Remove Metadata Remove Snapshot Remove Strategy Remove Testing
article thumbnail

Use AWS Glue ETL to perform merge, partition evolution, and schema evolution on Apache Iceberg

AWS Big Data

Apache Iceberg manages these schema changes in a backward-compatible way through its innovative metadata table evolution architecture. With Lake Formation, you can manage fine-grained access control for your data lake data on Amazon S3 and its metadata in the Data Catalog. Iceberg maintains the table state in metadata files.

Snapshot 108
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 addresses customer needs by capturing rich metadata information about the dataset at the time the individual data files are created.

Data Lake 114
Insiders

Sign Up for our Newsletter

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

article thumbnail

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

AWS Big Data

In-place data upgrade In an in-place data migration strategy, existing datasets are upgraded to Apache Iceberg format without first reprocessing or restating existing data. In this method, the metadata are recreated in an isolated environment and colocated with the existing data files. Supported formats are Avro, Parquet, and ORC.

article thumbnail

From Hive Tables to Iceberg Tables: Hassle-Free

Cloudera

Depending on the size and usage patterns of the data, several different strategies could be pursued to achieve a successful migration. In this blog, I will describe a few strategies one could undertake for various use cases. Query engines (Impala, Hive, Spark) might mitigate some of these problems by using Iceberg’s metadata files.

article thumbnail

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.

article thumbnail

Backtesting index rebalancing arbitrage with Amazon EMR and Apache Iceberg

AWS Big Data

Backtesting is a process used in quantitative finance to evaluate trading strategies using historical data. This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance.

article thumbnail

Introducing Apache Iceberg in Cloudera Data Platform

Cloudera

In Iceberg, instead of listing O(n) partitions (directory listing at runtime) in a table for query planning, Iceberg performs an O(1) RPC to read the snapshot. It includes a catalog that supports atomic changes to snapshots – this is required to ensure that we know changes to an Iceberg table either succeeded or failed.

Snapshot 103