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Category: Hadoop

Understanding the Kafka Partitioner

Published 2023-04-28 by Kevin Feasel

Bill Bejeck talks partitions:

Apache Kafka is the de facto standard for event streaming today. Part of what makes Kafka so successful is its ability to handle tremendous volumes of data, with a throughput of millions of records per second, not unheard of in production environments. One part of Kafka’s design that makes this possible is partitioning.  

Kafka uses partitions to spread the load of data across brokers in a cluster, and it’s also the unit of parallelism; more partitions mean higher throughput. Since Kafka works with key-value pairs, getting records with the same key on the same partition is essential.  

Read on to learn a bit about how that partitioning works and why it’s important for application design, especially across multiple programming languages.

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Common Challenges Implementing PySpark Code

Published 2023-04-27 by Kevin Feasel

Amlan Patnaik looks at some common implementation problems:

Pyspark has become one of the most popular tools for data processing and data engineering applications. It is a fast and efficient tool that can handle large volumes of data and provide scalable data processing capabilities. However, Pyspark applications also come with their own set of challenges that data engineers face on a day-to-day basis. In this article, we will discuss some of the common challenges faced by data engineers in Pyspark applications and the possible solutions to overcome these challenges.

Read on for five such challenges.

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Optimizing Kafka Infrastructure Costs

Published 2023-04-26 by Kevin Feasel

Addison Huddy saves some money:

In this first blog, we’re going to run through the infrastructure costs of running Kafka—i.e., compute, storage, networking, and the additional tooling you need to keep Kafka up and running smoothly. We won’t bury the lede—if you’re running Kafka in the cloud across multiple AZs (as most do for high availability), networking likely represents over 50% of your Kafka infrastructure costs. Let’s see how this ends up being the case.

Click through for some thoughts on how to reduce network costs, using AWS as an example.

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Kafka Topics and Message Ordering

Published 2023-04-19 by Kevin Feasel

Francesco Tisiot calls us to order:

One of Apache Kafka’s most known mantras is “it preserves the message ordering per topic-partition,” but is it always true? In this blog post, we’ll analyze a few real scenarios where accepting the dogma without questioning it could result in unexpected and erroneous sequences of messages.

Click through for a dive into what can go wrong with ordering. The good news is, in most cases, exact ordering isn’t critical. For cases in which it is critical, you’re trading off reduced throughput for increased order integrity.

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An Overview of the Kappa Architecture

Published 2023-04-10 by Kevin Feasel

Amian Patnaik provides an overview:

The Kappa Architecture, introduced by Jay Kreps, co-founder of Confluent, is designed to handle real-time data processing in a scalable and efficient manner. Unlike the traditional Lambda Architecture, which separates data processing into batch and stream processing, the Kappa Architecture promotes a single pipeline for both batch and stream processing, eliminating the need for maintaining separate processing pipelines.

What’s interesting to me is that Lambda, an architecture which was an explicit product of its time (in the sense that it was a compromise architecture trying to do two things, the combination of which limited hardware and tooling didn’t allow), is still thriving today. Kappa, meanwhile, isn’t an architectural style that people throw around a lot anymore, at least in the circles I run around in.

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Spark ELT in Synapse Notebooks

Published 2023-04-10 by Kevin Feasel

Liliam Leme performs some data movement:

I often receive various requests from customers while working on FastTrack projects, and I have compiled some examples to help you build your solution on top of a data lake using useful tips. Most of the examples in this post use pandas, and I hope they will be helpful for you as they were for me.

Please note that all examples in this post use pyspark.

In my scenario, I exported multiple tables from SQLDB to a folder using a notebook and ran the requests in parallel.

Read on for the examples and some of the things you can do with Spark notebooks in Azure Synapse Analytics.

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Comparing HBase to Cassandra

Published 2023-04-05 by Kevin Feasel

The Instaclustr team performs a comparison:

Apache HBase® and Apache Cassandra® are both open source NoSQL databases well-equipped to handle incredible amounts of data–but that’s where the similarities end. 

In this blog, discover the architectures powering these technologies, when and how to use them, and which option may prove to be the better choice for your operations.  

Click through for their overview of the two systems and recommendations on when to use which.

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Generating Artificial Data in Databricks

Published 2023-03-29 by Kevin Feasel

Ben Hazan needs some fake data:

While attending the SQLBits 2023, I took part in André Kamman’s session about “Generate test data quick, easy and lots of it with the Databricks Labs Data Generator”.

In this blog, I will share with you my insights about the DataBricks Data Generator library and I’ll give an example.

Synthetic data is a valuable resource for data scientists, engineers, and analysts who need to test, benchmark, or demonstrate their solutions without compromising sensitive or confidential information. However, generating realistic and relevant synthetic data can be challenging and time-consuming.

That’s why Databricks Labs has developed a Python library called dbldatagen that can help you create large-scale synthetic data sets using Spark.

Click through to learn more about the library and see how you can use to to generate arbitrary amounts of artificial data following certain constraints.

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Testing Message Ordering in Kafka

Published 2023-03-24 by Kevin Feasel

Francesco Tisiot puts a claim to the test:

One of Apache Kafka®’s most known mantras is “it preserves the message ordering per topic-partition”, but is it always true? In this blog post we’ll analyze a few real scenarios where accepting the dogma without questioning it could result in unexpected, and erroneous, sequences of messages.

There’s a lot more to this than I realized, and Francesco does a great job of explaining it.

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