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Migrate an existing data lake to a transactional data lake using Apache Iceberg

AWS Big Data

A data lake is a centralized repository that you can use to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data and then run different types of analytics for better business insights.

Data Lake 102
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Simplify operational data processing in data lakes using AWS Glue and Apache Hudi

AWS Big Data

A modern data architecture is an evolutionary architecture pattern designed to integrate a data lake, data warehouse, and purpose-built stores with a unified governance model. The company wanted the ability to continue processing operational data in the secondary Region in the rare event of primary Region failure.

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

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

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Build a data lake with Apache Flink on Amazon EMR

AWS Big Data

With a unified data catalog, you can quickly search datasets and figure out data schema, data format, and location. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos. Refer to Catalogs for more information.

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How Amazon Devices scaled and optimized real-time demand and supply forecasts using serverless analytics

AWS Big Data

With data volumes exhibiting a double-digit percentage growth rate year on year and the COVID pandemic disrupting global logistics in 2021, it became more critical to scale and generate near-real-time data. You can visually create, run, and monitor extract, transform, and load (ETL) pipelines to load data into your data lakes.

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Introducing AWS Glue crawler and create table support for Apache Iceberg format

AWS Big Data

Iceberg has become very popular for its support for ACID transactions in data lakes and features like schema and partition evolution, time travel, and rollback. Iceberg captures metadata information on the state of datasets as they evolve and change over time. Choose Create.

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Open Data Lakehouse powered by Iceberg for all your Data Warehouse needs

Cloudera

Every table change creates an Iceberg snapshot, this helps to resolve concurrency issues and allows readers to scan a stable table state every time. In a bit more detail, Iceberg V1 added support for creating, updating, deleting and inserting data into tables. These files store the file paths and positions of the deleted records.