Finding software can be overwhelming. Software Advice has helped dozens of companies choose the right data integration tools to collect, prepare and store data for analysis.

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Domo

Domo is the Business Cloud®, empowering organizations of all sizes with BI leverage at cloud scale in record time. With Domo, BI-critical processes that took weeks, months or more can now be done on the fly, in minutes or seconds,... Read more

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Recent recommendations: 13 recommendations

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Zapier

Zapier is a cloud-based integration platform that helps businesses of all sizes automate daily workflows involving various applications. The solution enables users to transition data between web applications automatically while i... Read more

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Samepage

Samepage is a cloud-based collaboration software solution that helps teams eliminate project bottlenecks and communicate seamlessly with team members. Samepage provides a suite of project management tools, communication featu... Read more

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Anypoint Platform

Anypoint Platform is an application development solution for organizations that are looking to integrate applications, data sources and devices using application programming interfaces (API). The solution is suitable for industrie... Read more

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UiPath

UiPath is an on-premise data entry and robotic process automation solution designed for businesses of all sizes. The solution allows users to create, deploy and administer automation in business processes. UiPath features UiP... Read more

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Process Street

Process Street is a cloud-based business process management (BPM) solution that enables organizations to create checklists and process documents for recurring projects. The solution is used in business functions such as client set... Read more

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Boomi

Boomi is a unified cloud-based integration solution designed to centralize data, applications, and processes into one unified ecosystem, effectively breaking down data silos. Using Boomi's integration capabilities, businesses are ... Read more

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DiCentral

DiCentral is a cloud-based solution designed to help businesses manage the entire supply chain lifecycle through electronic data interchange, accounting, warehouse management, dropshipping, and more. It is suitable for businesses ... Read more

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Operations Hub

PieSync is a cloud-based data sync platform that automatically syncs data between applications including email, CRM, customer care platform and more. PieSync’s features include 360-degree customer view, 2-way sync, applicati... Read more

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IFTTT

IFTTT is a data integration software, which helps fintech, home automation, and energy companies connect applications and devices with third-party integrations. The software allows financial institutions to connect apps or website... Read more

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ImportOmatic

ImportOMatic is a data integration solution for Blackbaud's Raiser's Edge and Raiser's Edge NXT fundraising solutions. It is deployed as a plug-in for the Raiser’s Edge platform. ImportOMatic allows users to filter and transf... Read more

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Cirrus Insight

Cirrus Insight is a customer relationship management application. It helps users track and sync emails, create and edit Salesforce records, log calls and create email campaigns. Cirrus Insight’s key features include sales pro... Read more

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APPSeCONNECT

APPSeCONNECT is an enterprise integration platform that allows businesses to connect their on-premise and cloud applications into a single platform. It offers a range of connectors for e-commerce, cloud storage, customer relations... Read more

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Matillion

Matillion is an extract, transform, load (ETL) solution for cloud data warehouses such as Amazon Redshift, Google BigQuery and Snowflake. The centralized platform enables users to automatically schedule orchestration jobs and proj... Read more

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SyncSpider

SyncSpider is the app-2-app integration tool built for eCommerce. Grow your revenue with multichannel sales automation. Manage stock from a single place. Connect your eCommerce tools to work in sync. Create a flawless buying exper... Read more

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Cleo Integration Cloud

Cleo Integration Cloud is a platform purpose-built to design, build, operate and optimize critical supply-chain integration processes. With greater choice, companies can blend self-service and Cleo Services for integration agility... Read more

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Winautomation

WinAutomation by Softmotive is a data entry automation solution designed for businesses of all sizes. It enables users to automate both desktop and web-based tasks. The product is an on-premise solution and runs on Windows operati... Read more

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OneSaas

OneSaas is a cloud-based integration platform for small to large sized enterprises. Its key features include metadata management, workflow automation and information sharing. Firms can use OneSaas to eliminate manual transfer... Read more

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Cloud Workflow - iPaaS

Cloud Workflow is a web-based integration platform designed to help businesses connect various applications, accounting systems, big data, popular databases and more. Key features include integration management, multiple data sour... Read more

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Skyvia

Skyvia is a cloud-based data integration, backup and management platform for businesses of all sizes. Key features include direct data integration between apps, scheduling settings for backup automation, a wizard to simplify local... Read more

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Buyers guide


Last Updated: June 10, 2021

The data universe is expanding. It's no secret that the data businesses create, capture and analyze has been growing in volume and diversity, with no signs of slowing down.

The ubiquity of data in today's business environment dictates that even small businesses should be thinking of how they can use data for a competitive advantage. Increasingly, tools are becoming available to help with the collection and analysis of this data.

In this guide, we'll cover:

What Are Data Integration Tools?

Data integration is simply the process by which data is collected from multiple sources, normalized and prepared for analysis. Data integration software are tools that collect and transform the data for common storage, typically in a data warehouse, from which it can be extracted for analysis, as depicted in the diagram below:

Data integration and analysis process (diagram)

Traditionally, this is done through the extract, transform, load (ETL) process by a database administrator (DBA), who sets up the criteria the data should adhere to prior to storage. The criteria the DBA sets up, or defines for the data, are based on the most critical insights a business seeks to derive from the data.

The ETL process is an involved one in which data is collected, or "extracted" from the original sources, which often exist in widely varying formats. These include not only .CSV and XML files, but also online sources such as social media.

Once the data is extracted from the original source, it is "transformed" into a format that fits the parameters the DBA has defined for the data warehouse, or wherever the data will reside.

Conversely, the ELT (extract, load, transform) process manages the process in a different order—one in which the data is loaded into the database, where it's transformed (as opposed to having predetermined rules set up within the database, such as a data cube).

Data integration environment in TIBCO Jaspersoft (screenshot)

Data integration environment in TIBCO Jaspersoft

Increasingly for large enterprises, data lakes are becoming a popular data storage strategy for those dealing with big data.

The data is then integrated with other transformed data for like comparisons and analysis.

Common Features of Data Integration Tools

As a baseline, data integration tools should offer the following:

ETL (extract, transform, load) Collects data from outside sources, transforms it and then loads it into the target system (a database or warehouse). Because primary data is often organized using different schemas or formats, analysts can use ETL tools to normalize it for useful analysis.
ELT (extract, load, transform) Collects data from outside sources, loads it into the database or warehouse and then transforms it to conform to requests for analysis. This feature allows the data to be manipulated/integrated within the warehouse itself, rather than prior to migration.
Data capture/connection Allows software to "connect" to multiple—and sometimes disparate—data sources (including relational databases, XML, .CSV, data lakes, Hadoop, SQL etc.) for the purposes of data extraction.
Data transformation Normalizes data across disparate sources by standardizing data, converting values and correcting numeric values to conform to minimum and maximum values.
Data quality management Helps organizations maintain clean, standardized and error-free data. Standardization is especially important for BI implementations that integrate data from diverse sources, as this ensures that later analyses are correct.

Some data integration software offers additional features, including more self service options (such as drag-and-drop development for citizen data analysts).

What Type of Buyer Are You?

Typically, data integration resides in the realm of the DBA, who sits in the IT department.

Small businesses. These are businesses with little to no IT department. While traditionally, they have less need to manage vast amounts of data in a data warehouse, this trend is shifting, given the explosion of data in recent years. More and more tools designed to help "citizen" data administrators extract, integrate and manage data without the need for extensive programming knowledge are becoming available today.

Midsize businesses. These buyers are still likely to benefit from data integration tools that offer some level of self-service functionality, so that a robust IT department isn't required to architect complex data storage solutions. Real-time data demands and ad hoc granular data analysis are becoming the norm.

Enterprise businesses. These buyers will have a robust IT department capable of handling the traditional ETL process, which involves time and effort. Ironically, these larger enterprises may have more of a demand for real-time delivery of multistructured data as opposed to the “batch" delivery methods ETL is associated with. Increasingly, tools are becoming more and more sophisticated, with broader functionality sets from delivery to governance, to meet these demands.

Benefits of Data Integration Tools

Data Integration software provides two clear benefits to users:

  • Single source of truth. The principal benefit of data integration software is arriving at a single source of truth for businesses, especially those that deal in a variety of data sets from multiple, and often incongruous, sources. The "truth," or insight sought after, is typically a key performance indicator (KPI) defined by the business. Data integration aligns the data to best reveal this truth.
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  • Apples to apples. The additional benefit is that organizations both large and small can not only define what they need from the data to make the most important business decisions, but confidence in those insights. Integration of data into a consistent, predetermined view ensures to some degree that like-comparisons are being made. In short, integration allows for the best "apples-to-apples" comparison disparate data sources can provide.

Market Trends to Understand

Data integration as a field is undergoing some change. According to Gartner, data integration and quality tools as a market grew 2.5 percent in 2016 to $4.4 billion, though more traditional data integration tools, which serve merely as "connectors" for batch movement of data, had slower growth (report available to Gartner clients).

This is due in large part to the increasing "mass proliferation" of data according to Gartner, which has put greater demand on data integration tools to expand their offerings to serve various data delivery speeds, deployments and types.

Essentially, slow, plodding, structured data delivery is on the outs. More and more, enterprises are seeing the need for data integration flexibility, including virtual and real-time data delivery, as well as the ability to deal with hybrid data sources (cloud and on-premise). Also, businesses are looking more and more for data integration tools that can handle "multistructured" data, or data that comes in a diverse array of structures.