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

AWS Big Data

Kinesis Data Streams has native integrations with other AWS services such as AWS Glue and Amazon EventBridge to build real-time streaming applications on AWS. Refer to Amazon Kinesis Data Streams integrations for additional details. State snapshot in Amazon S3 – You can store the state snapshot in Amazon S3 for tracking.

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Cloudera Data Engineering 2021 Year End Review

Cloudera

Since the release of Cloudera Data Engineering (CDE) more than a year ago , our number one goal was operationalizing Spark pipelines at scale with first class tooling designed to streamline automation and observability. Modernizing pipelines.

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

AWS Big Data

Tracking data changes and rollback Build your transactional data lake on AWS You can build your modern data architecture with a scalable data lake that integrates seamlessly with an Amazon Redshift powered cloud warehouse. Additionally, you can query in Athena based on the version ID of a snapshot in Iceberg.

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Exploring real-time streaming for generative AI Applications

AWS Big Data

Furthermore, data events are filtered, enriched, and transformed to a consumable format using a stream processor. The result is made available to the application by querying the latest snapshot. It also allows you to transform, enrich, join, and aggregate data across many sources efficiently in near-real time.

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Build a multi-Region and highly resilient modern data architecture using AWS Glue and AWS Lake Formation

AWS Big Data

Data migration must be performed separately using methods such as S3 replication , S3 sync, aws-s3-copy-sync-using-batch or S3 Batch replication. This utility has two modes for replicating Lake Formation and Data Catalog metadata: on-demand and real-time. Nivas Shankar is a Principal Product Manager for AWS Lake Formation.

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How Amazon optimized its high-volume financial reconciliation process with Amazon EMR for higher scalability and performance

AWS Big Data

To optimize the reconciliation process, these users require high performance transformation with the ability to scale on demand, as well as the ability to process variable file sizes ranging from as low as a few MBs to more than 100 GB. For optimal parallelization, the step concurrency is set at 10, allowing 10 steps to run concurrently.

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Introducing Apache Iceberg in Cloudera Data Platform

Cloudera

Over the past decade, the successful deployment of large scale data platforms at our customers has acted as a big data flywheel driving demand to bring in even more data, apply more sophisticated analytics, and on-board many new data practitioners from business analysts to data scientists.

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