#7. PostgreSQL: <\/strong><\/span><\/h3>\n\n\n\nA robust database management system with more than 20 years of community development is PostgreSQL. It serves as the main data repository for geospatial, analytics, mobile, and web applications. A more complex version of SQL, PostgreSQL, supports features like triggers, subqueries, and foreign keys. <\/p>\n\n\n\n
Additionally, it is also appropriate for data warehousing and analysis applications, business intelligence software, and OLTP and OLAP systems that need quick read-and-write operations.<\/p>\n\n\n\n
#8. Amazon S3: <\/strong><\/span><\/h3>\n\n\n\nAmazon S3 is a NoSQL storage service that provides stability, accessibility, performance, security, and unlimited scalability at low prices. Additionally, it supports voluminous, unstructured, and semi-structured data, allows user organization, and offers subscription access to similar systems. While slower than DynamoDB, it sets the standard for business cloud storage.<\/p>\n\n\n\n
#9. Teradata: <\/strong><\/span><\/h3>\n\n\n\nFor big data warehousing applications, Teradata is a popular Relational Database Management system that uses parallelism and an MPP architecture to lighten the load and produce insightful results. Additionally, it satisfies integration and ETL requirements by ingesting, processing, and managing data through an intuitive interface.<\/p>\n\n\n\n
#10. Amazon RDS: <\/strong><\/span><\/h3>\n\n\n\nScaling relational databases on the AWS Cloud is made possible by RDS, a PaaS cloud data storage service. It also provides hardware that is affordable for managing difficult tasks like software installation, storage, replication, and disaster recovery. <\/p>\n\n\n\n
Additionally, RDS supports six database engines: Amazon Aurora, PostgreSQL, MySQL, MariaDB, Oracle Information, and SQL Server, as well as three instance classes.<\/p>\n\n\n\n
What Is SQL Data Warehousing? <\/span><\/h2>\n\n\n\nSQL Data Warehouse is an Enterprise Data Warehouse (EDW) that runs complex queries over petabytes of data quickly thanks to massively parallel processing (MPP). <\/p>\n\n\n\n
Furthermore, as a crucial element of a big data solution, utilize a SQL Data Warehouse. Columnar storage is used by the SQL Data Warehouse to store data in relational tables, which lowers data storage costs and boosts query performance. Note that to distribute data processing across several nodes, SQL Data Warehouse makes use of a scale-out architecture.<\/p>\n\n\n\n
What Is a Data Warehouse in ETL? <\/span><\/h2>\n\n\n\nETL, which stands for Extract, Transform, and Load, is a process used in data warehousing to gather data from various sources, format it for loading into a warehouse, and then load it there. <\/p>\n\n\n\n
What Are the Etl Concepts? <\/span><\/h2>\n\n\n\nThe process of ETL can be broken down into the following three stages:<\/p>\n\n\n\n