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Accelerate your data warehouse migration to Amazon Redshift – Part 7

AWS Big Data

With Amazon Redshift, you can use standard SQL to query data across your data warehouse, operational data stores, and data lake. Migrating a data warehouse can be complex. You have to migrate terabytes or petabytes of data from your legacy system while not disrupting your production workload.

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Automate deployment of an Amazon QuickSight analysis connecting to an Amazon Redshift data warehouse with an AWS CloudFormation template

AWS Big Data

Amazon Redshift is the most widely used data warehouse in the cloud, best suited for analyzing exabytes of data and running complex analytical queries. Amazon QuickSight is a fast business analytics service to build visualizations, perform ad hoc analysis, and quickly get business insights from your data.

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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.

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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

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Query your Apache Hive metastore with AWS Lake Formation permissions

AWS Big Data

Apache Hive is a SQL-based data warehouse system for processing highly distributed datasets on the Apache Hadoop platform. The Hive metastore is a repository of metadata about the SQL tables, such as database names, table names, schema, serialization and deserialization information, data location, and partition details of each table.

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Break data silos and stream your CDC data with Amazon Redshift streaming and Amazon MSK

AWS Big Data

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.

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Set up advanced rules to validate quality of multiple datasets with AWS Glue Data Quality

AWS Big Data

It supports both data quality at rest and data quality in AWS Glue extract, transform, and load (ETL) pipelines. Data quality at rest focuses on validating the data stored in data lakes, databases, or data warehouses. The extracted data is stored in Amazon S3, which serves as the data lake.