Remove 2012 Remove Data Architecture Remove Metadata
article thumbnail

Expand data access through Apache Iceberg using Delta Lake UniForm on AWS

AWS Big Data

The landscape of big data management has been transformed by the rising popularity of open table formats such as Apache Iceberg, Apache Hudi, and Linux Foundation Delta Lake. These formats, designed to address the limitations of traditional data storage systems, have become essential in modern data architectures.

Metadata 122
article thumbnail

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

AWS Big Data

They understand that a one-size-fits-all approach no longer works, and recognize the value in adopting scalable, flexible tools and open data formats to support interoperability in a modern data architecture to accelerate the delivery of new solutions.

Data Lake 127
Insiders

Sign Up for our Newsletter

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

article thumbnail

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

AWS Big Data

Organizations have multiple Hive data warehouses across EMR clusters, where the metadata gets generated. To address this challenge, organizations can deploy a data mesh using AWS Lake Formation that connects the multiple EMR clusters. An entity can act both as a producer of data assets and as a consumer of data assets.

Data Lake 116
article thumbnail

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

AWS Big Data

Data domain producers publish data assets using datasource run to Amazon DataZone in the Central Governance account. This populates the technical metadata in the business data catalog for each data asset. Data ownership remains with the producer. using following command $ nvm install 18.12.0

Data Lake 130
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 131
article thumbnail

Build streaming data pipelines with Amazon MSK Serverless and IAM authentication

AWS Big Data

He works with enterprise FSI customers and is primarily specialized in machine learning and data architectures. . // It serves as a simple API Gateway to Kafka Proxy, accepting requests and forwarding them to a Kafka topic. In this free time, Philipp spends time with his family and enjoys every geek hobby possible.

Testing 116
article thumbnail

Design a data mesh on AWS that reflects the envisioned organization

AWS Big Data

Cost and resource efficiency – This is an area where Acast observed a reduction in data duplication, and therefore cost reduction (in some accounts, removing the copy of data 100%), by reading data across accounts while enabling scaling. In this approach, teams responsible for generating data are referred to as producers.