As businesses embrace digital transformation, they must consolidate data from multiple sources to create a centralized data repository and ensure a single source of truth. Having the right data, collecting and storing them in a secure and organized manner, is crucial to gain timely data-driven insights. Consequently, ETL tools have become indispensable for forward-thinking organizations when it comes to data integration. An ETL allows data-driven businesses to extract data from disparate sources, transform it, and load it into target systems, such as a data lake or data warehouse, for reporting and analytics.

But what does ETL stand for? What is an ETL tool, and how does it work? And what are the different types of ETL? Let’s discuss these in detail.

What is ETL?

What is ETL

ETL (Extract, Transform, and Load) is a three-step process that involves extracting data from various source systems, transforming it into a useful format, and loading it to a target database destination for business intelligence and reporting. It is one of many data integration techniques that businesses use.

You can think of ETL as a data pipeline that connects disparate data sources, enabling them to flow into a unified system. It allows organizations to maintain a single version of truth for everyone, resulting in accurate, more informed decision-making.

How Does ETL Work?

The ETL Process in Astera Centerprise

Step 1: Extraction

The first step of the ETL process is data extraction.

Businesses receive data from various sources, including CRMs, file systems, databases, and cloud applications. This data is available in different file formats, such as spreadsheets, text files, XML/JSON, PDFs, and more. Most of this data (more than 80 percent) is in an unstructured format.

This is a challenge for organizations using legacy systems, as unstructured data is difficult to process and analyze. Fortunately, modern ETL solutions allow organizations to seamlessly extract structured, semi-structured, and unstructured data from multiple data sources.

Step 2: Transformation

The second step of the ETL process is data transformation.

Data extracted from different sources lack standardization and is often riddled with data quality issues. Moreover, it is in a normalized format with complex joins, which can hamper the efficiency of database infrastructure.

The transformation process involves cleaning, standardizing, and validating data, which improves its quality. This step ensures that the consolidated data is accurate, complete, and valuable for reporting and analysis before it reaches its target destination.

Step 3: Load

The third step of the ETL process is data loading.

It’s the final step of ETL, where the transformed data is loaded into a data warehouse, database, or target destination. Depending on the volume, you can load data into a data warehouse in two ways:

  • Full Load: A full load, also known as a destructive load, involves loading the entire source data to the destination at once. In this approach, a destination table is truncated before loading data and is completely replaced with updated datasets.
  • Incremental Load: An incremental load involves only updating the new datasets instead of truncating the target table and replacing it with new data. This approach is more efficient as it minimizes computing resources and time required to load data, facilitating real-time insights.

ETL Use Cases

While businesses have been using ETL for decades, the process has evolved. With more diverse data sources and destinations, the ETL horizon has expanded to include new use cases. Here are common ETL data integration use cases:

1.    Data Warehousing

Data warehousing involves integrating, rearranging, and consolidating massive volumes of data captured within disparate systems to maintain a single source of truth. Businesses are increasingly shifting towards cloud data warehouses, such as Amazon Redshift and Snowflake, to manage copious amounts of data effectively.

ETL is an essential step in data warehousing as it allows businesses to consolidate data from multiple sources into a single repository. Through ETL, the source data is prepared for multiple stages of the data warehouse architecture. Moreover, it supports process automation to create and maintain self-regulating data pipelines.

2.    Application Integration

According to The State of SaaS Sprawl in 2021, a typical organization deals with hundreds of applications, and the main challenge is to make these applications work together. Application integration helps achieve synergy between on-premises and cloud applications, such as Salesforce and Microsoft Dynamics CRM. With ETL, you can quickly extract data from all applications and consolidate it into a unified view.

3.    Legacy System Modernization

Businesses increasingly use ETL to update their technology stack as legacy systems are no longer compatible with diverse, newer technologies. The modernization initiatives usually involve data migration to cloud databases such as Oracle or Azure or No SQL databases such as MongoDB.

How Can an ETL Tool Help?

Traditionally, businesses set up ETL pipelines through manual coding, which is inefficient and time-consuming. The primary problem with the programming stack is that data is manipulated by the element instead of columns and rows, making it difficult to integrate heterogeneous sources.

Moreover, adding or changing data pipelines requires building on top of previous libraries and complex code integration. Therefore, modern businesses have started using automated ETL tools that can handle intricate ETL processes more efficiently.

Here are some reasons why you should opt for an ETL tool:

ETL Tools Build Data Pipelines Faster

Like other data integration tools, ETL tools allow you to integrate data and build data pipelines faster. They come equipped with pre-built connectors for diverse data sources and destinations, minimizing the need for custom coding and allowing for faster implementation. They also offer a visual interface for designing and managing data pipelines more seamlessly, so users with minimal to no coding experience can easily build and maintain data pipelines.

ETL Tools are Future-Proof

Modern ETL tools are designed to be adaptable and flexible to handle constantly changing data requirements and technologies. Most ETL tool vendors constantly update functionalities and add connectors in response to new technologies and best practices.

ETL Tool can Handle Complex Data Easily

Handling complex and unstructured raw data is a difficult task. ETL tools have evolved to address this challenge by incorporating features and capabilities to handle data in various formats and structures. Moreover, many ETL solutions allow users to write custom transformations to ingest, cleanse, and manipulate complex data.

ETL Tools Reduce Error Probability

ETL Tools significantly reduce the risk of errors in data pipelines, mainly through automation. Eliminating manual tasks also eliminate the risk of human errors. Moreover, many ETL tools come with data validation controls to ensure that data passes the specified criteria before reaching a target destination. The visual interface and workflow also help reduce the probability of errors.

Types of ETL Tools

The growing demand for effective data management and the increasing volume, variety, and velocity of data has led to an explosion of ETL tools, including:

Open Source ETL Tools

As ETL tools became common, many developers released open-source tools, which are free to use. You can easily access their source code and extend its functionality to meet your data requirements. While open-source ETL tools are flexible, they have a steep learning curve. Moreover, they might be incompatible with your existing data pipelines.

Custom ETL Tools

Many businesses design their ETL tools using programming languages such as Python and SQL. While allowing companies greater customization to meet specific data requirements, these tools are time-consuming to build and require significant investment and resources. Moreover, it’s hard to maintain custom ETL tools and update them to incorporate changing data management requirements.

Cloud-based ETL Tools

Cloud-based ETL tools allow you to manage data from various cloud-based applications. These solutions are deployed on the cloud to process large volumes of data without investing in additional infrastructure. These ETL tools are easy to set up and use but lack the technical functionalities to carry out complex ETL processes.

Enterprise ETL tools

Enterprise ETL tools are specialized solutions for large organizations to perform ETL processes efficiently. You can ingest data from disparate sources into a centralized data repository for reporting and analytics. These solutions have advanced capabilities to perform complex data transformations and handle data processing at scale.

Many vendors charge an annual license fee or follow a pay-as-you-go model. Top providers offer extensive training and resources. These enterprise ETL tools are very easy to use, especially no-code solutions that empower business users with no coding background to take charge of data.

ETL Tool Features to Look For

Before you invest in an ETL platform, you must evaluate its capabilities & features to determine whether it’ll meet your data management requirements. Here are some important features that an ETL solution should have:

  1. Library of Connectors: Modern ETL tools offer an expansive library of connectors, including file formats, databases, and cloud platforms. Ensure that the tool you purchase can natively support your data sources.
  2. Ease of Use: Managing custom-coded ETL mappings is a complex process that requires deep development expertise. To save developer resources and transfer data from the hands of developers to business users, you need an enterprise ETL solution that offers an intuitive, code-free environment to extract, transform, and load data.
  3. Data Transformations: Data transformation needs vary from simple transformations such as lookups and joins to more complex tasks like denormalizing data or converting unstructured data into structured tables. You should select an ETL tool that offers a range of simple and more advanced transformations as per your data manipulation requirements.
  4. Data Quality and Profiling: You only want clean and accurate data to be loaded into your repository. So, prioritize data quality management, and look for an ETL platform that offers inbuilt data quality and profiling capabilities to determine enterprise data’s consistency, accuracy, and completeness.
  5. Automation: Large enterprises must handle hundreds of ETL jobs daily, which is only possible through automation. Look for an advanced ETL automation solution with end-to-end automation capabilities, including job scheduling and process orchestration, to streamline the data management processes.

Automate ETL Process with Astera Centerprise

Astera Centerprise is an enterprise-ready, code-free data integration tool with a powerful ETL/ELT engine. Our zero-code solution has a vast library of connectors, transformations, and in-built functionalities, making it easier for users to extract, manipulate and load data to the destination of choice without writing code. Our tool’s automation and orchestration capabilities can save up to 80% time required to manage ETL processes.

Interested in trying Astera Centerprise and exploring how it meets your unique ETL requirements? Download a free 14-day trial version and experience it first-hand!

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