Remove Big Data Remove Cost-Benefit Remove Data Warehouse Remove Snapshot
article thumbnail

Implement data warehousing solution using dbt on Amazon Redshift

AWS Big Data

In this post, we look into an optimal and cost-effective way of incorporating dbt within Amazon Redshift. Snapshots – These implements type-2 slowly changing dimensions (SCDs) over mutable source tables. This mechanism allows developers to focus on preparing the SQL files per the business logic, and the rest is taken care of by dbt.

article thumbnail

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

AWS Big Data

Snowflake integrates with AWS Glue Data Catalog to access the Iceberg table catalog and the files on Amazon S3 for analytical queries. This greatly improves performance and compute cost in comparison to external tables on Snowflake , because the additional metadata improves pruning in query plans.

Insiders

Sign Up for our Newsletter

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

article thumbnail

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

AWS Big Data

Amazon Redshift is a popular cloud data warehouse, offering a fully managed cloud-based service that seamlessly integrates with an organization’s Amazon Simple Storage Service (Amazon S3) data lake, real-time streams, machine learning (ML) workflows, transactional workflows, and much more—all while providing up to 7.9x

article thumbnail

Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

Since the deluge of big data over a decade ago, many organizations have learned to build applications to process and analyze petabytes of data. Data lakes have served as a central repository to store structured and unstructured data at any scale and in various formats.

article thumbnail

From Hive Tables to Iceberg Tables: Hassle-Free

Cloudera

Introduction For more than a decade now, the Hive table format has been a ubiquitous presence in the big data ecosystem, managing petabytes of data with remarkable efficiency and scale. Depending on the size and usage patterns of the data, several different strategies could be pursued to achieve a successful migration.

article thumbnail

Use Apache Iceberg in a data lake to support incremental data processing

AWS Big Data

There is an increased need for data lakes to support database like features such as ACID transactions, record-level updates and deletes, time travel, and rollback. Apache Iceberg is designed to support these features on cost-effective petabyte-scale data lakes on Amazon S3. The snapshot points to the manifest list.

Data Lake 114
article thumbnail

Enforce fine-grained access control on Open Table Formats via Amazon EMR integrated with AWS Lake Formation

AWS Big Data

Transaction data lake use case Amazon EMR customers often use Open Table Formats to support their ACID transaction and time travel needs in a data lake. Another popular transaction data lake use case is incremental query. The following are some highlighted steps: Run a snapshot query. %%sql