What Is ETL & Types of ETL Tools for Your Data

If you want a data-driven business, then you need a data-driven approach.

One of the first things that come to mind when we think about data-driven businesses is the importance of having a centralized repository for all your data. A centralized repository makes it easy for everyone in an organization to access and share information quickly and efficiently.

But what happens when your company doesn’t have this centralized repository? What do you do if your team is scattered across several locations or if multiple units are working with different data sets?

You might be tempted to say, “we’ll just use an ETL tool.” 

But what exactly are ETL tools? And why should you use them?

In this article, you will understand everything about ETL and ETL tools.

What Are ETL Tools?

When managing a data-driven business, the right ETL tool is indispensable.

ETL stands for “Extract, Transform, and Load.” It’s a three-step process for managing your data:

• Extract data from structured and unstructured sources.
• Transform it into a format satisfying your business’s operational and analytical requirements.
• Load it to a target destination.

If you have an ETL tool, you can easily extract data from disparate sources, scrub it for consistency and quality, and consolidate this information into your data warehouse. 

Because of this streamlined approach to intake, sharing, and storage, implementing an ETL tool will simplify your data management strategies and improve the quality of your data.

Use Cases for ETL Testing

ETL Testing is the only way to ensure your ETL process works correctly.

Imagine this: you’ve spent months designing and building an ETL process. It works perfectly on your local machine, but something goes wrong when you deploy it in production. 

The problem may be with your source data, or it’s something as simple as a missing character in a field name. Without ETL testing, there’s no way of knowing which one it is—and if you don’t see what’s wrong with your process, how can you fix it?

What if the fix requires changes to multiple parts of the codebase?  What if those changes introduce bugs in other parts of your application? What if fixing those bugs introduces new bugs in your ETL process?  And how much time and money would it cost to fix the problem once it’s been deployed?

It doesn’t have to be this way! 

With ETL testing, you’ll be able to catch these issues before they become problems in production. 

You’ll know precisely where the issue lies and how best to resolve it. By catching problems early on in development, you can save money now and throughout future operations.  

What does ETL testing consist of?

Structure validation

Structure validation verifies the source and target table structures per the mapping document. Testers should validate the data type in the original and the target systems. The length of data types in the source and target system should be the same. 

For data to make sense to external users, data field types and formats need to be the same in the source as well as the target system and individual column names should be validated.

It ensures no conflicts between columns of different schemas in both systems and avoids unnecessary changes due to mismatches in the name or data type.

Validating Mapping document

Mapping documents are the heart of any data mapping process. Tracking errors and getting things done quickly can be impossible without proper documentation.

The first thing you need to do is validate the mapping document. It involves checking all the information and ensuring it matches the source and target systems. 

It includes validating change logs, data types, length, transformation rules, and more.

Data Consistency Check

A data consistency check is a process that involves checking the misuse of integrity constraints like foreign keys. An attribute’s length and data type may vary in tables, though their definition remains the same at the semantic layer.

It may result in an incorrect structure of data and inconsistency between tables.

Data Completeness Validation

Data completeness validation ensures that all expected data is loaded into the target table and checks for rejected records. It also checks data should not be truncated in the column of target tables and statements boundary value analysis.

The validation involves checking if all the data is loaded to the target system from the source system. Testers can compare record counts between the source and the target. Testers can also compare record counts by checking for any rejected records and inspecting data sets to ensure it is truncated in the column of target tables.

Data Transformation Validation

Data Transformation Validation is the process of checking your data transformations.

Data transformation validation involves creating a spreadsheet of scenarios for input values and expected results, verifying those results with end-users, and comparing the range of values in each field to ensure that they’re all within the content specified by the business users. You can also validate if the data types in your warehouse are the same as mentioned in your data model.

Duplicate Data Validation

Duplicate data validation ensures that the same data does not enter the target system more than once. It is imperative when multiple source system columns are used to populate a single field in the target system.

It ensures that no duplicates exist in your database and helps you save time and money by avoiding costly errors.

You can validate duplicate values in various ways, including validating primary keys and other columns if there are identical values per business requirement.

Data Cleaning

Removing unwanted data from your data set before loading it into a staging area is essential. This is done to guarantee that you are only sending relevant information to your staging area. When irrelevant data is sent, you’re wasting time and money.

ETL for Batch Processing

In the past, it was common for companies to process their data in real-time. But as the amount of data being processed increased, so did the load on their infrastructure. It resulted in slowdowns, which impacted performance and productivity across the board.

To deal with this issue, IT managers began looking for ways to process their data more efficiently by moving from a real-time approach to batch processing. The result? A faster way of working that allows you to get your job done more quickly and efficiently than ever!

Batch ETL processing means users collect and store data in batches during a batch window. It can save time, improve the efficiency of processing the data, and helps organizations and companies in managing large amounts of data while processing it quickly.

The Data Warehouse executes batch tasks in any order, and the workflow for each batch is defined by the order in which they are completed. 

If you run ETL in the data stream, you must do it correctly.

The standard approach uses batch processing to connect databases in a data warehouse and pull data from S3 once or twice daily. It’s not ideal, but it’s what most people do. And it works—but only if you don’t mind paying for an expensive cloud solution that doesn’t scale well.

The alternative is to use a training layer that can handle high rates and scale your business needs. With this kind of architecture, you have more control over the size of your data lake and the number of data streams to optimize for performance for a reasonable cost.

ETL for Streaming Data

Do you want to get the most out of your data?

Streaming ETL is a powerful data pipeline tool to extract and transform data from any source so that you can use it in your business applications.

Streaming ETL allows you to stream events from any source and helps you change the data while on the run. The entire process can be in one stream while you stream data, whether you stream data to a data warehouse or a database.

The streaming ETL process is helpful for real-time use cases. Fortunately, some tools easily convert routine batch jobs into a real-time ETL data pipeline.

You can load the results of the streaming processing into a data lake based on AmazonS3 or upload your results to a cloud with cloud-based ETL tools. This allows you to use a robust and scalable ETL pipeline in your core business applications.

Data transformation tools and load data can be extracted using a stream-based data pipeline to perform SQL queries and generate reports and dashboards. 

ETL for Change Data Capture (CDC)

ETL is a critical data integration process that you can use to extract, transform, and load data from various sources into a database or data warehouse.

It’s important to note that ETL has traditionally been used for batch-based processing, but modern platforms often utilize in-memory processing to enable near-real-time data movement. 

During the transformation phase, data is converted into the appropriate format for the target destination. It could be done using database queries or Change Data Capture (CDC). 

Finally, ETL involves loading data into the target destination.

Established IT Company ETL Tools List

Pentaho

Pentaho is a data integration, analytics, mining, and data consolidation tool that is an open-source platform. It offers a complete range of data integration, mining, dashboard, customized ETL, and reporting facilities.

Pentaho helps integrate data from different resources and execute real-time analysis to present results excitingly. This contemporary and robust BI software supports the decision-making process across the business.

Datorios

Datorios is the world’s first end-to-end SaaS ETL platform. It provides data sovereignty with its cloud VPC and on-prem installations while being easy to use and quick to deploy.

With Datorios, you can build pipelines with ease. Our no-code data transformations allow anyone to create the most simplistic or complex data flows, regardless of technical ability. With schedule or event triggering, tasks are handled event by event, allowing for the creation of any needed configurations.

Apache Nifi

Apache NiFi is a dataflow system designed to automate data flow between different software systems. It’s simple, powerful, and accurate. It can distribute and process data in a full graph that is easy to manage.

The web-based user interface is simple enough for anyone but offers many configuration options. The system has support for multi-tenant authorization and data attribution.

It offers a concurrent model with proper visual management that encourages loosely coupled components.

Talend

Talend is a powerful ETL tool that can help you with your data lifecycle needs. It provides accurate, clean, complete, and healthy data for your organization, and it does it with support for cloud data warehouses in place. 

It features practical application and API integration, as well as data governance. It also works with multiple cloud environments and hybrid clouds.

To top it off, Talend offers good support for on-premises and cloud databases with connectors. It works most effectively with batch procedures.

Azure Data Factory

Azure Data Factory is the perfect tool for ETL and ELT processes. It can easily construct ETL and ELT procedures, then integrate data with Azure Synapse Analytics to provide insightful information. It offers a cost-effective pay-as-you-go model that makes it ideal for your business.

It’s also easy to rehost SSIS with inbuilt CI/CD support, helping you accelerate data transformation with code-free data flows.

Custom ETL Solutions

The best approach for your organization depends on the needs of your business. Investing in a commercial ETL tool might be the answer if you’re looking to save time, money, and resources. However, if you have specific requirements for an ETL tool that are not met by existing solutions, building a custom solution may be the right choice for your organization.

Once you’ve decided which approach is suitable for your organization’s needs, evaluating ETL tools will help ensure you find the best fit. There are several key factors to consider when assessing an ETL or cloud-based tools:

• Ease of use: How easy is it to start using this tool? How intuitive is the interface? What type of training resources are available?

• Cost: What is the price point for this solution? Does it include any additional fees or charges associated with support or maintenance?

• Platform compatibility/flexibility: What platforms does this tool support? Is there flexibility in terms of what types of data can be imported or exported from this platform?

• Security/privacy: Are there any security concerns related to using this platform? Does it offer specific security features that other platforms don’t provide (such as encryption)?

Conclusion

ETL (Extract, Transform, Load) tools are essential for organizations to effectively manage and process their data. With the increasing amount of data being generated, ETL tools can help streamline the data integration process and save time and resources.

As outlined in this blog, there are various ETL tools available in the market, each with its unique features and capabilities. It is important to carefully evaluate the specific needs of an organization and select an ETL tool that best meets those needs. Ultimately, using an advanced ETL tool such as Datorios can significantly enhance an organization’s data management and analytics capabilities, leading to better decision-making and improved business outcomes.

Open your free Datorios account to see what we mean.

FAQs

What is an ETL tool?

ETL stands for Extract-Transform-Load. ETL tools enable data integration strategies by allowing companies to gather data from multiple data sources and consolidate it into a single, centralized location.

Which is the best ETL tool?

The best ETL tool differs for every organization as it is based on a variety of factors including the capabilities of the tool, the connections it offers, its capacity for handling data, and the data quality needed.

The following are some of the best ETL tools (in no particular order):

• Alteryx 
• Datorios
• Informatica PowerCenter
• SQL Server Integration Services (SSIS)
• Talend

Is SQL an ETL tool?

SQL and ETL are concepts that have been used for many years to manage data.

SQL stands for Structured Query Language and is a programming language that allows you to query relational databases and is commonly found prewritten within ETl tools.

Watch this video and see how easy it is to use Datorios to change date formats.

What is an ETL example?

ETL tools are used in many different areas of data management, including Data warehousing, Data migration projects, and when big organizations acquire small firms.

Which ETL tool is in demand in 2022?

To make sense of all of the information you have at your disposal, you need ETL processes to ensure that you can analyze it in a way that makes sense. ETL is the process of getting data out of its source, transforming it into something useful for analysis, and then loading it into a structured database that can be used effectively by analysts or business users.

Following are some of the ETL tools that are in demand in 2023:

1. Informatica
2. Talend
3. AWS Glue/EMR
4. Datorios

Is ETL easy to learn?

Learning ETL is not an easy task. Your educational background and schedule will significantly determine how long it takes you to understand the many facets including the difference between ETL tools and cloud ETL tools. 

It takes several weeks or months to grasp the concepts of ETL and apply them correctly. Take time and internalize the best practices before trying them on actual data. The more dedicated you are, the faster you will learn.

What languages do ETL tools use?

Python, Ruby, Java, SQL, and Go are all popular programming languages in ETL.

Python is an elegant, versatile language that you can use for ETL. Ruby is a scripting language like Python that allows developers to build ETL pipelines, but few ETL-specific Ruby frameworks exist to simplify the task.

Go features several machine learning libraries, support for Google’s TensorFlow, some data pipeline libraries, like Apache Beam, and a couple of ETL toolkits like Crunch and Pachyderm.

Java has influenced other programming languages, including Python, and spawned several spinoffs (such as Scala). Java forms the backbone of a slew of big data tools (such as Hadoop and Spark).