ETL (Extract, Transform, Load) is a process of extracting data from different data sources; manipulating them according to business calculations; loading the modified data into a different data warehouse. Because of the in-depth analytics data it provides, ETL function lies at the core of Business Intelligence systems. With ETL, enterprises can obtain historical, current, and predictive views of real business data.
Here, a module extracts data from different data sources independent of file formats. For instance, banking and insurance technology platforms operate on different databases, hardware, operating system, and communication protocols. Also, their system derives data from a variety of touchpoints like ATMs, text files, pdfs, spreadsheets, scanned forms, etc. The extraction phase maps the data from different sources into a unified format before processing.
ETL systems ensure the following while extracting data.
1.Removing redundant (duplicate) or fragmented data
2. Removing spam or unwanted data
3. Reconciling records with source data
4. Checking data types and key attributes.
This stage involves applying algorithms and modifying data according to business-specific rules. The common operations performed in ETL’s transformation stage is computation, concatenation, filters, and string operations like currency, time, data format, etc. It also validates the following-
1. Data cleaning like adding ‘0’ to null values
2. Threshold validation like age cannot be more than two digits
3. Data standardization according to the rules and lookup table.
Loading is a process of migrating structured data into the warehouse. Usually, large volumes of data need to be loaded in a short time. ETL applications play a crucial role in optimizing the load process with efficient recovery mechanisms for the instances of loading failures.
A typical ETL process involves three types of loading functions-
Initial load: it populates the records in the data warehouse.
Incremental load: it applies changes (updates) periodically as per the requirements.
Full refresh: It reloads the warehouse with fresh records by erasing the old contents.
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Transactional databases are not enough to resolve complex business queries. Also, dealing with unorganised data formats is more time-taking. Almost all industries can deploy the benefits of ETL systems. However, businesses like banking, insurance, customer relations, finance, and healthcare are the early adopters of this technology. ETL can help in obtaining:
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