Data federation involves the creation of a virtual database that maps many different resources of an organization and makes them accessible through a one-stop interface. Unlike other technologies, the data federation leaves all data at its source. Integration occurs in real time as data consumers apply a single query to multiple sources.
What are the benefits of data federation?
It is not uncommon for companies to have hundreds of data repositories. As companies grow and develop, storage infrastructures naturally become more heterogeneous and access to company data becomes even more difficult. Some of the challenges of integrating fragmented enterprise data environments include:
Special query formulations: Technology providers implement query tools specific to their solutions. Even if these use their own SQL variants, data access requires users to understand how each data source expects queries to appear.
Custom extensions: Technology providers only offer extensions that add features or improve performance in the context of their solutions. Again, data engineers must understand how technology provider-specific extensions of a data source affect data extraction.
Semantic variations: When organizational domains put their data systems into practice, they make design decisions that make sense in the moment and within their own context. Data users who try to access this data do not have this perspective. As a result, source-to-source differences in semantics, format, and other data properties make cross-domain data integration difficult.
Unified data architecture solves these challenges by masking discrepancies within an abstraction layer. Users can run queries within this virtual layer without worrying about technologies or the structure of the source data. This virtualization of the data infrastructure of accumulation leads to five key benefits of data federation:
1. Real-time access
Traditionally, data analytics takes time. Users would need the help of their company's data teams to overcome their companies' clutter problems.Data engineers had to develop extract, transform, and upload (ETL) and extract, upload, and transform (ELT) pipelines to copy, prepare, and upload data into a new dataset.
This time-consuming development process was slowing down the company's decision-making process by extending the time between business questions and insights. The proliferation of data warehouses is an indication of a company's struggle to shorten the time it takes to gain insights.
Data federation provides end-users with real-time access to data across the organization. Leaving data at the source and creating a virtual data consumption layer renders processes and intermediate datasets redundant. Users no longer need the help of data teams. They can run queries themselves using the analytics and business intelligence tools they already know.
Democratized, real-time access speeds up insight acquisition time and makes decision-making more effective.
2. Data integration
Machine learning algorithms and AI applications can provide the deepest business insights. But data scientists can only produce innovative data products with ready access and reliable data quality. When domains and proprietary systems delete data, extracting, cleaning, and preparing the large datasets that data scientists need requires great effort by data teams.
Data federation facilitates the integration of large datasets by unifying the organization's disparate data sources behind one data consumption layer. Data scientists can quickly run queries as they iteratively explore subsets of the company's vast data stores. With a better understanding of the data environment, data scientists can offer engineers more refined requirements for integrating much larger datasets.
3. Reduce costs
Split data infrastructures make data analytics more expensive. Companies are investing in extra storage capacity to support temporary datasets and new databases. Data warehouses promise to consolidate data that matters, but old data sources always seem to remain.
Another less visible, but equally important aspect is that companies accept the lower productivity of their data teams. Developing and maintaining data pipelines takes time and limits the accessibility of data teams to the rest of the organization.
Data federation reduces these costs. Leaving data at the source prevents the proliferation of specialized databases and data warehouses, which leads to increased storage costs.
Additional savings are indirectly achieved when the federation frees data teams from less productive tasks. Catalogs of ETL and ELT pipelines are no longer kept. The democratization of data means that engineers are not distracted by simple query requests. As a result, data teams have more time to support complex projects that can drive commercial innovation.
4. Scalability
One reason for the rising costs of big data analytics is the investments companies have to make to ensure that storage and processing capacity is available when analysts need them. Underutilized capacity blocks cash that the company can allocate to more efficient uses.
Data federation leverages cloud technology to separate storage and computing. IT departments can plan for steady growth in storage capacity and develop a data infrastructure with the optimal balance of performance, productivity and cost.
Data federation allows companies to scale computing capacity on demand, rather than overinvesting to manage changes in computing demand.
5. Flexibility
Fragmented data infrastructures are fragile and impervious to change. For example, any glitch in a data migration project can disrupt operations for days. The reason for this inflexibility is that data usage scenarios are inextricably linked to the data infrastructure. Companies shape their data products in the context of how each source stores and configures data. A change in source ripples these dependencies in unexpected ways.
Federation eliminates these dependencies by isolating resources within a data consumption layer. Changes to the resource occur transparently for business users.
For example, most users never know that a migration project moves data from an on-premises system to the cloud. One day, queries in the federated consumption layer pull data from the old system, while the next day queries can pull data from the new system.
What is the difference between data federation and data lake?
Data federation and data lakes are different solutions to similar challenges. Both make data more accessible for analysis and discovery, but they do it in different ways.
The data federation does not move or copy raw data. Instead, it focuses on virtualizing multiple data sources and providing a unified view through the abstracted consumption layer.
Data lakes take large volumes of raw data to support analysis and discovery. But data lakes do not have to replace the original sources. They become another element in a business's growing storage infrastructure.
What is the difference between data federation and virtualization?
Although the terms seem interchangeable, federation and virtualization are not the same. Federated data requires virtualization, but virtualized data does not necessarily have to be federated.
Data virtualization is a concept that encompasses federation and other data management capabilities. Virtualization abstracts the complexity of an underlying resource or resources to simplify access.
Data federation is specifically the virtualization of multiple data sources. Creating a data consumption layer makes it easy to pull data from different locations within the same query.
What is an example of a data federation?
Starburst is a data federation solution that virtualizes your company's different data sources in a single access point. Seamless integration with each resource and advanced query optimizations shorten the time to gain insights and optimize your data infrastructure.
Here are five game-changing features of data federation with Starburst:
1. Integration of Different Data Sources
Starburst eliminates silos that separate your users from your data by offering connectors to more than fifty enterprise-level relational databases, data warehouses, data lakes, cloud storage platforms, and other data systems.
With seamless access to every resource, your engineers can explore datasets without having to make time-consuming moves that reduce productivity and undermine security. Data engineers can quickly build infrastructure early on in a new project to reduce the risk of later making more expensive changes.
2. Questioning Across Multiple Sources
Starburst's virtualized data consumption layer gives business intelligence analysts and other users direct access to every data source. Users use tools they already know to write SQL-based merged queries that combine high-quality data from multiple sources.
Democratizing real-time data access enables analysts to generate business insights faster to help managers make more informed decisions.
3. Query Optimization and Performance
Powered by the open-source Trino query engine, Starburst offers advanced performance features that empower your queries:
- Dynamic filtering: reduce loads on networks and data sources.
- Query transfer: Export queries or query fragments to the data source for optimal performance.
- Cached views: quick access to frequently viewed data.
- Cost-based optimization: each query uses the most efficient merge enumerations and distributions.
Taken together, these and other features on the Starburst distributed analytics platform provide a high-performance and cost-effective tool for consolidating your data infrastructure.
4. Data security and Data Governance
Starburst democratizes access to data across your organization, while the platform's security and governance features enable access to be authorized.
Multiple authentication options combined with role and feature-based authorization policies limit users to the data their work requires. Precise controls allow you to manage access at table, row, and column levels.
Because Starburst's federation platform leaves data at its source, you avoid data replication security risks. End-to-end encryption protects all data in the transfer.
Data logging and real-time monitoring improve the compliance and implementation of your data management policies.
5. Scalability
Starburst evolves with your data workloads as you scale from gigabytes to petabytes.
Automated scaling and precision stopping capabilities allow you to manage clusters without impacting queries.
Error-tolerant execution ensures that cluster failures do not affect long-standing workloads.
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