Learn More About Data Fabric Software
What is Data Fabric Software?
Data fabric software is an architecture that connects sources, types, and the location of data and provides end-to-end data integration. It is a unified environment for data services and technologies, helping with data management. Using this platform, organizations can collect enterprise data from disparate sources and provide it to various teams within the company without external help. The data is pulled by APIs from data warehouses, data lakes, databases, and apps. Data fabric software can be enhanced by incorporating artificial intelligence (AI) or machine learning (ML). AI-powered versions of these tools provide personalized recommendations to select datasets which can boost the speed of data science projects.
Data assets are usually generated in silos, while data preparation cycles in the data pipeline are long and take up a lot of time, affecting an organization’s data optimization. Data fabric systems help standardize data management practices across cloud, on-premises, and edge services. These tools usually include various data management technologies like data catalog, governance, virtualization, integration, pipeline, and orchestration. Data fabric software helps users access data using unique workflows while also democratizing data, allowing data citizens to access information across the organization. Using this tool gives companies a holistic view of the business process.
What are the Common Features of Data Fabric Software?
The following are some core features of data fabric software that can help users in various ways:
Unified data environment: Data fabric software creates an architecture that integrates various data management processes like data collaboration, data discovery, data analytics, data visualization, data access, and data control on a single platform. This eliminates the need for multiple data integration products.
Data collaboration and sharing: Data fabric software allows data connectivity into a single unified view, helping data to be accessed by or shared with internal and external applications.
Governance and compliance: Data owners remain in full control of who can visit, edit, download, or query their datasets. Data fabric software enables compliance, preserves integrity, and controls access. These tools also incorporate data quality in each step of data management.
Environment agnostic: Data fabric software allows data management across multiple environments such as on-premises, in the cloud, hybrid, and multi cloud.
Metadata management: Data fabric has data lineage capabilities and currency of data, which means it contains data migration and transformation history. The currency of data defines the state of the data—active or archived.
Data analytics and visualization: These tools use continuous analytics over the existing metadata assets for better business insights.
What are the Benefits of Data Fabric Software?
While there are many data management technologies like master data management, data hubs, and data lakes, data fabric differs from them in various ways.
Enhanced data management: Data fabric software helps retrieve, validate, and enrich data automatically. It helps in enterprise data integration and management. It also helps to provide a single unified view of the data, which allows end users to identify and track data easily and use it efficiently. Automation and integration help in dynamic data orchestration across a distributed ecosystem.
Easy to use: Technical and non-technical users can use data fabric platforms. The architecture makes it possible to create various user interfaces. Business users can create sleek dashboards and use it for various other functions, while data scientists can also use it for deep data exploration.
Compatible with hybrid hosting environments: Data fabrics are environment agnostic. It can help in bi-directional integration with almost all the components to create a fabric-like structure and eliminate the need for coding. Data fabric software supports on-premises, hybrid cloud, and multi-cloud environments.
High scalability: Data fabric systems can manage data at an enterprise scale. It helps to ingest data automatically, which would typically remain unutilized. They are scalable with minimum interference and no investment requirement into expensive hardware or trained staff. The data architecture helps reduce big data complexity and ultimately drives strategic business outcomes.
Fast insights: Automation of data engineering tasks and integration augmentation helps deliver real-time insights faster. Also, continuous data analytics used by data fabric also helps provide value through rapid access. Data fabric software combines data warehouses and data lakes and integrated data from multiple apps, providing services that help organizations monitor and control their data.
Seamless integration: Data fabric software solves the common challenge of big data in organizations. This tool removes data silos through a holistic approach and helps in the seamless integration of data across various functions. Many workloads are moving to the cloud, and it requires data. Data fabric software streamlines this movement from the cloud to the data center or between hybrid clouds.
Who Uses Data Fabric Software?
Data fabric platforms have various stakeholders within an organization.
Data scientists: Data scientists use data fabric software to explore deep and hidden enterprise data to share with other departments for actionable insights.
Business users: The organization's business users, like marketers, can use these tools to make critical business decisions. Smart data fabric solutions are the emerging data architecture helping organizations fast-track their enterprise data initiatives.
Software Related to Data Fabric Software
Following are some tools that can be used with data fabric software:
Machine learning data catalog software: Machine learning data catalogs allow organizations to categorize, access, interpret, and collaborate data across multiple data sources and maintain a high level of governance and access management. Data fabric helps identify, collect, and analyze data sources and metadata.
Data quality software: Data quality software uses a set of technologies to identify, understand, prevent, and correct issues with the data used for decision making. Data quality tools carry out critical functions like data profiling, parsing, standardization, cleansing, built-in workflow, and knowledge bases.
Data governance software: Data governance software is used to enforce data-related policies. These products help establish guidelines, processes, and accountability measures to ensure data quality standards are met. Data governance tools enable organizations to develop a framework to know what data they own and how to use it optimally.
Data preparation software: Data preparation and delivery are important steps in data transformation and integration during the data pipeline lifecycle. Data preparation begins with loading data into a data platform from a data lake. Then data processing begins using extract, transform, load or extract, load, transform (ETL or ELT) tools. The result is prepared data.
Challenges with Data Fabric Software
Although data fabric systems aim at data management, there are some challenges when implementing its services. Below are a few challenges faced by organizations commonly:
Deployment and configuration of services: Services may have to be deployed on multiple servers to optimize performance. This may require configuring services in specific ways for them to work together.
Creating a data model and managing data: A data model determines how data will be structured and organized. Thus it becomes necessary to build a data model that fulfills the organization's needs and can be managed easily. Data fabric unifies data across various data types and points using a semantic knowledge graph. One of the challenges is managing and saving data. Data is available in different formats; hence, the software must be able to handle and manage all kinds of data. Building an architecture that supports different environments is a challenge.
Integration with external systems: Data fabric makes it possible to integrate with multiple systems. For integration with external systems, middleware software is usually created to mediate between these external systems and data fabric tools, managing their communication. The challenge here is that two communicating systems may have different architectures; thus, it is challenging to produce a single middleware.
Data security: Data protection is paramount to any organization. One of the challenges, when data is being transferred from one point to another using data fabric tools, is that the data is vulnerable to attacks. However, this can be avoided by introducing firewalls to ensure safety. It is also essential to go beyond data masking and encryption to ensure total data protection.
How to Buy Data Fabric Software?
Requirements Gathering (RFI/RFP) for Data Fabric Software
Data fabric software solves several data management concerns or challenges in an organization. Before purchasing data fabric software, it is important to understand the existing requirements of the organization. If an organization needs only deduplication and data validation, a data quality tool may help. Many organizations also choose data processing solutions such as ETL tools to process and integrate their data. Depending on where in the organization there is a need for data management, data fabric solutions can be chosen.
Compare Data Fabric Software Products
Create a long list
A list of data fabric software vendors can help understand their offerings. The team in the organization can then evaluate the vendors that would fulfill the organization’s needs.
Create a short list
After evaluating various data fabric solutions, the organization's decision makers can shortlist a few depending on which vendors fit the bill.
Conduct demos
After shortlisting vendors, companies should look for a demo. The demo gives a better understanding of the technical functionality of the software. Nowadays, data fabric tools come with artificial intelligence features. AI-based recommendations help faster data recovery. These could be some important features that the teams need to know. IT professionals, data scientists, as well as data management and business teams can attend the demo to evaluate the product from various perspectives.
Selection of Data Fabric Software
Choose a selection team
A selection team is a mix of technical users and business users like data scientists, data management teams, and marketing teams. Along with that, the team should have a key decision maker.
Negotiation
Once a vendor is selected for their software, it is advisable to understand their pricing and negotiate if necessary. The negotiation part entirely depends on the organization’s budget and the difference between the product pricing and the budget.
Final decision
After both parties arrive at a mutually agreeable term, it is time to decide whether to buy the software.
