ETL: What It Means and Why Is It Important?

By |2020-06-26T17:57:37+00:00June 26th, 2020|

IDC predicts that the collective sum of global data will grow from 33 zettabytes to 175 zettabytes by 2025. To derive value from this voluminous information growth, businesses are required to extract data from disparate sources and create an integrated view of their information assets. An end-to-end ETL tool helps accomplish this task and prepare data for BI and reporting. This allows businesses to stay on top of the changing market dynamics and make timely and accurate decisions.

What Does ETL Stand For?

ETL or Extract-Transform-Load is a three-step data management process that extracts data from multiple sources, transforms it into a format satisfying the operational and analytical requirements of the business, and loads it to a target destination, such as a database or data warehouse.

How Does ETL Work?

Let’s have a look at the ETL process in detail.

ETL Pipeline

Figure 1: The ETL Pipeline

Step 1: Extraction

Most businesses receive data from multiple sources, including CRMs, file systems, emails, and several others. And more than 80 percent of this data is unstructured. Although unstructured data is human-readable, machines require structured information to process it digitally for business analyses or integration with IT applications. Hence, data needs to be extracted from its source and converted into a structured format using an automated data extraction solution.

For example, a business receives PDF invoices containing order details, including order ID, quantity, description, item code, and price. These details can be extracted for analysis by leveraging a template-based data extraction tool, which enables designing and deploying an extraction logic for extracting desired data points from incoming PDF documents with a similar layout.

Data extraction template

Figure 2: Extraction Template

Step 2: Transformation

After extraction, data is transformed to meet the format and structural requirements of the destination. This involves data mapping, joining data from two or more sources, data conversion, and data cleansing. Generally, more than one transformation is required to convert data into a consistent format for reporting, such as join, lookup, filter, expression, and aggregate.

The screenshot below illustrates a data transformation use-case, which combines the source data from different sales regions and verifies it against a set of business rules.

Data transformation

Figure 3: Data Transformation

Step 3: Loading

The loading phase of an ETL process involves moving the processed data into the destination system, usually a data warehouse, data mart, or an operational data store.

The screenshot below shows how the processed, high quality-data is being loaded into an SQL Server database table.

Data loading

Figure 4: Data Loading

ETL tool

ETL Use-Cases

Organizations have relied on the ETL process for a long time to gain a 360-view of their data to drive successful business outcomes. However, with the advancements in technology, ETL has evolved from a hand-coded process to an automated one that processes 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 every ETL process as it maps two 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  mapping In an ETL dataflow.

data mapping

Figure 5: Data Mapping

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, corrects them to ensure only accurate data is transferred to the target systems.

To provide a one-stop solution, many ETL tools offer data cleansing capabilities along with the data transformation functionality to address complex data management scenarios.

The screenshot below illustrates a scenario in which data is cleansed before it is written into an Excel destination.

data cleansing

Figure 6: Data Cleansing

ETL and Data Transformation

Businesses need data transformation for several reasons, including migrating data to a new data store, converting unstructured data into a structured format, and performing calculations, such as aggregations on data. A complete ETL tool offers an extensive library of built-in transformations to simplify the process of integrating complex data formats such as XML, JSON, and COBOL in a visual drag-and-drop environment.

The screenshot below illustrates a scenario in which data from two different sources, including Excel and SQL Server are combined, verified against defined data quality rules, and loaded into the destination table.

Data Transformation

Figure 7: Data Transformation

5-Step Future-Ready ETL Strategy

The ETL process is more than just moving data from one system into another. Executing a successful ETL project generally includes five steps, stated below:

  1. Define the Scope of the Project

According to a study, 58 percent of IT professionals say that their big data initiatives fail because of the inaccurate project scope. To provide a sense of direction and simplify monitoring, it is crucial to define clear objectives for the ETL project.

  1. 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:

  • 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?
  1. Map Data Objects and Fields

After examining the data source, the next step is to map the source and target tables, attributes, or fields. When a business has more data sources, types, and formats to work with, it is advisable to leverage an automated data mapping tool to automatically match source and destination entities, thereby saving time and resources.

  1. Convert, Cleanse, and Load

This step involves ensuring data quality by removing bad data, converting enterprise-wide data into a format suitable for the destination, and loading the transformed data into a data store.

  1. Monitor and Adjust the ETL Workflows

The final step is to check ETL workflows at periodic intervals to ensure that changing business requirements are being incorporated in the data processing pipeline.

Streamlining Extract-Transform-Load with an Automated ETL Solution

Astera Centerprise is a next-gen ETL solution that enables businesses to accomplish complex data integration tasks while ensuring data quality. The software features a high-performance cluster-based architecture, industrial-strength ETL engine, and advanced automation capabilities to streamline complex ETL projects involving large data volumes.

With support for pushdown optimization, incremental data load, and connectivity to modern and legacy data sources, Astera Centerprise helps integrate data of any format, size, or complexity with minimal IT support.

Interested in giving Astera Centerprise a try? Download a free 14-day trial version and experience it first-hand!

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