If you’re in the market for a data fabric, then you might be interested in a recent report from Forrester, which published a Wave report in June detailing the pros and cons of more than a dozen data fabrics offerings.
Enterprises that are struggling to manage big data for advanced analytics and AI projects are increasingly turning to data fabrics, which help by centralizing access the variety of tools and capabilities needed to work with data in a governed, responsible manner (if not centralizing the data itself).
It’s a fairly new space, yet data fabrics from 15 vendors made the cut in the Forrester Wave: Enterprise Data Fabric Q2, 2022. The report was written by analyst Noel Yuhanna, who was involved in defining the new category, with help from Forrester analysts Aaron Katz, Angela Lozada, and Katie Pierpont.
Forrester ranked the data fabric offerings across 26 criteria, and based on the results, separated the contenders into three groups, including leaders, strong performers, and contenders. Here’s a brief rundown on the offerings and how they stack up.
Data Fabric Leaders
Informatica: The integration giant has moved solidly into the data fabric space, where Forrester considers it a leader, with strengths in core data fabric areas like catalog, discovery, transformation, lineage, processing, events, and transaction processing.
“Informatica has a strong product vision that demonstrates a commitment to expanded data fabric use cases,” Yuhanna wrote. The company lags in open source support and partner ecosystem tooling, however.
Oracle: This vendor is leveraging its strength in databases, data management, security, and replication as it moves into the data fabric space, Yuhanna said.
“Oracle’s superior vision focuses on a unified, intelligent, and automated platform to accelerate use cases, leveraging AI/ML, transactions, knowledge graph, data products, and semantics,” the analyst wrote.
Denodo: The longtime data virtualization player is now one of the leaders in the data fabric space by expanding its capabilities in integration, management, and delivery. Its strengths lie in data connectivity, integration, processing, transactional workload, transformation, access, search, and delivery, Yuhanna wrote.
“Denodo’s data fabric roadmap suggests improved data pipeline automation, extended graph capabilities, extended AI/ML capabilities within the platform, and simplified administration for a geodistributed environment,” Yuhanna wrote.
IBM: Big Blue has been a longtime player in the master data management (MDM) space, and it’s moved aggressively to become a data fabric player, according to Yuhanna.
IBM is strong in areas like data modeling, catalog, governance, pipeline, discovery and classification, event and transaction processing, and deployment options, Yuhanna wrote. “However, it lags in data quality, scale, automation, ecosystem tooling, and supporting an end-to-end integrated data fabric solution.”
SAP: The ERP giant has a comprehensive data fabric solution that can handle complex use cases for large companies, either on-prem or with hybrid deployments. Yuhanna said it’s ideal for existing SAP customers that want to bring non-SAP data into the fold.
SAP’s data fabric does many things well, including semantic data modeling, catalog, governance and security, connectivity, discovery and classification, quality, transformation and lineage, events and transactions, access and delivery, and deployment options, according to Yuhanna. But it lags in administration and end-to-end integration.
Talend: The longtime data and application integration vendor is also a leader in the nascient data fabric space, according to the Forrester analysis. “Talend has demonstrated a strong ability to execute on its vision as well as a consistent track record of install base and outstanding client satisfaction,” Yuhanna wrote.
Strengths for Talend lie in data connectivity, processing and persistence, and deployment options. Concerns exist around its catalog and reference data, administration, automation, and offering an integrated end-to-end solution.
Data Fabric Strong Performers
Teradata: The data warehousing giant has made inroads into the data fabric space, with strengths in areas like semantics, data quality, and catalogs. Its roadmap is focused on filling feature gaps in areas like data integration, catalog, governance, semantics, automation, and support for new connectors, Yuhanna wrote.
“Although Teradata on par with the competition around product enhancements, execution roadmap, innovation roadmap, and performance,” he wrote, “its strongest strategic point is its partner ecosystem, which is extensive and covers the needs of most customer.”
Cloudera: The one-time Hadoop darling is a big supporter of hybrid data platforms, which is what data fabrics are all about these days. The company’s data fabric strengths lay in data pipelines and streaming data, data processing and persistence, and data delivery, Yuhuanna wrote.
TIBCO Software: The TIBCO Connected Intelligence Platform offers “good capabilities,” according to Forrester, which noted high scores in data connectivity, catalog and reference data pipelines, streaming data, integration, data processing, and persistence, and data delivery.
However, the product lags in several areas, including automation, governance, transformation and lineage, and end-to-end connectivity. TIBCO’s roadmap also is underwhelming, per the analyst group.
Qlik: The business intelligence vendor delivers data fabric capabilities in its Active Intelligence Platform, which received high scores in data connectivity and delivery, governance, data access and search, processing, deployment options, and administration.
But Qlik lags in delivering an end-to-end data fabric solution, with gaps in data modeling, discovery and classification, and transformation, Forrester says. Support was also flagged as a potential issue.
Cinchy: The data fabric from Cinchy benefits from being an “end-to-end” solution “with a high degree of automation” that simplifies development and deployment and offers “excellent data delivery capabilities.” One customer applauded Cinchy’s query, auditing, and workflow capabilities.
But it lags in basic capabilities, such as data discovery and classification, and processing, Forrester says. It also has limited scalability, and the partner ecosystem is small, according to Forrester.
Data Fabric Contenders
Cambridge Semantics: This data fabric offering is based on Cambridge Semantics’ AnzoGraph database, and offers features like data integration, graph algorithms, analytics, and ML. Data harmonization, including structured and unstructured data, is a strength for Cambridge Semantics, which Forrester says is strong in data discovery and classification, and data access and search.
However, Cambridge Semantics’ vision is “too knowledge-graph-centric, which is no longer differentiating,” Forrester says. The product lags in data governance, transactional, and data event processing capabilities, the analyst group says.
Hitachi Vantara: An established data fabric with foundations in “total visibility with data modernization services,” Hitachi Vantara has some bright spots, Forrester says. Its strengths lay in data connectivity and data processing and persistence.
However, it lags in several areas, including the data catalog, governance, data quality, administration, data access and search, and end-to-end integration. High complexity was also cited by a customer.
Solix Technologies: This vendor comes from an archival background, and its Common Data Platform (CDP) received good scores from Forrester in areas like data connectivity, discovery, classification, processing, delivery, transformation, lineage, and deployment options.
However, the vendor lags in several areas, including semantic data modeling, data pipeline, transactional capabilities, data quality, end-to-end integration, and scalability. The vendor also faces struggles with “a small market presence, little innovation on its roadmap, and enhancements that largely fill feature gaps,” Forrester says.
HPE: The computer giant’s Ezmeral Data Fabric is critical in helping to ease access to HPE customers’ data stored on-prem, the cloud, and on the edge. It hits the mark for data catalog, discovery, classification, transformation, lineage, data processing and persistence, data access, search, and data delivery, according to Forrester.
However, HPE (which declined to participate in the full evaluation process, Forrester says) lags in several key areas, including semantic data modeling, pipelines, connectivity, quality, handling events and transactions, administration, and offering an end-to-end integrated solution, the analyst group says.