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Apache Iceberg optimization: Solving the small files problem in Amazon EMR

AWS Big Data

Iceberg tables store metadata in manifest files. As the number of data files increase, the amount of metadata stored in these manifest files also increases, leading to longer query planning time. The query runtime also increases because it’s proportional to the number of data or metadata file read operations. with Spark 3.3.2,

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Speed up queries with the cost-based optimizer in Amazon Athena

AWS Big Data

Starting today, the Athena SQL engine uses a cost-based optimizer (CBO), a new feature that uses table and column statistics stored in the AWS Glue Data Catalog as part of the table’s metadata. Let’s discuss some of the cost-based optimization techniques that contributed to improved query performance.

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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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Introducing Amazon MWAA larger environment sizes

AWS Big Data

Running Apache Airflow at scale puts proportionally greater load on the Airflow metadata database, sometimes leading to CPU and memory issues on the underlying Amazon Relational Database Service (Amazon RDS) cluster. A resource-starved metadata database may lead to dropped connections from your workers, failing tasks prematurely.

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Optimize data layout by bucketing with Amazon Athena and AWS Glue to accelerate downstream queries

AWS Big Data

However, as data volumes continue to grow, optimizing data layout and organization becomes crucial for efficient querying and analysis. AWS Glue allows you to define bucketing parameters, such as the number of buckets and the columns to bucket on, providing an optimized data layout for efficient querying with Athena.

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Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

Apache Iceberg is an open table format for very large analytic datasets, which captures metadata information on the state of datasets as they evolve and change over time. Apache Iceberg addresses customer needs by capturing rich metadata information about the dataset at the time the individual data files are created.

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Gartner D&A Summit Bake-Offs Explored Flooding Impact And Reasons for Optimism!

Rita Sallam

Are there mitigation strategies that show reasons for optimism? Are there mitigation strategies that can be implemented successfully that could provide policy guidance and reasons for optimism in the face of ever increasing frequency of extreme weather events?