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Data Warehouses

Amazon Redshift ETL - Top 3 ETL Approaches for 2023

Amazon Redshift is a completely managed Data Warehouse, offered as a cloud service by Amazon. It can scale up to petabytes of data and offers great performance even for complex queries. Table of Contents Its columnar nature with Postgres as the querying standard makes it very popular for analytical and reporting use cases. These days it is very common to use Amazon Redshift as the backbone Data Warehouse for highly reliable ETL or ELT systems.

Analyze your data with Cloudera Data Warehouse

Cloudera Data Warehouse is a comprehensive data analytics and management solution that offers security and governance policies, automations, and high-speed SQL engines for analyzing data easily, self-service, and quickly. Its open data lakehouse architecture eliminates data silos, making all data accessible without moving it, and due to integration with Apache Iceberg, supports both structured and unstructured, real-time, and batch data.

DynamoDB to Redshift: 4 Best Methods

When you use different kinds of databases, there would be a need to migrate data between them frequently. A specific use case that often comes up is the transfer of data from your transactional database to your data warehouse such as transfer/copy data from DynamoDB to Redshift. This article introduces you to AWS DynamoDB and Redshift. It also provides 4 methods (with detailed instructions) that you can use to migrate data from AWS DynamoDB to Redshift.

Isn't the Data Warehouse the Same Thing as the Data Lakehouse?

A data lakehouse is a data storage repository designed to store both structured data and data from unstructured sources. It allows users to access data stored in different forms, such as text files, CSV or JSON files. Data stored in a data lakehouse can be used for analysis and reporting purposes.