Data Management

The Top 7 Data Fabric Solutions

Explore the Top Data Fabric Solutions offering seamless data integration, unified management, and analytics capabilities to facilitate efficient and flexible data management across distributed environments.

Last updated on Mar 06, 2024

Written by Mirren McDade

Technical review by Laura Iannini

The Top 7 Data Fabric Solutions include:

1. Cloudera Data Platform (CDP)
2. Denodo
3. Google Dataplex
4. IBM Cloud Pak for Data
5. Oracle Coherence
6. SAP Datasphere
7. Talend by Qlik

Data fabric is an architecture and set of data services that provide consistent capabilities across a range of endpoints spanning on-premises and multiple cloud environments. It enables businesses to gain actionable insights from large volumes of data distributed across many locations, thereby improving efficiency, reducing risks, and creating new revenue streams. Data fabric uses analytics and artificial intelligence, supporting high levels of automation and access to disparate data across all platforms – without disrupting business operations.

The data fabric market is intensely competitive, with many providers offering innovative solutions targeted at different business needs. These solutions, which are often feature-rich and integrated with open-source technologies, offer capabilities like data management, data integration, real-time analytics, and data security.

This guide will list the top data fabric solutions, examining their capabilities and the feature sets they offer. We will highlight the best solutions based on technical specifications, customer reviews, and our own analysis, in order to give a comprehensive review.

Cloudera Data Platform (CDP)

Cloudera is a U.S-based software company, providing an enterprise data management and analytics platform. Its main offering, the Cloudera Data Platform (CDP), allows businesses to consolidate data sources, in a secure and intelligent manner, across multiple clouds and on-premises.

CDP includes a Data Catalog which helps data practitioners to discover information, and offers data management to ensure compliance with organizational, industrial, and regulatory constraints. The observability feature allows businesses to understand their data’s health and utilization, while the replication feature facilitates the movement of data where required, while maintaining its security and governance. Cloudera’s Data Catalog supports the finding, curating, and auditing of data across all infrastructures, helping to understand, document, and monitor data use while adhering to requisite regulations and standards. It also allows for safe data sharing within organizations. Cloudera’s Replication Manager also ensures safe data movement without compromising security and governance controls by replicating all related metadata, classification tags, security policies, compliance rules, and lineage information.

Cloudera Data Platform (CDP) provides uniform data workflow experience across any private, public, or multi-cloud deployment. It enables the secure and governed portability of data, security rules, and workflows, and also liberates businesses from reliance on proprietary data systems.

Denodo

Denodo is recognized for its performance and capacity to unify access to a wide range of data sources including enterprise, big data, cloud, and unstructured data. This solution offers the most flexible data services provisioning and governance at competitive pricing, challenging traditional data integration costs.

Denodo leads its contemporaries in next-generation data management, handling distributed data across various environments – on-premises, hybrid, and multi-cloud. It employs a logical/semantic-model approach for integrated data management and uses artificial intelligence for task simplification and automation. This approach accelerates business value without moving any data, saving resources, and providing flexibility to adapt to shifting business climates. The Denodo Platform acts as a logical hub for all enterprise data, leading to efficient decisions and operations, whilst also allowing swift responses to business and market changes. The platform incorporates disparate data regardless of location, format, or latency. It organizes related data into a universal semantic model, and delivers data through various tools, catalog, and APIs.

The Denodo Platform democratizes data, enabling anyone to analyze and query distributed data as if it were a single, integrated data source. The platform also enhances security by centralizing data access monitoring and management. It reduces costs by integrating FinOps and reducing data replication.

Google Dataplex

Google developed Dataplex to assist organizations in managing their data effortlessly. Dataplex’s key feature is its intelligent data fabric, which provides a unified management system for data across different platforms – data lakes, warehouses, and marts. This offers a convenient accessibility to trusted data, significant for analytics and AI on a larger scale.

Dataplex not only simplifies data discovery, but it also automates the process. Enriching and classifying metadata that is stored in Google Cloud is no longer a challenging task. The platform is designed to manage technical, operational, and business metadata using a powerful and adaptable Data Catalog. Dataplex provides logical organization of data spanning multiple storage services into business-specific domains. Allowing for easy management, curation, tiering, and archiving of data. Centralized policy management provided by the system also aids in monitoring, and auditing for data authorization and classification, across different data silos.

Dataplex’s built-in data quality and lineage automation offers reliable and trustworthy data. It not only captures data lineage automatically for better data understanding and troubleshooting, but also extends to third-party data sources. This unique feature provides a comprehensible view of where your data originates from and the transformations it undergoes.

IBM Cloud Pak for Data

IBM Cloud Pak for Data is a robust platform designed to simplify and unify how organizations collect data from various sources across multi-cloud environments, making it accessible anytime, anywhere.

Key features of IBM Cloud Pak for Data include data integration, data governance, data observability, and Master Data Management. The data integration function ensures the smooth connection of data from disparate sources and its delivery to teams. Under its data governance feature, IBM facilitates the creation of business-ready data foundations for self-service access to high-quality, safely secured data. The platform also features a Databand continuous data observability system designed to detect and resolve data incidents swiftly, delivering reliable data to the business. The Master Data Management helps in delivering accurate views of master data and their relationships for quicker insights and enhanced data quality.

With the IBM data fabric, clients can effectively leverage trusted data built on a robust data foundation. With integrated data governance and data quality capabilities, users can automate data discovery, enrichment, and protection to provide dependable data for AI workflows. The design of this architecture is compositional, making it adept at accommodating clients at different stages of their data journey.

Oracle Coherence

Oracle is a globally recognized cloud technology company offering computing infrastructure and software that assists companies in innovation, increasing efficiency, and promoting efficacy. Oracle Coherence is their large-scale data grid extensively used by prominent organizations in fields such as finance, telecommunications, logistics, travel, and media.

Oracle Coherence is known for its unlimited scalability, high availability, minimal latency, diverse capabilities, and quality service. It operates as an information fabric, or more simply, a data grid, using a switched fabric concept to manage data in a distributed environment. Oracle Coherence dynamically forms a reliable and resilient switched fabric composed of any number of servers within a grid environment, with technological capacities like dynamic partitioning of data across data grid nodes. The partitioning is achieved automatic, with load-balancing capabilities, ensuring fair distribution of data management responsibilities. Redundancy of data is maintained at a configurable level, thereby removing single points of failure, and is achieved by keeping data synchronously updated in multiple data grid nodes. Each data grid node can handle a substantial number of client connections that can be load-balanced by a hardware load balancer.

Oracle’s Coherence represents a sophisticated solution for companies seeking comprehensive data management in a globally distributed environment.

SAP Datasphere

SAP specializes in creating software to streamline business operations and enhance customer relationship management. One of its solutions is SAP Datasphere, formerly known as SAP Data Warehouse Cloud. This service offers seamless, scalable access to fundamental business data to all data professionals.

SAP Datasphere allows professionals to expedite their processes by utilizing semantic definitions and associations from SAP applications effortlessly. The service harmonizes heterogeneous data into a comprehensive business semantic model, accommodating a broad data landscape, also enabling data accessibility across hybrid and cloud environments. SAP Datasphere creates an open data ecosystem, serving as a foundation for a business data fabric. SAP Datasphere extends support in English, Simplified Chinese, Japanese, and Korean languages.

The pricing of SAP Datasphere is based on an adaptable model of capacity units (CU) per year, and the SAP Datasphere Estimator can help assess the number of required capacity units. The subscription plan includes one productive tenant, with no restriction on the number of tenants that can be created for a subaccount.

Talend by Qlik

Qlik’s Talend is a comprehensive data management solution known as Talend Data Fabric. This platform offers businesses the ability to effectively manage their data, minimizing risk, boosting control, and unlocking its full potential. The platform combines data integration, governance, and integrity into a robust and solitary platform that is backed by a strong partner ecosystem and service infrastructure.

Talend helps businesses in various ways such as improving operational efficiency, reducing risk and regulatory compliance, building customer loyalty, and streamlining the IT infrastructure. It is a flexible, modular solution that supports the entire data lifecycle and caters to diverse user needs across an organization. Being scalable and cloud-independent, it is compatible with diverse deployment architectures. Key features of Qlik’s Talend include data inventory, which simplifies data collection, transformation, mapping, and data preparation which allows users to cleanse and profile incoming data in real-time.

The data quality feature ensures trustworthy and excellent data throughout its lifecycle, bolstered by tools like the Talend Trust Score. Application and API integrations make data sharing easier, while Data Stewardship lets users check dataset reliability at any time, improving overall data trust.

Everything You Need To Know About Data Fabric Solutions (FAQs)

What Are Data Fabric Solutions?

Data fabric solutions are designed to provide a comprehensive and integrated approach to the managing, connecting, and utilization of data across diverse and distributed environments. When we refer to ‘data fabric’ we are talking about the architecture that facilitates end-to-end integration of various data pipelines and cloud environments via the use of intelligence and automated systems. This allows organizations to seamlessly access, share, and analyze data, regardless of its format or location.

Data fabric solutions have become integral in the era of big data and continue to shape how organizations manage, secure, and extract value from their vast and complex databases. They are designed to address the challenges of managing and integrating data. They support a wide variety of applications including real-time analytics, machine learning, and advanced data processing.

Data fabric solutions work to help organizations overcome the challenges associated with data fragmentation, silos, and complexity. They do this by offering a unified, interoperable framework where data across the entire enterprise can be managed and utilized. Data fabric solutions promote data accessibility, agility, and innovation, thereby helping to support organizations in achieving their data-driven initiatives.

Why Use A Data Fabric Solution?

By implementing a data fabric solution, organizations can enjoy a number of benefits that allow them to better manage and derive value from diverse and distributed data environments. Some key benefits these solutions provide include:

Consolidate data from various sources onto a simple platform
Process and analyze data in real-time
Access data from anywhere, at any time
Scale data processing and analysis capabilities where needed

These benefits collectively contribute to the creation of a more efficient, agile, and data driven organization, which supports businesses in harnessing their full potential of their data assets for more strategic decision making and innovation. Data fabric solutions provide organizations with a powerful tool to manage and analyze data. They are becoming increasingly popular as businesses are looking for new ways to bolster their data management and usage, reduce their costs, make quicker and better informed decisions, and scale data operations.

What Features Should You Look For In Data Fabric Solutions?

Some key capabilities to look for in any data fabric solution you consider implementing include:

Comprehensive Overview of Data – It is useful to have a single source of information from across the organization, for full visibility into data. A good data fabric solution should also allow users to build single views with separate dataset. This is useful for when different teams (like engineering teams and data science teams) need to access a model with different information (like user interaction data or product information). This ability to build singular views of separate datasets is highly beneficial for the development cycle.
Data Available and Accessible in Real-Time – Real-time data accessibility and availability are essential for organizations as this facilitates better informed decision making and quicker response to changing business conditions. A data fabric solution should utilize technology like in-memory data processing, caching, and streaming to achieve this. Seamless integration with Change Data Capture (CDC) systems is also important, as this allows organizations to effectively detect changes to a data source.
High Performance and Scalability – High performance and scalability are essential considerations for any data fabric solution. A data fabric solution with high performance is capable of handling large volumes of data, quickly processing that data, and delivering it to users in real time. Scalability is a vital component of this, as the solutions needs to be able to scale in response to an increased load, without resulting in a drop in performance.
Strong Data Governance – Data governance must be robust in order to ensure that the data within the data fabric is consistent and accurate. Features like data lineage tracks data movement from origin to its current locations. Data security, and compliance are all features that work to ensure that the data fabric solution has strong data governance.
Cloud Native Architecture – Cloud native architecture is ideal as it allows the data fabric solutions to run seamlessly in cloud environments, but it can also leverage useful cloud services such as data warehousing, auto-scaling, and serverless computing. Cloud native architecture makes it easier to handle containerized workloads and microservices. This also makes it easier to scale the data fabric solutions as data volume increases and workloads move between different environments to meet changing business needs.
Analytics – Data fabric solutions will often come with analytics capabilities or will integrate seamlessly with analytics tools. This is useful for providing the organizations with meaningful insights from diverse and distributed data sources managed within the data fabric.

Mirren McDade

Journalist & Content Writer

Mirren McDade is a writer and journalist at Expert Insights, spending each day researching, writing, editing and publishing content, covering a variety or topics and solutions, and interviewing industry experts. She is an experienced copywriter with a background in a range of industries, including cloud business technologies, cloud security, information security and cyber security, and has conducted interviews with several industry experts. Mirren holds a First Class Honors degree in English from Edinburgh Napier University.

Laura Iannini

Cybersecurity Analyst

Laura Iannini is an Information Security Engineer. She holds a Bachelor’s degree in Cybersecurity from the University of West Florida. Laura has experience with a variety of cybersecurity platforms and leads technical reviews of leading solutions. She conducts thorough product tests to ensure that Expert Insights’ reviews are definitive and insightful.