Remove Interactive Remove Metadata Remove Snapshot Remove Testing
article thumbnail

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

AWS Big Data

In this post, we show you how you can convert existing data in an Amazon S3 data lake in Apache Parquet format to Apache Iceberg format to support transactions on the data using Jupyter Notebook based interactive sessions over AWS Glue 4.0. AWS Command Line Interface (AWS CLI) configured to interact with AWS Services.

article thumbnail

Power enterprise-grade Data Vaults with Amazon Redshift – Part 2

AWS Big Data

Building a starter version of anything can often be straightforward, but building something with enterprise-grade scale, security, resiliency, and performance typically requires knowledge and adherence to battle-tested best practices, and using the right tools and features in the right scenario.

Insiders

Sign Up for our Newsletter

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

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

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 106
article thumbnail

Modernize a legacy real-time analytics application with Amazon Managed Service for Apache Flink

AWS Big Data

We introduce you to Amazon Managed Service for Apache Flink Studio and get started querying streaming data interactively using Amazon Kinesis Data Streams. Frequent materialized view refreshes on top of constantly changing base tables due to streamed data can lead to snapshot isolation errors. We use two datasets in this post.

article thumbnail

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

Cloudera

Cloudera Contributors: Ayush Saxena, Tamas Mate, Simhadri Govindappa Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), we are excited to see customers testing their analytic workloads on Iceberg. Iceberg basics Iceberg is an open table format designed for large analytic workloads.

article thumbnail

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

AWS Big Data

Finally, by testing the framework, we summarize how it meets the aforementioned requirements. Amazon Athena is used for interactive querying and AWS Lake Formation is used for access controls. The File Manager Lambda function consumes those messages, parses the metadata, and inserts the metadata to the DynamoDB table odpf_file_tracker.