Data management has never been more complex in 2024. Not only are organisations swimming in excessive amounts of data, but they also struggle to access crucial data assets that are central to the success of their business.
The problem is that data comes from all sorts of places – from databases to spreadsheets and the cloud. And often, this data isn't necessarily in the same format or location. This prevents organizations from finding the data they need, leading to as much as two-thirds of their data going untapped despite being crucial for discovering insights, driving innovation and solving key business challenges.
For organizations struggling to handle multiple data sources, data integration tools are proving crucial to keeping tabs on the valuable data at their disposal.
What is data integration?
Data integration is the process of combining data residing in different sources and providing a unified view of them. It means bringing all your data together from various locations and formats into a single, usable source.
The goal of data integration is to provide a unified and comprehensive dataset that you can use to quickly access the information you need, analyse trends, and identify insights that might otherwise remain hidden.
It gives you a complete and accurate view of your data, allowing you to improve decision-making, create new products and services and ensure that your data is accurate, up-to-date, and readily available at all times.
Data integration can also help organisations optimise their operations by identifying inefficiencies and areas for improvement. For example, an organisation might use data integration to track customer interactions and identify areas where the customer experience can be improved.
What are data integration tools?
Data integration tools are software applications that automate the data integration process of combining data from multiple sources and presenting it in a unified view.
They do this by unifying data from different data sources such as databases, spreadsheets, cloud storage, and even social media, providing a complete perspective of your operations, customers, or research.
This allows organizations to gain a deeper understanding of their data for data-driven decisions, enabling them to analyze trends, identify opportunities, and address challenges with a comprehensive view.
Types of data integration tools
There are a variety of different data integration tools available today, each of which caters to specific data volumes, processing needs, and desired levels of real-time functionality:
1. Extract, Transform, Load (ETL) Tools
ETL tools are software designed to automate and simplify the process of managing data flow between various sources and a target system, typically a data warehouse or data lake.
They do this by pulling data from multiple sources like databases, applications, flat files, and APIs and cleaning, filtering, and converting this data into a single view to ensure consistency and accuracy. They then load this data into the destination system, ready for analysis or reporting.
ETL tools are crucial for organizations that deal with data from a variety of different sources. They streamline data management by automating the data flow and ensuring the data is usable for analytics.
2. Extract, Load, Transform (ELT) Tools
ELT tools are similar to ETL tools but with a key difference in the order of their processes. Like ETL tools, pull data from various sources like databases, applications, and files, but then they load the extracted data into the target system, typically a data warehouse or data lake.
This can make them better than ETL tools for big data scenarios where processing large amounts of data upfront might be slow or resource-intensive, making the data integration process faster and more scalable but less accurate.
Read: ETL vs ELT: What’s the Difference?
After the data lands in the target system, ELT tools then clean, format, and transform it to a consistent and usable format. This can involve filtering, correcting errors, converting formats, and calculations.
3. Data Preparation Tools
Data preparation tools are software applications specifically designed to streamline the process of getting your data ready for analysis. Unlike ETL or ELT tools that handle data movement, data preparation tools primarily focus on cleaning, formatting, and transforming your data. This involves tasks like:
- Identifying and handling missing values: Data might have missing entries that need to be addressed (e.g., filling with averages, removing rows).
- Correcting errors: Inconsistent formatting, typos, or outliers can be identified and fixed.
- Standardizing formats: Data from different sources might have varying formats (dates, currencies, units) that need to be standardized for consistency.
- Deriving new features: You can create new data elements (features) based on existing data through calculations or transformations.
Many data preparation tools offer a visual, user-friendly interface that allows data analysts and business users with less technical expertise to clean and manipulate data without extensive coding.
Data preparation tools also often integrate seamlessly with popular data analysis and visualization tools, allowing for a smooth transition from data cleaning to analysis.
4. Data Migration Tools
Data migration tools are software applications designed to automate and simplify the process of moving large volumes of data between different storage systems, databases, applications, or platforms.
While some data migration tools focus solely on data movement, some offer built-in transformation capabilities as well, including filtering, cleaning, or converting data formats to ensure compatibility with the target system.
This connects to various source and destination systems, allowing you to extract data from one location and load it into another. They support a wide range of data formats and structures to handle diverse data types.
5. Data Integration Platforms (DIPs)
Data Integration Platforms (DIPs) are essentially orchestrators for your data movement needs. They're comprehensive solutions that provide a wide range of functionalities to seamlessly connect, consolidate, transform, and manage data from various sources.
DIPs come equipped with connectors that act as bridges to various data sources like databases, applications, files, and APIs, allowing them to extract data efficiently from disparate systems.
They go beyond simple data movement too, providing tools to transform data by cleaning, formatting, and standardizing it to ensure consistency and accuracy.
Unlike other types of data integration tools, DIPs allow you to define data mapping rules, which specify how data elements from different sources should be matched and transformed for a unified structure. They also enable you to build automated workflows that orchestrate the entire data integration process, streamlining data movement.
Best Data Integration tools
Choosing the best data integration tool depends on several factors, such as the type of data you are integrating, the volume and complexity of the data, the frequency of data updates, and your budget.
Here's a short list of ten of the best data integration tools available today based on their popularity with users, features and user reviews.
Matilion
Matillion is a cloud-native Data Integration Platform (DIP) that allows you to centralize and manage all your data integration needs through a single platform. The platform focuses on enabling both coders and non-coders to build and manage data pipelines efficiently, offering a visual designer with drag-and-drop functionality for building data pipelines that require zero code to develop. It also comes with a no-code interface for simple data flows and integrates with coding languages like Python and SQL for complex transformations. This allows it to cater to a range of technical skill sets and is accessible to both technical and non-technical users.
Matillion stands out for its range of powerful tools for data integration including filtering, cleaning, aggregation, and joining data sets. It also supports incorporating dbt (data build tool) for more advanced transformation and allows you to automate data pipelines and schedule data refreshes at specific intervals to ensure your data stays up-to-date and eliminate the need for manual intervention. The Matillion data integration tool also integrates seamlessly with various cloud platforms like Amazon Redshift, Google BigQuery, Microsoft Azure Synapse Analytics, and Snowflake, allowing for flexibility in choosing your cloud environment.
Alteryx Designer
Alteryx Designer is a powerful and versatile data integration tool that forms part of the broader Alteryx Analytics Platform. The tool caters to data analysts, data scientists, and business users alike, offering a visual, drag-and-drop interface where users can visually design data workflows using pre-built tools. It also supports both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, allowing you to choose the approach that best suits your data and transformation needs. This makes it suitable for enterprises with significant data processing needs and scalable for big data workloads through distributed processing capabilities.
Alteryx provides a comprehensive toolkit for data cleansing, transformation, and manipulation. You can clean and format data, perform calculations, join datasets, and derive new features to prepare your data for analysis. It also allows for complex data transformations through calculations, expressions, and data science techniques, and can seamlessly connect to various data sources, including databases, cloud storage platforms, applications, and flat files. This flexibility allows you to integrate data from disparate locations for a holistic view.
Oracle
Oracle Data Integrator (ODI) is a data integration platform that caters particularly well to organizations already invested in the Oracle ecosystem, providing a powerful solution for managing data movement and transformations across various sources and targets. ODI provides a user-friendly GUI with tools like the Topology Manager and Designer. These interfaces enable the visual development of data flows, transformations, and integration processes, even for users with less coding experience. Like many other data integration tools on this list, ODI supports both ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, allowing can choose the approach that best suits your data volumes, processing needs, and target system requirements.
Oracle Data Integrator is known for its ability to handle large data volumes efficiently. It leverages Oracle's database technologies for high-performance data extraction, transformation, and loading tasks. As an Oracle product, the platform integrates seamlessly with other Oracle products like Oracle Warehouse Builder (OWB) and Oracle GoldenGate. This tight integration streamlines data movement within the Oracle data ecosystem.
Microsoft SQL Server Integration Services (SSIS)
While Microsoft SQL Server is a relational database management system, it offers a powerful data integration tool called SQL Server Integration Services (SSIS). SSIS is a comprehensive data integration and data transformation platform designed for use alongside SQL Server, providing a robust set of tools to automate data extraction, transformation, and loading processes. You can clean and format data, and perform calculations, aggregations, joins, and data type conversions to prepare your data for analysis or loading into the target system. SSIS also allows you to schedule data integration packages to run automatically at specific times or intervals. This automation ensures your data pipelines are executed consistently and keeps your data up-to-date.
SSIS integrates seamlessly with other Microsoft products within the SQL Server ecosystem, including SQL Server Analysis Services (SSAS) for data warehousing and SQL Server Reporting Services (SSRS) for report generation. This tight integration streamlines data management workflows and makes data integration as seamless as possible across the Microsoft ecosystem. SSIS also offers a graphical user interface where you can build data integration workflows using drag-and-drop functionality, simplifying the creation of ETL processes, even for users without extensive coding experience.
AB Initio
Ab Initio is a popular tool for data integration that goes beyond simple ETL (Extract, Transform, Load) processes to encompass a wider range of data management functionalities. The platform provides a powerful suite of tools for data management, high-volume data processing, and analytics, using a graphical development environment where you build data processing applications using a graphical workflow. Ab Initio applications can also be distributed across servers or containerized environments, enabling them to integrate data from massive datasets and complex workloads efficiently.
Ab Initio is known for its exceptional performance, particularly for handling large and complex datasets. It leverages parallel processing techniques to ensure efficient data movement and transformation, allowing you to build applications that handle historical data loads (batch) and integrate live data streams in real-time within the same platform. The platform also supports a wide range of data formats, including relational databases, flat files, XML, JSON, and more, and comes with built-in data quality features for data profiling, cleansing, and validation. This helps ensure the accuracy and consistency of your data for reliable analysis and integration.
Qlik Data Integration
Qlik Data Integration (QDI) is a comprehensive data integration solution designed to centralize and automate the process of moving data from various sources to your analytics environment within Qlik, such as QlikView or Qlik Sense. The tool provides a central platform to manage all your data integration needs, streamlining data ingestion, transformation, and automation, while even offering features such as a data catalog for easy data discovery. It’s also cloud-optimized and integrates seamlessly with cloud-based data sources and analytics tools within the Qlik ecosystem, and it can support hybrid deployments for businesses with on-premise data infrastructure.
QDI offers built-in tools for data cleansing, filtering, aggregation, and other transformations to prepare your data for analysis within Qlik applications. You can automate data pipelines and schedule data refreshes at specific intervals, making it easy to ensure your data stays up-to-date while eliminating the need for manual intervention. QDI also stands among other data integration tools for its agentless architecture, which eliminates the need to install software on your source or target systems to simplify deployment and reduce the impact on system performance.
Denodo
Denodo’s award-winning integration platform stands out from other tools on this list for its data virtualization approach to data integration. The platform unifies access to data from various sources without physically moving the data by creating a virtual layer on top of your disparate data sources to hide the complexity of physical data locations and formats. This not only leads to simplified data management compared to traditional ETL methods but also reduces data storage needs and leads to improved data access performance. It also allows Denodo to provide a central point to manage and deliver data services across your organization, enabling you to define reusable data services that encapsulate complex data access logic to simplify data consumption for applications and users.
Empowering users with self-service capabilities, Denondo’s data integration platform fosters data democratization within organizations while accelerating data-driven decision-making. The platform also integrates seamlessly with various cloud platforms and on-premise data sources, allowing you to leverage your existing data infrastructure while enabling a hybrid or multi-cloud data management strategy. This support for hybrid and multi-cloud environments, paired with the platform’s data virtualization approach, self-service capabilities and unified data services, make it one of the best data integration solutions available today.
Informatica PowerCenter
Informatica PowerCenter is a widely-recognized Data Integration Platform (DIP) that provides a comprehensive set of features to manage data movement, transformation, and integration needs across various data sources and target systems. The platform excels at handling large and complex data volumes efficiently, leveraging parallel processing techniques to ensure fast and reliable data movement and transformation. It also offers a vast library of connectors that enable seamless integration with a wide range of data sources, including relational databases, cloud storage platforms, legacy systems, real-time data streams, and more.
PowerCenter offers a rich set of tools for data transformation tasks. You can perform data cleansing, filtering, aggregation, joining, data type conversions, and even implement custom code for complex transformations. It also comes with built-in features for data profiling, cleansing, and validation. It helps ensure the accuracy and consistency of your data for reliable analysis and decision-making. Informatica offers PowerCenter in both cloud-native and hybrid deployment options, providing flexibility to cater to on-premise, cloud, or hybrid data infrastructure environments.
Fivetran
With its focus on automated data movement from various cloud applications and databases to your target data warehouse or cloud analytics platform, Fivetran stands out as one of the leading data integration tools available today. The platform boasts a vast library of pre-built connectors for popular cloud applications and databases, each of which can automate data extraction, transformation, and loading (ETL) processes while eliminating the need for manual coding or complex data pipeline development. Fivetran also uses Change Data Capture (CDC) technology to capture only the incremental changes in your source data. This ensures efficient data updates in your CDW to minimize resource consumption and processing time.
Unlike many other data integration tools on this list, Fivetran automatically handles schema changes in your source data and adapts your data warehouse schema accordingly. This eliminates the need for manual intervention for managing and scanning your data while ensuring your data remains consistent across your organization. This, along with Fivetran’s focus on automation, ease of use, efficient data movement through CDC, and robust security features make the platform a strong choice for streamlined data integration in the cloud.
FME by Safe Software
Safe Software’s FME is widely recognized as one of the best data integration tools available today. The platform boasts one of the most impressive selections of connectors on the market, allowing connection to over 450 data sources, including relational databases, cloud storage platforms (like AWS S3, and Azure Blob Storage), enterprise applications, spatial data formats (like GIS data), and even social media APIs. This industry-leading connectivity allows you to integrate data from virtually any source, opening the doors to data assets that are difficult to access with other tools. FME also offers a user-friendly drag-and-drop interface for building data integration workflows. You can visually connect data sources, define transformations, and configure outputs with little to no coding experience required.
Along with data integration, FME provides a robust toolkit for data transformation tasks. You can cleanse, filter, format, join data sets, perform spatial operations (for geospatial data), and even leverage custom Python scripts for complex transformations. FME integrates seamlessly with various data platforms and business intelligence tools and also offers flexible deployment options, including on-premise, cloud, and hybrid environments.
