What are the required stages for transferring data in datawarehouse into big data structure. Are there any tools and methods that support it? How to use the schema for such transformation, how to deal with different data types like facts and dimensions for instance. What is the criteria for data separation into machines, indexes or unique keys?
Based on your comments transitioning big data sets from on-premises systems to a cloud-based system is cumbersome and fraught with challenges. However, you can use Amazon RedShift:
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. You can start with just a few hundred gigabytes of data and scale to a petabyte or more.
- The first step is to create a data warehouse is to launch a set of nodes, called an Amazon Redshift cluster.
- After you provision your cluster, you can upload your data set and then perform data analysis queries. Regardless of the size of the data set, Amazon Redshift offers fast query performance using the same SQL-based tools and business intelligence applications that you use today.
- Use a Hadoop environment as the landing zone to pull in data from various sources, process it, and transfer the processed data to the existing data warehouse or other repositories.
- Explore scenarios of different ways to implement a landing zone. Learn about the architecture of the zone and the tools and techniques for integrating it with various environments.