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17 Jan, 2022 | Community

The Stream — January 2022 edition

The January 2022 edition of The Stream: covering this month in stream processing on the internet.

1611064394032
Words by
Mike Rosam, CEO & Co-Founder
The Stream January 2022

What’s a data-driven company if it doesn’t take an opportunity to drive its content with data? Instead of filling our January newsletter with things we think you should know, we turned back the clock to 2021 and considered what other people like you — data engineers, data scientists and developers — want to know.

The data revealed our most popular tutorialsexplainer blog posts, research reports and how-to guides. It told us that the data engineering community is ravenous to learn how to streamline data. They’ve realized that dumping raw data into a warehouse for cleanup and query later makes everyone’s jobs harder than they need to be.

The problem is that it’s complicated. Until now, no one except an elite handful of tech juggernauts had harnessed the power of stream processing because it took a literal army of engineers to do it.

We think the future of data is stream processing for everyone. Soon, you’ll see businesses of every size processing data in memory, on a message broker like Kafka to extract its value before their data gets sunk into a lake or buried under ever-increasing volumes of data in a warehouse.

Based on our reader data, you think so, too. Many of you searched us out for our tutorials on doing real-time stream processing with Kafka and Python. Many also looked for our research and head-to-head comparison on how well Python-based stream processing client libraries perform.

So, without further ado, here’s our digest of the top tutorials, explainers and research reports from 2021 that were most helpful to our users.

01 Feature story

Kafka + Python = %$#&?!

Python gets the most love from data scientists and other data-friendly developers, but when it comes to Kafka, Python gets the cold shoulder. Here’s how they work together.

Will it blend? A tutorial
02 Streaming data hard to handle

Why is streaming data so hard to handle?

And why aren’t these difficulties already solved? Our CTO explains in not-too-technical terms why stream processing has been out of reach for most organizations — until now.

An analogy is worth 1,000 words →

03 Python library best for stream processing

Which Python library is best for stream processing?

When you build a product, you do research — exhaustive competitive research. Our CEO offers a deep-deep-dive on Spark vs. Flink vs Quix performance.

Show me the receipts →

04 Kafka for real time stream processing

Everything you wanted to know about Kafka but were afraid to ask

Kafka isn’t just tricky technology — it’s tricky terminology. Dig into what makes Kafka different, when you should (and shouldn’t) use it, and how it works.

The data metamorphosis →

More insights

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1611064394032
words by
Mike Rosam, CEO & Co-Founder

Mike Rosam is Co-Founder and CEO at Quix, where he works at the intersection of business and technology to pioneer the world's first streaming data development platform. He was previously Head of Innovation at McLaren Applied, where he led the data analytics product line. Mike has a degree in Mechanical Engineering and an MBA from Imperial College London.

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