role assigned is unnecessary.

For information on creating an IAM role, attaching that role to an EC2 instance, and launching an instance with an attached role, see “IAM roles for Amazon EC2” in the Amazon EC2 User Guide.

Other common mistakes

If your use case does not use any of these AWS execution environments, you might still experience an impact from this change. We recommend that you examine the roles in your account and identify scenarios where your code (or human use through the AWS CLI) results in a role assuming itself. We provide Amazon Athena and AWS CloudTrail Lake queries later in this post to help you locate instances where a role assumed itself. For each instance, you can evaluate whether a role assuming itself is the right operation for your needs.

Self-assumption with a scoped-down policy

The first pattern we have observed that is not a mistake is the use of self-assumption combined with a scoped-down policy. Some systems use this approach to provide different privileges for different use cases, all using the same underlying role. Customers who choose to continue with this approach can do so by adding the role to its own trust policy. While the use of scoped-down policies and the associated least-privilege approach to permissions is a good idea, we recommend that customers switch to using a second generic role and assume that role along with the scoped-down policy rather than using role self-assumption. This approach provides more clarity in CloudTrail about what is happening, and limits the possible iterations of role assumption to one round, since the second role should not be able to assume the first. Another possible approach in some cases is to limit subsequent assumptions is by using an IAM condition in the role trust policy that is no longer satisfied after the first role assumption. For example, for Lambda functions, this would be done by a condition checking for the presence of the “lambda:SourceFunctionArn” property; for EC2, by checking for presence of “ec2:SourceInstanceARN.”

Assuming an expected target compute role during development

Another possible reason for role self-assumption may result from a development practice in which developers attempt to normalize the roles that their code is running in between scenarios in which role credentials are not automatically provided by the environment, and scenarios where they are. For example, imagine a developer is working on code that she expects to run as a Lambda function, but during development is using her laptop to do some initial testing of the code. In order to provide the same execution role as is expected later in product, the developer might configure the role trust policy to allow assumption by a principal readily available on the laptop (an IAM Identity Center role, for example), and then assume the expected Lambda function execution role when the code is initializing. The same approach could be used on a build and test server. Later, when the code is deployed to Lambda, the actual role is already available and in use, but the code need not be modified in order to provide the same post-role-assumption behavior that existing outside of Lambda: the unmodified code can automatically assume what is in this case the same role, and proceed. While this approach is not illogical, as with the scope-down policy case we recommend that customers configure distinct roles for assumption both in development and test environments as well as later production environments. Again, this approach provides more clarity in CloudTrail about what is happening, and limits the possible iterations of role assumption to one round, since the second role should not be able to assume the first.

What should I do next?

If you receive an email or AWS Health Dashboard notification for an account, we recommend that you review your existing role trust policies and corresponding code. For those roles, you should remove the dependency on the old behavior, or if you can’t, update those role trust policies with an explicit self-referential permission grant. After the grace period expires on February 15, 2023, you will no longer be able to use the implicit self-referential permission grant behavior.

If you currently use the old behavior and need to continue to do so for a short period of time in the context of existing infrastructure as code or other automated processes that create new roles, you can do so by adding the role’s ARN to its own trust policy. We strongly encourage you to treat this as a temporary stop-gap measure, because in almost all cases it should not be necessary for a role to be able to assume itself, and the correct solution is to change the code that results in the unnecessary self-assumption. If for some reason that self-service solution is not sufficient, you can reach out to AWS Support to seek an accommodation of your use case for new roles or accounts.

If you make any necessary code or configuration changes and want to remove roles that are currently allow-listed, you can also ask AWS Support to remove those roles from the allow list so that their behavior follows the new model. Or, as previously noted, you can opt out of the old behavior at any time by creating a new role with a different ARN that has the desired identity-based and trust-policy–based permissions and substituting it for the allow-listed role. Another stop-gap type of option is to add an explicit deny that references the role to its own trust policy.

If you would like to understand better the history of your usage of role self-assumption in a given account or organization, you can follow these instructions on querying CloudTrail data with Athena and then use the following Athena query against your account or organization CloudTrail data, as stored in Amazon Simple Storage Services (Amazon S3). The results of the query can help you understand the scenarios and conditions and code involved. Depending on the size of your CloudTrail logs, you may need to follow the partitioning instructions to query subsets of your CloudTrail logs sequentially. If this query yields no results, the role self-assumption scenario described in this blog post has never occurred within the analyzed CloudTrail dataset.

SELECT eventid, eventtime, userIdentity.sessioncontext.sessionissuer.arn as RoleARN, split_part(userIdentity.principalId, ':', 2) as RoleSessionName from cloudtrail_logs t CROSS JOIN UNNEST(t.resources) unnested (resources_entry) where eventSource = 'sts.amazonaws.com' and eventName = 'AssumeRole' and userIdentity.type = 'AssumedRole' and errorcode IS NULL and substr(userIdentity.sessioncontext.sessionissuer.arn,12) = substr(unnested.resources_entry.ARN,12)

As another option, you can follow these instructions to set up CloudTrail Lake to perform a similar analysis. CloudTrail Lake allows richer, faster queries without the need to partition the data. As of September 20, 2022, CloudTrail Lake now supports import of CloudTrail logs from Amazon S3. This allows you to perform a historical analysis even if you haven’t previously enabled CloudTrail Lake. If this query yields no results, the scenario described in this blog post has never occurred within the analyzed CloudTrail dataset.

SELECT eventid, eventtime, userIdentity.sessioncontext.sessionissuer.arn as RoleARN, userIdentity.principalId as RoleIdColonRoleSessionName from $EDS_ID where eventSource = 'sts.amazonaws.com' and eventName = 'AssumeRole' and userIdentity.type = 'AssumedRole' and errorcode IS NULL and userIdentity.sessioncontext.sessionissuer.arn = element_at(resources,1).arn

Understanding the change: more details

To better understand the background of this change, we need to review the IAM basics of identity-based policies and resource-based policies, and then explain some subtleties and exceptions. You can find additional overview material in the IAM documentation.

The structure of each IAM policy follows the same basic model: one or more statements with an effect (allow or deny), along with principals, actions, resources, and conditions. Although the identity-based and resource-based policies share the same basic syntax and semantics, the former is associated with a principal, the latter with a resource. The main difference between the two is that identity-based policies do not specify the principal, because that information is supplied implicitly by associating the policy with a given principal. On the other hand, resource policies do not specify an arbitrary resource, because at least the primary identifier of the resource (for example, the bucket identifier of an S3 bucket) is supplied implicitly by associating the policy with that resource. Note that an IAM role is the only kind of AWS object that is both a principal and a resource.

In most cases, access to a resource within the same AWS account can be granted by either an identity-based policy or a resource-based policy. Consider an Amazon S3 example. An identity-based policy attached to an IAM principal that allows the s3:GetObject action does not require an equivalent grant in the S3 bucket resource policy. Conversely, an s3:GetObject permission grant in a bucket’s resource policy is all that is needed to allow a principal in the same account to call the API with respect to that bucket; an equivalent identity-based permission is not required. Either the identity-based policy or the resource-based policy can grant the necessary permission. For more information, see IAM policy types: How and when to use them.

However, in order to more tightly govern access to certain security-sensitive resources, such as AWS Key Management Service (AWS KMS) keys and IAM roles, those resource policies need to grant access to the IAM principal explicitly, even within the same AWS account. A role trust policy is the resource policy associated with a role that specifies which IAM principals can assume the role by using one of the sts:AssumeRole* API calls. For example, in order for RoleB to assume RoleA in the same account, whether or not RoleB’s identity-based policy explicitly allows it to assume RoleA, RoleA’s role trust policy must grant access to RoleB. Within the same account, an identity-based permission by itself is not sufficient to allow assumption of a role. On the other hand, a resource-based permission—a grant of access in the role trust policy—is sufficient. (Note that it’s possible to construct a kind of hybrid permission to a role by using both its resource policy and other identity-based policies. In that case, the role trust policy grants permission to the root principal ARN; after that, the identity-based policy of a principal in that account would need to explicitly grant permission to assume that role. This is analogous to the typical cross-account role trust scenario.)

Until now, there has been a nonintuitive exception to these rules for situations where a role assumes itself. Since a role is both a principal (potentially with an identity-based policy) and a resource (with a resource-based policy), it is in the unique position of being both a subject and an object within the IAM system, as well as being an object owned by itself rather than its containing account. Due to this ownership model, roles with identity-based permission to assume themselves implicitly trusted themselves as resources, and vice versa. That is to say, roles that had the privilege as principals to assume themselves implicitly trusted themselves as resources, without an explicit self-referential Allow in the role trust policy. Conversely, a grant of permission in the role trust policy was sufficient regardless of whether there was a grant in the same role’s identity-based policy. Thus, in the self-assumption case, roles behaved like most other resources in the same account: only a single permission was required to allow role self-assumption, either on the identity side or the resource side of their dual-sided nature. Because of a role’s implicit trust of itself as a resource, the role’s trust policy—which might otherwise limit assumption of the role with properties such as actions and conditions—was not applied, unless it contained an explicit deny of itself.

The following example is a role trust policy attached to the role named RoleA in account 123456789012. It grants explicit access only to the role named RoleB.

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": "arn:aws:iam::123456789012:role/RoleB"
            },
            "Action": ["sts:AssumeRole", "sts:TagSession"],
            "Condition": {
                "StringEquals": {
                    "aws:PrincipalTag/project": "BlueSkyProject"
                }
            }
        }
    ]
}

Assuming that the corresponding identity-based policy for RoleA granted the sts:AssumeRole action with regard to RoleA, this role trust policy provided that there were two roles that could assume RoleA: RoleB (explicitly referenced in the trust policy) and RoleA (assuming it was explicitly referenced in its identity policy). RoleB could assume RoleA only if it had the principal tag project:BlueSkyProject because of the trust policy condition. (The sts:TagSession permission is needed here in case tags need to be added by the caller as parted of the RoleAssumption call.) RoleA, on the other hand, did not need to meet that condition because it relied on a different explicit permission—the one granted in the identity-based policy. RoleA would have needed the principal tag project:BlueSkyProject to meet the trust policy condition if and only if it was relying on the trust policy to gain access through the sts:AssumeRole action; that is, in the case where its identity-based policy did not provide the needed privilege.

As we previously noted, after considering feedback from customers on this topic, AWS has decided that requiring self-referential role trust policy grants even in the case where the identity-based policy also grants access is the better approach to delivering consistency and visibility with regard to role behavior and privileges. Therefore, as of today, r­ole assumption behavior requires an explicit self-referential permission in the role trust policy, and the actions and conditions within that policy must also be satisfied, regardless of the permissions expressed in the role’s identity-based policy. (If permissions in the identity-based policy are present, they must also be satisfied.)

Requiring self-reference in the trust policy makes role trust policy evaluation consistent regardless of which role is seeking to assume the role. Improved consistency makes role permissions easier to understand and manage, whether through human inspection or security tooling. This change also eliminates the possibility of continuing the lifetime of an otherwise temporary credential without explicit, trackable grants of permission in trust policies. It also means that trust policy constraints and conditions are enforced consistently, regardless of which principal is assuming the role. Finally, as previously noted, this change allows customers to create and understand role assumption permissions in a single place (the role trust policy) rather than two places (the role trust policy and the role identity policy). It increases the simplicity of role trust permission management: “what you see [in the trust policy] is what you get.”

Continuing with the preceding example, if you need to allow a role to assume itself, you now must update the role trust policy to explicitly allow both RoleB and RoleA. The RoleA trust policy now looks like the following:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
                "AWS": [
                    "arn:aws:iam::123456789012:role/RoleB",
                    "arn:aws:iam::123456789012:role/RoleA"
                ]
            },
            "Action": ["sts:AssumeRole", "sts:TagSession"],
            "Condition": {
                "StringEquals": {
					"aws:PrincipalTag/project": "BlueSkyProject"
				}
            }
        }
    ]
}

Without this new principal grant, the role can no longer assume itself. The trust policy conditions are also applied, even if the role still has unconditioned access to itself in its identity-based policy.

Conclusion

In this blog post we’ve reviewed the old and new behavior of role assumption in the case where a role seeks to assume itself. We’ve seen that, according to our analysis of service metadata, the vast majority of role self-assumption behavior that relies solely on identity-based privileges is totally unnecessary, because the code (or human) who calls sts:AssumeRole is already, without realizing it, using the role’s credentials to call the AWS STS API. Eliminating that mistake will improve performance and decrease resource consumption. We’ve also explained in more depth the reasons for the old behavior and the reasons for making the change, and provided Athena and CloudTrail Lake queries that you can use to examine past or (in the case of allow-listed roles) current self-assumption behavior in your own environments. You can reach out to AWS Support or your customer account team if you need help in this effort.

If you currently use the old behavior and need to continue to do so, your primary option is to create an explicit allow for the role in its own trust policy. If that option doesn’t work due to operational constraints, you can reach out to AWS Support to seek an accommodation of your use case for new roles or new accounts. You can also ask AWS Support to remove roles from the allow-list if you want their behavior to follow the new model.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new IAM-tagged discussion on AWS re:Post or contact AWS Support.

AWS would like to thank several customers and partners who highlighted this behavior as something they found surprising and unhelpful, and asked us to consider making this change. We would also like to thank independent security researcher Ryan Gerstenkorn who engaged with AWS on this topic and worked with us prior to this update.

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Mark Ryland

Mark Ryland

Mark is the director of the Office of the CISO for AWS. He has over 30 years of experience in the technology industry and has served in leadership roles in cybersecurity, software engineering, distributed systems, technology standardization and public policy. Previously, he served as the Director of Solution Architecture and Professional Services for the AWS World Public Sector team.

Stephen Whinston

Stephen Whinston

Stephen is a Senior Product Manager with the AWS Identity and Access Management organization. Prior to Amazon, Stephen worked in product management for cloud service and identity management providers. Stephen holds degrees in computer science and an MBA from the University of Colorado Leeds School of Business. Outside of work, Stephen enjoys his family time and the Pacific Northwest.

AWS Online Tech Talks – June 2018

Post Syndicated from Devin Watson original https://aws.amazon.com/blogs/aws/aws-online-tech-talks-june-2018/

AWS Online Tech Talks – June 2018

Join us this month to learn about AWS services and solutions. New this month, we have a fireside chat with the GM of Amazon WorkSpaces and our 2nd episode of the “How to re:Invent” series. We’ll also cover best practices, deep dives, use cases and more! Join us and register today!

Note – All sessions are free and in Pacific Time.

Tech talks featured this month:

 

Analytics & Big Data

June 18, 2018 | 11:00 AM – 11:45 AM PTGet Started with Real-Time Streaming Data in Under 5 Minutes – Learn how to use Amazon Kinesis to capture, store, and analyze streaming data in real-time including IoT device data, VPC flow logs, and clickstream data.
June 20, 2018 | 11:00 AM – 11:45 AM PT – Insights For Everyone – Deploying Data across your Organization – Learn how to deploy data at scale using AWS Analytics and QuickSight’s new reader role and usage based pricing.

 

AWS re:Invent
June 13, 2018 | 05:00 PM – 05:30 PM PTEpisode 2: AWS re:Invent Breakout Content Secret Sauce – Hear from one of our own AWS content experts as we dive deep into the re:Invent content strategy and how we maintain a high bar.
Compute

June 25, 2018 | 01:00 PM – 01:45 PM PTAccelerating Containerized Workloads with Amazon EC2 Spot Instances – Learn how to efficiently deploy containerized workloads and easily manage clusters at any scale at a fraction of the cost with Spot Instances.

June 26, 2018 | 01:00 PM – 01:45 PM PTEnsuring Your Windows Server Workloads Are Well-Architected – Get the benefits, best practices and tools on running your Microsoft Workloads on AWS leveraging a well-architected approach.

 

Containers
June 25, 2018 | 09:00 AM – 09:45 AM PTRunning Kubernetes on AWS – Learn about the basics of running Kubernetes on AWS including how setup masters, networking, security, and add auto-scaling to your cluster.

 

Databases

June 18, 2018 | 01:00 PM – 01:45 PM PTOracle to Amazon Aurora Migration, Step by Step – Learn how to migrate your Oracle database to Amazon Aurora.
DevOps

June 20, 2018 | 09:00 AM – 09:45 AM PTSet Up a CI/CD Pipeline for Deploying Containers Using the AWS Developer Tools – Learn how to set up a CI/CD pipeline for deploying containers using the AWS Developer Tools.

 

Enterprise & Hybrid
June 18, 2018 | 09:00 AM – 09:45 AM PTDe-risking Enterprise Migration with AWS Managed Services – Learn how enterprise customers are de-risking cloud adoption with AWS Managed Services.

June 19, 2018 | 11:00 AM – 11:45 AM PTLaunch AWS Faster using Automated Landing Zones – Learn how the AWS Landing Zone can automate the set up of best practice baselines when setting up new

 

AWS Environments

June 21, 2018 | 11:00 AM – 11:45 AM PTLeading Your Team Through a Cloud Transformation – Learn how you can help lead your organization through a cloud transformation.

June 21, 2018 | 01:00 PM – 01:45 PM PTEnabling New Retail Customer Experiences with Big Data – Learn how AWS can help retailers realize actual value from their big data and deliver on differentiated retail customer experiences.

June 28, 2018 | 01:00 PM – 01:45 PM PTFireside Chat: End User Collaboration on AWS – Learn how End User Compute services can help you deliver access to desktops and applications anywhere, anytime, using any device.
IoT

June 27, 2018 | 11:00 AM – 11:45 AM PTAWS IoT in the Connected Home – Learn how to use AWS IoT to build innovative Connected Home products.

 

Machine Learning

June 19, 2018 | 09:00 AM – 09:45 AM PTIntegrating Amazon SageMaker into your Enterprise – Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment.

June 21, 2018 | 09:00 AM – 09:45 AM PTBuilding Text Analytics Applications on AWS using Amazon Comprehend – Learn how you can unlock the value of your unstructured data with NLP-based text analytics.

 

Management Tools

June 20, 2018 | 01:00 PM – 01:45 PM PTOptimizing Application Performance and Costs with Auto Scaling – Learn how selecting the right scaling option can help optimize application performance and costs.

 

Mobile
June 25, 2018 | 11:00 AM – 11:45 AM PTDrive User Engagement with Amazon Pinpoint – Learn how Amazon Pinpoint simplifies and streamlines effective user engagement.

 

Security, Identity & Compliance

June 26, 2018 | 09:00 AM – 09:45 AM PTUnderstanding AWS Secrets Manager – Learn how AWS Secrets Manager helps you rotate and manage access to secrets centrally.
June 28, 2018 | 09:00 AM – 09:45 AM PTUsing Amazon Inspector to Discover Potential Security Issues – See how Amazon Inspector can be used to discover security issues of your instances.

 

Serverless

June 19, 2018 | 01:00 PM – 01:45 PM PTProductionize Serverless Application Building and Deployments with AWS SAM – Learn expert tips and techniques for building and deploying serverless applications at scale with AWS SAM.

 

Storage

June 26, 2018 | 11:00 AM – 11:45 AM PTDeep Dive: Hybrid Cloud Storage with AWS Storage Gateway – Learn how you can reduce your on-premises infrastructure by using the AWS Storage Gateway to connecting your applications to the scalable and reliable AWS storage services.
June 27, 2018 | 01:00 PM – 01:45 PM PTChanging the Game: Extending Compute Capabilities to the Edge – Discover how to change the game for IIoT and edge analytics applications with AWS Snowball Edge plus enhanced Compute instances.
June 28, 2018 | 11:00 AM – 11:45 AM PTBig Data and Analytics Workloads on Amazon EFS – Get best practices and deployment advice for running big data and analytics workloads on Amazon EFS.

Build your own weather station with our new guide!

Post Syndicated from Richard Hayler original https://www.raspberrypi.org/blog/build-your-own-weather-station/

One of the most common enquiries I receive at Pi Towers is “How can I get my hands on a Raspberry Pi Oracle Weather Station?” Now the answer is: “Why not build your own version using our guide?”

Build Your Own weather station kit assembled

Tadaaaa! The BYO weather station fully assembled.

Our Oracle Weather Station

In 2016 we sent out nearly 1000 Raspberry Pi Oracle Weather Station kits to schools from around the world who had applied to be part of our weather station programme. In the original kit was a special HAT that allows the Pi to collect weather data with a set of sensors.

The original Raspberry Pi Oracle Weather Station HAT – Build Your Own Raspberry Pi weather station

The original Raspberry Pi Oracle Weather Station HAT

We designed the HAT to enable students to create their own weather stations and mount them at their schools. As part of the programme, we also provide an ever-growing range of supporting resources. We’ve seen Oracle Weather Stations in great locations with a huge differences in climate, and they’ve even recorded the effects of a solar eclipse.

Our new BYO weather station guide

We only had a single batch of HATs made, and unfortunately we’ve given nearly* all the Weather Station kits away. Not only are the kits really popular, we also receive lots of questions about how to add extra sensors or how to take more precise measurements of a particular weather phenomenon. So today, to satisfy your demand for a hackable weather station, we’re launching our Build your own weather station guide!

Build Your Own Raspberry Pi weather station

Fun with meteorological experiments!

Our guide suggests the use of many of the sensors from the Oracle Weather Station kit, so can build a station that’s as close as possible to the original. As you know, the Raspberry Pi is incredibly versatile, and we’ve made it easy to hack the design in case you want to use different sensors.

Many other tutorials for Pi-powered weather stations don’t explain how the various sensors work or how to store your data. Ours goes into more detail. It shows you how to put together a breadboard prototype, it describes how to write Python code to take readings in different ways, and it guides you through recording these readings in a database.

Build Your Own Raspberry Pi weather station on a breadboard

There’s also a section on how to make your station weatherproof. And in case you want to move past the breadboard stage, we also help you with that. The guide shows you how to solder together all the components, similar to the original Oracle Weather Station HAT.

Who should try this build

We think this is a great project to tackle at home, at a STEM club, Scout group, or CoderDojo, and we’re sure that many of you will be chomping at the bit to get started. Before you do, please note that we’ve designed the build to be as straight-forward as possible, but it’s still fairly advanced both in terms of electronics and programming. You should read through the whole guide before purchasing any components.

Build Your Own Raspberry Pi weather station – components

The sensors and components we’re suggesting balance cost, accuracy, and easy of use. Depending on what you want to use your station for, you may wish to use different components. Similarly, the final soldered design in the guide may not be the most elegant, but we think it is achievable for someone with modest soldering experience and basic equipment.

You can build a functioning weather station without soldering with our guide, but the build will be more durable if you do solder it. If you’ve never tried soldering before, that’s OK: we have a Getting started with soldering resource plus video tutorial that will walk you through how it works step by step.

Prototyping HAT for Raspberry Pi weather station sensors

For those of you who are more experienced makers, there are plenty of different ways to put the final build together. We always like to hear about alternative builds, so please post your designs in the Weather Station forum.

Our plans for the guide

Our next step is publishing supplementary guides for adding extra functionality to your weather station. We’d love to hear which enhancements you would most like to see! Our current ideas under development include adding a webcam, making a tweeting weather station, adding a light/UV meter, and incorporating a lightning sensor. Let us know which of these is your favourite, or suggest your own amazing ideas in the comments!

*We do have a very small number of kits reserved for interesting projects or locations: a particularly cool experiment, a novel idea for how the Oracle Weather Station could be used, or places with specific weather phenomena. If have such a project in mind, please send a brief outline to [email protected], and we’ll consider how we might be able to help you.

The post Build your own weather station with our new guide! appeared first on Raspberry Pi.

DNS over HTTPS in Firefox

Post Syndicated from corbet original https://lwn.net/Articles/756262/rss

The Mozilla blog has an
article
describing the addition of DNS over HTTPS (DoH) as an optional
feature in the Firefox browser. “DoH support has been added to
Firefox 62 to improve the way Firefox interacts with DNS. DoH uses
encrypted networking to obtain DNS information from a server that is
configured within Firefox. This means that DNS requests sent to the DoH
cloud server are encrypted while old style DNS requests are not
protected.
” The configured server is hosted by Cloudflare, which
has posted this
privacy agreement
about the service.

Protecting coral reefs with Nemo-Pi, the underwater monitor

Post Syndicated from Janina Ander original https://www.raspberrypi.org/blog/coral-reefs-nemo-pi/

The German charity Save Nemo works to protect coral reefs, and they are developing Nemo-Pi, an underwater “weather station” that monitors ocean conditions. Right now, you can vote for Save Nemo in the Google.org Impact Challenge.

Nemo-Pi — Save Nemo

Save Nemo

The organisation says there are two major threats to coral reefs: divers, and climate change. To make diving saver for reefs, Save Nemo installs buoy anchor points where diving tour boats can anchor without damaging corals in the process.

reef damaged by anchor
boat anchored at buoy

In addition, they provide dos and don’ts for how to behave on a reef dive.

The Nemo-Pi

To monitor the effects of climate change, and to help divers decide whether conditions are right at a reef while they’re still on shore, Save Nemo is also in the process of perfecting Nemo-Pi.

Nemo-Pi schematic — Nemo-Pi — Save Nemo

This Raspberry Pi-powered device is made up of a buoy, a solar panel, a GPS device, a Pi, and an array of sensors. Nemo-Pi measures water conditions such as current, visibility, temperature, carbon dioxide and nitrogen oxide concentrations, and pH. It also uploads its readings live to a public webserver.

Inside the Nemo-Pi device — Save Nemo
Inside the Nemo-Pi device — Save Nemo
Inside the Nemo-Pi device — Save Nemo

The Save Nemo team is currently doing long-term tests of Nemo-Pi off the coast of Thailand and Indonesia. They are also working on improving the device’s power consumption and durability, and testing prototypes with the Raspberry Pi Zero W.

web dashboard — Nemo-Pi — Save Nemo

The web dashboard showing live Nemo-Pi data

Long-term goals

Save Nemo aims to install a network of Nemo-Pis at shallow reefs (up to 60 metres deep) in South East Asia. Then diving tour companies can check the live data online and decide day-to-day whether tours are feasible. This will lower the impact of humans on reefs and help the local flora and fauna survive.

Coral reefs with fishes

A healthy coral reef

Nemo-Pi data may also be useful for groups lobbying for reef conservation, and for scientists and activists who want to shine a spotlight on the awful effects of climate change on sea life, such as coral bleaching caused by rising water temperatures.

Bleached coral

A bleached coral reef

Vote now for Save Nemo

If you want to help Save Nemo in their mission today, vote for them to win the Google.org Impact Challenge:

  1. Head to the voting web page
  2. Click “Abstimmen” in the footer of the page to vote
  3. Click “JA” in the footer to confirm

Voting is open until 6 June. You can also follow Save Nemo on Facebook or Twitter. We think this organisation is doing valuable work, and that their projects could be expanded to reefs across the globe. It’s fantastic to see the Raspberry Pi being used to help protect ocean life.

The post Protecting coral reefs with Nemo-Pi, the underwater monitor appeared first on Raspberry Pi.

New – Pay-per-Session Pricing for Amazon QuickSight, Another Region, and Lots More

Post Syndicated from Jeff Barr original https://aws.amazon.com/blogs/aws/new-pay-per-session-pricing-for-amazon-quicksight-another-region-and-lots-more/

Amazon QuickSight is a fully managed cloud business intelligence system that gives you Fast & Easy to Use Business Analytics for Big Data. QuickSight makes business analytics available to organizations of all shapes and sizes, with the ability to access data that is stored in your Amazon Redshift data warehouse, your Amazon Relational Database Service (RDS) relational databases, flat files in S3, and (via connectors) data stored in on-premises MySQL, PostgreSQL, and SQL Server databases. QuickSight scales to accommodate tens, hundreds, or thousands of users per organization.

Today we are launching a new, session-based pricing option for QuickSight, along with additional region support and other important new features. Let’s take a look at each one:

Pay-per-Session Pricing
Our customers are making great use of QuickSight and take full advantage of the power it gives them to connect to data sources, create reports, and and explore visualizations.

However, not everyone in an organization needs or wants such powerful authoring capabilities. Having access to curated data in dashboards and being able to interact with the data by drilling down, filtering, or slicing-and-dicing is more than adequate for their needs. Subscribing them to a monthly or annual plan can be seen as an unwarranted expense, so a lot of such casual users end up not having access to interactive data or BI.

In order to allow customers to provide all of their users with interactive dashboards and reports, the Enterprise Edition of Amazon QuickSight now allows Reader access to dashboards on a Pay-per-Session basis. QuickSight users are now classified as Admins, Authors, or Readers, with distinct capabilities and prices:

Authors have access to the full power of QuickSight; they can establish database connections, upload new data, create ad hoc visualizations, and publish dashboards, all for $9 per month (Standard Edition) or $18 per month (Enterprise Edition).

Readers can view dashboards, slice and dice data using drill downs, filters and on-screen controls, and download data in CSV format, all within the secure QuickSight environment. Readers pay $0.30 for 30 minutes of access, with a monthly maximum of $5 per reader.

Admins have all authoring capabilities, and can manage users and purchase SPICE capacity in the account. The QuickSight admin now has the ability to set the desired option (Author or Reader) when they invite members of their organization to use QuickSight. They can extend Reader invites to their entire user base without incurring any up-front or monthly costs, paying only for the actual usage.

To learn more, visit the QuickSight Pricing page.

A New Region
QuickSight is now available in the Asia Pacific (Tokyo) Region:

The UI is in English, with a localized version in the works.

Hourly Data Refresh
Enterprise Edition SPICE data sets can now be set to refresh as frequently as every hour. In the past, each data set could be refreshed up to 5 times a day. To learn more, read Refreshing Imported Data.

Access to Data in Private VPCs
This feature was launched in preview form late last year, and is now available in production form to users of the Enterprise Edition. As I noted at the time, you can use it to implement secure, private communication with data sources that do not have public connectivity, including on-premises data in Teradata or SQL Server, accessed over an AWS Direct Connect link. To learn more, read Working with AWS VPC.

Parameters with On-Screen Controls
QuickSight dashboards can now include parameters that are set using on-screen dropdown, text box, numeric slider or date picker controls. The default value for each parameter can be set based on the user name (QuickSight calls this a dynamic default). You could, for example, set an appropriate default based on each user’s office location, department, or sales territory. Here’s an example:

To learn more, read about Parameters in QuickSight.

URL Actions for Linked Dashboards
You can now connect your QuickSight dashboards to external applications by defining URL actions on visuals. The actions can include parameters, and become available in the Details menu for the visual. URL actions are defined like this:

You can use this feature to link QuickSight dashboards to third party applications (e.g. Salesforce) or to your own internal applications. Read Custom URL Actions to learn how to use this feature.

Dashboard Sharing
You can now share QuickSight dashboards across every user in an account.

Larger SPICE Tables
The per-data set limit for SPICE tables has been raised from 10 GB to 25 GB.

Upgrade to Enterprise Edition
The QuickSight administrator can now upgrade an account from Standard Edition to Enterprise Edition with a click. This enables provisioning of Readers with pay-per-session pricing, private VPC access, row-level security for dashboards and data sets, and hourly refresh of data sets. Enterprise Edition pricing applies after the upgrade.

Available Now
Everything I listed above is available now and you can start using it today!

You can try QuickSight for 60 days at no charge, and you can also attend our June 20th Webinar.

Jeff;

 

Hiring a Director of Sales

Post Syndicated from Yev original https://www.backblaze.com/blog/hiring-a-director-of-sales/

Backblaze is hiring a Director of Sales. This is a critical role for Backblaze as we continue to grow the team. We need a strong leader who has experience in scaling a sales team and who has an excellent track record for exceeding goals by selling Software as a Service (SaaS) solutions. In addition, this leader will need to be highly motivated, as well as able to create and develop a highly-motivated, success oriented sales team that has fun and enjoys what they do.

The History of Backblaze from our CEO
In 2007, after a friend’s computer crash caused her some suffering, we realized that with every photo, video, song, and document going digital, everyone would eventually lose all of their information. Five of us quit our jobs to start a company with the goal of making it easy for people to back up their data.

Like many startups, for a while we worked out of a co-founder’s one-bedroom apartment. Unlike most startups, we made an explicit agreement not to raise funding during the first year. We would then touch base every six months and decide whether to raise or not. We wanted to focus on building the company and the product, not on pitching and slide decks. And critically, we wanted to build a culture that understood money comes from customers, not the magical VC giving tree. Over the course of 5 years we built a profitable, multi-million dollar revenue business — and only then did we raise a VC round.

Fast forward 10 years later and our world looks quite different. You’ll have some fantastic assets to work with:

  • A brand millions recognize for openness, ease-of-use, and affordability.
  • A computer backup service that stores over 500 petabytes of data, has recovered over 30 billion files for hundreds of thousands of paying customers — most of whom self-identify as being the people that find and recommend technology products to their friends.
  • Our B2 service that provides the lowest cost cloud storage on the planet at 1/4th the price Amazon, Google or Microsoft charges. While being a newer product on the market, it already has over 100,000 IT and developers signed up as well as an ecosystem building up around it.
  • A growing, profitable and cash-flow positive company.
  • And last, but most definitely not least: a great sales team.

You might be saying, “sounds like you’ve got this under control — why do you need me?” Don’t be misled. We need you. Here’s why:

  • We have a great team, but we are in the process of expanding and we need to develop a structure that will easily scale and provide the most success to drive revenue.
  • We just launched our outbound sales efforts and we need someone to help develop that into a fully successful program that’s building a strong pipeline and closing business.
  • We need someone to work with the marketing department and figure out how to generate more inbound opportunities that the sales team can follow up on and close.
  • We need someone who will work closely in developing the skills of our current sales team and build a path for career growth and advancement.
  • We want someone to manage our Customer Success program.

So that’s a bit about us. What are we looking for in you?

Experience: As a sales leader, you will strategically build and drive the territory’s sales pipeline by assembling and leading a skilled team of sales professionals. This leader should be familiar with generating, developing and closing software subscription (SaaS) opportunities. We are looking for a self-starter who can manage a team and make an immediate impact of selling our Backup and Cloud Storage solutions. In this role, the sales leader will work closely with the VP of Sales, marketing staff, and service staff to develop and implement specific strategic plans to achieve and exceed revenue targets, including new business acquisition as well as build out our customer success program.

Leadership: We have an experienced team who’s brought us to where we are today. You need to have the people and management skills to get them excited about working with you. You need to be a strong leader and compassionate about developing and supporting your team.

Data driven and creative: The data has to show something makes sense before we scale it up. However, without creativity, it’s easy to say “the data shows it’s impossible” or to find a local maximum. Whether it’s deciding how to scale the team, figuring out what our outbound sales efforts should look like or putting a plan in place to develop the team for career growth, we’ve seen a bit of creativity get us places a few extra dollars couldn’t.

Jive with our culture: Strong leaders affect culture and the person we hire for this role may well shape, not only fit into, ours. But to shape the culture you have to be accepted by the organism, which means a certain set of shared values. We default to openness with our team, our customers, and everyone if possible. We love initiative — without arrogance or dictatorship. We work to create a place people enjoy showing up to work. That doesn’t mean ping pong tables and foosball (though we do try to have perks & fun), but it means people are friendly, non-political, working to build a good service but also a good place to work.

Do the work: Ideas and strategy are critical, but good execution makes them happen. We’re looking for someone who can help the team execute both from the perspective of being capable of guiding and organizing, but also someone who is hands-on themselves.

Additional Responsibilities needed for this role:

  • Recruit, coach, mentor, manage and lead a team of sales professionals to achieve yearly sales targets. This includes closing new business and expanding upon existing clientele.
  • Expand the customer success program to provide the best customer experience possible resulting in upsell opportunities and a high retention rate.
  • Develop effective sales strategies and deliver compelling product demonstrations and sales pitches.
  • Acquire and develop the appropriate sales tools to make the team efficient in their daily work flow.
  • Apply a thorough understanding of the marketplace, industry trends, funding developments, and products to all management activities and strategic sales decisions.
  • Ensure that sales department operations function smoothly, with the goal of facilitating sales and/or closings; operational responsibilities include accurate pipeline reporting and sales forecasts.
  • This position will report directly to the VP of Sales and will be staffed in our headquarters in San Mateo, CA.

Requirements:

  • 7 – 10+ years of successful sales leadership experience as measured by sales performance against goals.
    Experience in developing skill sets and providing career growth and opportunities through advancement of team members.
  • Background in selling SaaS technologies with a strong track record of success.
  • Strong presentation and communication skills.
  • Must be able to travel occasionally nationwide.
  • BA/BS degree required

Think you want to join us on this adventure?
Send an email to jobscontact@backblaze.com with the subject “Director of Sales.” (Recruiters and agencies, please don’t email us.) Include a resume and answer these two questions:

  1. How would you approach evaluating the current sales team and what is your process for developing a growth strategy to scale the team?
  2. What are the goals you would set for yourself in the 3 month and 1-year timeframes?

Thank you for taking the time to read this and I hope that this sounds like the opportunity for which you’ve been waiting.

Backblaze is an Equal Opportunity Employer.

The post Hiring a Director of Sales appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

[$] Bpfilter (and user-mode blobs) for 4.18

Post Syndicated from corbet original https://lwn.net/Articles/755919/rss

In February, the bpfilter mechanism was
first posted to the mailing lists. Bpfilter is meant to be a replacement
for the current in-kernel firewall/packet-filtering code. It provides
little functionality itself; instead, it creates a set of hooks that can
run BPF programs to make the packet-filtering decisions. A version of that patch set has been merged
into the net-next tree for 4.18. It will not be replacing any existing
packet filters in its current form, but it does feature a significant
change to one of its more controversial features: the new user-mode helper
mechanism.

Randomly generated, thermal-printed comics

Post Syndicated from Alex Bate original https://www.raspberrypi.org/blog/random-comic-strip-generation-vomit-comic-robot/

Python code creates curious, wordless comic strips at random, spewing them from the thermal printer mouth of a laser-cut body reminiscent of Disney Pixar’s WALL-E: meet the Vomit Comic Robot!

The age of the thermal printer!

Thermal printers allow you to instantly print photos, data, and text using a few lines of code, with no need for ink. More and more makers are using this handy, low-maintenance bit of kit for truly creative projects, from Pierre Muth’s tiny PolaPi-Zero camera to the sound-printing Waves project by Eunice Lee, Matthew Zhang, and Bomani McClendon (and our own Secret Santa Babbage).

Vomiting robots

Interaction designer and developer Cadin Batrack, whose background is in game design and interactivity, has built the Vomit Comic Robot, which creates “one-of-a-kind comics on demand by processing hand-drawn images through a custom software algorithm.”

The robot is made up of a Raspberry Pi 3, a USB thermal printer, and a handful of LEDs.

Comic Vomit Robot Cadin Batrack's Raspberry Pi comic-generating thermal printer machine

At the press of a button, Processing code selects one of a set of Cadin’s hand-drawn empty comic grids and then randomly picks images from a library to fill in the gaps.

Vomit Comic Robot Cadin Batrack's Raspberry Pi comic-generating thermal printer machine

Each image is associated with data that allows the code to fit it correctly into the available panels. Cadin says about the concept behing his build:

Although images are selected and placed randomly, the comic panel format suggests relationships between elements. Our minds create a story where there is none in an attempt to explain visuals created by a non-intelligent machine.

The Raspberry Pi saves the final image as a high-resolution PNG file (so that Cadin can sell prints on thick paper via Etsy), and a Python script sends it to be vomited up by the thermal printer.

Comic Vomit Robot Cadin Batrack's Raspberry Pi comic-generating thermal printer machine

For more about the Vomit Comic Robot, check out Cadin’s blog. If you want to recreate it, you can find the info you need in the Imgur album he has put together.

We ❤ cute robots

We have a soft spot for cute robots here at Pi Towers, and of course we make no exception for the Vomit Comic Robot. If, like us, you’re a fan of adorable bots, check out Mira, the tiny interactive robot by Alonso Martinez, and Peeqo, the GIF bot by Abhishek Singh.

Mira Alfonso Martinez Raspberry Pi

The post Randomly generated, thermal-printed comics appeared first on Raspberry Pi.

Measuring the throughput for Amazon MQ using the JMS Benchmark

Post Syndicated from Rachel Richardson original https://aws.amazon.com/blogs/compute/measuring-the-throughput-for-amazon-mq-using-the-jms-benchmark/

This post is courtesy of Alan Protasio, Software Development Engineer, Amazon Web Services

Just like compute and storage, messaging is a fundamental building block of enterprise applications. Message brokers (aka “message-oriented middleware”) enable different software systems, often written in different languages, on different platforms, running in different locations, to communicate and exchange information. Mission-critical applications, such as CRM and ERP, rely on message brokers to work.

A common performance consideration for customers deploying a message broker in a production environment is the throughput of the system, measured as messages per second. This is important to know so that application environments (hosts, threads, memory, etc.) can be configured correctly.

In this post, we demonstrate how to measure the throughput for Amazon MQ, a new managed message broker service for ActiveMQ, using JMS Benchmark. It should take between 15–20 minutes to set up the environment and an hour to run the benchmark. We also provide some tips on how to configure Amazon MQ for optimal throughput.

Benchmarking throughput for Amazon MQ

ActiveMQ can be used for a number of use cases. These use cases can range from simple fire and forget tasks (that is, asynchronous processing), low-latency request-reply patterns, to buffering requests before they are persisted to a database.

The throughput of Amazon MQ is largely dependent on the use case. For example, if you have non-critical workloads such as gathering click events for a non-business-critical portal, you can use ActiveMQ in a non-persistent mode and get extremely high throughput with Amazon MQ.

On the flip side, if you have a critical workload where durability is extremely important (meaning that you can’t lose a message), then you are bound by the I/O capacity of your underlying persistence store. We recommend using mq.m4.large for the best results. The mq.t2.micro instance type is intended for product evaluation. Performance is limited, due to the lower memory and burstable CPU performance.

Tip: To improve your throughput with Amazon MQ, make sure that you have consumers processing messaging as fast as (or faster than) your producers are pushing messages.

Because it’s impossible to talk about how the broker (ActiveMQ) behaves for each and every use case, we walk through how to set up your own benchmark for Amazon MQ using our favorite open-source benchmarking tool: JMS Benchmark. We are fans of the JMS Benchmark suite because it’s easy to set up and deploy, and comes with a built-in visualizer of the results.

Non-Persistent Scenarios – Queue latency as you scale producer throughput

JMS Benchmark nonpersistent scenarios

Getting started

At the time of publication, you can create an mq.m4.large single-instance broker for testing for $0.30 per hour (US pricing).

This walkthrough covers the following tasks:

  1.  Create and configure the broker.
  2. Create an EC2 instance to run your benchmark
  3. Configure the security groups
  4.  Run the benchmark.

Step 1 – Create and configure the broker
Create and configure the broker using Tutorial: Creating and Configuring an Amazon MQ Broker.

Step 2 – Create an EC2 instance to run your benchmark
Launch the EC2 instance using Step 1: Launch an Instance. We recommend choosing the m5.large instance type.

Step 3 – Configure the security groups
Make sure that all the security groups are correctly configured to let the traffic flow between the EC2 instance and your broker.

  1. Sign in to the Amazon MQ console.
  2. From the broker list, choose the name of your broker (for example, MyBroker)
  3. In the Details section, under Security and network, choose the name of your security group or choose the expand icon ( ).
  4. From the security group list, choose your security group.
  5. At the bottom of the page, choose Inbound, Edit.
  6. In the Edit inbound rules dialog box, add a role to allow traffic between your instance and the broker:
    • Choose Add Rule.
    • For Type, choose Custom TCP.
    • For Port Range, type the ActiveMQ SSL port (61617).
    • For Source, leave Custom selected and then type the security group of your EC2 instance.
    • Choose Save.

Your broker can now accept the connection from your EC2 instance.

Step 4 – Run the benchmark
Connect to your EC2 instance using SSH and run the following commands:

$ cd ~
$ curl -L https://github.com/alanprot/jms-benchmark/archive/master.zip -o master.zip
$ unzip master.zip
$ cd jms-benchmark-master
$ chmod a+x bin/*
$ env \
  SERVER_SETUP=false \
  SERVER_ADDRESS={activemq-endpoint} \
  ACTIVEMQ_TRANSPORT=ssl\
  ACTIVEMQ_PORT=61617 \
  ACTIVEMQ_USERNAME={activemq-user} \
  ACTIVEMQ_PASSWORD={activemq-password} \
  ./bin/benchmark-activemq

After the benchmark finishes, you can find the results in the ~/reports directory. As you may notice, the performance of ActiveMQ varies based on the number of consumers, producers, destinations, and message size.

Amazon MQ architecture

The last bit that’s important to know so that you can better understand the results of the benchmark is how Amazon MQ is architected.

Amazon MQ is architected to be highly available (HA) and durable. For HA, we recommend using the multi-AZ option. After a message is sent to Amazon MQ in persistent mode, the message is written to the highly durable message store that replicates the data across multiple nodes in multiple Availability Zones. Because of this replication, for some use cases you may see a reduction in throughput as you migrate to Amazon MQ. Customers have told us they appreciate the benefits of message replication as it helps protect durability even in the face of the loss of an Availability Zone.

Conclusion

We hope this gives you an idea of how Amazon MQ performs. We encourage you to run tests to simulate your own use cases.

To learn more, see the Amazon MQ website. You can try Amazon MQ for free with the AWS Free Tier, which includes up to 750 hours of a single-instance mq.t2.micro broker and up to 1 GB of storage per month for one year.

Security and Human Behavior (SHB 2018)

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/05/security_and_hu_7.html

I’m at Carnegie Mellon University, at the eleventh Workshop on Security and Human Behavior.

SHB is a small invitational gathering of people studying various aspects of the human side of security, organized each year by Alessandro Acquisti, Ross Anderson, and myself. The 50 or so people in the room include psychologists, economists, computer security researchers, sociologists, political scientists, neuroscientists, designers, lawyers, philosophers, anthropologists, business school professors, and a smattering of others. It’s not just an interdisciplinary event; most of the people here are individually interdisciplinary.

The goal is to maximize discussion and interaction. We do that by putting everyone on panels, and limiting talks to 7-10 minutes. The rest of the time is left to open discussion. Four hour-and-a-half panels per day over two days equals eight panels; six people per panel means that 48 people get to speak. We also have lunches, dinners, and receptions — all designed so people from different disciplines talk to each other.

I invariably find this to be the most intellectually stimulating conference of my year. It influences my thinking in many different, and sometimes surprising, ways.

This year’s program is here. This page lists the participants and includes links to some of their work. As he does every year, Ross Anderson is liveblogging the talks. (Ross also maintains a good webpage of psychology and security resources.)

Here are my posts on the first, second, third, fourth, fifth, sixth, seventh, eighth, ninth, and tenth SHB workshops. Follow those links to find summaries, papers, and occasionally audio recordings of the various workshops.

Next year, I’ll be hosting the event at Harvard.

Protecting your API using Amazon API Gateway and AWS WAF — Part I

Post Syndicated from Chris Munns original https://aws.amazon.com/blogs/compute/protecting-your-api-using-amazon-api-gateway-and-aws-waf-part-i/

This post courtesy of Thiago Morais, AWS Solutions Architect

When you build web applications or expose any data externally, you probably look for a platform where you can build highly scalable, secure, and robust REST APIs. As APIs are publicly exposed, there are a number of best practices for providing a secure mechanism to consumers using your API.

Amazon API Gateway handles all the tasks involved in accepting and processing up to hundreds of thousands of concurrent API calls, including traffic management, authorization and access control, monitoring, and API version management.

In this post, I show you how to take advantage of the regional API endpoint feature in API Gateway, so that you can create your own Amazon CloudFront distribution and secure your API using AWS WAF.

AWS WAF is a web application firewall that helps protect your web applications from common web exploits that could affect application availability, compromise security, or consume excessive resources.

As you make your APIs publicly available, you are exposed to attackers trying to exploit your services in several ways. The AWS security team published a whitepaper solution using AWS WAF, How to Mitigate OWASP’s Top 10 Web Application Vulnerabilities.

Regional API endpoints

Edge-optimized APIs are endpoints that are accessed through a CloudFront distribution created and managed by API Gateway. Before the launch of regional API endpoints, this was the default option when creating APIs using API Gateway. It primarily helped to reduce latency for API consumers that were located in different geographical locations than your API.

When API requests predominantly originate from an Amazon EC2 instance or other services within the same AWS Region as the API is deployed, a regional API endpoint typically lowers the latency of connections. It is recommended for such scenarios.

For better control around caching strategies, customers can use their own CloudFront distribution for regional APIs. They also have the ability to use AWS WAF protection, as I describe in this post.

Edge-optimized API endpoint

The following diagram is an illustrated example of the edge-optimized API endpoint where your API clients access your API through a CloudFront distribution created and managed by API Gateway.

Regional API endpoint

For the regional API endpoint, your customers access your API from the same Region in which your REST API is deployed. This helps you to reduce request latency and particularly allows you to add your own content delivery network, as needed.

Walkthrough

In this section, you implement the following steps:

  • Create a regional API using the PetStore sample API.
  • Create a CloudFront distribution for the API.
  • Test the CloudFront distribution.
  • Set up AWS WAF and create a web ACL.
  • Attach the web ACL to the CloudFront distribution.
  • Test AWS WAF protection.

Create the regional API

For this walkthrough, use an existing PetStore API. All new APIs launch by default as the regional endpoint type. To change the endpoint type for your existing API, choose the cog icon on the top right corner:

After you have created the PetStore API on your account, deploy a stage called “prod” for the PetStore API.

On the API Gateway console, select the PetStore API and choose Actions, Deploy API.

For Stage name, type prod and add a stage description.

Choose Deploy and the new API stage is created.

Use the following AWS CLI command to update your API from edge-optimized to regional:

aws apigateway update-rest-api \
--rest-api-id {rest-api-id} \
--patch-operations op=replace,path=/endpointConfiguration/types/EDGE,value=REGIONAL

A successful response looks like the following:

{
    "description": "Your first API with Amazon API Gateway. This is a sample API that integrates via HTTP with your demo Pet Store endpoints", 
    "createdDate": 1511525626, 
    "endpointConfiguration": {
        "types": [
            "REGIONAL"
        ]
    }, 
    "id": "{api-id}", 
    "name": "PetStore"
}

After you change your API endpoint to regional, you can now assign your own CloudFront distribution to this API.

Create a CloudFront distribution

To make things easier, I have provided an AWS CloudFormation template to deploy a CloudFront distribution pointing to the API that you just created. Click the button to deploy the template in the us-east-1 Region.

For Stack name, enter RegionalAPI. For APIGWEndpoint, enter your API FQDN in the following format:

{api-id}.execute-api.us-east-1.amazonaws.com

After you fill out the parameters, choose Next to continue the stack deployment. It takes a couple of minutes to finish the deployment. After it finishes, the Output tab lists the following items:

  • A CloudFront domain URL
  • An S3 bucket for CloudFront access logs
Output from CloudFormation

Output from CloudFormation

Test the CloudFront distribution

To see if the CloudFront distribution was configured correctly, use a web browser and enter the URL from your distribution, with the following parameters:

https://{your-distribution-url}.cloudfront.net/{api-stage}/pets

You should get the following output:

[
  {
    "id": 1,
    "type": "dog",
    "price": 249.99
  },
  {
    "id": 2,
    "type": "cat",
    "price": 124.99
  },
  {
    "id": 3,
    "type": "fish",
    "price": 0.99
  }
]

Set up AWS WAF and create a web ACL

With the new CloudFront distribution in place, you can now start setting up AWS WAF to protect your API.

For this demo, you deploy the AWS WAF Security Automations solution, which provides fine-grained control over the requests attempting to access your API.

For more information about deployment, see Automated Deployment. If you prefer, you can launch the solution directly into your account using the following button.

For CloudFront Access Log Bucket Name, add the name of the bucket created during the deployment of the CloudFormation stack for your CloudFront distribution.

The solution allows you to adjust thresholds and also choose which automations to enable to protect your API. After you finish configuring these settings, choose Next.

To start the deployment process in your account, follow the creation wizard and choose Create. It takes a few minutes do finish the deployment. You can follow the creation process through the CloudFormation console.

After the deployment finishes, you can see the new web ACL deployed on the AWS WAF console, AWSWAFSecurityAutomations.

Attach the AWS WAF web ACL to the CloudFront distribution

With the solution deployed, you can now attach the AWS WAF web ACL to the CloudFront distribution that you created earlier.

To assign the newly created AWS WAF web ACL, go back to your CloudFront distribution. After you open your distribution for editing, choose General, Edit.

Select the new AWS WAF web ACL that you created earlier, AWSWAFSecurityAutomations.

Save the changes to your CloudFront distribution and wait for the deployment to finish.

Test AWS WAF protection

To validate the AWS WAF Web ACL setup, use Artillery to load test your API and see AWS WAF in action.

To install Artillery on your machine, run the following command:

$ npm install -g artillery

After the installation completes, you can check if Artillery installed successfully by running the following command:

$ artillery -V
$ 1.6.0-12

As the time of publication, Artillery is on version 1.6.0-12.

One of the WAF web ACL rules that you have set up is a rate-based rule. By default, it is set up to block any requesters that exceed 2000 requests under 5 minutes. Try this out.

First, use cURL to query your distribution and see the API output:

$ curl -s https://{distribution-name}.cloudfront.net/prod/pets
[
  {
    "id": 1,
    "type": "dog",
    "price": 249.99
  },
  {
    "id": 2,
    "type": "cat",
    "price": 124.99
  },
  {
    "id": 3,
    "type": "fish",
    "price": 0.99
  }
]

Based on the test above, the result looks good. But what if you max out the 2000 requests in under 5 minutes?

Run the following Artillery command:

artillery quick -n 2000 --count 10  https://{distribution-name}.cloudfront.net/prod/pets

What you are doing is firing 2000 requests to your API from 10 concurrent users. For brevity, I am not posting the Artillery output here.

After Artillery finishes its execution, try to run the cURL request again and see what happens:

 

$ curl -s https://{distribution-name}.cloudfront.net/prod/pets

<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.01 Transitional//EN" "http://www.w3.org/TR/html4/loose.dtd">
<HTML><HEAD><META HTTP-EQUIV="Content-Type" CONTENT="text/html; charset=iso-8859-1">
<TITLE>ERROR: The request could not be satisfied</TITLE>
</HEAD><BODY>
<H1>ERROR</H1>
<H2>The request could not be satisfied.</H2>
<HR noshade size="1px">
Request blocked.
<BR clear="all">
<HR noshade size="1px">
<PRE>
Generated by cloudfront (CloudFront)
Request ID: [removed]
</PRE>
<ADDRESS>
</ADDRESS>
</BODY></HTML>

As you can see from the output above, the request was blocked by AWS WAF. Your IP address is removed from the blocked list after it falls below the request limit rate.

Conclusion

In this first part, you saw how to use the new API Gateway regional API endpoint together with Amazon CloudFront and AWS WAF to secure your API from a series of attacks.

In the second part, I will demonstrate some other techniques to protect your API using API keys and Amazon CloudFront custom headers.

Use Slack ChatOps to Deploy Your Code – How to Integrate Your Pipeline in AWS CodePipeline with Your Slack Channel

Post Syndicated from Rumi Olsen original https://aws.amazon.com/blogs/devops/use-slack-chatops-to-deploy-your-code-how-to-integrate-your-pipeline-in-aws-codepipeline-with-your-slack-channel/

Slack is widely used by DevOps and development teams to communicate status. Typically, when a build has been tested and is ready to be promoted to a staging environment, a QA engineer or DevOps engineer kicks off the deployment. Using Slack in a ChatOps collaboration model, the promotion can be done in a single click from a Slack channel. And because the promotion happens through a Slack channel, the whole development team knows what’s happening without checking email.

In this blog post, I will show you how to integrate AWS services with a Slack application. I use an interactive message button and incoming webhook to promote a stage with a single click.

To follow along with the steps in this post, you’ll need a pipeline in AWS CodePipeline. If you don’t have a pipeline, the fastest way to create one for this use case is to use AWS CodeStar. Go to the AWS CodeStar console and select the Static Website template (shown in the screenshot). AWS CodeStar will create a pipeline with an AWS CodeCommit repository and an AWS CodeDeploy deployment for you. After the pipeline is created, you will need to add a manual approval stage.

You’ll also need to build a Slack app with webhooks and interactive components, write two Lambda functions, and create an API Gateway API and a SNS topic.

As you’ll see in the following diagram, when I make a change and merge a new feature into the master branch in AWS CodeCommit, the check-in kicks off my CI/CD pipeline in AWS CodePipeline. When CodePipeline reaches the approval stage, it sends a notification to Amazon SNS, which triggers an AWS Lambda function (ApprovalRequester).

The Slack channel receives a prompt that looks like the following screenshot. When I click Yes to approve the build promotion, the approval result is sent to CodePipeline through API Gateway and Lambda (ApprovalHandler). The pipeline continues on to deploy the build to the next environment.

Create a Slack app

For App Name, type a name for your app. For Development Slack Workspace, choose the name of your workspace. You’ll see in the following screenshot that my workspace is AWS ChatOps.

After the Slack application has been created, you will see the Basic Information page, where you can create incoming webhooks and enable interactive components.

To add incoming webhooks:

  1. Under Add features and functionality, choose Incoming Webhooks. Turn the feature on by selecting Off, as shown in the following screenshot.
  2. Now that the feature is turned on, choose Add New Webhook to Workspace. In the process of creating the webhook, Slack lets you choose the channel where messages will be posted.
  3. After the webhook has been created, you’ll see its URL. You will use this URL when you create the Lambda function.

If you followed the steps in the post, the pipeline should look like the following.

Write the Lambda function for approval requests

This Lambda function is invoked by the SNS notification. It sends a request that consists of an interactive message button to the incoming webhook you created earlier.  The following sample code sends the request to the incoming webhook. WEBHOOK_URL and SLACK_CHANNEL are the environment variables that hold values of the webhook URL that you created and the Slack channel where you want the interactive message button to appear.

# This function is invoked via SNS when the CodePipeline manual approval action starts.
# It will take the details from this approval notification and sent an interactive message to Slack that allows users to approve or cancel the deployment.

import os
import json
import logging
import urllib.parse

from base64 import b64decode
from urllib.request import Request, urlopen
from urllib.error import URLError, HTTPError

# This is passed as a plain-text environment variable for ease of demonstration.
# Consider encrypting the value with KMS or use an encrypted parameter in Parameter Store for production deployments.
SLACK_WEBHOOK_URL = os.environ['SLACK_WEBHOOK_URL']
SLACK_CHANNEL = os.environ['SLACK_CHANNEL']

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

def lambda_handler(event, context):
    print("Received event: " + json.dumps(event, indent=2))
    message = event["Records"][0]["Sns"]["Message"]
    
    data = json.loads(message) 
    token = data["approval"]["token"]
    codepipeline_name = data["approval"]["pipelineName"]
    
    slack_message = {
        "channel": SLACK_CHANNEL,
        "text": "Would you like to promote the build to production?",
        "attachments": [
            {
                "text": "Yes to deploy your build to production",
                "fallback": "You are unable to promote a build",
                "callback_id": "wopr_game",
                "color": "#3AA3E3",
                "attachment_type": "default",
                "actions": [
                    {
                        "name": "deployment",
                        "text": "Yes",
                        "style": "danger",
                        "type": "button",
                        "value": json.dumps({"approve": True, "codePipelineToken": token, "codePipelineName": codepipeline_name}),
                        "confirm": {
                            "title": "Are you sure?",
                            "text": "This will deploy the build to production",
                            "ok_text": "Yes",
                            "dismiss_text": "No"
                        }
                    },
                    {
                        "name": "deployment",
                        "text": "No",
                        "type": "button",
                        "value": json.dumps({"approve": False, "codePipelineToken": token, "codePipelineName": codepipeline_name})
                    }  
                ]
            }
        ]
    }

    req = Request(SLACK_WEBHOOK_URL, json.dumps(slack_message).encode('utf-8'))

    response = urlopen(req)
    response.read()
    
    return None

 

Create a SNS topic

Create a topic and then create a subscription that invokes the ApprovalRequester Lambda function. You can configure the manual approval action in the pipeline to send a message to this SNS topic when an approval action is required. When the pipeline reaches the approval stage, it sends a notification to this SNS topic. SNS publishes a notification to all of the subscribed endpoints. In this case, the Lambda function is the endpoint. Therefore, it invokes and executes the Lambda function. For information about how to create a SNS topic, see Create a Topic in the Amazon SNS Developer Guide.

Write the Lambda function for handling the interactive message button

This Lambda function is invoked by API Gateway. It receives the result of the interactive message button whether or not the build promotion was approved. If approved, an API call is made to CodePipeline to promote the build to the next environment. If not approved, the pipeline stops and does not move to the next stage.

The Lambda function code might look like the following. SLACK_VERIFICATION_TOKEN is the environment variable that contains your Slack verification token. You can find your verification token under Basic Information on Slack manage app page. When you scroll down, you will see App Credential. Verification token is found under the section.

# This function is triggered via API Gateway when a user acts on the Slack interactive message sent by approval_requester.py.

from urllib.parse import parse_qs
import json
import os
import boto3

SLACK_VERIFICATION_TOKEN = os.environ['SLACK_VERIFICATION_TOKEN']

#Triggered by API Gateway
#It kicks off a particular CodePipeline project
def lambda_handler(event, context):
	#print("Received event: " + json.dumps(event, indent=2))
	body = parse_qs(event['body'])
	payload = json.loads(body['payload'][0])

	# Validate Slack token
	if SLACK_VERIFICATION_TOKEN == payload['token']:
		send_slack_message(json.loads(payload['actions'][0]['value']))
		
		# This will replace the interactive message with a simple text response.
		# You can implement a more complex message update if you would like.
		return  {
			"isBase64Encoded": "false",
			"statusCode": 200,
			"body": "{\"text\": \"The approval has been processed\"}"
		}
	else:
		return  {
			"isBase64Encoded": "false",
			"statusCode": 403,
			"body": "{\"error\": \"This request does not include a vailid verification token.\"}"
		}


def send_slack_message(action_details):
	codepipeline_status = "Approved" if action_details["approve"] else "Rejected"
	codepipeline_name = action_details["codePipelineName"]
	token = action_details["codePipelineToken"] 

	client = boto3.client('codepipeline')
	response_approval = client.put_approval_result(
							pipelineName=codepipeline_name,
							stageName='Approval',
							actionName='ApprovalOrDeny',
							result={'summary':'','status':codepipeline_status},
							token=token)
	print(response_approval)

 

Create the API Gateway API

  1. In the Amazon API Gateway console, create a resource called InteractiveMessageHandler.
  2. Create a POST method.
    • For Integration type, choose Lambda Function.
    • Select Use Lambda Proxy integration.
    • From Lambda Region, choose a region.
    • In Lambda Function, type a name for your function.
  3.  Deploy to a stage.

For more information, see Getting Started with Amazon API Gateway in the Amazon API Developer Guide.

Now go back to your Slack application and enable interactive components.

To enable interactive components for the interactive message (Yes) button:

  1. Under Features, choose Interactive Components.
  2. Choose Enable Interactive Components.
  3. Type a request URL in the text box. Use the invoke URL in Amazon API Gateway that will be called when the approval button is clicked.

Now that all the pieces have been created, run the solution by checking in a code change to your CodeCommit repo. That will release the change through CodePipeline. When the CodePipeline comes to the approval stage, it will prompt to your Slack channel to see if you want to promote the build to your staging or production environment. Choose Yes and then see if your change was deployed to the environment.

Conclusion

That is it! You have now created a Slack ChatOps solution using AWS CodeCommit, AWS CodePipeline, AWS Lambda, Amazon API Gateway, and Amazon Simple Notification Service.

Now that you know how to do this Slack and CodePipeline integration, you can use the same method to interact with other AWS services using API Gateway and Lambda. You can also use Slack’s slash command to initiate an action from a Slack channel, rather than responding in the way demonstrated in this post.

Replacing macOS Server with Synology NAS

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/replacing-macos-server-with-synology-nas/

Synology NAS boxes backed up to the cloud

Businesses and organizations that rely on macOS server for essential office and data services are facing some decisions about the future of their IT services.

Apple recently announced that it is deprecating a significant portion of essential network services in macOS Server, as they described in a support statement posted on April 24, 2018, “Prepare for changes to macOS Server.” Apple’s note includes:

macOS Server is changing to focus more on management of computers, devices, and storage on your network. As a result, some changes are coming in how Server works. A number of services will be deprecated, and will be hidden on new installations of an update to macOS Server coming in spring 2018.

The note lists the services that will be removed in a future release of macOS Server, including calendar and contact support, Dynamic Host Configuration Protocol (DHCP), Domain Name Services (DNS), mail, instant messages, virtual private networking (VPN), NetInstall, Web server, and the Wiki.

Apple assures users who have already configured any of the listed services that they will be able to use them in the spring 2018 macOS Server update, but the statement ends with links to a number of alternative services, including hosted services, that macOS Server users should consider as viable replacements to the features it is removing. These alternative services are all FOSS (Free and Open-Source Software).

As difficult as this could be for organizations that use macOS server, this is not unexpected. Apple left the server hardware space back in 2010, when Steve Jobs announced the company was ending its line of Xserve rackmount servers, which were introduced in May, 2002. Since then, macOS Server has hardly been a prominent part of Apple’s product lineup. It’s not just the product itself that has lost some luster, but the entire category of SMB office and business servers, which has been undergoing a gradual change in recent years.

Some might wonder how important the news about macOS Server is, given that macOS Server represents a pretty small share of the server market. macOS Server has been important to design shops, agencies, education users, and small businesses that likely have been on Macs for ages, but it’s not a significant part of the IT infrastructure of larger organizations and businesses.

What Comes After macOS Server?

Lovers of macOS Server don’t have to fear having their Mac minis pried from their cold, dead hands quite yet. Installed services will continue to be available. In the fall of 2018, new installations and upgrades of macOS Server will require users to migrate most services to other software. Since many of the services of macOS Server were already open-source, this means that a change in software might not be required. It does mean more configuration and management required from those who continue with macOS Server, however.

Users can continue with macOS Server if they wish, but many will see the writing on the wall and look for a suitable substitute.

The Times They Are A-Changin’

For many people working in organizations, what is significant about this announcement is how it reflects the move away from the once ubiquitous server-based IT infrastructure. Services that used to be centrally managed and office-based, such as storage, file sharing, communications, and computing, have moved to the cloud.

In selecting the next office IT platforms, there’s an opportunity to move to solutions that reflect and support how people are working and the applications they are using both in the office and remotely. For many, this means including cloud-based services in office automation, backup, and business continuity/disaster recovery planning. This includes Software as a Service, Platform as a Service, and Infrastructure as a Service (Saas, PaaS, IaaS) options.

IT solutions that integrate well with the cloud are worth strong consideration for what comes after a macOS Server-based environment.

Synology NAS as a macOS Server Alternative

One solution that is becoming popular is to replace macOS Server with a device that has the ability to provide important office services, but also bridges the office and cloud environments. Using Network-Attached Storage (NAS) to take up the server slack makes a lot of sense. Many customers are already using NAS for file sharing, local data backup, automatic cloud backup, and other uses. In the case of Synology, their operating system, Synology DiskStation Manager (DSM), is Linux based, and integrates the basic functions of file sharing, centralized backup, RAID storage, multimedia streaming, virtual storage, and other common functions.

Synology NAS box

Synology NAS

Since DSM is based on Linux, there are numerous server applications available, including many of the same ones that are available for macOS Server, which shares conceptual roots with Linux as it comes from BSD Unix.

Synology DiskStation Manager Package Center screenshot

Synology DiskStation Manager Package Center

According to Ed Lukacs, COO at 2FIFTEEN Systems Management in Salt Lake City, their customers have found the move from macOS Server to Synology NAS not only painless, but positive. DSM works seamlessly with macOS and has been faster for their customers, as well. Many of their customers are running Adobe Creative Suite and Google G Suite applications, so a workflow that combines local storage, remote access, and the cloud, is already well known to them. Remote users are supported by Synology’s QuickConnect or VPN.

Business continuity and backup are simplified by the flexible storage capacity of the NAS. Synology has built-in backup to Backblaze B2 Cloud Storage with Synology’s Cloud Sync, as well as a choice of a number of other B2-compatible applications, such as Cloudberry, Comet, and Arq.

Customers have been able to get up and running quickly, with only initial data transfers requiring some time to complete. After that, management of the NAS can be handled in-house or with the support of a Managed Service Provider (MSP).

Are You Sticking with macOS Server or Moving to Another Platform?

If you’re affected by this change in macOS Server, please let us know in the comments how you’re planning to cope. Are you using Synology NAS for server services? Please tell us how that’s working for you.

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