article thumbnail

Multicloud data lake analytics with Amazon Athena

AWS Big Data

Many organizations operate data lakes spanning multiple cloud data stores. In these cases, you may want an integrated query layer to seamlessly run analytical queries across these diverse cloud stores and streamline your data analytics processes. Refer to the respective documentation for details.

Data Lake 102
article thumbnail

Choosing an open table format for your transactional data lake on AWS

AWS Big Data

A modern data architecture enables companies to ingest virtually any type of data through automated pipelines into a data lake, which provides highly durable and cost-effective object storage at petabyte or exabyte scale.

Data Lake 115
Insiders

Sign Up for our Newsletter

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

Trending Sources

article thumbnail

Build a real-time GDPR-aligned Apache Iceberg data lake

AWS Big Data

Data lakes are a popular choice for today’s organizations to store their data around their business activities. As a best practice of a data lake design, data should be immutable once stored. A data lake built on AWS uses Amazon Simple Storage Service (Amazon S3) as its primary storage environment.

article thumbnail

The Differences Between Data Warehouses and Data Lakes

Sisense

Instead, businesses are increasingly turning to data lakes to store massive amounts of unstructured data. Analytics from your cloud data sources are key to transforming your business, but the reality of how most companies use them lags behind expectations. The rise of data warehouses and data lakes.

article thumbnail

Create an Apache Hudi-based near-real-time transactional data lake using AWS DMS, Amazon Kinesis, AWS Glue streaming ETL, and data visualization using Amazon QuickSight

AWS Big Data

Data analytics on operational data at near-real time is becoming a common need. Due to the exponential growth of data volume, it has become common practice to replace read replicas with data lakes to have better scalability and performance. For more information, see Changing the default settings for your data lake.

article thumbnail

Data Modeling 301 for the cloud: data lake and NoSQL data modeling and design

erwin

For NoSQL, data lakes, and data lake houses—data modeling of both structured and unstructured data is somewhat novel and thorny. This blog is an introduction to some advanced NoSQL and data lake database design techniques (while avoiding common pitfalls) is noteworthy. Data Modeling.

article thumbnail

Data Cataloging in the Data Lake: Alation + Kylo

Alation

When it was no longer a hard requirement that a physical data model be created upon the ingestion of data, there was a resulting drop in richness of the description and consistency of the data stored in Hadoop. You did not have to understand or prepare the data to get it into Hadoop, so people rarely did.