Why Do We Need Tools for Data Integration?
Before we dive into data integration tools, let’s discuss why they exist and how they help with the process of data integration: the act of bringing together disparate data sources across an organization for business intelligence. Data integration represents a core aspect of data management, which can involve cleansing and ingesting, transforming, harmonizing, governing, protecting and storing data, regardless of the type, structure and volume of data.
How you perform data integration is crucial in this modern technological age. New forms of data are continually being developed, and the amount of data grows exponentially. To stay competitive, organizations must learn how to effectively make use of the incoming data and ensure it is free from errors and inconsistencies. With the right data integration approach, a centralized view of rich, cohesive and accurate information across an- organization can be created. This enables deeper insights into business functions and decision-making that support business objectives. Data integration tools help make this happen.
What Is a Data Integration Tool?
A data integration tool is software designed to facilitate the process of integrating data from multiple sources into a single, unified view for analysis.
Data integration tools can perform weighty tasks to support the data integration process, including:
- Data extraction: The ability to extract data from various sources, including databases, cloud services, APIs and file systems.
- Data transformation: The ability to transform data into a standardized format, including data mapping, data quality checks and data enrichment.
- Data loading: The ability to load the transformed data into a target data repository, such as a data warehouse or database.
- Data management: The ability to manage the data integration process, including scheduling and monitoring data integrations, managing dependencies, and providing auditing and reporting capabilities.
Types of Data Integration Tools
There are a variety of data integration tools to choose from, each with unique features and capabilities.
ETL tools are designed to extract data from multiple sources, transform it into a common format, and then load it into a centralized database or data warehouse. These tools are particularly useful for organizations that need to integrate large amounts of structured data from multiple sources.
The advent of big data and the growth of non-relational data sources, such as Hadoop and NoSQL databases, challenged the traditional ETL approach, leading to the development of new data integration techniques, such as ELT. These tools help to push down queries directly to the target instead of moving the data back and forth for processing.
- Data virtualization tools:
These tools allow organizations to access and combine data from multiple sources without copying the data into a centralized repository.
- Master data management (MDM) tools:
MDM tools are designed to help organizations access and manage the consistent and accurate representation of key business entities, such as customers, products and suppliers. These tools are particularly useful for organizations that need to ensure data consistency across multiple systems and applications.
An API allows organizations to integrate data and systems by exposing data and functionality from one system to be used by another system. This is particularly useful for organizations that need to integrate data from cloud-based systems or applications with their on-premises systems.
- Cloud-based data integration solutions:
The growth of cloud computing has created new opportunities for data integration, with cloud-based data integration platforms providing faster and more scalable solutions for integrating data from cloud-based sources.
The right tool can also be the hero in tackling the common data integration and management challenges to get to business value.
Data Integration Tools Overcome Common Integration Challenges
Data integration can present several challenges, including:
- Data quality:
- Data formatting:
- Data volume:
- Data security:
- Integration with legacy systems:
- Integration with cloud-based systems:
- Maintenance and scalability:
- Resource constraints:
- Hand coding:
Integrating data from multiple sources can result in data inconsistencies, errors, and duplicates. It can be difficult to ensure that the data being integrated is accurate, complete, and consistent.
Different systems may store data in different formats, making it difficult to integrate the data into a unified view. This may require significant data transformation efforts to standardize the data.
Integrating large volumes of data can be time-consuming and resource-intensive, particularly when dealing with real-time data streams or high-frequency data updates.
Integrating data from multiple sources can raise security concerns, as sensitive or confidential data may need to be shared across systems. It is important to have strong security measures in place to protect sensitive data during the integration process.
Integrating data from older systems that were not designed for data integration can be a challenge, as these systems may not have the capability to export data in a format that can be easily integrated with modern systems.
Integrating data from multi-cloud environments can present additional challenges, such as network latency, data privacy and security, and vendor lock-in.
Data integration solutions need to be maintainable over time, as the systems and data sources being integrated may change. The solution also needs to be scalable to accommodate changes in data volume or complexity over time.
The integration workloads have increased manyfold, whereas teams have not grown proportionately. Companies must rely on specific expertise which is hard to come by. A lot of the tools still require manual effort, and teams often get demotivated by mundane integration tasks.
With the variety of integration use cases, you need a diverse set of data integration tools to address them. Many companies end up custom coding for those use cases initially to save cost. But these data pipelines get harder to maintain and are vulnerable to bugs in the long run.
With careful planning, the right tools and resources, and a well-designed integration solution, these challenges can be overcome.
Things to Consider When Evaluating Data Integration Tools
Data integration has come a long way since the early days of extracting, transforming and loading data from disparate systems into a centralized data repository. The evolution of data integration has been driven by the changing needs of modern businesses, which require faster, more flexible and more reliable solutions for integrating data from a growing number of sources. Here are some things to consider when evaluating the right data integration tools for your needs:
Low-code to code-friendly data integration tools
These are software applications that offer both low-code and advanced coding options for data integration tasks. This type of tool is ideal for organizations with a mix of technical and non-technical users who need to perform data integration tasks. Users can create data integrations with a drag-and-drop interface, or they can write code in Java, Python, Groovy or other programming languages.
Cloud data integration tools can enable users to create automated workflows, including data integration workflows. Low code to code-friendly data integration tools can offer organizations the best of both worlds: the ease of use and visual interface of low-code tools, and the flexibility and power of traditional coding options. This type of tool can help organizations streamline their data integration processes and reduce the time and cost associated with traditional coding methods.
Standalone data integration tools versus a data integration platform
A data integration platform is a comprehensive solution that provides a range of data integration capabilities. The list includes data extraction, data transformation, data loading and data management. A data integration tool provides a specific set of data integration capabilities. Organizations use a data integration platform instead of standalone integration tools when they require a more comprehensive & flexible solution to manage their data integration processes.
Data integration platforms provide several advantages over standalone data integration tools, including:
- Scalability: Data integration platforms are designed to scale to meet the demands of large and complex data integration requirements. Standalone data integration tools may have limitations in terms of their scalability.
- Complex integration: Data integration platforms provide a unified solution for managing multiple data integrations through a single task flow. This makes it easier for organizations to manage dependencies and control the flow of data between multiple sources and target systems. It provides end-to-end visibility into data lineage.
- Ease of use: Data integration platforms provide a centralized interface for managing and monitoring data integrations. This makes it easier for organizations to manage and maintain their data integration processes.
- Robustness: Data integration platforms provide a robust and reliable solution for managing data integrations, with built-in error handling and auditing capabilities. Standalone data integration tools may not provide the same level of robustness.
- Security: Data integration platforms provide a secure solution for managing data integrations, with built-in security features such as encryption and access controls. Standalone data integration tools may not provide the same level of security.
- Efficiency: Every use case is different. With cloud integration services you can pick an integration tool or capability that is apt for your use case. With the right tool your data pipeline is optimized for performance and cost.
Data integration tools & multi-cloud environments
Data integration in a multi-cloud environment can be challenging due to the varying architecture, security and compliance requirements of different cloud platforms. To address these challenges, data integration platforms and tools need to offer advanced features specifically designed for multi-cloud environments.
Here are a few advanced features that can support data integration in a multi-cloud environment:
- Cloud-agnostic data integration:
A cloud-agnostic data integration solution allows organizations to integrate data from multiple cloud platforms, regardless of the specific cloud platforms being used. This allows organizations to move data freely between cloud platforms, as needed, without being locked into a specific cloud provider.
- Secure data transfer:
When integrating data between cloud platforms, security is a major concern. Advanced data integration tools for multi-cloud environments should provide secure data transfer capabilities, such as encryption, authentication and authorization, to ensure the privacy and security of sensitive data.
- Compliance and governance:
Compliance and governance are critical when integrating data between cloud platforms. Advanced data integration tools for multi-cloud environments should provide features that support regulatory compliance and data governance, such as data masking, data archiving, and data retention.
- Scalable and flexible data integration:
multi-cloud environments can be complex and dynamic, requiring data integration tools to be highly scalable and flexible. Advanced data integration tools for multi-cloud environments should provide scalability and flexibility to support the dynamic nature of multi-cloud environments.
Advanced data integration tools for multi-cloud environments should be able to integrate data between on-premises and cloud-based systems, as well as between multiple cloud platforms. This enables organizations to take full advantage of the benefits of hybrid and multi-cloud environments.
Explore Use Cases & Integration Stories by Industry
Data integration tools are used across a wide range of industries to manage and integrate data from multiple sources. Here are a few specific industry examples:
In the healthcare industry, data integration platforms are used to integrate patient data from multiple sources, including electronic medical records (EMRs), medical imaging systems and laboratory systems. This enables healthcare plans and providers to have a comprehensive view of a patient's health history, which can improve patient care and outcomes. For a real-life example of data integration in healthcare, read Humana’s story.
Retail companies use data integration platforms to integrate data from point-of-sale (POS) systems, customer relationship management (CRM) systems and supply chain systems. This enables retailers to gain insights into customer behavior and preferences, as well as manage their inventory and supply chain more effectively. Check out Sunrun’s data integration story.
- Financial Services:
In the financial services industry, data integration platforms are used to integrate data from multiple sources, including banking systems, investment management systems and accounting systems. This enables financial institutions to have a comprehensive view of their customers' financial information, which can help improve customer service and decision-making. Explore Principal Financial Group’s data integration story.
Manufacturers use data integration platforms to integrate data from various systems, including enterprise resource planning (ERP) systems, quality management systems and supply chain systems. This enables manufacturers to gain real-time visibility into their operations, which can help improve efficiency, reduce costs and enhance product quality. Read about Seagate’s data integration journey.
These are just a few examples of how data integration platforms are leveraged across different industries. The use of data integration platforms can help organizations gain insights into their operations, improve decision-making and enhance customer satisfaction.
Check out these additional resources on data integration tools and solutions:
- What is data integration?
- Reasons to go serverless for your data integration needs
- Faster, more cost-effective cloud data integration
- Data & analytics modernizatin with cloud data integration
- Simpler data integration for analytics
- Don’t code: Stay agile with a versatile cloud-based platform for data integration
Learn more about Informatica’s approach: Cloud Data Integration Hub