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Reducing The Barrier To Entry For Building Stream Processing Applications With Decodable

Data Engineering Podcast

Summary Building streaming applications has gotten substantially easier over the past several years. Despite this, it is still operationally challenging to deploy and maintain your own stream processing infrastructure. What was the process for adding full Java support in addition to SQL?

Process 182
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Revolutionizing Real-Time Streaming Processing: 4 Trillion Events Daily at LinkedIn

LinkedIn Engineering

Authors: Bingfeng Xia and Xinyu Liu Background At LinkedIn, Apache Beam plays a pivotal role in stream processing infrastructures that process over 4 trillion events daily through more than 3,000 pipelines across multiple production data centers.

Process 119
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Rapid Event Notification System at Netflix

Netflix Tech

To this end, we developed a Rapid Event Notification System (RENO) to support use cases that require server initiated communication with devices in a scalable and extensible manner. In this blog post, we will give an overview of the Rapid Event Notification System at Netflix and share some of the learnings we gained along the way.

Systems 133
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Building an an Early Stage Startup: Lessons from Akita Software

The Pragmatic Engineer

Venture funding is on a downward trend , and we seem to be at the start – or the middle – of a “startup purge” event. This news is hot off the press, publicly announced by Postman and by Akita yesterday, and you are among the early ones to hear about this event. On hiring Every startup hires differently.

Building 208
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Building Linked Data Products With JSON-LD

Data Engineering Podcast

Summary A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. In this episode Brian Platz explains how JSON-LD can be used as a shared representation of linked data for building semantic data products. Hex brings everything together.

Building 189
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Building ETL Pipelines With Generative AI

Data Engineering Podcast

Now that AI has reached the level of sophistication seen in the various generative models it is being used to build new ETL workflows. In this episode Jay Mishra shares his experiences and insights building ETL pipelines with the help of generative AI. Check out the agenda and register at Neo4j.com/NODES.

Building 162
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They Handle 500B Events Daily. Here’s Their Data Engineering Architecture.

Monte Carlo

A data engineering architecture is the structural framework that determines how data flows through an organization – from collection and storage to processing and analysis. Before building your own data architecture from scratch though, why not steal – er, learn from – what industry leaders have already figured out?