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

Data Engineering Podcast

Preamble Hello and welcome to the Data Engineering Podcast, the show about modern data management When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out Linode.

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Exploring Processing Patterns For Streaming Data Integration In Your Data Lake

Data Engineering Podcast

In this episode Ori Rafael shares his experiences from Upsolver and building scalable stream processing for integrating and analyzing data, and what the tradeoffs are when coming from a batch oriented mindset. Can you start by giving an overview of the state of the market for data lakes today?

Data Lake 100
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Beyond Kafka: Conversation with Jark Wu on Fluss - Streaming Storage for Real-Time Analytics

Data Engineering Weekly

Fluss is a compelling new project in the realm of real-time data processing. Confluent Tableflow can bridge Kafka and Iceberg data, but that is just a data movement that data integration tools like Fivetran or Airbyte can also achieve.

Kafka 75
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Data Pipeline Architecture: Understanding What Works Best for You

Ascend.io

This article aims to shed light on the essential components of successful data pipeline architectures and offer actionable advice for creating scalable, reliable, and resilient data pipelines. Ready to fortify your data management practice? Let’s dive into the world of data pipeline architecture.

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Data Ingestion: 7 Challenges and 4 Best Practices

Monte Carlo

Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern data management workflows. Some data teams will leverage micro-batch strategies for time sensitive use cases.