Remove 2023 Remove Analytics Remove Metadata Remove Snapshot
article thumbnail

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

AWS Big Data

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. Analytics use cases on data lakes are always evolving. In this method, the metadata are recreated in an isolated environment and colocated with the existing data files.

Data Lake 102
article thumbnail

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.

Snapshot 100
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

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 integration is supported by AWS analytics services including Amazon EMR , Amazon Athena , and AWS Glue. Starting with Amazon EMR version 6.5.0,

Data Lake 118
article thumbnail

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.

article thumbnail

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

AWS Big Data

Iceberg employs internal metadata management that keeps track of data and empowers a set of rich features at scale. AWS provides flexibility and a wide breadth of features to ingest data, build AI and ML applications, and run analytics workloads without having to focus on the undifferentiated heavy lifting.

Data Lake 102
article thumbnail

Amazon OpenSearch Service H1 2023 in review

AWS Big Data

Since its release in January 2021, the OpenSearch project has released 14 versions through June 2023. With managed domains, you can use advanced capabilities at no extra cost such as cross-cluster search, cross-cluster replication, anomaly detection, semantic search, security analytics, and more. in OpenSearch Service).

article thumbnail

Introducing Amazon MWAA support for Apache Airflow version 2.7.2 and deferrable operators

AWS Big Data

You can see the time each task spends idling while waiting for the Redshift cluster to be created, snapshotted, and paused. Airflow will cache variables and connections locally so that they can be accessed faster during DAG parsing, without having to fetch them from the secrets backend, environments variables, or metadata database.

Metrics 103