In the previous article, we talked about the differences between ETL and ELT, the wo data integration methods. Both of these methods do the same thing – they transfer data from one place to another. Now, let’s talk about a database and data warehouse, two different places to store data.
We will focus on structured data, which is a more traditional type of data. It is called structured, because data is predefined and formatted to live in a relational database structure. As a result, structured data is easy to access, use, and analyze. The best example of a relational database is SQL Server.
Structured data is typically stored in databases and data warehouses. People often confuse the two terms and use them interchangeably. Below, we discuss databases and data warehouses, and when you should use what.
A database refers to a place where a collection of related and structured data is stored. Databases have a schema that is a defined set of tables and columns into which the rows of data are loaded. Engineers will design a schema based on different use cases required for business. That is why data in databases is often highly detailed.
A database captures and stores data via the online transaction processing system (OLTP). This system processes data from transactions in real-time. For each row of transaction individual records are created in columns. Banking or retail transactions are the good examples of OLTP.
A key thing to understand about a transactional database is that it isn’t ideal for further analysis and reporting. A database isn’t structured to do analytics well; often, you must perform multiple table joins and write complex queries. This requires the help of a data administrator.
Upon capturing and storing data in a database, companies need to perform analytics and reporting. This is where a data warehouse comes in.
Essentially, the data warehouse is also a database. However, it is a type of database that integrates various transactional data from databases into a centralized, structured place. Unlike a database, which is ideal to capture real-time data, a data warehouse typically stores both current and historical data from different sources.
After taking all the data from these databases, the data warehouse creates a layer that is designed for analytics and reporting. A data warehouse supports the online analytical processing (OLAP) system. OLAP is a powerful technology that involves querying numerous records for discovery and complex analytical calculations.
To put simply, a data warehouse is a large database optimized for advanced analytics. Generally, an OLAP structure has fewer table joins and easier queries, which allows users to write simple SQL queries to perform analytics and reporting.
A few examples of data warehouses include Microsoft Azure Synapse, Snowflake, Oracle Data Warehouse, and Amazon Redshift.
If you need help in setting up or managing databases and data warehouses, Centida can provide assistance. We offer a wide range of consulting services and have successfully delivered projects for large Fortune 500 companies and SMEs around the world.