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Accelerate Amazon Redshift Data Lake queries with AWS Glue Data Catalog Column Statistics

AWS Big Data

Over the last year, Amazon Redshift added several performance optimizations for data lake queries across multiple areas of query engine such as rewrite, planning, scan execution and consuming AWS Glue Data Catalog column statistics. Performance was tested on a Redshift serverless data warehouse with 128 RPU.

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Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints – Part 2

AWS Big Data

We’ve already discussed how checkpoints, when triggered by the job manager, signal all source operators to snapshot their state, which is then broadcasted as a special record called a checkpoint barrier. Then it broadcasts the barrier downstream. However, it continues to process partitions that are behind the barrier.

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Optimized joins & filtering with Bloom filter predicate in Kudu

Cloudera

Pushing down column predicate filters to Kudu allows for optimized execution by skipping reading column values for filtered out rows and reducing network IO between a client, like the distributed query engine Apache Impala, and Kudu. Broadcast the generated hash table to all worker nodes. CDP Runtime 7.1.5 Bloom filter. Join Queries.

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Optimize checkpointing in your Amazon Managed Service for Apache Flink applications with buffer debloating and unaligned checkpoints – Part 1

AWS Big Data

Internally, Apache Flink uses clever mechanisms to maintain exactly-once state consistency, while also optimizing for throughput and reduced latency. After the barriers from all upstream partitions have arrived, the sub-task takes the snapshot of its state and then broadcasts the barrier downstream.

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Porsche Carrera Cup Brasil gets real-time data boost

CIO Business Intelligence

In the annual Porsche Carrera Cup Brasil, data is essential to keep drivers safe and sustain optimal performance of race cars. Together, they established a core architecture that the company could build on to develop its engineering capabilities and, eventually, support for entertainment and broadcasting, which remains on Morrone’s roadmap.

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Run Trino queries 2.7 times faster with Amazon EMR 6.15.0

AWS Big Data

When you use Trino on Amazon EMR or Athena, you get the latest open source community innovations along with proprietary, AWS developed optimizations. and Athena engine version 2, AWS has been developing query plan and engine behavior optimizations that improve query performance on Trino. Starting from Amazon EMR 6.8.0

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Amazon EMR 7.1 runtime for Apache Spark and Iceberg can run Spark workloads 2.7 times faster than Apache Spark 3.5.1 and Iceberg 1.5.2

AWS Big Data

times faster with Amazon EMR runtime for Apache Spark , we detailed some of the optimizations, showing a runtime improvement of 4.5 However, many of the optimizations are geared towards DataSource V1, whereas Iceberg uses Spark DataSource V2. We have added eight new optimizations incrementally since the Amazon EMR 6.15