If you want to excel in web 3.0 then start with your data. Not only does it set the foundation to drive automation, but it also positions you among those who lead. Now, for any data-centric organisation, fabrics are an essential element.
In the simplest terms, data fabrics make it easy for businesses to organise their data. It consists of a single environment spanning multiple infrastructures such as clouds, data centres and systems such as Internet of Things devices, local machines, or even mobile devices.
By using a data fabric you can provide a consistent, enhanced user experience. It also allows members to access data from anywhere in the world in real-time.
A survey by Gartner shows that 25% of data management vendors will start providing data fabric solutions by 2024. According to IBM, the organisations that will adopt data fabric solutions by 2023 will also start connecting, optimising and investing in automating data management processes to integrate data delivery and reduce time by 30%.
Here’s a quick run-through of the top data management and automation solutions for startups in the market:
Invest with the K2View data fabric solution if you are looking for an end-to-end, scalable, open solution that gives you complete data control. This service provides all support related to data integration, transformation, enrichments, delivery and orchestration on a single platform.
Their data fabric solution supports a comprehensive range of management solutions such as test data management, data pipelining, privacy management, integration, virtualisation, and orchestration among others.
Most importantly, with a mesh architecture, their fabric adheres to decentralisation thereby preparing itself for the most consumer-centric form of web 3.0.
K2view has a unique approach to data fabric. It uses micro-databases wherein every database(DB) holds data for one business partner entity only. Likewise, the fabric holds millions of such micro-DBS thereby improving data fetching and streaming in real-time.
With an idea to target countless environments with a unified platform, K2View supports informative and faster operations in real-time. Since large corporations and businesses depend upon granular data insights, that further creates micro-DBS for every business partner.
IBM Cloud Park
As a pioneering data engineering company, IBM’s Cloud Pak has a strong foundation in data management products and processes. Over the years, the tech giant’s solutions have delivered accurate, synchronised and in-the-moment data that not only reduces costs but also drives a stronger decision-making process.
Moreover, it supports multi-cloud data integration as well as on-premise landscapes. With assured security and privacy, the data fabric solution has produced significant improvements in business analytics and has ultimately provided acquisition opportunities.
By using the IBM Cloud Pak for data, you can simplify and automate the data collection and analysis processes by accelerating the infusion of Artificial Intelligence (AI) in your business. You can easily connect data anywhere, deploy workloads, and build and manage AI at a scale easily in a hybrid cloud environment. AutoSQL is used in such hybrid-cloud environments to collect and deliver data securely to an organisation.
The IBM Cloud Pak facilitates the automation of distributed queries without the need to move any data. It also allows you to automate discovery and understand business-ready data along with automated universal usage and privacy policies to be used across an ecosystem. It allows you to enable an optimised training model that is accurate and explainable.
The NetApp Data Fabric solution enables data streams to connect irrespective of their location. This helps network admins link information on both private and public clouds, in-situ via different apps.
Netapp’s data fabric supports all types of structured, unstructured and semi-structured data. Besides bringing in operational agility, it solidifies the security protocol for passing information across different cloud and on-premise environments. There are also additional control options that business managers can use for their cloud landscape security and decision-making.
Furthermore, it reduces the chances of financial risks as it is powered by artificial intelligence.
Deploy NetApp with other cloud platforms like AWS, Google Cloud, Azure, and Alibaba Cloud among others to gain scalability and agility. Use this data fabric solution to automate on-premise containerised environments and virtual machines.
SAP’s Data Fabric Solution
This solution combines the powers of SAP HANA and SAP Data Intelligence. SAP Data Intelligence is a data management solution that transforms data into useful information. This information can be tracked down and accessed anytime by using reference data. To deliver innovation at scale, this data fabric works as the orchestration layer of SAP’s Business Technology Platform and aids in transforming distributed data sprawls into useful data insights.
SAP HANA, on the other hand, imbibes the data fabric with built-in features for accessing data. It also gives smart access to users and they can send queries to different external data sources like web services, external databases, etc in a cost-effective way.
Drive new information while performing column actions, harmonising disparate sources and combining/merging several datasets.
A data catalogue can automatically identify content that includes personal information, data type and tag information during the extraction of the metadata.
Identify and integrate connected data using graph engines.
Standardise, coarse and validate different attributes like name, address, duplicate identification, and entity relationships and also perform geocoding.
Data Automation Solutions To Consider In 2022-23
So far we discussed leading data management and automation tools in 2022. These include names from big corporates to recent startups, each of them catering to exclusive sectors and business environments. Apart from the names discussed, others such as Talend, Atlan and Cinchy are also recommended. What’s your take on them? Which data management solution are you using for your startup?
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