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2. Diving Deeper into Psyberg: Stateless vs Stateful Data Processing

Netflix Tech

By Abhinaya Shetty , Bharath Mummadisetty In the inaugural blog post of this series, we introduced you to the state of our pipelines before Psyberg and the challenges with incremental processing that led us to create the Psyberg framework within Netflix’s Membership and Finance data engineering team.

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Real World Change Data Capture At Datacoral

Data Engineering Podcast

Unfortunately, this is a non-trivial undertaking, particularly for teams that don’t have extensive experience working with streaming data and complex distributed systems. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming.

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Migrating Critical Traffic At Scale with No Downtime?—?Part 1

Netflix Tech

Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience. The batch job creates a high-level summary that captures some key comparison metrics.

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Hadoop Developer Job Responsibilities Explained

ProjectPro

Here’s a blog post that answers the question and details out the job responsibilities of a hadoop developer. Table of Contents Who is a Hadoop Developer? Hadoop developers spend lot of time in cleaning data as per business requirements using streaming API’s or user defined functions. Defining Hadoop Job Flows.

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Stream Processing vs. Real-Time Analytics Databases

Rockset

This is part two in Rockset’s Making Sense of Real-Time Analytics on Streaming Data series. In part 1 , we covered the technology landscape for real-time analytics on streaming data. In this post, we’ll explore the differences between real-time analytics databases and stream processing frameworks. With that, let’s dive in.

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Unified Streaming And Batch Pipelines At LinkedIn: Reducing Processing time by 94% with Apache Beam

LinkedIn Engineering

In the past, we often used lambda architecture for processing jobs, meaning that our developers used two different systems for batch and stream processing. To reduce this complexity, we began utilizing Apache Beam , which allows the user to write processing logic in the same code for both batch and stream jobs.

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5 Key Takeaways from #Current2023

Cloudera

With few conferences curating content specific to streaming developers, Current has historically been an important event for anyone trying to keep a pulse on what’s happening in the streaming space. The adoption of Flink mirrors growth in streaming data volumes and maturity of the streaming market.