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A Guide to Data Federation

  • Mallika Rangaiah
  • Aug 18, 2022
A Guide to Data Federation title banner

It can be difficult to keep track of where resources are, how to access them, and how to put data assets to work to run a more efficient and reliable enterprise in our increasingly hyper-connected, data-dependent world. Traditional approaches to IT operations analytics are becoming obsolete as data sources and types become more mobile, transient, diverse, and distributed. 

 

Also Read | Guide to Data Profiling


 

What is a Data Federation

 

Data federation is a method of collecting, storing, and utilizing data that uses virtualization rather than physical storage in a dedicated database. Data federation is a method of keeping up with data while still turning it into useful intelligence.

 

With today's capabilities, such as real-time monitoring and comprehensive discovery, data federation brings information from disparate systems from across the enterprise together better than legacy systems and techniques could. 

 

Whether the data required for a specific query or to inform contextual analysis of system performance is located in the organization's data center, at the network edge, or somewhere in the cloud, data federation allows you to see and use it as if it were in the organization's data center.

 

A Data federation is a software process that allows multiple databases to work together as if they were one. This virtual database collects data from various sources and converts it to a common model. This provides a centralized data source for front-end applications.

 

The data virtualization framework includes a data federation. With data federation, this data virtualization grew, but it also sprouted new features, applications, and functions. 

 

As a result, data virtualization serves a wide range of purposes other than data warehouse compilation. Metadata repositories, data abstraction, read and write access to source data systems, and advanced security are all part of it.

 

The strategy of federating information first and then querying the federated information store separates data federation concerns from application operational concerns. Queries written in the application do not need to know where a specific triple came from. 

 

This enables a single query to operate seamlessly across multiple data sources without the need for extensive planning on the part of the query author. This also means that changes to the application to federate additional data sources will have no effect on the application's queries. While data federation is a component of data virtualization, the two are similar but not the same.

 

Also Read | What is Data Pre-Processing?

 

 

What is Data Visualization?

 

Although data federation and data virtualization are sometimes used interchangeably, they are not the same thing. Data virtualization, which replaces the old extract, transform, and load method of collecting and monitoring system data, is what enables data federation. 

 

Visualization enables data to be federated and represented where it is needed for application, or to run the analytics required to make decisions or perform whatever function is required. Federated data is data that has been virtualized.

 

Data visualization is the use of common graphics to represent data, such as charts, plots, infographics, and even animations. These information visualizations communicate complex data relationships and data-driven insights in an easy-to-understand manner.

 

Data visualization can be used for a variety of purposes, and it is important to note that it is not limited to data teams. It is also used by management to communicate organizational structure and hierarchy, and data analysts and data scientists use it to discover and explain patterns and trends. According to Harvard Business Review (link outside IBM), data visualization can be used for four different purposes: idea generation, idea illustration, visual discovery, and everyday dataviz. 

 

Every day, your company generates more information about sales revenue, marketing performance, customer interactions, inventory levels, production metrics, staffing levels, costs, and other key performance indicators (KPIs). However, because there is so much data to sort through, it can be difficult for people to see the story it tells.

 

Data visualization transforms granular data into easily understandable, visually compelling, and useful business information. Today's data visualization tools, by utilizing external data sources, not only allow you to see your KPIs more clearly, but they also unify data and apply AI-driven analytics to reveal relationships between your KPIs, the market, and the world.

 

Important insights hidden within your data can help drive your business forward. However, looking at raw numbers alone does not always allow you to connect the dots. Patterns, connections, and other "aha" insights emerge when you look at your data in a visual format that would otherwise be hidden.

 

Data visualization makes data come to life, transforming you into the master storyteller of the insights hidden within your numbers. Data visualization, through live data dashboards, interactive reports, charts, graphs, and other visual representations, enables users to quickly and effectively develop powerful business insights.

 

Also Read | Why is Data Processing Important?

 

 

Benefits of Data Federation

 

Previously, you'd install an appliance or software, log all relevant data, monitor performance, and periodically poll your environment for changes that may have occurred since the last check-in. 

 

All of that data would be stored in a centralized database or data warehouse for future access, and the CMDB would be updated whenever the IT operations team had time. It took a long time, required a lot of labor, was prone to mistakes, and was inefficient. However, it was how IT operations monitoring and management were done back then.

 

With data federation, data can stay where it belongs while still informing IT operations about the enterprise's health, availability, and reliability in real time. Data federation is especially useful for performing common IT operations tasks such as change management tracking and automatically maintaining an accurate CMDB. 

 

Whether a new service is activated, a mobile device connects, a virtual computing instance is created, or new hardware is installed, data federation, in conjunction with modern enterprise monitoring tools such as an AIOps platform, enables real-time detection and updating of all relevant systems.

 

Some of the benefits of Data Federation are listed below :- 


The image shows the Benefits of Data Federation which includes No extra storage space required, A single truth source, Improved Data Reliability for machine learning and AI, Quick Data Access, Accessible with very little coding, Less expensive than alternatives and Reduce Risk

Benefits of Data Federation


 

  1. No Extra Storage Space Required

 

Businesses do not need to invest in hardware because the software does not make a complete copy of the data from the source. There is no need for costly infrastructure or additional data processing capabilities because the data federation software manages everything.
 

  1. A Single Truth Source

 

A single reliable data source is priceless. This not only saves time when looking for specific information, but it is also far more accurate. The data federation database will have the most recent data, regardless of where it is entered. This means fewer errors, happier customers, and more trustworthy business data.

 

Data silos are common, particularly when businesses take a less holistic approach to IT. Data federations eliminate silos and enable easy sharing across the enterprise.

 

  1. Improved Data Reliability for Machine Learning and AI

 

A large part of a data scientist's job is to cleanse data: to remove unnecessary data points and duplicates, to find the most recent information, and to eliminate outliers. The majority of this is handled automatically by data federation. The resulting data is precise, consistent, and provides better predictions and outcomes.

 

  1. Quick Data Access

 

There is no hardware or complicated infrastructure, just incredibly fast data access. Furthermore, if software is required, there is no need to create the warehouse as well as the extract, transform, and load functionality. Creating a data federation is much faster.

 

  1. Accessible with Very Little Coding

 

The barriers to creating a data federation are low. There is little coding and no need for dedicated, specialized IT personnel. Install the data federation development runtime software on a regular server, then create views and services and fine-tune the queries.

 

  1. Less Expensive Than Alternatives

 

A data federation does not require physical hardware to store data on, as well as software licenses, additional data governance, or expensive IT staff.

 

  1. Reduce Risk

 

There is little to no risk of data loss because the system is not replicating or physically moving anything. If the data federation system is properly configured, any existing reports can be mapped to run in the same manner. There are no missing, lost, or misplaced data or reports, and there is no risk.

 

Also Read | What is Data Ingestion? Challenges and Types


 

Data Federation and the Modern Enterprise

 

Data is the modern enterprise's lifeblood, and for IT to function optimally, timely, accurate, and trustworthy data is required. Data federation is a critical component for collecting and operationalizing all configuration items that make up an organization's infrastructure. 

 

Because of the ephemeral nature of the systems and services on which today's enterprises rely, as well as the large volume of data generated, that data must be available immediately, and data federation enables this.

 

Previously, you'd install an appliance or software, log all relevant data, monitor performance, and periodically poll your environment for changes that may have occurred since the last check-in. 

 

All of that data would be stored in a centralized database or data warehouse for future access, and the CMDB would be updated whenever the IT operations team had time. It took a long time, required a lot of labor, was prone to mistakes, and was inefficient. However, it was how IT operations monitoring and management were done back then.

 

With data federation, data can stay where it belongs while still informing IT operations about the enterprise's health, availability, and reliability in real time. Data federation is especially useful for performing common IT operations tasks such as change management tracking and automatically maintaining an accurate CMDB. 

 

Whether a new service is activated, a mobile device connects, a virtual computing instance is created, or a new piece of hardware is installed, data federation, in conjunction with modern enterprise monitoring tools such as an AIOps platform, enables real-time detection and updating of all relevant systems.

 

Data federation is a method of collecting, storing, and utilizing data that uses virtualization rather than physical storage in a dedicated database. Data federation is a method of keeping up with data while still turning it into useful intelligence.

 

With today's capabilities, such as real-time monitoring and comprehensive discovery, data federation brings information from disparate systems from across the enterprise together better than legacy systems and techniques could. 

 

Whether the data required for a specific query or to inform contextual analysis of system performance is located in the organization's data center, at the network edge, or somewhere in the cloud, data federation allows you to see and use it as if it were in the organization's data center.

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