IDC predicts that the sum of global data will grow from 33 zettabytes to 175 zettabytes by 2025. This enormous information growth requires efficient data handling by consumers. An end-to-end ETL tool helps accomplish data management which is essential for BI and reporting. This allows businesses to stay on top of the changing market dynamics and make accurate decisions.
Now the question is, how does ETL work?
This article will explain what ETL stands for, what it means, discuss its use cases, and highlight the five crucial steps to implement the ETL strategy.
What Does ETL Stand For?
ETL stands for extract-transform-load. The ETL process meaning can be understood as aiming to extract unstructured data, transforming it into a business-specified format, and loading it to a target destination, such as a database or data warehouse.
How Does ETL Work?
Now that we have understood what ETL stands for and how it works, we will go over the main steps in setting up an ETL pipeline. Let’s start by discussing the three ETL steps in detail
Step 1: Extraction
The first step of the ETL process is data extraction. Most businesses receive data from multiple sources, including CRMs, file systems, emails, etc. And more than 80 percent of this data is unstructured.
Although unstructured data is human-readable, it isn’t machine-readable. Machines require structured information to process it for business analysis or integration with IT applications. Hence, an automated data extraction solution must extract unstructured data from its source and convert it into a structured format.
Let’s take a look at an example of data extraction in ETL. For instance, a business receives PDF invoices containing order details, such as order ID, quantity, description, item code, and price. These details can be extracted using a template-based data extraction tool.
The image below demonstrates pattern-based extraction from a PDF invoice through a code-free tool. A similar pattern may be applied to all invoices with the same format.
Step 2: Transformation
This involves data mapping i.e. joining data from two or more sources, data conversion, and data cleansing. Generally, more than one transformation, such as join, lookup, filter, expression, aggregate, etc. is required to transform data into a consistent format.
The screenshot below illustrates a data transformation use case, which combines source data from different sales regions and verifies it against a set of business rules.
Step 3: Loading
The loading phase in the ETL process involves moving the processed data into a destination, usually a data warehouse, data mart, or an operational data store.
The screenshot below shows how processed; high quality-data is being loaded into an SQL Server database table.
To gain a 360-degree view of data and drive successful business outcomes, organizations have always relied on ETL processes. However, with the advancements in technology, ETL has evolved from a hand-coded approach to an automated process that works with large datasets in minimal time.
Here are a few common ETL use-cases used across a wide range of industries:
ETL and Data Mapping
Data mapping is an integral part of the ETL process as it involves mapping distinct data elements or models when converting data from one format into another. ETL tools with advanced data mapping capabilities can reduce or eliminate the need for hand coding, resulting in fewer errors and more reliable data.
The screenshot below shows an example of the join transformation from three distinct data sources in an ETL flow.
ETL and Data Cleansing
Businesses receive data from multiple sources, which may contain errors, such as missing information, duplicate records, or incorrect data. To leverage this data for critical business decisions, an enterprise should have an extensive data cleansing process in place. Data cleansing helps identify errors in a dataset and correct them to ensure only accurate data is transferred to the target systems.
To provide a one-stop solution for all data management-related tasks, top ETL tools in the market offer data cleansing capabilities and data transformation functionalities as well.
The screenshot below illustrates a scenario in which data is cleansed before being written into an Excel destination.
ETL and Data Transformation
Businesses need data transformation for several reasons, such as migrating data to a new data store, converting unstructured data into a structured format, performing calculations on data, etc. A unified ETL tool, like Astera Centerprise, offers a wide range of built-in transformations to simplify the integration of complex data formats, such as XML, JSON, and COBOL. The tool aims to simplify these complex scenarios and prepare data that is ready for analysis.
The screenshot below illustrates a scenario in which data from two different sources is merged, verified against defined data quality rules, and loaded into the destination table.
5-Step Future-Ready ETL Strategy
Knowing what ETL stands for and its process is not enough. There is much more to the ETL process than just moving data from one system into another. You need to fulfill certain ETL requirements for executing a successful ETL project, which generally includes five steps, stated below:
Define the Scope of a Project
According to a study, 58 percent of IT professionals say, their big data initiatives fail because of inaccurate project scope. To provide a sense of direction, it is crucial to define clear objectives and understand the requirements of an ETL project.
Assess and Analyze Your Data
To be able to transform data effectively, it is critical to study your enterprise data.
Answering the following questions can help in defining ETL requirements:
- Where is your source data stored?
- Is the data unstructured, semi-structured, or structured?
- Does your source data comply with the destination’s requirement, or will it require conversion?
Map Data Objects and Fields
After examining a data source in the workflow, the next step is to map the source and target tables, attributes, or fields. When a business has a large number of data sources in varying formats, it is advisable to use an automated data mapping tool. These tools match the source and destination entities using smart matching rules.
Convert, Cleanse, and Load
This step involves converting data into a destination-compatible format, cleansing out erroneous data, and finally loading the transformed data into a data store.
Monitor and Adjust the ETL Workflows
The final step is to check ETL workflows at periodic intervals to ensure that changing business requirements are incorporated into the data processing pipeline.
Streamlining Extract-Transform-Load with an Automated ETL Solution
To truly understand what ETL stands for, it is important to put the steps outlined above into practice and try to implement a data pipeline. Astera Centerprise is a next-generation enterprise ETL software that enables businesses to accomplish complex data integration tasks while ensuring data quality. The software meets all ETL requirements by featuring a high-performance cluster-based architecture and industrial-strength ETL engine that streamlines complex ETL projects involving large data volumes.
With support for pushdown optimization mode, incremental data load, and connectivity to a range of data sources, Astera Centerprise helps integrate data of any format, size, or complexity with minimal IT support.
Interested in giving Astera Centerprise a try and exploring how it meets your unique ETL requirements? Download a free 14-day trial version and experience it first-hand!