What is A Data Fabric? How It Will Change The Way We Work With Data: a blog around the new term Data Fabric and how it will change how we work with data.
Data Fabric is a component of the architecture of Big Data Systems. Big data facilitates parallel processing, real-time analytics and event-driven processing which often results in faster response time than traditional data processing. To process big data speedily means to process it constantly, using the latest information and to use the same computing infrastructure across its whole life-cycle. The hardware architecture of Data Fabric consists of three major components: hardware accelerator or accelerator node, central processor nodes (or cluster nodes) and inter-node communication.
The accelerator nodes are the most computing-intensive components of Data Fabric, and they are responsible for performing parallel computations. A processor node is an ordinary computer with a CPU and memory. It performs the I/O operations, database queries and other tasks that don’t require high-performance computing. The inter-node communication is used to transfer data between different parts of the system, such as transferring data from GPU to CPU or vice versa.
Why does it matter?
Data Fabric is an important concept to understand. It can be used to describe the relationship between data and information, as well as its use within industries. Let’s examine each of these definitions.
Data is what you have collected or created. If you’ve ever worked with a spreadsheet, you’ve probably come across the term “data.” Data is raw and unprocessed, like information but without context. For example, a list of names and numbers would be considered data. Information, on the other hand, is data that has been organized and analyzed for meaning. For example, knowing that James Smith was born in Florida on August 17th, 1992 is information because it provides context for what might otherwise be meaningless data points (e.g., “James Smith” or “Florida”).
When we think about Data Fabric in terms of how it relates to other industries, we can think about how companies like Amazon or Google make money by selling products based on your browsing history or search queries (which are considered data). If you search for “black dresses” on Google Shopping, then you’re likely going to be shown ads for black dresses in your future browsing sessions (even if you don’t click them).
An Overview of Popular Software to Create a Data Fabric
Data Fabric is an emerging concept that describes the ability to access and manipulate data across different cloud providers.
A data fabric makes it possible to move data between different environments, such as on-premises, public cloud and private cloud. It also makes it possible to move data between different storage technologies, such as object storage and relational databases.
There are several software products available to create a Data Fabric. Here’s an overview of some popular options:
Databricks Unified Analytics Platform (UAP) – Databricks UAP enables organizations to run analytics workloads in any environment and on any platform — from bare metal servers, virtual machines or Kubernetes clusters. It supports all major analytic frameworks including Apache Spark®, TensorFlow®, and PyTorch®.
Google Cloud Dataflow – Google Cloud Dataflow is a fully managed service for stream and batch data processing over Google Cloud Platform (GCP). This service allows you to build continuous applications using stream and batch processing models, using simple APIs or powerful programming models like Apache Kafka and Apache Flink.
Apache Beam – Apache Beam is an open source unified programming model for defining and executing complex data processing pipelines that can run on multiple execution engines including Apache Spark, Apache Flink.
Conclusion
Data Fabric is a concept that has been developed by Microsoft and Bluzelle to be a Blockchain based Data Platform. The advantages of this avenue for data storage are numerous, but largely relate to security and real-time response times concerning the handling of data.