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

AWS Big Data

Iceberg tables maintain metadata to abstract large collections of files, providing data management features including time travel, rollback, data compaction, and full schema evolution, reducing management overhead. Snowflake integrates with AWS Glue Data Catalog to retrieve the snapshot location.

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Choosing an open table format for your transactional data lake on AWS

AWS Big Data

Iceberg doesn’t optimize file sizes or run automatic table services (for example, compaction or clustering) when writing, so streaming ingestion will create many small data and metadata files. Offers different query types , allowing to prioritize data freshness (Snapshot Query) or read performance (Read Optimized Query).

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Hadoop Data Mining Tools Can Enhance The Value Of Digital Assets

Smart Data Collective

Some of the benefits are detailed below: Optimizing metadata for greater reach and branding benefits. One of the most overlooked factors is metadata. Metadata is important for numerous reasons. Search engines crawl metadata of image files, videos and other visual creative when they are indexing websites.

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Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg

AWS Big Data

With scalable metadata indexing, Apache Iceberg is able to deliver performant queries to a variety of engines such as Spark and Athena by reducing planning time. To avoid look-ahead bias in backtesting, it’s essential to create snapshots of the data at different points in time. Tag this data to preserve a snapshot of it.

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Don’t let your data pipeline slow to a trickle of low-quality data

IBM Big Data Hub

Data observability takes traditional data operations to the next level by using historical trends to compute statistics about data workloads and data pipelines directly at the source, determining if they are working, and pinpointing where any problems may exist. . The data observability difference . Instead, Databand.ai

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Simplify operational data processing in data lakes using AWS Glue and Apache Hudi

AWS Big Data

The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker. Current snapshot – This table in the data lake stores latest versioned records (upserts) with the ability to use Hudi time travel for historical updates.

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Amazon DataZone now integrates with AWS Glue Data Quality and external data quality solutions

AWS Big Data

By selecting the corresponding asset, you can understand its content through the readme, glossary terms , and technical and business metadata. By analyzing the historical report snapshot, you can identify areas for improvement, implement changes, and measure the effectiveness of those changes.