Python has an SSM client in the Boto 3 package: https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/ssm.html. Is there something similar for R? If not, any recommendations for how to make something similar? Thanks!
At the moment a package called paws on github
Access over 150 AWS services, including Machine Learning Translation
Natural Language Processing Databases File Storage
Or the cloudyr project
Welcome to the cloudyr project! The goal of this initiative is to make
cloud computing with R easier, starting with robust tools for working
with cloud computing platforms. The project’s inital work is with
Amazon Web Services, various crowdsourcing platforms, and popular
continuous integration services for R package development. Tools for
Google Cloud Services and Microsoft Azure are also on the long-term
agenda.
I only checked paws and that has the ssm functionality according to the paws documentation about ssm. The cloudyr project has many aws packages on cran. Not sure if in one of them is the ssm functionality.
Related
I am trying to understand if the sf package in R operates at the local (desktop) level or if it uses API to transmit information online. The documentation mentions the use of API to pull in algorithms but it is unclear to me what that involves.
The package operates locally; once compiled it is perfectly capable of being run in firewalled contexts if that is your question.
We are investigating how to integrate our app with Github Enterprise.
There are 2 different deployment models - 'Cloud' and 'On Premise'
I have been looking around but couldn't find the differences between the two.
Maybe there is no such difference
The basic difference is that GitHub Enterprise Server is software you deploy on a virtual machine you provision and control (on-premise here is a bit of a misnomer since your VM could be in AWS).
GitHub Enterprise Cloud, on the other hand, is an enterprise-level of service at GitHub.com.
You'll find more here.
I have some question to a service called AWS CodeArtifact?
What is it and what is it useful for.
Any links to the documentation?
From 1
AWS CodeArtifact is a fully managed software artifact repository service that makes it easy for organizations of any size to securely store, publish, and share packages used in their software development process. CodeArtifact eliminates the need for you to set up, operate, and scale the infrastructure required for artifact management so you can focus on software development. With CodeArtifact, you only pay for what you use and there are no license fees or upfront commitments.
AWS CodeArtifact works with commonly used package managers and build tools such as Maven and Gradle (Java), npm and yarn (JavaScript), pip and twine (Python), making it easy to integrate CodeArtifact into your existing development workflows. CodeArtifact can be configured to automatically fetch software packages from public artifact repositories such as npm public registry, Maven Central, and Python Package Index (PyPI), ensuring teams have reliable access to the most up-to-date packages.
IT leaders can use AWS CodeArtifact to create centralized repositories for sharing software packages approved for use across their development teams. CodeArtifact’s integration with AWS Identity and Access Management (IAM) provides them with the ability to control who has access to the packages. Further, CodeArtifact’s support for AWS CloudTrail gives leaders visibility into which packages are in use and where, making it easy to identify packages that need to be updated or removed. CodeArtifact also supports encryption with AWS Key Management Service so customers can control the keys used to encrypt their packages.
Product Page: 2
It is an artifact repository from AWS. Refer this -
https://aws.amazon.com/codeartifact/
I have been looking to use Storm which is available with Hortonworks 2.1 installation but in order to avoid installing Hortonworks in addition to a Cloudera installation (which has Spark in it), I tried to find a way to use Storm in Cloudera.
If one can use both Storm and Spark on a single platform then it will save additional resources required to have both Cloudera and Hortonworks installations on a machine.
You can use storm with Cloudera installation. You will have to install it on your own and maintain it as such. It will not be part of the Cloudera stack but that should not stop you from using it along with Hadoop if you need it.
You can use Storm on any of the vendor platform. However, storm cluster management is something you have to consider. Storm is not part of the CDH distribution. Cloudera Manager does not manage the lifecycle of the storm services and configurations, nor does it monitor the storm cluster, unless you are willing to write a Clouderea Manager extension yourself. On the contrary, if you choose a vendor such as HDP, the Ambari management tool on HDP provides all the above management features.
If you have a streaming project on CDH, you should strongly consider Apache Spark first, as it provides the same programming model for both batch and streaming processing. You do not need to learn a new API. However, Apache Spark streaming is micro-batch. Thus in use cases that requires sub-second low latency real-time processing, Storm is more suitable.
You can use Storm alongside Cloudera.
All the above are true, but why would you?
Spark includes Spark Streaming, which allows you to handle data processing and stream/event processing workloads using a single API. Spark/Streaming is already inside CDH.
So, why burden yourself with two different APIs?
You can install Apache Storm on Cloudera VM.
For a basic setup and test run, follow below link:
https://github.com/vrmorusu/StormOnClouderaVM/wiki/Apache-Storm-on-Cloudera-VM
This should get you started on developing Storm applications on Cloudera VM.
I want to try install Storm.
Does Storm have distributions like Hadoop (cloudera, mapr, etc.)?
Or should I install all by myself (ZEROMQ, GZMQ, etc.)
What about versions? Where can I find the versions to use?
I see that Storm has 0.8.1. ZeroMq is already at version 3.2.2.
The Storm-starter project on GitHub is a good place to start. You can easily deploy and run local topologies (entirely on your own machine). It is useful for getting your first topology up and running.
If you want to deploy Storm to Amazon AWS you should take a look at the Storm-deploy project. This will take care of the installation of the correct dependencies on AWS (Zookeeper, etc.).
There's a steep enough learning curve, but if you work through the online documentation you should be able to get the sample topology deployed to AWS pretty quickly.
The Storm wiki is the primary source of Storm documentation.