Data Fabric Vendors and Market Guide

Analyst firm Gartner defines data fabric as an important emerging trend that requires a combination of multiple data management technologies – such as data integration, data pipelining, data orchestration, data governance, and data catalog.

Gartner: “A data fabric stitches together integrated data from
many different sources and delivers it to various data consumers.”

The main goal of a data fabric is to deliver integrated and enriched data – at the right time, in the right way, and to the right data consumer – to support both operational and analytical use cases.

Successful data-driven enterprises map intended business outcomes to their data architecture and technology. This is why business and IT stakeholders need to know the reasons for implementing data fabric, and thoroughly understand its value proposition. Key drivers are described below.

Business drivers for data fabric

• Pipelining data cleaner and faster, leads to shorter time to insights, more informed decision making, and ultimately better business outcomes.
• Gaining a real-time, 360-degree view of any business entity – such as a customer, product, store, order, claim – enables new digital experiences, such as hyper-segmentation, proactive service, and sales personalization.
• Data fabric forges a common language between business and IT, through the use of a semantic data layer that hides the complexities of the underlying data sources.
• Data governance ensures that an organization's data quality and data privacy policies are enforced, elevating data trust across the company.
• Decoupling data from the underlying applications enables incremental and rapid modernization of legacy siloed systems.

Data management drivers for data fabric

• Automation frees up data scientists, data engineers, and other IT resources, from tedious grunt work.
• A data fabric infrastructure integrates existing data management tools that are disjointed, and makes others redundant, for greater cost efficiency.

Organizational drivers for data fabric

• Better communication between data creators and consumers fosters improved collaboration between IT and business.
• Self-service functionality empowers data consumers to get the data they need, when they need it for increased business agility and velocity.

Knowledge graphs are important because most relationship insights are lost when using traditional data modeling and integration tools.

When researching data fabric vendors, look for a data fabric architecture that enables the following key capabilities:

• Data catalog, to inventory and classify data assets, and visually map information supply chains
• Data engineering, to create scalable data pipelines for operational and analytical workloads
• Data governance, to ensure quality, enforce privacy, and make data safely accessible at scale
• Data orchestration, to package fragmented data from multiple source systems and make it accessible via secured web services
• Data preparation, to cleanse, transform, and enrich enterprise data for analytics and AI
• Data integration and delivery, to integrate data from any source and deliver it to any target, in bulk (batch) or real time
• Data persistence layer, to dynamically persist data in different relational and nonrelational models

The table below summarizes the strengths and concerns of the 5 top data fabric vendors.

	Strengths	Concerns
K2view	Single, integrated platform, combining all data fabric capabilities Data is uniquely organized by business entity, for real-time data pipelining, and “x360” workloads at scale Support for massive data workloads requiring real-time data integration and movement Full support for both analytical and operational workloads Quick deployment (typically in a few weeks) and easy adaption, supporting agile, and CI/CD Low total cost of ownership (TCO)	Focus on large enterprises, with relatively few mid-sized customers High concentration of deployments in telco, healthcare, and financial services markets Few system integration partners outside North America and Europe
Denodo	Focus and strength in data virtualization Catalog serves as a single-entry point for enforcing security and governance Broad go-to-market partnerships Optimization for analytics use cases	Complexity in managing and operating the data fabric Not applicable for high-volume operational workloads Processes and effort required to ensure distributed query performance on the platform
Talend	Focus and strength in data integration across multi-cloud and hybrid ecosystems Wide-ranging capabilities for data engineering Broad set of connectors for a large variety of data sources	Not applicable for high-volume operational workloads; best suited for analytics use cases Support required for complex data orchestration and data pipeline operationalization Limited data virtualization capabilities
Informatica	Use of AI and ML for augmented data integration and data quality support Strengths in data integration for optimized analytics, data migration, and MDM Ability to scale in support of complex data integration scenarios	Complex and costly deployment and adaption Data virtualization support required Limited real-time data pipelining capabilities, making it less suitable for operational workloads, where real-time data integration is required Multiple disjointed tools acquired over time and not yet integrated into a single platform
IBM Cloud Pak for Data	Strong product scalability and performance Diverse data integration delivery styles and architectures Data virtualization and metadata management Improved integration capabilities repackaged as Cloud Pak for Data	Data fabric is comprised of multiple standalone products, creating uncertainty around the platform’s structure, cost, and deployment Complex architecture, resulting in difficult and costly upgrades Self-service, and cloud-based data integration capabilities required

K2view stands out as the only platform capable of responding to data queries in real time, at massive scale, in support of both analytical and operational workloads.

Unique technology

At the core of K2view’s patented technology is the Micro-Database™, that unifies everything the enterprise knows about a particular business entity (such as a customer, product, location, credit card, etc.), including business transactions, cross-channel interactions, network data, and master data.

The Micro-Database integrates all data elements, regardless of source systems, technologies, and formats.

Everything a company knows about a customer is encapsulated in a Micro-Database.

How it works

At runtime, business entity (say, customer) data is integrated from underlying data sources, enriched, transformed, compressed (by as much as 90%), and stored in a Micro-Database – one  per customer.

Each Micro-Database is secured with its own encryption key, meaning that data is protected at the customer record level. Data sync rules manage the frequency of data updates between the source systems and the Micro-Databases, and the architecture is distributed to support workloads of massive scale – in the cloud, on-premises, or in a hybrid architecture.

Key features

• Modular, open, and scalable architecture
  Data integration, transformation, enrichment, preparation, and delivery – integrated in a single, extensible platform
• Split second, end-to-end, response times
  Enterprise data fabric, built to support real-time operations, with bi-directional data movement between sources and targets
• Macro big data management in Micro-Databases
  Data for each business entity is integrated into its own Micro-Database, for a single source of truth, and a unified view of that entity