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DEW #124: State of Analytics Engineering, ChatGPT, LLM & the Future of Data Consulting, Unified Streaming & Batch Pipeline, and Kafka Schema Management

Data Engineering Weekly

🤺🤺🤺🤺🤺🤺 [link] LinkedIn: Unified Streaming And Batch Pipelines At LinkedIn: Reducing Processing time by 94% with Apache Beam One of the curses of adopting Lambda Architecture is the need for rewriting business logic in both streaming and batch pipelines.

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

LinkedIn Engineering

In 2010, they introduced Apache Kafka , a pivotal Big Data ingestion backbone for LinkedIn’s real-time infrastructure. To transition from batch-oriented processing and respond to Kafka events within minutes or seconds, they built an in-house distributed event streaming framework, Apache Samza.

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An Exploration Of The Expectations, Ecosystem, and Realities Of Real-Time Data Applications

Data Engineering Podcast

Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

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StreamNative Brings Streaming Data To The Cloud Native Landscape With Pulsar

Data Engineering Podcast

How have projects such as Kafka and Pulsar impacted the broader software and data landscape? How have projects such as Kafka and Pulsar impacted the broader software and data landscape? What motivates you to dedicate so much of your time and enery to Pulsar in particular, and the streaming data ecosystem in general?

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Simplifying Continuous Data Processing Using Stream Native Storage In Pravega with Tom Kaitchuck - Episode 63

Data Engineering Podcast

How does it compare with systems such as Kafka and Pulsar for ingesting and persisting unbounded data? For someone who wants to build an application on top of Pravega, what interfaces does it provide and what architectural patterns does it lend itself toward? Can you start by explaining what Pravega is and the story behind it?

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Data Engineering Weekly #124

Data Engineering Weekly

Join Live Session LinkedIn: Unified Streaming And Batch Pipelines At LinkedIn: Reducing Processing time by 94% with Apache Beam One of the curses of adopting Lambda Architecture is the need for rewriting business logic in both streaming and batch pipelines.

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Apache Spark Use Cases & Applications

Knowledge Hut

Spark streaming also has in-built connectors for Apache Kafka which comes very handy while developing Streaming applications. The order management system pushes the order status to the queue(could be Kafka) from where Streaming process reads every minute and picks all the orders with their status.

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