Organizational data has never been more important in today’s data-driven age. The rise of big data, paired with the recent surge in AI and machine learning, means that data is now at the cornerstone of most of the technologies enterprises use today.
And enterprises are taking note. A 2023 survey by NewVantage Partners found that 3 in 5 organizations are currently using data analytics to drive business innovation, and around 1 in 4 data analytics leaders claim to have successfully created a data-driven organization.
While organizations today understand the importance of data in achieving their business objectives, many still struggle to know how to properly store, handle and gain insights from the various data sources at their disposal. That’s where extract, transform, and load (ETL) tools can help.
What is ETL?
ETL stands for Extract, Transform, and Load. It's a crucial process used in data integration that involves combining data from various sources into a single, consistent format for analysis. The method began in the 1970s and continues to be crucial for on-premise databases with finite memory and processing power.
ETL helps create a centralized view of data, making it easier to analyze trends, identify patterns, and gain valuable insights from your data assets.
ETL processes clean and transform the data to ensure consistency and accuracy. This might involve fixing errors, formatting dates and currencies, or removing duplicates. Data is also often enriched once it’s combined with information from other sources.
Read: ETL vs ELT: What’s the Difference?
ETL is often confused with ELT. With ETL, the data is transformed before it’s loaded into the data warehouse. With ELT, however, data is extracted from its origins and loaded directly into the target system, and transformation happens after the data is loaded.
How does ETL work?
1. Extract
During extraction, data is pulled from various sources. These sources can be databases, spreadsheets, social media feeds, or any system that stores relevant information. The extracted data is often messy and inconsistent.
The extraction process often involves:
- Identifying Data Sources. This initial step involves pinpointing the systems holding relevant data. It could be internal databases (customer, sales), external sources (social media feeds, market research), flat files (spreadsheets), or even real-time data streams (sensor readings).
- Connection Methods. ETL tools utilize various methods to connect to these sources. Common methods include ODBC (Open Database Connectivity) for traditional databases, APIs (Application Programming Interfaces) for web services, and file system access for flat files.
- Data Extraction Strategies. Depending on the source, different strategies are employed for data extraction. Full table extracts (copying all data) are used for initial data loads, while incremental extracts (fetching only new or updated data) are preferred for ongoing updates.
- Scheduling and Automation. Scheduling regular data extraction ensures that your data warehouse or lake stays up-to-date. ETL tools allow setting schedules based on data update frequency or specific business needs.
2. Transform
During transformation, the data is cleaned, formatted, and organized. This might involve correcting errors, removing duplicates, and converting data types to ensure consistency. Transformations can also involve enriching the data by combining it with data from other sources.
The transformation process often involves:
- Data Cleaning. Data cleaning addresses errors, inconsistencies, and missing values. This might involve correcting typos, handling null values, and standardizing formats.
- Data Validation. Data validation ensures the transformed data meets predefined quality standards. This can involve checking for data integrity, identifying and handling outliers, and guaranteeing data consistency across the dataset.
- Data Derivation. New data elements can be created through calculations and transformations. For example, deriving weekly sales figures from daily data or calculating customer lifetime value from purchase history.
3. Load
The transformed data is then loaded into a target system, which could be a data warehouse, data lake, or any analytics platform. This creates a centralized repository of clean and usable data for further analysis.
The Load process often involves:
- Target System Selection. The target system could be a data warehouse designed for historical data analysis, a data lake for storing all types of data (structured, semi-structured, unstructured), or a specific analytics application.
- Loading Strategies. Different loading strategies are used based on the target system and data volume. Full loads replace the entire dataset periodically, while incremental loads update specific records efficiently.
- Error Handling and Recovery. ETL tools typically offer mechanisms for error logging, retries, and rollback of failed loads.
- Data Archiving and Purging: Plan for data archiving and purging strategies to manage data storage requirements and ensure the target system remains efficient.
ETL workflows are typically automated, allowing them to run on pre-defined schedules. This allows you to schedule data extraction, define transformation rules, and load the data into your target system. You can also orchestrate the flow of data from extraction to loading, ensuring a smooth and efficient transfer.
What are ETL tools?
ETL tools are software applications that automate the process of collecting data from various sources, transforming it into a usable format, and loading it into a target system.
These tools streamline the data integration process, making it faster, more efficient, and less error-prone. They also improve data quality by ensuring that the data used for analysis is accurate and consistent.
Many ETL tools can handle structured data from databases, semi-structured data like JSON files, and even unstructured data from log files. This allows you to create a unified view of your data, eliminating silos and enabling comprehensive analysis.
They can also handle a wide range of data sources and formats, too, and offer options for configuring transformation rules to meet your specific data needs. This flexibility makes ETL adaptable to various data management scenarios.
Benefits of ETL Tools
ETL tools offer several advantages that make data management and analysis smoother and more effective.
Here are some of the key benefits you can expect from implementing an ETL Tool or software solution:
1. Improved Data Quality
ETL tools can significantly enhance data quality by cleaning and standardizing data during the transformation stage. This includes removing duplicates, correcting errors, and ensuring consistency in format across data from various sources. With clean data, businesses can be confident in the accuracy of their analysis and make better data-driven decisions.
2. Increased Efficiency
ETL tools automate the entire data integration process, eliminating the need for manual coding and repetitive tasks. This translates to significant time savings for IT teams, allowing them to focus on more strategic initiatives. Additionally, automation reduces the risk of human error during data extraction and transformation.
3. Enhanced Decision-Making
By providing access to high-quality, reliable data through ETL tools, businesses can gain deeper insights and make more informed decisions. Consistent and accurate data empowers better reporting, forecasting, and overall business strategy.
4. Simplified Compliance
ETL tools can be instrumental in managing compliance with data privacy regulations like the GDPR or HIPAA. The data transformation functionalities can be used to anonymize sensitive data or filter information according to compliance requirements. Additionally, audit trails within the ETL process can help demonstrate adherence to data governance standards.
5. Scalability
ETL tools are designed to handle large volumes of data efficiently. As a business grows and its data needs expand, ETL systems can be easily scaled to accommodate the increasing data flow. This ensures smooth data integration even with growing data complexity.
Best ETL Tools for 2024
Choosing the best ETL tool depends on your specific needs and priorities.
Here's a detailed breakdown of some of the best ETL tools available today, each of which can help you leverage your organizational data in 2024.
Talend
While the open-source version of Talend’s ETL tool, Talend Open Studio, was retired in January 2024, their commercial offerings remain a dominant force in the data integration market. The company offers a comprehensive data integration platform known as Talend Data Fabric, that incorporates ETL/ELT processes alongside data quality, data management, and application integration functionalities. This unified approach streamlines your data pipeline creation and management, making it incredibly easy to manage and extract value from your data. It also makes to integrate batch or streaming data from almost any source, while an integrated preparation functionality ensures that your data is usable from day one.
Talend boasts a vast library of connectors for various cloud applications, data sources, and data warehouses, ensuring you can connect virtually any data source to your target destination. The platform also integrates seamlessly with various data sources and destinations, both on-premises and in the cloud. This open architecture allows you to connect to virtually any data source and build scalable data pipelines that can handle growing data volumes.
Dataddo
Dataddo is a leading ETL tool that stands out for its fully managed, no-code data integration solution built for professionals with any level of technical expertise. The platform focuses on user-friendliness, providing a no-code, drag-and-drop interface that allows even those with minimal technical expertise to build data pipelines. And unlike some ETL tools that require ongoing maintenance and management by your IT team, Dataddo takes care of everything behind the scenes. This frees you from worrying about infrastructure, maintenance, or API updates, allowing your data teams to focus on more important tasks like data analysis or business intelligence (BI).
Dataddo is SOC 2 Type II certified and ISO 27001 compliant, making it an ideal choice for those looking for robust, reliable security for their data. Its architecture is also designed to handle large data volumes, making it suitable for businesses with growing data needs that might struggle to scale as their data grows. Dataddo also offers a wide range of connectors for cloud applications, BI tools, data warehouses, and data lakes, making it easy to connect virtually any data source to your desired destination. Overall, Dataddo is a powerful ETL solution for businesses seeking a user-friendly, managed platform with a broad feature set, strong security, and scalability for their data integration needs.
Integrate.io
With its cloud-based data integration platform designed for modern data pipelines, Integrate.io is another top ETL tool in the data management space. The company goes beyond traditional ETL by also offering robust capabilities for Extract, Load, Transform (ELT) to get your data into the data warehouse quickly for analysis when needed by making transformations happen later. It also offers capabilities for Change Data Capture (CDC). This allows for near real-time data movement between sources and destinations, ensuring your data analytics are based on the most up-to-date information.
While user-friendly, Integrate.io offers customization options for complex data transformations thanks to its powerful scripting capabilities that allow you to tailor data pipelines to your specific needs. It also comes with built-in data quality checks and transformations so you can clean, standardize, and enrich your data during the integration process. This not only improves the accuracy and reliability of your data analysis, making Integrate.io a strong choice for organizations looking to streamline data pipelines and gain valuable insights from their data.
Matillion
A leader in the data and AI space, Matillion offers an incredibly powerful ETL tool designed specifically for organizations working within cloud data platforms. The company’s cloud ETL software integrates with virtually any data source, ingesting data into leading cloud data platforms, and transforming it so it can be used by leading analytics and BI tools and synced back to the business. Unlike traditional ETL tools built for on-premise environments, it also leverages the power and scalability of the cloud. This ensures optimal performance, scalability, and ease of use when working with cloud data warehouses and data lakes like Snowflake, Amazon Redshift, Google BigQuery, Azure Synapse Analytics, and Delta Lake on Databricks.
Like some of the other top ETL tools on this list, Matillion boasts a user-friendly interface with drag-and-drop functionality. It makes it easy for both business analysts and data engineers to build and manage data pipelines visually, reducing the need for extensive coding knowledge. At the same time, it doesn't compromise on power. For complex data transformations, you can leverage Python, SQL, and dbt code within your data pipelines to handle intricate data manipulation tasks. Matillion also provides a library of pre-built transformation components for common data wrangling tasks, saving you time and effort, while the Matillion Copilo allows you to write data transformation logic in plain English, which Matillion then translates into code.
Ascend.io
Ascend.io goes beyond traditional ETL with an incredible data automation platform that specifically targets streamlining data pipelines and analytics workflows. The platform prioritizes automating repetitive tasks within the data pipeline creation and management process, freeing up data engineers to focus on more strategic initiatives and reducing the risk of errors caused by manual coding. It also provides powerful capabilities for reverse ETL and Live data sharing, allowing users to push data back out to applications for scenarios like updating customer records in a CRM system and sharing data streams across different data lakes, warehouses, and clouds in real-time.
While standard ETL tools handle specific stages (extract, transform, load), Ascend.io aims to automate the entire data pipeline, integrating data quality checks and monitoring to help identify and address data quality issues proactively and ensure the accuracy and reliability of your data analysis The platform also boasts extensive integrations with various cloud data sources, databases, and data warehouses. This allows you to connect virtually any data source to your desired destination and scale your data pipelines to handle growing data volumes, Asencd.io also provides an extensive library of pre-built functions for common data transformations, and you can use scripting languages like Python and SQL to handle intricate data manipulation tasks within your pipelines.
Safe Software FME
Safe Software's FME platform is one of the industry’s best ETL tools for spatial data integration. Unlike traditional ETL tools that handle general data types, FME excels at transforming and manipulating geospatial data, including vector data (points, lines, polygons) and raster data (images). This makes it ideal for organizations working with geographic information systems (GIS) and location-based analytics, as it provides extensive functionality for data translation, transformation, and manipulation specific to geospatial needs, like coordinate system conversion and feature geometry editing.
While strong in geospatial data, FME isn't limited to it for ETL. It supports a wide range of data formats from various sources, including databases, cloud applications, and even legacy systems, allowing you to integrate not only geospatial data but also other data relevant to your analysis. FME also comes with a vast library of over 800 out-of-the-box transformers allowing users to build and automate custom integration workflows without having to code. These transformers can handle everything from basic data cleaning and filtering to complex spatial operations and data enrichment, making FME an incredibly versatile tool catering to a wide range of data transformations. code. Whether your data challenges have to do with spatial data, big data,stream processing, distributed processing, parallel processing, cloud migration, or business intelligence, FME can help you spend more time using data, and less time fighting it.
Adeptia
Adeptia holds a strong position in the ETL (Extract, Transform, Load) market, offering one of the industry's best ETL tools for users of varying coding expertise. The platform provides a visual interface with drag-and-drop functionality, allowing business analysts with minimal coding experience to create simple data connections. It also prioritises user-friendliness with a graphical user interface and pre-built connectors for various data sources and destinations. For data engineers, it offers advanced features like data validation, transformation scripting, error handling, and robust backend connectivity, allowing you to build intricate data pipelines without compromising on ease of use. It also supports languages like Java and Python allowing for intricate data transformations within pipelines for complex data manipulation tasks.
Adeptia supports both traditional ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) workflows, giving you the flexibility to choose the approach that best suits your data processing needs. It also offers functionalities beyond basic ETL, including data cleansing, routing, and real-time data processing, making it a compelling solution for organizations seeking to streamline data integration and gain valuable insights from their data.
Airbyte
Airbyte has recently gained significant traction in the ETL space thanks to its unique, open-source data integration platform and focus on flexibility. Airbyte is an open-source platform, allowing for greater transparency, customization, and community-driven development. This means you can use pre-built connectors for a vast array of cloud applications, databases, and data warehouses (over 300 connectors and growing), develop your own custom connectors to handle specific data sources not yet supported, and integrate Airbyte with your existing data pipelines and orchestration tools.
Unlike some ETL tools that handle data transformation within the tool itself, Airbyte prioritizes efficiently moving your data from various sources (extract) and loading it into your data warehouse (load) with minimal transformations. This allows for more flexibility in how you handle and analyze your data later using separate tools like dbt, while not compromising security thanks to pre-built features like encryption and access control. Despite being open-source, Airbyte offers a robust community forum and comprehensive documentation too, and a paid cloud version with enterprise support is available for organizations seeking additional assistance.
Rivery
Rivery’s Cloud ETL tool is a fully managed solution for data ingestion, data transformation, data orchestration, and reverse ETL and that comes with built-in support for your data operations development and deployment lifecycles. Designed to be nimble for non-technical users and with advanced capabilities for experts, the platform allows you to instantly connect to key data sources with 200+ managed integrations and build advanced data pipelines in just a few clicks. You can also replicate data with total ease from any relational or NoSQL database with Rivery’s change data capture (CDC) or built customer SQL query replication for databases where CDC isn’t an option.
Rivery goes beyond traditional ETL by offering functionalities to push data back out to applications for scenarios like updating customer records in a CRM system. You can share all your digital data, in real, across teams, departments, and organizations to boost efficiency, communication, and analysis. You can also seamlessly schedule and manage your data pipelines efficiently with features like visual orchestration and automated scheduling, making Rivery one of the best one-stop solutions for all of your ETL and data integration needs.
Fivetran
With a powerful cloud data integration platform and the industry’s best selection of fully managed, zero-configuration connectors, Fivetran is widely recognized as one of the best ETL tools on the market in 2024. The platform supports over 150 data connectors that dynamically adapt as schemas and APIs change, ensuring reliable data access for a wide range of cloud applications, databases, and data warehouses. It also continuously synchronizes data from source to warehouse, and accelerates data analysis by programmatically managing ready-to-query schemas and automating in-warehouse transformations. You can define scheduled data transformations to be applied to your data after it's loaded into the warehouse too, giving you complete flexibility over the ETL process.
Fivetran prioritizes ease of use with a user-friendly interface that allows both business analysts and data engineers to build and manage data pipelines visually. It comes with Drag-and-drop functionality and clear menus that simplify the process of connecting data sources and defining data flows, as well as prebuilt data models that can be used to swiftly deliver new reports and respond to ad hoc queries while saving crucial engineering time. Overall, Fivetran's focus on automated ELT, extensive pre-built connectors, user-friendly interface, data quality and security features, and scalability make it our no 1 pick for the best ETL solution for businesses seeking a streamlined and reliable way to integrate data from various cloud sources into their data warehouses.
