There’s a new buzzword in the world of data analytics, and that is – data fabric. The word, which denotes a definitive environment comprising a united ecosystem consisting of all the technologies involved in data analytics, has caught on this year.
What is a data fabric?
According to this article, Data Fabric provides a catalog of consistent data services across private and public clouds. This explanation says enterprises have grappled with the problem of the integration of their entire data sets into a single platform. A data fabric simply describes a comprehensive way to make that goal.
Try and imagine a large piece of hypothetical fabric extended over a theoretical space that joins multiple data points across locations, including the cloud, all kinds of structured and unstructured data, along with methods for accessing that data and analyzing it. Unlike a piece of cloth, a data fabric does not have a fixed shape, is scalable, and has in-built fluidity that accounts for data processing, management, and storage. It can be accessed or shared by internal and external teams for a wide variety of enterprise analytical and operation use cases.
Here’s what Data Fabric is not:
a) A fancy word that describes the same old processes or solutions
b) A one-time resolution of a data problem
We must understand that as data analytics gets cost-effective and more democratic, and as the world marches towards becoming a data-centric one, the entire operation needs to get more cohesive and easy to use. That’s where the scalable data fabric comes in for it not only helps to manage the collection, governance, integration, and sharing of data but also to solve challenges in the way. And, it prevents data from accumulating in silos.
Why Data Fabric?
The goals are simple enough – to provide a single environment for accessing data, and to enable simpler and unified data management. But the overarching aim is to maximize the value of your data.
Let’s first examine the hurdles before an enterprise that is on its way to digital transformation:
- Data in multiple on-premise and off-premise locations
- Data cleansing issues
- Structured and unstructured data
- Data in a variety of formats
- Use of different technologies and multiple data integration tools
- Lack of scalability
All of the above uses about 70-80% of a data professional’s time for doing non-essential tasks. Clearly, it is in the interest of an enterprise to strive towards a single environment for accessing, collecting, and analyzing all data. That’s what a data fabric does – making the enterprise extremely agile.
A data fabric solves several problems like:
·Lack of reliability in data storage and security
·Reliance on underperforming legacy systems
How Does A Data Fabric Help?
Here are the many ways:
- It helps with data inputs and integration abilities between data sources and apps
- Helps with bolstering data quality, data preparation, and data governance capabilities
- It helps connect with any data source via pre-packaged modules and does not require any coding
- It helps handle multiple environments such as cloud, on-premise, and hybrid
- It represents the most viable financial option for the otherwise distributed needs of the data ecosystem
Image by Gerd Altmann from Pixabay