40+ Data Integration Tools & Vendors of 2024: Sortable List
Data integration refers to the process of taking data from different sources and making it usable. There are four broad subcategories of data integration that each have their specific tools:
- Data warehousing
- Data migration
- Enterprise Application Integration
- Master Data Management
For a more comprehensive look at the topics above, check out our data integration article.
In this article, you can find out more about these different categories and browse over 40 vendors across them.
Data Warehousing Tools
Data warehousing consists of aggregating structured data from one or more sources for use in Business Intelligence (BI) endeavors. It also provides a view of the overall health and performance of a business because of the wide range of data available for use in analysis. This further enables a historical context through a long-term view of data over time.
When choosing a data warehouse, there are a few functionalities that should be included:
- Support of both relational and multidimensional databases, including built-in readiness for star and snowflake schema database designs and query optimization
- Online analytical processing (OLAP) functionality so that developers and end users can code less complex queries
- Data movement capabilities such as simple load and unload or replication
- Optimization of queries from an operational or transactional database management system (DBMS)
- In-memory functionality to improve performance
- Support for zone maps so that queries can be optimized via pruning data blocks
Data warehouse platforms today come in a range of formats, so choosing the right one can feel intimidating. Some of the most common options are relational database management systems (RDBMS), analytical DBMS, data warehouse as a services (DWaaS), and appliances. Some criteria under which to evaluate these options include:
- Cloud vs on-premises
- Performance
- Reliability
- Usability, integration
- Scalability
- Security
- Supported data types
- Ecosystem
- Backup and recovery
A few major data warehouse platforms include:
Name | Founded | Status | Number of Employees |
---|---|---|---|
Google BigQuery | 2010 | Public | 10,001+ |
Amazon Redshift | 2012 | Public | 10,001+ |
Cloudera | 2008 | Public | 1,001-5,000 |
Panoply | 2005 | Private | 11-50 |
Ab Initio | 1995 | Private | 501-1,000 |
AnalytiX DS | 2006 | Private | 51-200 |
DATAllegro | 2003 | Private | 51-200 |
Teradata | 1979 | Private | 10,001+ |
Informatica | 1993 | Public | 1,001-5,000 |
Data Migration
Data migration refers to the movement of data between locations, formats, or applications. It can be caused by the introduction of a new system or location for the data, such as the change from on-premises to cloud-based options.
Depending on the specific needs of your migration, there are different tools that have different functionalities to meet these needs. Some common types of data migration and their associated tools include:
Database migration: Also known as schema migration, refers to managing incremental and reversible changes to relational database schemas. This allows for fixing mistakes and adapting data to new requirements. This type of migration is generally done when it’s time to upgrade or replace existing hard disks and servers, perform server maintenance, data center relocation, or asset consolidation.
Some tools that specialize in database migration are:
Name | Founded | Status | Number of Employees |
---|---|---|---|
AWS DMS and Schema Conversion | 2015 | Public | 10,001+ |
Attunity Database Migration | 1998 | Public | 201-500 |
Flyway Database Migration by Boxfuse | 2010 | Private | |
FlySpeed by Active Database Software | 2005 | Private | 2-10 |
SAP Hana | 1972 | Public | 10,000+ |
Scribe Software | 1996 | Private | 51-200 |
Database migration also often includes storage migration, where volume data from an older storage system is moved to a new storage system with minimal disruption to ongoing daily processes.
Application migration: As the name suggests, this method focuses on moving an application from one environment to another. This can often mean moving from an on-premises to a cloud location. Such changes can be challenging because of the inherent differences in applications that enabled them to function in their initial location. Subsequently, many brands who support applications in multiple types of environments will have migration guides and tools to help assist in the transition.
Some tools that have been developed specifically to help with application migration include:
Name | Founded | Status | Number of Employees |
---|---|---|---|
1E | 1997 | Private | 201-500 |
CloudSwitch | 2008 | Private | 11-50 |
Altoros | 2001 | Private | 201-500 |
CloudAtlas Inc | 2015 | Private | 11-50 |
Red Hat Application Migration Toolkit | 1993 | Public | 5,001-10,000 |
CloudEndure | 2012 | Private | 11-50 |
Enterprise Application Integration
Enterprise application integration (EAI) is a category of approaches to obtaining interoperability between different business systems. Specifically, it requires approaching problems related to the modular architecture of the organization. The end goal of EAI l is to minimize the number of single point-to-point connectors between services and applications through the use of different middleware.
Some functionalities that any EAI solution should help users to achieve include:
- Activity monitoring and real-time analytics
- Transformation of data
- Process orchestration
- Storage, routing, filtering
Perspectives on EAI
There are two common methodologies for achieving effective EAI: with an enterprise service bus (ESB) or via the ‘hub and spoke’ (broker) system.
Image Source: Neuron ESB
An ESB works by enables different applications to be connected via a ‘bus’ with which each application can communicate. This means that every application only needs to be able to communicate with the bus, not with every other application. Such a system allows for easier scaling and less dependency than point-to-point integration.
Some ESB tools that can assist in the creation of the ideal EAI architecture include:
Name | Founded | Status | Number of Employees |
---|---|---|---|
Red Hat Jboss Fuse | 1993 | Public | 5,001-10,000 |
Mulesoft ESB | 2006 | Public | 1,001-5,000 |
Microsoft BizTalk | 2000 | Public | 10,001+ |
IBM Websphere ESB | 1911 | Public | 10,001+ |
Oracle ESB | 1977 | Public | 10,001+ |
Talend Open Source ESB | 2005 | Public | 1,001-5,000 |
Fiorano | 1995 | Private | 51-200 |
Software AG WebMethods | 1969 | Public | 1,001-5,000 |
WSO2 Carbon | 2005 | Private | 501-1,000 |
Tibco ActiveMatrix Service Bus | 1997 | Private | 1,001-5,000 |
In a hub and spoke arrangement, unlike in the case of ESB where there is a messaging solution, a central ‘hub’ distributes the right information to all of its ‘spokes’. This hub helps to translate and communicate all of the messages across services and operations.
Master Data Management
Master Data Management (MDM) is an integrative method of linking all key data within an organization through a common point of reference. It can also help in enabling connectivity between differing system platforms, applications, and architectures. For an effective MDM strategy, members of the organization must learn how data is to be formatted, described, and accessed.
The capabilities that you require for your MDM platform will heavily influence the criteria and functionality by which tools are evaluated. However, there are some features to look out for in order to meet some of the most common tasks undertaken by MDM:
- Multi-domain MDM support
- Data model flexibility
- Data standardization inclusive of matching, cleansing, merge, and unmerge
- Support for data governance and related workflows
- Matching and survivorship strategies
- On-premises or cloud deployment
- Integrations and data connectivity
- Performance and scalability
And others based on the specific needs of your business.
Some vendors in the MDM space include:
Name | Founded | Status | Number of Employees |
---|---|---|---|
Orchestra Networks EBX | 2000 | Private | 51-200 |
Dell Boomi | 1984 | Public | 10,001+ |
Stibo Systems STEP | 1976 | Private | 501-1,000 |
Profisee MDM | 2007 | Private | 51-200 |
Ataccma MDM | 2007 | Private | 51-200 |
Semarchy Intelligent MDM | 2011 | Private | 11-50 |
EnterWorks Enable | 1996 | Private | 51-200 |
Riversand MDM | 2001 | Private | 201-500 |
Information Builders Data Management Platform | 1975 | Private | 1,001-5,000 |
SAP Master Data Governance | 1972 | Public | 10,001+ |
Challenges with Data Integration
As with any major technical endeavor, there are a few challenges (and solutions) associated with data integration.
Challenge 1: Disjointed initiative with data integration being viewed in large as a technical effort, without need for business involvement.
SOLUTION: Incorporate a champion that understands the data assets of the organization and will lead discussions regarding long-term integration plans. This will help to demonstrate the benefits of the initiative.
Challenge 2: Achieving an accurate analysis of requirements.
SOLUTION: Ask the following questions:
- What is the goal of the data integration?
- What are the deliverables and objectives?
- What are the business rules?
- Where will the data be sourced from?
Challenge 3: Achieving an accurate analysis of source systems
SOLUTION: Ask the following questions:
- What are the extraction options?
- How is the data quality?
- What are the data volumes being processed?
- What is the frequency of extraction?
As with any analysis prior to embarking on a new data integration effort, these are just a few questions to begin your efforts.
For more on data integration
To learn more about data integration, read:
- Business Best Practices for Web Data Integration
- 4 Components of Data Integration
- Integration Platform as a Service (IPaaS)
And if you believe your business would benefit from an integration solution, we have a data-driven list of vendors prepared.
Go through them, and we will help you choose the one that fits your needs the most:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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