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Data Warehouses


SQL Server to Snowflake: 2 Easy Methods

Microsoft SQL Server is kind of a swiss army knife for most SME needs and workloads. However there are a handful of things that SQL Server will be better at, and there’s a handful of things Snowflake will be better at. Table of Contents Snowflake is great if you have big data needs. It offers scalable computing and limitless size in a traditional SQL and Data Warehouse setting. If you have a relatively small dataset or low concurrency/load then you won’t see the benefits of Snowflake.

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.