article thumbnail

How Volkswagen streamlined access to data across multiple data lakes using Amazon DataZone – Part 1

AWS Big Data

Over the years, organizations have invested in creating purpose-built, cloud-based data lakes that are siloed from one another. A major challenge is enabling cross-organization discovery and access to data across these multiple data lakes, each built on different technology stacks.

Data Lake 111
article thumbnail

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

AWS Big Data

licensed, 100% open-source data table format that helps simplify data processing on large datasets stored in data lakes. Data engineers use Apache Iceberg because it’s fast, efficient, and reliable at any scale and keeps records of how datasets change over time.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Seamless integration of data lake and data warehouse using Amazon Redshift Spectrum and Amazon DataZone

AWS Big Data

Unlocking the true value of data often gets impeded by siloed information. Traditional data management—wherein each business unit ingests raw data in separate data lakes or warehouses—hinders visibility and cross-functional analysis. Amazon DataZone natively supports data sharing for Amazon Redshift data assets.

article thumbnail

Design a data mesh pattern for Amazon EMR-based data lakes using AWS Lake Formation with Hive metastore federation

AWS Big Data

Use cases for Hive metastore federation for Amazon EMR Hive metastore federation for Amazon EMR is applicable to the following use cases: Governance of Amazon EMR-based data lakes – Producers generate data within their AWS accounts using an Amazon EMR-based data lake supported by EMRFS on Amazon Simple Storage Service (Amazon S3)and HBase.

article thumbnail

Petabyte-scale log analytics with Amazon S3, Amazon OpenSearch Service, and Amazon OpenSearch Ingestion

AWS Big Data

At the same time, they need to optimize operational costs to unlock the value of this data for timely insights and do so with a consistent performance. With this massive data growth, data proliferation across your data stores, data warehouse, and data lakes can become equally challenging.

Data Lake 113
article thumbnail

Use AWS Glue Data Catalog views to analyze data

AWS Big Data

Additionally, you can use the power of SQL in a view to express complex boundaries in data across multiple tables that can’t be expressed with simpler permissions. Data lakes provide customers the flexibility required to derive useful insights from data across many sources and many use cases.

article thumbnail

Introducing hybrid access mode for AWS Glue Data Catalog to secure access using AWS Lake Formation and IAM and Amazon S3 policies

AWS Big Data

AWS Lake Formation helps you centrally govern, secure, and globally share data for analytics and machine learning. With Lake Formation, you can manage access control for your data lake data in Amazon Simple Storage Service (Amazon S3 ) and its metadata in AWS Glue Data Catalog in one place with familiar database-style features.