The 7 Best Data Integration Tools for 2024

Data is generated and collected at a very fast speed these days. It is a company's most valuable asset and therefore rightfully termed as the oil of the 21st century. Organizational-level data automation can revolutionize a company's operational proficiency.

This is why it is important for companies today to implement and maintain efficient data integration processing pipelines. These pipelines help them to effectively collect, manage, and utilize data collected from different sources. Leading processing tools and data integration techniques help you significantly reduce data quality costs . This is why many technology consultants and engineers recommend using these tools for enterprise infrastructures.

What is data integration?

Data integration is the process by which companies combine data from multiple sources into one place. It allows them to create a unified view of existing information and perform analytical and statistical operations on a central data set.

This process has several steps. The first step is called requirements gathering, in which analysts gather business requirements and identify gaps in existing data.

In the next step, the data is profiled, i.e. information about existing datasets is collected and high-level overviews are generated. Analysts also need to perform metadata quality assessments and add metadata fields if necessary.

At this stage, analysts also need to check whether the data is available in the legacy system, as legacy systems often have data quality issues. This makes data discovery and mapping difficult. Once the data is profiled, the analysis needs to verify the availability of staging areas and ensure that there is sufficient space available for storage and processing.

After the data profile is created, you need to prepare it. This involves the process of collecting, reformatting, transforming and loading data according to the business use case. This step is also called ETL (Extract Transform Load).

The last step in the process is called data consolidation, in which engineers combine data from different data sources through pipelines and store it in a data warehouse.

Once the data is integrated, engineers feed this consolidated data into an analysis engine that filters it and adds predefined transformations to it. This step essentially denormalized the data for use. You can also integrate this data into a master relational database.

Benefits of Data Integration

Companies often generate large volumes of data in various formats. Furthermore, the more distributed the data, the more complicated the data ingestion process becomes.

The data integration process helps combat these issues by using pipelines to aggregate and transform data from different sources into a master data set. As a result, it significantly reduces the time required to prepare and analyze data and allows organizations to achieve data continuity.

Data integration increases business connectivity and improves the accessibility of existing data. It also improves collaboration and reduces data complexity. Furthermore, it allows companies to use this data to make business decisions.

Data integration tools are a great asset to a company. They improve data visibility and reduce data complexity. However, it is important for users to have some considerations before choosing an integration tool for their business.

What should you consider before choosing a data integration tool?

• Connectors

You must ensure that your data integration solution has connectors available for all necessary tools. This is important because you need to make sure your integration tool supports the data sources you need to connect.

• Easy to use

You need to check whether the selected tool is a no-code/low-code solution. Can non-engineers use the tool without facing problems? Or is it too complex and requires technical knowledge to use? How quickly can you train someone to use it?

• Supplier support

Vendor support is required to resolve integration issues when adding a new tool to existing infrastructure. Therefore, it is important to check whether the software vendor provides useful resources such as videos, documentation, and module information along with the tool. Some providers also offer on-site support.

• Real-time data integration

You need to be sure that the solution you select supports real-time data collection and integration.

• Business requirements

You need to check whether the selected solution can scale in the future to handle your future business requirements/demands.

Top 7 Onboarding Tools for 2023

#1 SnapLogic

SnapLogic is an enterprise integration platform-as-a-service (iPaaS) solution that allows you to automate data integration through a simple browser-based user interface. You can also use its pre-built connectors, called snaps, to integrate various other tools such as data warehouses, databases, APIs, and IoT devices.

SnapLogic allows users to perform real-time batch integration and streaming. Its intelligent integration platform uses a no-code/low-code approach to generate business insights and drive decisions.

Characteristics

1. This low-code solution allows you to connect internal and cloud-based applications. Through this tool, you can also connect NoSQL databases, big data tools (Hadoop), and mainframe applications.
2. This tool allows users to make real-time pipeline changes through pipeline templates and 600+ pre-built connectors.
3. You can also use this tool to create visual elements such as charts.
4. Engineers can use this tool for EDI modernization.
5. Issue tracking, pipeline management, and event logging are easy through SnapLogic's drag-and-drop interface.

#2 IT

Informatica is an incredible data integration tool that accelerates data transfer between systems and reduces data complexity. It is a metadata-driven tool with built-in intelligence to improve performance and advanced data management.

It uses job designs to perform actions such as data masking, data visualization, and data cleaning. You can also use its big data management components to handle large and rapidly changing data sets.

Characteristics

1. This data integration solution can be easily integrated with other business tools such as Salesforce, Tableau, Redshift, SAP, and Workday. It allows point-to-point data integration between two devices.
2. It has end-to-end system (email) reporting and notification options.
3. This cloud-based service is easy to set up and use.
4. Engineers can also use this tool for ETL.

#3 Talend

This platform allows users to check data quality and perform data preparation and integration. You can also use it to integrate big data tools like Spark and Hadoop.

This open source tool can be integrated into existing infrastructures as an on-premises or cloud-based solution. It has hundreds of connectors that allow users to build scalable solutions.

This tool has an interactive GUI tool that allows users to perform complex transformations through a codeless approach. Furthermore, it is also very useful for data analysis and machine learning projects.

Characteristics

1. It is an amazing data ingestion tool with intelligent data migration attributes for optimal performance.
2. It has a large community for discussing questions and queries. You can also request dedicated customer support (the vendor offers global and multilingual support to customers).
3. This tool can also be used for ETL or ELT processes.
4. You can integrate it with popular platforms like AWS, Google Cloud, and Azure. It can run on different operating systems such as Windows, Linux and macOS.

#4 Bummi

This cloud-based data integration tool comes with many pre-configured components that allow users to integrate software-as-a-service (SaaS) applications and data sources into their tools. The single-user, multi-tenant architecture of this iPaaS tool allows users to quickly scale APIs and automate workflows.

Its low-code design allows engineers to create custom data connectors. These connectors can also be time-triggered for real-time data integration.

Characteristics

1. It has built-in support for modern EDI standards such as XML, HL7 and X12. It also has a very useful visual interface that allows users to easily connect to external data systems for B2B integration.
2. It has options for live updates and notifications. The user can be notified via email or SMS.
3. This lightweight tool can be quickly integrated with other business solutions such as Shopify, Salesforce, SAP, and Expedia.
4. It can also generate product/event reviews.

#5 Tremor

Jitterbit is a data integration tool that allows users to create data source connections through pre-built integration templates. It is an amazing workflow designer through which engineers can transfer large amounts of data with ease.

It uses high-performance parallel processing algorithms to support data transfers. It also has automatic mapping options to map similar fields and formulas to EDI.

Characteristics

1. It's a cost-effective solution with an easy-to-use GUI that allows engineers to integrate enterprise data sources with drag and drop.
2. Offers batch processing for bulk data migration.
3. This tool allows users to work in virtual test environments. You can also schedule cron jobs for real-time data updates.
4. It has amazing debugging and logging functions for error handling. You can also add custom alerts and logs to your environments.
5. Jitterbit can be integrated with advanced software such as speech recognition tools and language translation solutions.

#6 Pentaho

Pentaho is an end-to-end data integration platform. Engineers can use this platform for a variety of tasks such as ETL, data engineering, data replication, and data warehousing.

Using this tool, engineers can use filters and design options to perform quick data transformations. Furthermore, this low-code tool has an easy-to-use visual interface that allows you to manage complex data changes.

Characteristics

1. It uses parallel data processing and multithreading techniques to remove common pipeline bottlenecks and transfer data quickly.
2. It offers many customs options for data transformation and integration.
3. It has comprehensive data review and visualization capabilities for real-time reporting. You can also use it to generate data insights and drive business intelligence.
4. It has a simple learning curve and is easy to understand.

#7 Oracle Data Integrator

Oracle Data Integrator is a very popular SaaS data interaction tool for data ingestion and integration. It can be easily integrated with other oracle package applications and also supports big data integration.

It can perform different types of data integration such as data-based, event-based, and service-based. It supports many databases such as Teradata, Oracle, and IBM DB2. It uses decoupled mappings and design principles for big data transfer.

Characteristics

1. It uses an ETL architecture that allows users to integrate different data sources such as XML, XTD, and CSV.
2. Autonomous data storage and transaction processing options allow users to easily manage and process data.
3. It can be integrated with different Oracle middleware tools such as Goldengate, Exadata and Fusion.
4. It has an easy-to-use GUI to manage project development, security, metadata design, and scheduling.

Conclusion

Data integration tools are a great asset to an organization's data strategy, as they help to unify data and improve its visibility. They synchronize data and allow the company to create reports in real time, which is extremely useful for data-driven decision making.

This is why integrating these tools into a business infrastructure is considered a good practice for most companies. But before you integrate a solution, you need to make sure your tool has the necessary connectors and is easy to use.