bicep VS beam

I thought it was a good idea, but Bicep did not agree. I have submitted a proposal to the Bicep team for how this can be allowed. Vote for this issue if you agree!

Bicep has limitations which makes it non-declarative even though it is marketed as declarative: https://learn.microsoft.com/en-us/azure/azure-resource-manag...

MSFT is trying to add features to make this better, but it is not in production yet: https://github.com/Azure/bicep/issues/10460

Additionally, Bicep does not support interacting with Azure Active Directory: https://github.com/Azure/bicep/issues/7724

So it really is not very useful. Terraform is better in almost every single conceivable way.

Github: https://github.com/Azure/bicep/issues/9569

Since its launch Bicep has become popular within the IT community. You can find blog posts, tweets, conference sessions, and plenty of interaction on the official Bicep GitHub space. Bicep became production ready at v0.3. It is supported by Microsoft Support Plans.

Please give Azure Bicep a try. You get a really simple experience with all the benefits of using the platform native capabilities https://github.com/Azure/bicep

I was on the bicep call last month but that doesn't mean I didn't miss the announcement, it looks like they are getting close though - https://github.com/Azure/bicep/issues/8598

Bicep, the open source project used by Visual Studio Code to extend its capabilities, has finally arrived in Visual Studio, enabling users of Microsoft’s flagship IDE to use some of Bicep’s most popular features in the same program they have been using since they were introduced to it — in other words, Visual Studio itself.

In addition, check the similar issue on GitHub.

There is an ongoing thread here https://github.com/Azure/bicep/issues/1853

The "streaming systems" book answers your question and more: https://www.oreilly.com/library/view/streaming-systems/97814.... It gives you a history of how batch processing started with MapReduce, and how attempts at scaling by moving towards streaming systems gave us all the subsequent frameworks (Spark, Beam, etc.).

As for the framework called MapReduce, it isn't used much, but its descendant https://beam.apache.org very much is. Nowadays people often use "map reduce" as a shorthand for whatever batch processing system they're building on top of.

Apache Beam is one of many tools that you can use

Flexible runtime ☁️: Temporian programs can run seamlessly in-process in Python, on large datasets using Apache Beam.

To perform the tests I'm using a Kafka cluster on Kubernetes from the Beam repo (here).

Apache Beam: Streaming framework which can be run on several runner such as Apache Flink and GCP Dataflow

Apache Beam: Batch/streaming data processing 🔗Link

What you are looking for is Dataflow. It can be a bit tricky to wrap your head around at first, but I highly suggest leaning into this technology for most of your data engineering needs. It's based on the open source Apache Beam framework that originated at Google. We use an internal version of this system at Google for virtually all of our pipeline tasks, from a few GB, to Exabyte scale systems -- it can do it all.

That being said, there is a learning curve in understanding how Apache Beam works. Take a look at the beam website for more information.