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Data Integrity for AI: What’s Old is New Again

Precisely

Then came Big Data and Hadoop! The traditional data warehouse was chugging along nicely for a good two decades until, in the mid to late 2000s, enterprise data hit a brick wall. The big data boom was born, and Hadoop was its poster child. A data lake!

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Enabling Security for Hadoop Data Lake on Google Cloud Storage

Uber Engineering

Ready to boost your Hadoop Data Lake security on GCP? Our latest blog dives into enabling security for Uber’s modernized batch data lake on Google Cloud Storage!

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Charting A Path For Streaming Data To Fill Your Data Lake With Hudi

Data Engineering Podcast

Summary Data lake architectures have largely been biased toward batch processing workflows due to the volume of data that they are designed for. With more real-time requirements and the increasing use of streaming data there has been a struggle to merge fast, incremental updates with large, historical analysis.

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Cloud Data Warehouse Migrations: Success Stories from WHOOP and Nexon

Snowflake

Many of our customers — from Marriott to AT&T — start their journey with the Snowflake AI Data Cloud by migrating their data warehousing workloads to the platform. That’s why we’ve collected these migration success stories to help you get started on your migration to Snowflake.

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Straining Your Data Lake Through A Data Mesh

Data Engineering Podcast

Summary The current trend in data management is to centralize the responsibilities of storing and curating the organization’s information to a data engineering team. This organizational pattern is reinforced by the architectural pattern of data lakes as a solution for managing storage and access.

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Maintaining Your Data Lake At Scale With Spark

Data Engineering Podcast

Summary Building and maintaining a data lake is a choose your own adventure of tools, services, and evolving best practices. The flexibility and freedom that data lakes provide allows for generating significant value, but it can also lead to anti-patterns and inconsistent quality in your analytics.

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Exploring Processing Patterns For Streaming Data Integration In Your Data Lake

Data Engineering Podcast

Summary One of the perennial challenges posed by data lakes is how to keep them up to date as new data is collected. 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.

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