Big data has taken over the world, and there’s no stopping its reign. Small and large enterprises, governments, and healthcare professionals all rely on big data to give them insights into everything, from your favorite hot beverage to the effects of driving on road conditions over time. ETL databases are essential to many business intelligence operations. The key to getting all these intricate details is data analytics. However, there must be a process by which data scientists find, collect, and transform data to turn it into business intelligence.
Data integration is an integral business intelligence process. There are many integration strategies, and choosing the best one is a matter of your company’s current and future data needs, talent, and data science tools. ETL is one of the key processes. In this short guide, we’ll explain the basics of ETL processes, databases, and some of their real-world use cases.,
What is ETL?
As mentioned in the introduction, ETL is one of the most popular data integration strategies. It’s an acronym for extract, transform, load, and it applies to a set of processes and tools.
The staging area is where data transformation takes place. Data cleansing, setting of business rules, and implementation of algorithms for analytics are among the data transformation processes data scientists can undertake in the staging area. Finally, data is loaded into the target system (or ETL database) during the load stage for data analysts and business users to apply to critical business decisions.
What is ELT?
Another cool thing about ETL is it has a sister acronym in ELT, which is short for—you guessed it—extract, load, transform. The most significant line of demarcation between ETL and ELT is the order of the processes. In ETL processes, data scientists extract data from various source systems into a staging area. ELT processes extract data from various data sources and store the raw data in data lakes for data scientists to transform it later when they need it.
This process provides quicker load times but isn’t ideal if you need real-time business intelligence. Now, on to what happens after unstructured data undergoes its transformation.
What is an ETL database?
Now, onto the final destination in the ETL data integration process—ETL databases. An ETL database is where data is where you store data for analytics and other processes. They’re also often called data warehouses and data marts.
Data warehouses and marts enable business users and data analysts to see data in a unified view and format, following business rules they set during the transformation process. Data warehouses can store an incredible amount of data, and business users can access data from an analytics database by inputting a simple query.
What are some ETL database use cases?
There are plenty of use cases for an ETL database or data warehouse. Indeed, they can be found everywhere, from the stock market to your favorite online merchant.
One of the most compelling use cases for using ETL data integration processes is data integration for company mergers. Personnel and office furniture aren’t the only things that get moved around when two companies become one. They have to share massive amounts of data, and ETL enables them to create logical data warehouses for their combined data.
Another great ETL process/data warehouse use case is integrating data from legacy systems into new ones. ETL enables data scientists to bring the business intelligence, master data, and metadata from old source systems up to speed to work synchronously with new systems.
ETL processes and databases are essential to many business intelligence operations. From finding data sources to data cleansing and extraction, ETL is work-intensive. It also requires careful planning, great communication, and near-perfect execution of the data integration strategy to get data from different source systems to a centralized target system for data analysis.
Data warehouses, data stores, and other databases enable companies to store data for real-time insights that shape their products, marketing, and customer experience. It also provides scalability and data extraction from legacy systems and its transformation and integration into new systems. ETL tools and databases are some of the best resources for transforming raw data into actionable insights.