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

AWS Big Data

Systems of this nature generate a huge number of small objects and need attention to compact them to a more optimal size for faster reading, such as 128 MB, 256 MB, or 512 MB. For more information on streaming applications on AWS, refer to Real-time Data Streaming and Analytics. impl":"org.apache.iceberg.aws.s3.S3FileIO",

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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. When barriers from all upstream partitions have arrived, the sub-task takes a snapshot of its state.

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Use Amazon Athena with Spark SQL for your open-source transactional table formats

AWS Big Data

AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (Amazon S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. It will never remove files that are still required by a non-expired snapshot.

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Analyze Elastic IP usage history using Amazon Athena and AWS CloudTrail

AWS Big Data

You can use this solution regularly as part of your cost-optimization efforts to safely remove unused EIPs to reduce your costs. To gather EIP usage reporting, this solution compares snapshots of the current EIPs, focusing on their most recent attachment within a customizable 3-month period.

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

AWS Big Data

It aims to provide a framework to create low-latency streaming applications on the AWS Cloud using Amazon Kinesis Data Streams and AWS purpose-built data analytics services. The collected data is available in milliseconds to allow real-time analytics use cases, such as real-time dashboards, real-time anomaly detection, and dynamic pricing.

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Achieve near real time operational analytics using Amazon Aurora PostgreSQL zero-ETL integration with Amazon Redshift

AWS Big Data

When data is used to improve customer experiences and drive innovation, it can lead to business growth,” – Swami Sivasubramanian , VP of Database, Analytics, and Machine Learning at AWS in With a zero-ETL approach, AWS is helping builders realize near-real-time analytics. Choose a suitable instance size (the default is db.r5.2xlarge ).

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Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

Amazon Redshift is a cloud data warehousing service that provides high-performance analytical processing based on a massively parallel processing (MPP) architecture. In this post, we look into an optimal and cost-effective way of incorporating dbt within Amazon Redshift. For more information, refer SQL models.