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Use Apache Iceberg in your data lake with Amazon S3, AWS Glue, and Snowflake

AWS Big Data

They understand that a one-size-fits-all approach no longer works, and recognize the value in adopting scalable, flexible tools and open data formats to support interoperability in a modern data architecture to accelerate the delivery of new solutions. Snowflake can query across Iceberg and Snowflake table formats.

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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

Over the years, data lakes on Amazon Simple Storage Service (Amazon S3) have become the default repository for enterprise data and are a common choice for a large set of users who query data for a variety of analytics and machine leaning use cases. Analytics use cases on data lakes are always evolving.

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AI at Scale isn’t Magic, it’s Data – Hybrid Data

Cloudera

The takeaway – businesses need control over all their data in order to achieve AI at scale and digital business transformation. The challenge for AI is how to do data in all its complexity – volume, variety, velocity. And that data is likely in clouds, in data centers and at the edge.

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Architectural patterns for real-time analytics using Amazon Kinesis Data Streams, part 1

AWS Big Data

For high availability and durability, Kinesis Data Streams achieves high durability by synchronously replicating the streamed data across three Availability Zones in an AWS Region and gives you the option to retain data for up to 365 days. Refer to Amazon Kinesis Data Streams integrations for additional details.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

One important feature is to run different workloads such as business intelligence (BI), Machine Learning (ML), Data Science and data exploration, and Change Data Capture (CDC) of transactional data, without having to maintain multiple copies of data.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

In this post, we discuss why data streaming is a crucial component of generative AI applications due to its real-time nature. In-context learning LLMs are trained with point-in-time data and have no inherent ability to access fresh data at inference time. For more information, refer to Dynamic Tables.

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Cloudera Data Engineering 2021 Year End Review

Cloudera

In working with thousands of customers deploying Spark applications, we saw significant challenges with managing Spark as well as automating, delivering, and optimizing secure data pipelines. We wanted to develop a service tailored to the data engineering practitioner built on top of a true enterprise hybrid data service platform.

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