Executive Summary

Data Integration

Data integration is a fundamental enabler of a data intelligence strategy. Analysis of individual data sources—customer or product data, for example—can provide insights to improve operational efficiency. However, the combination of data from multiple sources enables enterprises to innovate, improving customer experience and revenue generation, for example, by targeting the most lucrative customers with offers to adopt the latest product.

ISG Research defines data integration as software that enables enterprises to extract data from applications, databases and other sources and combine it for analysis in a data warehouse, including a logical data warehouse or data lakehouse, to generate business insights. Without data integration, business data would be trapped in the applications and systems in which it was generated.

More than two-thirds (69%) of enterprises cite preparing data for analysis as the most time-consuming aspect of the analytics process. Reducing the time and effort spent on data integration and preparation can significantly accelerate time to business insight.

Traditional approaches to data management are rooted in point-to-point batch data processing whereby data is extracted from its source, transformed for a specific purpose and loaded into a target environment for analysis. The transformation could include the normalization, cleansing and aggregation of data. More than two-thirds (69%) of enterprises cite preparing data for analysis as the most time-consuming aspect of the analytics process. Reducing the time and effort spent on data integration and preparation can significantly accelerate time to business insight.

Although point-to-point data integration continues to serve tactical data integration use cases, it is unsuitable for more strategic enterprise-wide data integration initiatives. These require the orchestration of a complex mesh of agile data pipelines that traverse multiple data-processing locations and can evolve in response to changing data sources and business requirements.

Traditional batch extract, transform and load integration products were designed to extract data from a source and transform it in a dedicated staging area before loading it into a target environment (typically a data warehouse or data lake) for analysis. The dedicated ETL staging layers were important to avoid placing an undue transformation processing burden on the target data platform, ensuring that sufficient processing power was available to perform the necessary analytic queries.

Since they are designed for a specific data transformation task, ETL pipelines are often highly efficient. However, they are also rigid, difficult to adapt and ill-suited to continuous and agile processes. As data and business requirements change, ETL pipelines must be rewritten accordingly. The need for greater agility and flexibility to meet the demands of real-time data processing is one reason we have seen increased interest in extract, load and transform data pipelines.

Extract, load and transform pipelines use a more lightweight staging tier, which is required simply to extract data from the source and load it into the target data platform. Rather than a separate transformation stage prior to loading, ELT pipelines make use of pushdown optimization, leveraging the data processing functionality and processing power of the target data platform to transform the data.

Pushing data transformation execution to the target data platform results in a more agile data extraction and loading phase, which is more adaptable to changing data sources. This approach is well aligned with the application of schema-on-read applied in data lake environments, as opposed to the schema-on-write approach in which schema is applied as it is loaded into a data warehouse. Since the data is not transformed before being loaded into the target data platform, data sources can change and evolve without delaying data loading. This potentially enables data analysts to transform data to meet their requirements rather than have dedicated data integration professionals perform the task. As such, many ELT offerings are positioned for use by data analysts and developers rather than IT professionals. This can also reduce delays in deploying business intelligence projects by avoiding the need to wait for data transformation specialists to (re)configure pipelines in response to evolving business intelligence requirements and new data sources.

By 2026, more than three-quarters of enterprises’ information architectures will support ELT patterns to accelerate data processing and maximize the value of large volumes of data. Whereas once there was considerable debate between software providers as to the relative methods of ETL versus ELT, today, many providers offer both approaches and recognize that there are multiple factors that influence whether one approach is more suitable than the other to any individual integration scenario.

Like ETL pipelines, ELT pipelines may also be batch processes. Both can be accelerated by using change data capture techniques. Change data capture is not new but has come into greater focus given the increasing need for real-time data processing. As the name suggests, CDC is the process of capturing data changes. Specifically, in the context of data pipelines, CDC identifies and tracks changes to tables in the source database as data is inserted, updated or deleted. CDC reduces complexity and increases agility by only synchronizing changed data rather than the entire dataset. The data changes can be synchronized

The term zero-ETL, along with some of the marketing around it, implies that users can do away with extraction, transformation and loading of data entirely. That might sound too good to be true, and in many cases it will be.

incrementally or in a continuous stream.

More recently, we have seen the emergence of the term zero-ETL by some providers offering automated replication of data from the source application, with immediate availability for analysis in the target analytic database. The term zero-ETL, along with some of the marketing around it, implies that users can do away with extraction, transformation and loading of data entirely. That might sound too good to be true, and in many cases it will be.

Removing the need for data transformation can only be met if all the data required for an analytics project is generated by a single source. Many analytics projects rely on combining data from multiple applications. If this is the case, then transformation of the data will be required after loading to integrate and prepare it for analysis. Even if all the data is generated by a single application, the theory that data does not need to be transformed relies on the assumption that schema is strictly enforced when the data is generated. If not, enterprises are likely to need declarative transformations to cleanse and normalize the data for longer-term analytics or data governance requirements. As such, zero-ETL could arguably be seen as a form of ELT that automates extraction and loading and has the potential to remove the need for transformation in some use cases.

Our Data Integration Buyers Guide is designed to provide a holistic view of a software provider’s ability to deliver the combination of functionality to provide a complete view of data integration with either a single product or suite of products. As such, the Data Integration Buyers Guide includes the full breadth of data integration functionality. Our assessment also considered whether the functionality in question was available from a software provider in a single offering or as a suite of products or cloud services.

This Data Integration Buyers Guide evaluates products based on whether the data integration platform enables the integration of real-time data in motion in addition to data at rest as well as the use of artificial intelligence (AI) to automate and enhance data integration, and the availability and depth of functionality to enable enterprises to integrate data with business partners and other external entities. To be included in this Buyers Guide, products must include data pipeline development, deployment and management.

This research evaluates the following software providers that offer products that address key elements of data integration as we define it: Actian, Alibaba Cloud, Alteryx, Amazon Web Services (AWS), Boomi, Cloud Software Group, Confluent, Databricks, Denodo, Fivetran, Google Cloud, Hitachi Vantara, Huawei Cloud, IBM, Informatica, Jitterbit, Matillion, Microsoft, Oracle, Precisely, Qlik, Reltio, Rocket Software, Salesforce, SAP, SAS Institute, SnapLogic, Solace, Syniti, Tray.ai and Workato.

Buyers Guide Overview

For over two decades, ISG Research has conducted market research in a spectrum of areas across business applications, tools and technologies. We have designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of the business requirements in any enterprise. Utilization of our research methodology and decades of experience enables our Buyers Guide to be an effective method to assess and select software providers and products. The findings of this research undertaking contribute to our comprehensive approach to rating software providers in a manner that is based on the assessments completed by an enterprise.

ISG Research has designed the Buyers Guide to provide a balanced perspective of software providers and products that is rooted in an understanding of business requirements in any enterprise.

This ISG Research Buyers Guide: Data Integration is the distillation of over a year of market and product research efforts. It is an assessment of how well software providers’ offerings address enterprises’ requirements for data integration software. The index is structured to support a request for information (RFI) that could be used in the request for proposal (RFP) process by incorporating all criteria needed to evaluate, select, utilize and maintain relationships with software providers. An effective product and customer experience with a provider can ensure the best long-term relationship and value achieved from a resource and financial investment.

In this Buyers Guide, ISG Research evaluates the software in seven key categories that are weighted to reflect buyers’ needs based on our expertise and research. Five are product-experience related: Adaptability, Capability, Manageability, Reliability, and Usability. In addition, we consider two customer-experience categories: Validation, and Total Cost of Ownership/Return on Investment (TCO/ROI). To assess functionality, one of the components of Capability, we applied the ISG Research Value Index methodology and blueprint, which links the personas and processes for data integration to an enterprise’s requirements.

The structure of the research reflects our understanding that the effective evaluation of software providers and products involves far more than just examining product features, potential revenue or customers generated from a provider’s marketing and sales efforts. We believe it is important to take a comprehensive, research-based approach, since making the wrong choice of data integration technology can raise the total cost of ownership, lower the return on investment and hamper an enterprise’s ability to reach its full performance potential. In addition, this approach can reduce the project’s development and deployment time and eliminate the risk of relying on a short list of software providers that does not represent a best fit for your enterprise.

ISG Research believes that an objective review of software providers and products is a critical business strategy for the adoption and implementation of data integration software and applications. An enterprise’s review should include a thorough analysis of both what is possible and what is relevant. We urge enterprises to do a thorough job of evaluating data integration systems and tools and offer this Buyers Guide as both the results of our in-depth analysis of these providers and as an evaluation methodology.

How To Use This Buyers Guide

Evaluating Software Providers: The Process

We recommend using the Buyers Guide to assess and evaluate new or existing software providers for your enterprise. The market research can be used as an evaluation framework to establish a formal request for information from providers on products and customer experience and will shorten the cycle time when creating an RFI. The steps listed below provide a process that can facilitate best possible outcomes.

1. Define the business case and goals.
   Define the mission and business case for investment and the expected outcomes from your organizational and technology efforts. 
2. Specify the business needs.
Defining the business requirements helps identify what specific capabilities are required with respect to people, processes, information and technology.
3. Assess the required roles and responsibilities.
Identify the individuals required for success at every level of the organization from executives to front line workers and determine the needs of each. 
4. Outline the project’s critical path.
What needs to be done, in what order and who will do it? This outline should make clear the prior dependencies at each step of the project plan. 
5. Ascertain the technology approach.
Determine the business and technology approach that most closely aligns to your organization’s requirements. 
6. Establish technology vendor evaluation criteria.
Utilize the product experience: Adaptability, Capability, Manageability, Reliability and Usability, and the customer experience in TCO/ROI and Validation. 
7. Evaluate and select the technology properly.
Weight the categories in the technology evaluation criteria to reflect your organization’s priorities to determine the short list of vendors and products.
8. Establish the business initiative team to start the project.
Identify who will lead the project and the members of the team needed to plan and execute it with timelines, priorities and resources. 

The Findings

All of the products we evaluated are feature-rich, but not all the capabilities offered by a software provider are equally valuable to types of workers or support everything needed to manage products on a continuous basis. Moreover, the existence of too many capabilities may be a negative factor for an enterprise if it introduces unnecessary complexity. Nonetheless, you may decide that a larger number of features in the product is a plus, especially if some of them match your enterprise’s established practices or support an initiative that is driving the purchase of new software.

Factors beyond features and functions or software provider assessments may become a deciding factor. For example, an enterprise may face budget constraints such that the TCO evaluation can tip the balance to one provider or another. This is where the Value Index methodology and the appropriate category weighting can be applied to determine the best fit of software providers and products to your specific needs.

Overall Scoring of Software Providers Across Categories

The research finds Informatica atop the list, followed by Microsoft and Oracle. Companies that place in the top three of a category earn the designation of Leader. Oracle has done so in five categories; Informatica in four; Databricks in three; Actian and Microsoft in two; and Denodo, Google Cloud, Qlik, SnapLogic and SAP in one category.

The overall representation of the research below places the rating of the Product Experience and Customer Experience on the x and y axes, respectively, to provide a visual representation and classification of the software providers. Those providers whose Product Experience have a higher weighted performance to the axis in aggregate of the five product categories place farther to the right, while the performance and weighting for the two Customer Experience categories determines placement on the vertical axis. In short, software providers that place closer to the upper-right on this chart performed better than those closer to the lower-left.

The research places software providers into one of four overall categories: Assurance, Exemplary, Merit or Innovative. This representation classifies providers’ overall weighted performance.

Exemplary: The categorization and placement of software providers in Exemplary (upper right) represent those that performed the best in meeting the overall Product and Customer Experience requirements. The providers rated Exemplary are: Actian, Alteryx, AWS, Boomi, Databricks, Google Cloud, IBM, Informatica, Matillion, Microsoft, Oracle, Qlik, Salesforce and SAP.

Innovative: The categorization and placement of software providers in Innovative (lower right) represent those that performed the best in meeting the overall Product Experience requirements but did not achieve the highest levels of requirements in Customer Experience. The providers rated Innovative are: Cloud Software Group and Huawei Cloud.

Assurance: The categorization and placement of software providers in Assurance (upper left) represent those that achieved the highest levels in the overall Customer Experience requirements but did not achieve the highest levels of Product Experience. The providers rated Assurance are: SnapLogic and Solace.

Merit: The categorization of software providers in Merit (lower left) represents those that did not exceed the median of performance in Customer or Product Experience, or surpass the threshold for the other three categories. The providers rated Merit are: Alibaba Cloud, Confluent, Denodo, Fivetran, Hitachi Vantara, Jitterbit, Precisely, Reltio, Rocket Software, SAS Institute, Syniti, Tray.ai and Workato.

We warn that close provider placement proximity should not be taken to imply that the packages evaluated are functionally identical or equally well suited for use by every enterprise or for a specific process. Although there is a high degree of commonality in how enterprises handle data integration, there are many idiosyncrasies and differences in how they do these functions that can make one software provider’s offering a better fit than another’s for a particular enterprise’s needs.

We advise enterprises to assess and evaluate software providers based on organizational requirements and use this research as a supplement to internal evaluation of a provider and products.

Product Experience

The process of researching products to address an enterprise’s needs should be comprehensive. Our Value Index methodology examines Product Experience and how it aligns with an enterprise’s life cycle of onboarding, configuration, operations, usage and maintenance. Too often, software providers are not evaluated for the entirety of the product; instead, they are evaluated on market execution and vision of the future, which are flawed since they do not represent an enterprise’s requirements but how the provider operates. As more software providers orient to a complete product experience, evaluations will be more robust.

The research results in Product Experience are ranked at 80%, or four-fifths, of the overall rating using the specific underlying weighted category performance. Importance was placed on the categories as follows: Usability (5%), Capability (25%), Reliability (15%), Adaptability (25%) and Manageability (10%). This weighting impacted the resulting overall ratings in this research. Informatica, Oracle and Microsoft were designated Product Experience Leaders.

Many enterprises will only evaluate capabilities for workers in IT or administration, but the research identified the criticality of Adaptability (25% weighting) in enabling enterprises to respond to changing business requirements.

Customer Experience

The importance of a customer relationship with a software provider is essential to the actual success of the products and technology. The advancement of the Customer Experience and the entire life cycle an enterprise has with its software provider is critical for ensuring satisfaction in working with that provider. Technology providers that have chief customer officers are more likely to have greater investments in the customer relationship and focus more on their success. These leaders also need to take responsibility for ensuring this commitment is made abundantly clear on the website and in the buying process and customer journey.

The research results in Customer Experience are ranked at 20%, or one-fifth, using the specific underlying weighted category performance as it relates to the framework of commitment and value to the software provider-customer relationship. The two evaluation categories are Validation (10%) and TCO/ROI (10%), which are weighted to represent their importance to the overall research.

The software providers that evaluated the highest overall in the aggregated and weighted Customer Experience categories are Databricks, Microsoft and SAP. These category Leaders best communicate commitment and dedication to customer needs. While not Leaders, Oracle and AWS were also found to meet a broad range of enterprise customer experience requirements.

A few of the software providers we evaluated did not have sufficient information available through their website and presentations. While many have customer case studies to promote success, others lack depth in articulating their commitment to customer experience and an enterprise’s data integration journey. As the commitment to a software provider is a continuous investment, the importance of supporting customer experience in a holistic evaluation should be included and not underestimated.

Appendix: Software Provider Inclusion

For inclusion in the ISG Research Data Integration Buyers Guide for 2024, a software provider must be in good standing financially and ethically; have at least $50 million in annual or projected revenue, verified using independent sources; sell products and provide support on at least two countries and have at least 50 customers. The principal source of the relevant business unit’s revenue must be software-related, and there must have been at least one major software release in the last 12 months.

Data integration is a set of processes and technologies that enable enterprises to combine, transform and process data from multiple internal and external data platforms and applications to maximize the value of analytic and operational use.

To be included in this Buyers Guide requires functionality that addresses the following sections of the capabilities document:

• Configuration
• Data integration pipeline development
• Data integration pipeline deployment
• Data integration pipeline management
• Real-time data integration
• AI

The research is designed to be independent of the specifics of software provider packaging and pricing. To represent the real-world environment in which businesses operate, we include providers that offer suites or packages of products that may include relevant individual modules or applications. If a software provider is actively marketing, selling and developing a product for the general market and it is reflected on the provider’s website that the product is within the scope of the research, that provider is automatically evaluated for inclusion.

All software providers that offer relevant data integration products and meet the inclusion requirements were invited to participate in the evaluation process at no cost to them.

Software providers that meet our inclusion criteria but did not completely participate in our Buyers Guide were assessed solely on publicly available information. As this could have a significant impact on classification and ratings, we recommend additional scrutiny when evaluating those providers.

Products Evaluated

Providers of Promise

We did not include software providers that, as a result of our research and analysis, did not satisfy the criteria for inclusion in this Buyers Guide. These are listed below as “Providers of Promise.”