January 17, 2022
Industry insights

The Stream — January 2022 edition

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

The Stream January 2022 banner.
Insert Quix boilerplate.

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 tutorials, explainer 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.

Shiny colorful lines.

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

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 →

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 →

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

What’s a Rich Text element?

The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.

Static and dynamic content editing

A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!

How to customize formatting for each rich text

Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.

Related content

Banner image for the article "Streaming ETL 101" published on the Quix blog
Industry insights

Streaming ETL 101

Read about the fundamentals of streaming ETL: what it is, how it works and how it compares to batch ETL. Discover streaming ETL technologies, architectures and use cases.
Tun Shwe
Words by
LLMOps: large language models in production with Quix
Industry insights

LLMOps: running large language models in production

LLMOps is a considered, well structured response to the hurdles that come with building, managing and scaling apps reliant on large language models. From data preparation, through model fine tuning, to finding ways to improve model performance, here is an overview of the LLM lifecycle and LLMOps best practices.
Tun Shwe
Words by
What is stream processing
Industry insights

What is stream processing?

An overview of stream processing: core concepts, use cases enabled, what challenges stream processing presents, and what the future looks like as AI starts playing a bigger role in how we process and analyze streaming data
Tun Shwe
Words by