AWS Big Data

APRIL 10, 2024

and zero-ETL support) as the source, and a Redshift data warehouse as the target. The integration replicates data from the source database into the target data warehouse. Additionally, you can choose the capacity, to limit the compute resources of the data warehouse. For this post, set this to 8 RPUs.

Data Warehouse 98

Data Warehouse Analytics Metrics Snapshot 98

AWS Big Data

APRIL 3, 2024

The AWS Glue crawler generates and updates Iceberg table metadata and stores it in AWS Glue Data Catalog for existing Iceberg tables on an S3 data lake. Snowflake integrates with AWS Glue Data Catalog to retrieve the snapshot location. Snowflake can query across Iceberg and Snowflake table formats.

Data Lake 98

Data Lake Snapshot Metadata Data Architecture 98

AWS Big Data

DECEMBER 13, 2023

A CDC-based approach captures the data changes and makes them available in data warehouses for further analytics in real-time. usually a data warehouse) needs to reflect those changes in near real-time. This post showcases how to use streaming ingestion to bring data to Amazon Redshift.

Data Warehouse 103

Data Warehouse Snapshot Data Processing Management 103

AWS Big Data

JUNE 28, 2023

There are two broad approaches to analyzing operational data for these use cases: Analyze the data in-place in the operational database (e.g. With Aurora zero-ETL integration with Amazon Redshift, the integration replicates data from the source database into the target data warehouse.

Data Warehouse 88

Data Warehouse Analytics Metrics Dashboards 88

AWS Big Data

MARCH 21, 2024

The extract, transform, and load (ETL) process has been a common pattern for moving data from an operational database to an analytics data warehouse. ELT is where the extracted data is loaded as is into the target first and then transformed. ETL and ELT pipelines can be expensive to build and complex to manage.

Data Warehouse 92

Data Warehouse Metrics Statistics Optimization 92

AWS Big Data

MAY 19, 2023

Organizations across all industries have complex data processing requirements for their analytical use cases across different analytics systems, such as data lakes on AWS , data warehouses ( Amazon Redshift ), search ( Amazon OpenSearch Service ), NoSQL ( Amazon DynamoDB ), machine learning ( Amazon SageMaker ), and more.

Machine Learning 88

Machine Learning Metrics Management Big Data 88

AWS Big Data

JULY 28, 2023

Amazon Redshift is a petabyte-scale, enterprise-grade cloud data warehouse service delivering the best price-performance. Today, tens of thousands of customers run business-critical workloads on Amazon Redshift to cost-effectively and quickly analyze their data using standard SQL and existing business intelligence (BI) tools.

Data Lake 67

Data Lake Data Governance Data Warehouse Modeling 67