Difference between relational database and data warehouse
The main difference between a data warehouse vs. data lake vs. relational database system is that a relational database is used to store and organize structured data from a single source, such as a transactional system, while data warehouses are built to hold structured data from multiple sources.
What Is Amazon Redshift?
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud.
The first step 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
Redshift databases also take full advantage of Amazon’s cloud server infrastructure, including access to their S3 to back up their data.
..a...Provides 10x times faster performance than the other warehouses
..b...You can set caching to increase the data retrieval speed.
2..Easy to create, deploy, and manage
..a...You can create and deploy a warehouse in minutes.
..b...Most of the commons tasks are automated. Tasks that are automated are monitoring and managing your warehouse.
..a...There are upfront costs or contract periods. It is 10 times cheaper than a traditional data warehouse which is set up on-premises.
4..Scalability at it’s best
..a...This is the same as Redshift Spectrum. You can query any amount of data and AWS redshift will take care of scaling up or down. Also, the compute and storage instances are scaled separately.
5..Query your data lake
..a...Redshift in AWS allows you to query your Amazon S3 data bucket or data lake. You can query petabytes of unstructured data using Redshift on Amazon S3.
..a...Redshift in AWS lets you isolate your warehouse using VPC
..b..You can create Customer Management Keys (CMKs) using AWS Key Management Service to encrypt your data in the warehouse