Using Data Fabrics to Drive Data Management

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Ranked among Gartner Top ten data and analytics trends for 2021, a data web offers a way to mitigate the data management challenges businesses face today. It also allows companies to profitably leverage technologies such as artificial intelligence and the flexibility to scale at will.

What is a Data Fabric?

A data factory refers to a framework that serves as an integrated layer of data and connection process. It includes a unified architecture with technologies and services running on the architecture. A data fabric helps companies oversee their data and aims to maximize the value of data.

To reduce analysis time and costs, organizations use data factories to automate and optimize data management processes. Businesses looking to be data-driven are looking for the ability to provide their data analysts and data scientists with quick and easy access to big data. To do this without violating or compromising governance, security and privacy policies, they use data factories.

Organizations are also using data factories to manage large data sets in various locations more quickly. Data Fabrics help them optimize the entire data lifecycle, fueling applications that need real-time analytics and creating an environment where machine learning and artificial intelligence can operate much more efficiently.

Additionally, companies are increasingly leveraging multiple clouds. Recognizing that their data is spread across multiple clouds, these organizations are adopting data fabrics to manage all of their environments.

Data fabrics are also attractive to businesses because they simplify data management, improving the speed of digital service delivery. The result is not only digital transformation, but also obtaining a competitive advantage. Additionally, businesses can use data matrix scalability as a solution to address the complex challenges of unifying diverse technology environments.

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How Data Fabrics Manage Different Types of Data

Data factories ingest data from all sources to ensure consistency across all environments in an organization. These sources can be on-premises or cloud environments such as Oracle, SAP, Azure, Google Cloud, AWS, and containerization technologies such as Kubernetes, among other examples. All of this data can benefit from a data factory’s rich set of data management capabilities, such as automation and faster development, testing, and deployment. Additionally, users can benefit from monitoring this data through the use of self-service data management.

Beyond data, data factories collect and analyze all forms of metadata to generate contextual information. For example, a data factory can use metadata such as business or technical metadata to identify and connect metadata relationships.

To present metadata in an easy-to-understand way, a data factory creates a chart template after analyzing the metadata for key metrics. The graphic representation is based on unique relationships and offers business relevance. This metadata is then used to train artificial intelligence and machine learning algorithms to improve the automation of data management and to suggest other opportunities for better data management.

Data Fabrics and Efficient Data Management

Overcome data silos and data movement

Data silos are proving to be a data management challenge for today’s businesses. Contextless and isolated data sources fail to provide the complete picture, because big data requires a business to look at all data sources holistically to generate actionable insights. Additionally, siled data can have the same information stored in different databases, threatening data integrity.

The movement of data in a traditional data management architecture copies data from one storage system to another, through intermediary servers. This approach is not immune to errors and takes time. Implementing a data factory solves the problems of data isolation and movement by providing a single environment for data collection and access. It brings together disparate data from many systems to create an information network supporting the applications of a connected enterprise.

Read also : 2021 Best Data Management Platforms and Systems

Quick reaction to changes in data sources and volumes

As businesses generate, consume, and store data at an all-time high, maintaining control over volume needs and ever-increasing data sources becomes a challenge.

However, through the use of a data fabric, companies benefit from a permanent and scalable mechanism that brings all their data together under a single platform. A data structure enables organizations to have greater scalability and adapt to more applications, growing data volumes, and more data sources.

Supports comprehensive end-to-end data management capabilities

Data fabrics must accelerate business use cases relevant to an enterprise, such as risk analysis, customer intelligence. To improve data management, the scope of end-to-end data management for a data factory should at least encompass data ingestion, preparation, data catalog, integration, and security. Data Fabric solutions with business-specific use cases as well as automated data management capabilities deliver even greater value.

Scalable data management infrastructure

To avoid periodically falling back into the data management nightmare of struggling to integrate, process, and transform data to generate trusted insights, organizations need to keep abreast of the latest data management infrastructure. Through the use of data factories, existing connections and deployments can be maintained without disruption, because the introduction of new endpoints, data sources, and new technologies is seamless.

Optimization and acceleration of data pipelines

Queries on databases with billions of records may take a long time to come back. With a data matrix, companies can minimize the effort and time spent on data preparation, resulting in faster insight, which is appreciated in today’s fast-paced business world.

Data pipelines can be configured, tested, and configured for reuse to speed up data preparation. They can also be automated to automatically perform data cleansing, transformations, masking, and other operations to improve the quality of data preparation.

Reduce dependence on existing solutions and infrastructure

Today’s technological change is forcing businesses to switch to newer technology, but businesses can still depend on existing infrastructure. Why? Because such an infrastructure can still contain critical data and companies may not have a suitable plan to move critical data out of the existing system. Thus, companies may be tempted to maintain these infrastructures.

In addition to the data “trapped” in these systems, the maintenance of the existing infrastructure has financial implications. It also presents a security risk as some applications may no longer be updated, introducing points of vulnerability. Through an interconnected data network, data fabrics facilitate data management by enabling organizations to reduce their dependence on existing infrastructure by connecting these systems to modern cloud applications, data lakes and networks. data warehouses.

Robust data integration

Data integration issues are a common pain point in data projects. Using data factories can mitigate this problem by being compatible with many data delivery techniques. For example, replication, data virtualization, streaming and ETL, among others.

Data Fabrics offering robust data integration also improve data management efficiency by supporting all kinds of users, often business and IT users. Additionally, through ecosystem integration, a company is able to deliver better business value and better results through optimization of business processes and greater flexibility.

Read also : Use Database Virtualization for Efficient Cloud Migration

Centralized data security and governance

When data governance and security policies are not centralized, the complexity of not only protecting, but also integrating an enterprise’s data increases exponentially. Data Fabric solutions with centralized security and data governance policies are implemented consistently across on-premises, hybrid, cloud, and multi-cloud environments, enhancing an enterprise’s data governance capabilities.

Using a data factory facilitates approaches to data management, by setting up roles for data cleansing or tracing the origin of data to verify data integrity and compliance.

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