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.
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.
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.
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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.
How do Businesses Use ETL for Better Decision-Making?
ETL is the process businesses use to extract, transform, and load data to make it accurate, relevant, and business-progressive. Here is how ETL helps in decision-making:
- Scalability: ETL supports a large volume of data, so businesses have taken on bigger and bigger datasets and continue to analyze them for strategic decisions over time.
- Timely access: You will easily be able to access many integrated and adapted data to enable you to make necessary decisions as a business user. Data transformation ensures ETL tools handle most processing, so the data imported to the data store is ready for use.
- Consolidated Data: It integrates data from diverse sources to provide a company with a unified perspective on its operations, enabling greater accuracy and completeness in analysis.
- Data Quality: ETL processes sanitize and transform raw data to ensure decision-makers work with accurate, consistent, and reliable data.
- Faster Decision Making: By automating data preparation, ETL reduces the time required to access insights, enabling businesses to make prompt, data-driven decisions.
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).
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2. Talend
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
5. integrate.io
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
7. Pentaho
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
Learn about open-source ETL tools available in market with our blog.
Popular Alternatives to ETL
While ETL (Extract, Transform, Load) is a widely used process for data integration, several alternatives have emerged, offering different approaches for handling data. Here are some popular alternatives to ETL:
1. ELT
In ELT, data is first extracted from the source and loaded directly into a data lake or data warehouse. The transformation happens after the data is loaded.
Key Features
- Faster data loading (transformation is done later).
- Allows data to be stored in raw format for future use.
- Ideal for large data sets and big data applications
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2. Streaming ETL
Streaming ETL tools process data in real-time as it flows from source systems to target systems.
Key Features
3. Reverse ETL
Reverse ETL is basically the opposite of ETL. Instead of loading data from transactional systems into a data warehouse, it pulls data out of a data warehouse and pushes it into operational systems like CRM, marketing, or SaaS tools in real-time.
Key Features
- Puts data into the hands of business teams (sales, marketing, etc.).
- Helps operationalize data insights by integrating them into business workflows.
- Popular Tools: Census, Hightouch, Grouparoo.
4. iPaaS
iPaaS, i.e., Integration Platform as a Service platform, provides a cloud-based solution for connecting applications and automating workflows. They handle integration and data flow between multiple SaaS and on-premise applications without complex ETL processes.
Key Features
- Popular Tools: Zapier, Workato, MuleSoft, Boomi
- Simplifies integration between different cloud applications.
- Ideal for connecting and automating workflows between SaaS platforms
Final Thoughts
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.
Frequently Asked Questions
1. What are the benefits of ETL?
-Centralizes data from multiple sources
-Ensures data consistency and quality
-Prepares data for analysis and reporting
-Automates and streamlines data workflows
2. What is the value of ETL?
ETL simplifies and automates the process of extracting, transforming, and loading data, making it easier for businesses to analyze and use data for decision-making.
3. What is ETL Testing and why is it important?
ETL testing verifies data accuracy, completeness, and integrity during the ETL process. It’s important because it ensures that data is correctly transformed and loaded, maintaining data quality and avoiding errors in analysis.
Preetipadma is a dedicated technical content writer specializing in the data industry. With a keen eye for detail and strong problem-solving skills, she expertly crafts informative and engaging content on data science. Her ability to simplify complex concepts and her passion for technology makes her an invaluable resource for readers seeking to deepen their understanding of data integration, analysis, and emerging trends in the field.
