What is the ETL Process?
ETL comprises five steps: Extraction, Cleanup, Transform, Load, and Analysis. Data extraction, transformation, and loading are the essential steps.
In Extraction, desired data is extracted from unstructured databases and sources. Only estimated data volumes are extracted from each data source. Then it is transferred to a temporary staging data repository. Data extracted has no negative impact on databases.
Data extraction occurs in three ways:
Update Notification – you extract data when systems notify you that changes are made to the records.
Incremental Extraction – systems extract data on their own when records are modified without providing any notification.
Full Extraction – system reloads all the data to get it out of the system. This method involves large data transfers and is only used in small businesses.
Cleanup makes sure that quality data is extracted from an unstructured data pool. Also, it ensures that only quality data is transformed. It is one of the crucial steps in which null values, phone numbers, or zip codes are all converted to a standardized form.
Transformation refers to preparing data for analysis in two ways:
- Cleansing data
- Aggregating data
These two processes either take place in a staging area or analytics warehouse. There are two types of transformation: Basic and Advanced. Basin transformation includes cleaning, eliminating duplicate records, formatting, and key structuring.
The advanced transformation includes the following:
- Deriving new values by applying business rules to data
- Filtering data
- Linking data from multiple sources
- Summarizing data to obtain figures
- Aggregating data elements
- Data integration
After transformation, data is loaded into the warehouse. The loaded data is checked for defects. Else, a business tool or an alerting system is inserted into it. There are two ways to load data:
- Full Load: entire data is loaded into the warehouse
- Incremental Load: data between target and source data is dumped at regular intervals
Each step is executed one after another. However, the exact nature of each step (the format required for the target database) depends on your company’s specific needs and requirements.
Once the data is loaded, it is analyzed in warehouses. This process helps in gaining business insights from the data. There are data analysis tools that help in analyzing data.
Following applications provide data analysis tools:
- Alteryx: It is a data analytics platform that provides a visual workflow tool to analyze data.
- Amazon Quicksight: It helps developers easily build visualizations, perform ad-hoc analysis and get business insights from data.
- Amazon SageMaker: It is a data analytics platform that helps data developers to build, train, and deploy machine learning models quickly.
- Apache Spark: It is an open-source analytics engine that runs batch, stream workloads and provides modules for machine learning and graph processing.
In a nutshell, ETL is a data-driven strategy that aids in running business enterprises successfully. The data influences the business strategies and decisions. Hence, it is essential to take an ETL expert on board. It helps in making result-oriented decisions in less time.