Enabling Data Fabric Analytics Platform

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What is Data Fabric?

Data Fabric is the Data Management Architecture that enables the Business with the necessary data incontinently to make the opinions. Fabric is an integrated data which was woven from multiple sources and a variety of data that provides perceptivity. In this digital trip, Data grows exponentially on a day-to-day base in a variety of formats. There are numerous sources which captures and share the data. For case, a marketing leader can infer the market value of the new product launch using a data fabric.  

This fabric is darned from internal product master data, external social media, and internal CRM data from the Cloud. It takes several weeks or months in a traditional ETL process to make this result due to the complexity of data structures and the availability of the source connectors. Data Fabric architecture simplifies and enables the ways for the availability of data. It also focuses in participating the business data rapidly to others.

Gartner’s definition of Data Fabrics describes it as a conceptual design that acts as a cohesive layer, integrating data and connecting various processes seamlessly. A data fabric utilizes continues analytics over being, discoverable, and inferenced metadata means to support the design, deployment, and application of integrated and applicable data across all environments, including hybrid and multi-cloud platforms. In a nutshell, Data Fabric Analytics platform helps Businesses to use any data from anywhere and share their data to anyone snappily. 

Conceptual View

about the Data Fabric

Factors of Data Fabric Architecture

The crucial tenets of Data Fabric architecture are:

Robust Data Integration Layer

This segment serves as the foundational pillar of the Data Fabric architecture. The data integration layer covers the data ingestion and transformation frame to handle structured,semi-structured, and unstructured data from different sources like On-Prem/ Cloud Databases, Streaming bias, External Data providers, Cloud Storage area, Enterprise Products, Data Lake, etc. The Data Fabric offers a wide array of native connectors and SDKs designed to establish connections with various sources, facilitating seamless data retrieval. Furthermore, it provides the capability to process semi-structured data derived from JSON, XML, or APIs. It handles the transformations to produce the sanctified Data Mart or Lake which ultimately acts as input to induce Data Fabric. 

Micro Services Layer

It constitutes as structure block of data fabric result. It serves as an insulated reality to change data in real time. Data can be entered or participated by enabling this subcaste. Exposing data to external world must have robust security methodologies and authorization ways. It has the option of cracking datasets during the data transfer. The metadata for this layer should be user-friendly and aid users in comprehending the attributes and object structures.

Intelligent Knowledge Graph Layer

It creates the semantic subcaste with fortified data and metadata making more precious to Business. It creates the collection of interlinked generalities and  realities by connecting the insulated datasets to meet the factual business  requirements. With the metadata combination, Knowledge Graph becomes much more important which help business to search and get perceptivity on the data snappily. 

Data Governance Layer

This governs the Data Fabric platform in defining the norms, the approaches for different ingestion  styles, the security principles, managing the different data stores and authorizing the users to the applicable data.  

Data Consumption Layer

This talks about the delivery of the data to the Business or external brigades. The delivery can be through the business intelligence results, web results or APIs. It provides a different perspective and further perceptivity of the data.  

How to enable Data Analytics Platform as Data Fabric?

Data Fabric is a design conception that comprises of frame with different products and results to produce a fabric. The existing Data Analytics platform could have Data Integration tools and custom build soltion. It could have challenges to consume IoT data in real time. The platform has to be supplemented with new tools or enhance custom-built solution to ingest IoT data in the platform. Data Analytics Platform has to be  recently built or enhanced to handle the below  scripts, 

  • Data Ingestion  styles for different variety of data and multiple sources 
  • Meta data management
  • Data Parser and Transformation process
  • Build Semantic Knowledge Graphs
  • Data Sharing across the teams
  • Data Governance
Benefits of Data Fabric solution
  • Enables to deliver the data insights to Business rapidly
  • Enables Self-Service in data ingestion and data consumptions
  • Enables Data Access to any where and in any format
How Data Finz accelerates to build Data Fabric solution

DataFinz is a “No Code Data Integration Platformdesigned to resolve specialized challenges and handle data integration use cases with simple configurations. This can be a part of the Integration and Micro Services layer in the Data Fabric Analytics Platform. Being No Code, it brings agility and productivity for the development brigades and accelerates to reach Data Fabric state snappily. It has built- in channels that are designed for specific use cases. We’ve to configure these channels with the needed connection, that’s all. 

DataFinz takes care of the Data Integration use case automatically. It has the below features 

  • Numerous Connectors – REST API with different authentication  styles, Salesforce, JDBC, NoSQL, Cloud Storage( S3, Blob, Dropbox, Sharepoint)  
  • Consume any format of data  
  • Parse API or JSON or XML structures to a structured data  
  • Generate Entity Relationship Diagram( ERD) from JSON or XML or API metadata  
  • Generate Payload from structured data  
  • Publish any data( Table, View, Stored Proc, Flat train) as an API
  • Generate Swagger or OpenAPI documentation  
  • Copy data across  Cloud environments between On- Prem and Cloud environment. 
  • Execute SQLs as an ELT approach in target Data Marts or Big Data or EDW  
  • Figure functional Data Store for transactional systems  
  • Modernization of EDW using Lift & Shift approach  
  • Confirmation of Data in the modernization process  
  • Profiling of datasets with visualizations