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Summary Data lakes offer a great deal of flexibility and the potential for reduced cost for your analytics, but they also introduce a great deal of complexity. What used to be entirely managed by the database engine is now a composition of multiple systems that need to be properly configured to work in concert.
In contrast, Fluss adopts a Lakehouse-native design with structured tables, explicit schemas, and support for all kinds of data types; it directly mirrors the Lakehouse paradigm. Instead of Kafka's topics, Fluss organizes data into database tables with partitions and buckets. The second difference is the Storage Model.
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement 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 our friends at Linode. What are your goals with this book?
Announcements Hello and welcome to the Data Engineering Podcast, the show about modern datamanagement 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 our friends at Linode.
Pulsar is a well engineered and robust platform for building the core of any system that relies on durable access to easily scalable streams of data. You monitor your website to make sure that you’re the first to know when something goes wrong, but what about your data? Can you start by giving an overview of what Pulsar is?
This conversation was useful for getting a better idea of the challenges that exist in large scale data analytics, and the current state of the tradeoffs between data lakes and data warehouses in the cloud. Support the show and get your data projects in order! What is in store for the future of Delta Lake?
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 datamanagement practice? Let’s dive into the world of data pipeline architecture.
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 datamanagement workflows. Data ingestion is the process of acquiring and importing data for use, either immediately or in the future.
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