![]() For databases, Data captured as-is from a single transaction source. For the data warehouse, Data typically flows from transactional systems and other relational databases and typically includes structured, semi-structured, and unstructured data. The data warehouse is designed for online analytical processing and database is designed for online transaction process. Why build a data warehouse at all-why not just run analytics queries directly against the database? Let’s take a look at the difference between data warehouse and databases. We have a lot more choices in modern database technologies such as relational database, NoSQL, streaming. ![]() Data warehousing is really old technology concept. Why build a data warehouse?Ī data warehouse is a central repository of information coming from one or more data sources. AWS also provides QuickSight, cloud-powered business analytics service, to build visualizations. It’s wrapped with other AWS services such as S3 for importing/exporting data, KMS(key management service) for encrypting database, IAM(identity and access management) for role access and policy setup, VPC(Virtual private cloud) for isolating in secured virtual network, SWF(simple workflow service) for durable, distributed-state management, and CloudWatch for monitoring performance. It allows you to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution. Redshift is a fast, fully managed petabyte-scale data warehousing solution to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. ![]() It is specifically designed for online analytical processing (OLAP) and business intelligence (BI) applications. ![]() AWS Redshift Data Warehouse solution is based on PostgreSQL but beyond just PostgreSQL. ![]()
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