ETL stands for extract, transform and load. It is a process that collects data from multiple sources, transforms it, and loads it into a target database or some other type of data source.
As important as data is to modern business, the growing number of data sources makes it difficult to understand it as one entity. If you’re looking for a more thoughtful way to process and leverage your data, consider a data-streaming solution like Striim Cloud ().
History of ETL
In the late 1980s, when data warehouses were in the spotlight, industry leaders designed purpose-built tools to help load data into these new warehouses. Data storage and transformation happened primarily in on-premises data warehouses. Today, the amount of data generated and collected has grown exponentially. The traditional data warehouse infrastructure can’t process that much data in a cost-effective and timely manner, hence the need for cloud computing.
What is the ETL process?
The ETL process covers extracting data from varying sources, transforming it into a more appropriate structure for reporting and analysis, and finally inserting it into a database.
Extraction is retrieving raw data from different internal and external data sources. These data sources, which are diverse in content, can include business systems, marketing tools, transaction databases, mobile devices and apps, data storage platforms, etc. Data from these sources can be structured, ready for extraction, or unstructured, which needs more preparation to make it ready for extraction. Extraction happens in three different ways.
Notification-based data extraction
The data sources will notify the ETL system of any data change, and the system extracts the new data only.
Incremental data extraction
The data sources will identify and have a record of which data changed. The ETL system periodically checks such sources to identify the data change. The data portion that has changed is then incrementally extracted.
Full data extraction
The ETL system will extract the complete data when a data source doesn’t have the mechanism to identify data change. The ETL will keep a copy of the previous extract to compare it with the new document. Full data extraction involves a higher volume of data transfer as the entire dataset needs to be copied each time.
This step involves putting clean data into a specific format for its analytical use case. The tasks involved are:
- Filtering and cleaning – irrelevant information is removed, and the missing values and inconsistencies are fixed.
- Deduplication – all the redundant and repeated information is removed.
- Format revision – this involves matching the data to the schema of the organization.
- Sorting – calculations and translations are performed using the raw data.
- Verification – the ETL system checks for data integrity by flagging data anomalies.
Cloud computing insiders generally understand transformation as the most critical part of the ETL process.
The formatted data is inserted into the target database or another data source where data can be loaded in two ways.
When the ETL system fully loads, all the prepared data from the transform step is loaded into the target database as a single unit. Although this process is less complex, full loading can lead to overgrowth of data volume in the database, making it difficult to manage.
The ETL system monitors changes in the incoming data and creates a new data record when unique information is detected. As much as the cumulative load is manageable, it can cause data inconsistencies when a system fails.
Today, businesses have access to data from many different sources. All that data must be extracted, transformed, and loaded into a specific schema to meet operational needs.
ETL tools allow for transforming large quantities of data into actionable business intelligence. By delivering a single point of view, providing historical context, and improving efficiency and productivity, ETL enables heightened data management and capabilities.