Business leaders use business intelligence (BI) to turn data into valuable insights and make strategic decisions within the company. Many organizations and enterprises are pursuing an agile business intelligence strategy to learn about market trends and enhance their services. And this strategy starts with data aggregation and integration. Realising the importance of ETL many businesses across the world are including ETL tools for data integration in their long-term business intelligence goals.
But will ETL benefit your business use case? What are the top tools that will meet your business requirements and budget constraints? We have got all the answers for you in this article.
Table of Contents
What is ETL?
ETL, or extract, transform, and load, is the most popular method of integrating data. It consolidates data from several disparate source systems into a data warehouse or another destination system with the purpose of improving data access. In other words, these are data pipelines that perform a very particular function: take data from its source system or database, modify it, and load it into a centralized repository like a data warehouse.
To know the importance of ETL, let’s first explore each stage in the ETL process:
First, data is acquired from one or more sources, including SQL or NoSQL servers, CRM, ERP, Flat files, web pages, SaaS, etc. Next, the data is put into a staging area. Most of the time, it’s not possible to concurrently extract data from all source systems. The staging area gathers data at various periods to avoid overburdening data sources. This also prevents overtaxing data sources by not extracting and transforming them at the same time.
During this stage, data is processed to ensure that its values and organizational structure match the use case for which it is intended. Typically, transformation procedures are hosted in a staging environment where data may be maintained and processed through the many phases of:
- standardizing (converting all data kinds to the same format),
- cleaning (resolving errors and inaccuracies),
- de-duplication (identifying and deleting duplicate data records),
- mapping (combining data pieces from two or more data models),
- augmenting (bringing in data from other sources),
- validating (conducting audits to check data compliance and quality),
- verifying (encrypting or safeguarding information under the control of authorities such as the government or industry),
- masking (creating synthetic yet structurally similar data),
- filtering, ranking, and others.
Here, transformations are often specified by the particular requirements of analysts who are trying to use data to address a specific business problem. As a result, transactional data is frequently combined with operational data, making it suitable for business intelligence and data analysis.
In the last stage, the transformed data is sent from the staging area to a target destination, which might be an on-premises or cloud-based target database, data warehouse, data store, data hub, or data lake.
Why do You Need ETL?
The data landscape is expanding quickly with data availability from APIs, IoT, server logs, and other sources. As a result, we now have access to new types of big data. But, none of this information is provided in a format that can be used immediately to address the issues faced by your company. This where firms realise the importance of ETL for a smooth data flow across their business.
Therefore, data must be migrated around, processed, refined, combined with other data, complying with quality standards, and sent to a repository for reporting and analytics. By overlooking the importance of ETL, companies can end up with a lot of data that are not in a ready-to-use business format. The only alternative to ETL is manual data input, which—even with an enterprise-level workforce—could take months literally.
Apart from this, there are multiple reasons why ETL is being widely used and what the importance of ETL is:
- Single point-of-view: Gaining a holistic perspective of the business is challenging in the current business climate since companies frequently have data scattered across several systems and apps. After understanding the importance of ETL in data integration, you can employ ETL and allow data from many sources to be combined and analyzed for a more comprehensive view of the business.
- Automation: ETL optimizes repetitive data processing activities for more efficient analysis. ETL tools can automate the data migration process and incorporate data updates on a regular or on-demand basis. This will allow data engineers to spend less time on mundane activities like data movement and transformation.
- Data Governance: Data governance is concerned with the usefulness, availability, consistency, integrity, and security of data. By placing a layer of abstraction between the source and target systems, ETL can aid in data governance by preserving data security and lineage. The importance of ETL in data governance is becoming increasingly clear as it promotes data democracy, which increases data accessibility and makes the data available to all stakeholders for business analysis.
- Scalability: As the volume and complexity of data rises, the importance of ETL in data integration becomes increasingly vital for companies to stay competitive. You can scale up ETL pipelines to ensure that the business can continue to extract, transform, and load enormous volumes of data successfully. Because of this, the company can manage a growing volume of data without having to upgrade, make data available for analysis and reporting on time, and maintain its systems regularly. Here, scalability can be achieved in several ways viz., distributed processing, parallel processing, data partitioning, and cloud solutions.
Importance of ETL in Business Intelligence
Understanding the importance of ETL in an organization’s business intelligence strategy requires having a working knowledge of how it can benefit you.
- Timely access: You can quickly access many integrated and modified data as business users to help you make decisions. Since most processing is done by ETL tools during data transformation, the data is already in usable format when it is imported into the data store. With a clear vYour business intelligence applications can produce reports much more quickly since they don’t need to do complex computations, join records, or maintain formatting standards, when they query the database.
- Improved quality and consistency: ETL can assist you in identifying and correcting mistakes, inconsistencies, and other issues in data through data cleaning and transformation methods. It improves the quality, dependability, accuracy, and reliability of decision-making.
- Reduces the likelihood of human errors: In spite of your best efforts, managing data manually places you at risk for mistakes. A minor oversight during the initial phases of data processing might have serious consequences. This is because one mistake can snowball into bigger mistakes, and the cycle never ends. To reduce manual involvement and the likelihood of errors several firms have realised the importance of ETL solutions to automate several steps to deliver quality data.
Top ETL Tools in the Industry
Earlier, organizations created their own ETL code. However, today, by realising the importance of ETL, organisations are also employing several open-source and commercial ETL tools that could make a data engineer’s job easier.
Now that we understand the importance of ETL let us explore some of the popular ETL tools in 2023:
1. Hevo Data
With the help of the no-code bi-directional ETL tool Hevo Data, you can quickly replicate data from more than 150 sources to a wide range of destinations, including Snowflake, BigQuery, Redshift, Databricks, and Firebolt.
Hevo Data also enables you to keep an eye on your workflow to identify the source of any problems and fix them before they negatively impact the overall workflow. The tool aids teams in streamlining and automating organizational-wide data flows, which may help save up to 10 hours of engineering effort each week. In addition, it features an inbuilt capability that automatically maps incoming data.
Pricing Model: Free, Starter ($239/month), Business (Demo Request available)
It is an enterprise-ready ETL tool with outstanding data governance functionality. The Talend platform has hundreds of pre-built connectors and is interoperable with on-premises and cloud-based data sources. With the use of its graphical user interface, you can point and click on connections to widely used business data sources, including Microsoft Dynamics, Salesforce, Oracle, Dropbox, Excel, and others. Additionally, you can manage the complete data lifecycle without sacrificing data quality.
Pricing Model: Stitch (Contact Sales), Talend Data Management Platform (Contact Sales), Talend Big Data Platform (Contact Sales), and Talend Data Fabric (Contact Sales).
3. AWS Glue
AWS Glue is an end-to-end ETL software solution that sorts through your data, handles data preparation, ingestion, transformation, and produces a data catalog. Although it is mainly batch-oriented, it can also serve Lambda-based near real-time use cases. It also allows you to process semi-structured data such as clickstream (e.g., website hyperlinks) and process logs.
One of the most distinctive features of AWS Glue is that it is serverless. This means that Amazon automatically configures a server and takes it down after the job is finished. Other noteworthy features include an integrated Data Catalog and automated schema discovery.
Pricing Model: Pay-as-you-go pricing model that charges an hourly rate, billed by the second
4. Informatica PowerCenter
Informatica PowerCenter, aims to boost business and IT team cooperation while optimizing data pipelines. It provides a corporate data integration solution that is high-performance, scalable, and covers the complete data integration lifecycle. The program has the ability to parse complex data formats, including JSON, XML, and PDF. Additionally, it can automatically verify modified data to uphold certain requirements. In addition, Informatica PowerCenter provides pre-built transformation, high availability, and optimal performance.
Pricing Model: Starts with $2,000/month for its most basic plan
It is an ETL data integration platform that is cloud-based and makes it simple to combine different data sources. With Integrate.io, you can get packages for over 100 well-known SaaS services and data repositories, including MongoDB, MySQL, Amazon Redshift, Google Cloud Platform, and Facebook.
The platform comes with a wide range of capabilities in addition to being extremely scalable and secure. Field Level Encryption is one such feature that enables you to encrypt and decode data fields using their own encryption keys.
Pricing Model: Enterprise (Contact Sales), Starter ($15,000/yr), Professional ($25,000/yr)
6. IBM InfoSphere DataStage
IBM InfoSphere DataStage employs a client/server architecture where jobs are generated and managed by a Windows client against a central repository on a server. It is possible for this server to run LINUX, UNIX, or even an IBM mainframe. Regardless of the platform, the IBM InfoSphere DataStage ETL software can target applications using a high-performance parallel framework and integrate data on demand across several large volumes of data sources. InfoSphere DataStage additionally enables expanded metadata management and enterprise integration.
IBM Infosphere DataStage is available in a variety of editions, including the Enterprise Edition, Server Edition, and MVS Edition.
Pricing Model: Available on request
Pentaho Data Integration is a free and open-source ETL tool offering data mining, reporting, and information dashboards. Pentaho is a tool for internal ETL that can be hosted on either Intel or Apple servers. It can connect to both open-source corporate databases like DB2 and a range of relational databases like SQL via JDBC. Pentaho can even analyze incoming IoT devices and gather, clean, and load both organized and unstructured system data.
Pricing Model: Enterprise Edition (30 days free trial), Community Project
Data integration is one of the most reliable keys to success in a data-driven company. By realizing the importance of ETL and employing it to aggregate data from several sources, you can uncover insights that help you make informed business decisions. However, it is challenging to aggregate data without a reliable ETL tool. If you’re searching for a solution that addresses all your ETL, ELT, and Reverse ETL needs, Hevo Data is the ideal choice for you! With more than 150 plug-and-play integrations, the no-code environment enables teams to automate and optimize data flows.
Saving countless hours of manual data cleaning & standardizing, Hevo Data’s pre-load data transformations get it done in minutes via a simple drag n-drop interface or your custom python scripts. No need to go to your data warehouse for post-load transformations. You can simply run complex SQL transformations from the comfort of Hevo’s interface and get your data in the final analysis-ready form.
Want to take Hevo for a spin? SIGN UP for a 14-day free trial and simplify your data integration process. Check out the pricing details to understand which plan fulfills all your business needs.
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