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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
VP Data
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
VP Data
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
VP Data
Simplified diagram showing event-driven programming components (event listener, event queue, event loop, event handler)
Industry insights

The what, why and how of event-driven programming

Read about the fundamentals of event-driven programming (EDP): key concepts, advantages, and challenges. Discover EDP use cases and technologies, and learn about the relation between EDP and event-driven architecture (EDA).
Tomáš Neubauer
CTO & Co-Founder
Simplified diagram of a machine learning pipeline.
Industry insights

The anatomy of a machine learning pipeline

Explore the characteristics, challenges, and benefits of machine learning pipelines, and read about the steps involved in training and deploying ML models to production.
Alex Diaconu
Technical Writer
Three data processing icons in blue background.
Industry insights

The fundamentals of real-time machine learning

What is real-time machine learning? How is it different from batch ML? What are common real-time ML use cases? What are the challenges of building real-time ML capabilities? All these questions and more are answered in this article.
Mike Rosam
CEO & Co-Founder
Man standing in front of a labyrinth illustration.
Industry insights

Real-Time infrastructure tooling for data scientists

Explore the evolution of new tools for real-time pipelines that aim to solve the ongoing problem of data scientists' need for more infrastructure expertise.
Tun Shwe
VP Data
Language friction image timeline.
Industry insights

Feature engineering has a language problem

Should data scientists know Java? Java and Scala underpin many real-time, ML-based applications—yet data scientists usually work in Python. Someone has to port the Python into Java or adapt it to use a Python wrapper. Neither of these options is ideal, so what are some better solutions?
Tun Shwe
VP Data
Orange and green chart on blue background.
Industry insights

Time series analysis: a gentle introduction

Explore the fundamentals of time series analysis in this comprehensive article. Learn about key concepts, use cases, and types of time series analysis, and discover models, techniques, and methods to analyze time series data.
Javier Blanco
Senior Data Scientist
Black chart on colorful background.
Industry insights

Telemetry data explained

Gain a thorough understanding of telemetry data and how it works, learn about its benefits, challenges, and applications across different industries, and discover technologies you can use to operationalize telemetry.
Javier Blanco
Senior Data Scientist
The Stream May 2023 banner.
Industry insights

The Stream — May 2023 edition

A monthly round-up of the most interesting news coming out of the stream processing ecosystem
Mike Rosam
CEO & Co-Founder
Illustration of two people in the desert.
Industry insights

Bridging the gap between data scientists and engineers in machine learning workflows

Moving code from prototype to production can be tricky—especially for data scientists. There are many challenges in deploying code that needs to calculate features for ML models in real-time. I look at potential solutions to ease the friction.
Mike Rosam
CEO & Co-Founder
The Stream April 2023 banner.
Industry insights

The Stream — April 2023 edition

A monthly round-up of the most interesting news coming out of the stream processing ecosystem
Mike Rosam
CEO & Co-Founder
The Stream March 2023 banner.
Industry insights

The Stream — March 2023 edition

A monthly round-up of the most interesting news coming out of the stream processing ecosystem
Mike Rosam
CEO & Co-Founder
The Stream February 2023 banner.
Industry insights

The Stream — February 2023 edition

Build a simple event-driven system to get ML predictions with Python and Apache Kafka
Mike Rosam
CEO & Co-Founder
The Stream January 2023 banner.
Industry insights

The Stream — January 2023 edition

How can you send time-series data to Apache Kafka using Python and Pandas? Plus Apache Flink news, memes, and meetups
Mike Rosam
CEO & Co-Founder
The Stream December 2022 banner.
Industry insights

The Stream — December 2022 edition

The December 2022 edition of The Stream: covering this month in stream processing on the internet.
Mike Rosam
CEO & Co-Founder
The Stream November 2022 banner.
Industry insights

The Stream — November 2022 edition

The November 2022 edition of The Stream: covering this month in stream processing on the internet.
Mike Rosam
CEO & Co-Founder