The world we live in keeps facing unprecedented and rapid phase changes when it comes to business verticals and innovations. In such an era, data provides a competitive edge for businesses to stay at the forefront in their respective fields. Satisfying end-customer needs within the given time limits has also become a main priority. According to Forrester’s reports, the rate of insight-driven businesses is growing at an average of 30% per year.
Recognizing the potential of data, organizations are trying to extract values from their data in various ways to create new revenue streams and reduce the cost and resources required for operations. With the increased adoption of cloud and emerging technologies like the Internet of Things, data is no longer confined to the boundaries of organizations. The increased amounts and types of data, stored in various locations eventually made the management of data more challenging.
Challenges in maintaining data
As organizations keep using several applications, the data collected becomes unmanageable and inaccessible in the long run. The legacy systems and infrastructures can no longer be capable of handling such massive amounts of data. Shifting the data to the cloud from the existing legacy systems had its own challenges. Additionally, data sharing between different public cloud platforms or on-premise platforms can be difficult.
Companies these days have multiple on-premise as well as cloud platforms to store their data. The data contained can be both structured and unstructured and available in a variety of formats such as files, database applications, SaaS applications, etc. Processing such kinds of data require advanced technologies from ELT processing to real-time streaming. The daunting amounts of data make it very difficult for companies to quickly ingest, integrate, analyze, and share new data resources.
With the amount of increase in data, the complexity of managing data only keeps increasing. It has been found that data professionals end up spending 75% of their time on tasks other than data analysis. The ability of the organizations to manually extract the most out of their data results in being highly time and resource-consuming.
Advantages of data fabrication for data management
Data fabric is an architecture and set of data services that provide capabilities to seamlessly integrate and access data from multiple data sources like on-premise and cloud-native platforms. The data can also be processed, managed and stored within the data fabric. Using data fabric also provides advanced analytics for market forecasting, product development, sale and marketing. Moreover, it is important to note that data fabric is not a one-time solution to fix data integration and management issues. It is rather a permanent and flexible solution to manage data under a single environment. Other important advantages of data fabric are as follows
Unified environment
Data fabric applications provide a unified environment that caters to all the needs of the organization to transform raw data into valuable and healthy data. It also eliminates the need for the integration of multiple applications and tools for the product, contract and support mechanisms. Data fabric helps from discovery to integration of data that are gathered from various sources. Data Fabric also helps with cleansing the data, analyzing the integrity and enables sharing the trusted data with all the stakeholders.
Native code generation
A data fabric solution must be capable of optimizing code natively using preferred programming languages in the data pipeline to be easily integrated into cloud platforms such as Amazon Web Services, Azure, Google Cloud, etc. Also, the solution must have multiple built-in connectors and components that can function as intended for many environments and applications. This will enable the users to seamlessly work with code while developing data pipelines.
On-premise and cloud-native environment
Since a wide range of organizations stores data on both on-premise and cloud environments, a data fabric solution must be developed in such a way that it is natively capable of working in both environments. These solutions must also be able to ingest and integrate data from both on-premise and cloud environments such as Oracle, SAP and AWS, Google, Snowflake, etc. The data fabric solution must also embrace and adapt itself to new emerging technologies such as docker, Kubernetesinserverless computing, etc.
Data quality and governance
Data fabric solutions must integrate data quality into each step of the data management process right from the initial stages. Separate roles have to be set out for cleansing data and trace the source of data to maintain data integrity and compliance.
Best Data Fabric Tools for Enterprises – Tried and Tested
Atlan
Atlan’s data fabric solution focuses primarily on 4 major areas such as data cataloging & data discovery, data quality & profiling, data lineage & governance and data exploration & integration. This product offers a search feature that is as sophisticated as Google and automatic data profiling. Altan’s data fabric solution lets the user manage data usage across the ecosystem using governance and access controls.
K2View
K2View’s data fabric solution organizes isolated data sets from various data sources according to the digital entity. Each business entity has its own hyper-performance micro-database. The digital entity unifies all the known data related to the business entity. This data fabric solution ingests, transforms, orchestrates, secures all the data in the micro DB. This solution can also be integrated with the source system and can be scaled up to support millions of micro databases at the same time. This high-performance architecture can also be integrated into on-premise and cloud-native environments.
Cinchy
Cinchy offers a data collaboration platform that can handle enterprise applications and data integration. The product was originally developed as a secure tool to solve data access challenges and provide real-time governance and effective data delivery. Cinchy’s solution can seamlessly integrate fragmented data sets into its network architecture. The ‘autonomous data’ feature enables the platform to self-describing, self-protecting, self-connecting and self-managing.
Data fabric ultimately enables organizations to extract the most out of the collected data and meet business demands while maintaining a competitive advantage among companies in similar fields. Data fabric also helps in data maintenance and modernize data storage methodologies. Additionally, companies can also leverage the advantages of hybrid cloud environments with the right data fabric tools.
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