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Enable Multi-AZ deployments for your Amazon Redshift data warehouse

AWS Big Data

Originally published on December 9th, 2022. Amazon Redshift is a fully managed, petabyte scale cloud data warehouse that enables you to analyze large datasets using standard SQL. Amazon Redshift is a cloud-based data warehouse that supports many recovery capabilities to address unforeseen outages and minimize downtime.

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Open Data Lakehouse powered by Iceberg for all your Data Warehouse needs

Cloudera

In this blog, we will share with you in detail how Cloudera integrates core compute engines including Apache Hive and Apache Impala in Cloudera Data Warehouse with Iceberg. We will publish follow up blogs for other data services. Iceberg basics Iceberg is an open table format designed for large analytic workloads.

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Top 20 most-asked questions about Amazon RDS for Db2 answered

IBM Big Data Hub

Can Amazon RDS for Db2 be used for running data warehousing workloads? Answer : Yes, Amazon RDS for Db2 can support analytics workloads, but it is not a data warehouse. Amazon RDS At what level are snapshot-based backups taken?  Also, you can take manual snapshots as needed. 

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Unlock insights on Amazon RDS for MySQL data with zero-ETL integration to Amazon Redshift

AWS Big Data

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.

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Configure monitoring, limits, and alarms in Amazon Redshift Serverless to keep costs predictable

AWS Big Data

It automatically provisions and intelligently scales data warehouse compute capacity to deliver fast performance, and you pay only for what you use. Just load your data and start querying right away in the Amazon Redshift Query Editor or in your favorite business intelligence (BI) tool. Ashish Agrawal is a Sr.

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Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift

AWS Big Data

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.

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

AWS Big Data

They enable transactions on top of data lakes and can simplify data storage, management, ingestion, and processing. These transactional data lakes combine features from both the data lake and the data warehouse. The Data Catalog provides a central location to govern and keep track of the schema and metadata.

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