For a data-driven business, an ETL tool is indispensable, especially if you’re planning to extract and transform data from several sources before loading it into a centralized repository.
But what is ETL and what exactly is an ETL tool? Read on to find out.
In this article, we’ll explain what ETL means, what an ETL tool is and how it can benefit your business. Plus, we’ll also cover the major types and three use-cases of ETL tools. Lastly, we’ll share a list of key features that you should look for when choosing an ETL tool for your business.
ETL stands for Extract, Transform and Load. It is a three-step data management process that extracts unstructured 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.
What is an ETL Tool?
An ETL tool is used to execute an integration project, and it includes three steps: extraction, transformation, and loading.
Thus, an ETL tool extracts data from disparate sources, transforms it to make it compatible with the destination system, and then loads it into the destination system. This destination could be a data warehouse, data lake, database, or any other application system.
Why Do You Need an ETL tool?
Here are some ways in which an ETL tool can help your business grow:
An ETL tool allows you to collect, transform, and consolidate data in an automated way. As a result, you can save plenty of time and effort otherwise spent on importing data manually.
2. Handle Complex Data Easily
With time, your business will have to work with a vast data volume that’s intricate and diverse. For instance, you can be a multi-national organization with data coming in from 3 different countries with distinct product names, customer IDs, addresses, etc.
If you have to manage a range of attributes, you may end up formatting data all day long. An ETL tool can help streamline things for you by simplifying data cleaning and processing.
3. Reduced Error Probability
Even if you are careful with your data, you are prone to making errors when handling it manually. And, even a slight error in the early stages of the data processing can be dicey. Why? Because one error leads to another error, and the cycle continues. For example, if you enter sales data incorrectly, your entire calculations can go wrong.
ETL tools automate several parts of a data process, reducing manual intervention and hence, error probability.
4. Improved Business Intelligence And ROI
An ETL tool helps make sure that the data you obtain for analysis is of the finest quality possible. As a result, you can use this high-quality data to make better decisions and increase your ROI.
Types of ETL Tools
ETL tools can be categorized into the following main types:
Batch ETL Tools
In these tools, batch processing is used to acquire data from the source systems. The data is extracted, transformed, and loaded into the repository in batches of ETL jobs.
It’s a cost-effective method because it uses limited resources in a time-bound way.
Real-Time ETL Tools
In these tools, data is extracted, cleansed, enriched, and loaded to the target system in real-time. These tools offer you faster access to information and improve time-to-insights.
As the need to gather and analyze the data in the shortest possible time has augmented, these ETL tools are becoming more popular among businesses.
On-Premise ETL Tools
A large number of companies operate legacy systems that have both the data and the repository configured on-premise. The main reason behind such an implementation is data security. That’s why, companies prefer having an ETL tool that can be deployed on-site.
Cloud ETL Tools
These tools are deployed on the cloud and
Various cloud-based applications form an essential part of enterprise architecture. To manage data transfer from these apps, companies opt for ETL tools deployed in the cloud. These tools let them leverage the flexibility and agility that the cloud arrangement provides.
Use-Cases of ETL Tools
These are the three most common use-cases of ETL software in the enterprise sector:
Constructing A Data Warehouse
Data Warehouse is an organized environment that holds critical business data. But before data is loaded into the data warehouse, it has to be cleansed, enriched, and transformed. Once loaded, this data becomes a ‘single source of truth’ for the business.
One of the main steps in building a data warehouse is to make sure that the data retains quality and accuracy. An ETL tool can reinforce this concept and simplify the execution of this use-case effortlessly, allowing reliable data loading.
Another vital use-case of an ETL tool is upgrading systems or moving data from a legacy system to a modern one.
The challenge with data migration is mainly the disparity in the format of the old and new systems. An ETL tool, with its enhanced transformation capabilities, ensures the format, structure, and scheme of the source data is compatible with the target system.
ELT or Pushdown Optimization
In an ETL process, transformation takes place in the staging area before data is loaded into the destination system.
On the other hand, in an ELT process, data is fetched, entered into the database, and transformations are performed there. This process is preferred for high volume datasets. It reduces strain on the tool’s server because all the processing is taking place in the database.
What to Look for When Choosing an ETL Tool?
For a data-driven business, choosing the right ETL tool can be an irreplaceable aspect of your data analytics stack. But the question is, how do you find the right tool? Many software development companies offer ETL software.
To help you select the right one, we’ve compiled a list of key features that can narrow down your search:
The ETL tool you choose should connect to all the data sources used by your business. Ideally, it should have built-in connectors for all your required systems, including databases, sales and marketing applications, file formats, and more, making it easier to get any data to and from any system.
· Easy-To-Use Interface
A bug-free and easy-to-use interface provides a consistent and reliable experience for you when handling data-related tasks. Easy setup is an added benefit that can help you bring your data pipelines to life in a matter of minutes.
As your business grows, your data needs will also expand. Thus, the ETL tool should have performance optimization features, such as pushdown optimization, to address your growing business needs.
The ETL tool should be capable of handling errors efficiently, ensuring data consistency and accuracy. Plus, it should offer smooth and efficient data transformation capabilities, ensuring zero data loss.
· Real-Time Data Access
Fetching data in real-time is becoming imperative for businesses looking to gain timely insights. An ETL tool should be able to access data from web applications in real-time to ensure faster time-to-insights.
· Built-In Monitoring
The ETL tool should come with a built-in monitoring system that provides real-time updates about job progress and ensures smooth process execution.
Assuming that you now understand what ETL means, thus, it is important to know that an ETL software helps you get significant insights that support your business development by data from numerous sources in a practical arrangement. It streamlines and improves the process of blending the raw data distributed across several systems into a data repository. Therefore, selecting the right ETL tool plays a critical role in your business intelligence.
Looking for a robust ETL tool for your business? Give Astera Centerprise a try!
It’s a powerful ETL software that offers support to disparate systems, including REST APIs, SQL Server, MariaDB, SAP HANA, Excel, and more. It supports data manipulation with a range of built-in transformations – in a code-free and drag-and-drop environment.
With an in-built job scheduler, Astera Centerprise allows you to schedule anything from a simple data transformation job to a complex workflow, including numerous sub-flows. Plus, you can also push a data transformation job down into a relational database, making the best use of database resources and enhance performance. Consequently, it helps your better manage processing requirements, save more time, and increase developer efficiency.