Data federation benefits
What is data federation? To start, it’s best to understand how companies are currently storing data. Right now, it is not unusual for companies to have hundreds of data repositories. As companies grow and evolve, their storage infrastructure naturally becomes more heterogeneous, and company data becomes more difficult to access. Some of the challenges of integrating fragmented enterprise data landscapes include:
Proprietary query formulations: Vendors implement query tools specific to their solutions. Even when vendors use their own flavors of SQL, data access requires users to understand how each data source expects queries to look.
Proprietary extensions: Vendors offer extensions that add capabilities or improve performance — but only within the context of their solution. Again, data engineers must understand how a data source’s vendor-specific extensions impact data extraction.
Semantic variations: When organizational domains implement data systems, they make design decisions that make sense at the time and within their context. Data users trying to access this data do not have that perspective. As a result, source-to-source variations in semantics, formats, and other data properties complicate cross-domain data integration.
A federated data architecture resolves these challenges by masking inconsistencies within an abstraction layer. Users can run queries within this virtual layer without worrying about technologies or the structure of source data. This virtualization of an enterprise’s data infrastructure leads to five core benefits of data federation:
1. Real-time access through federated data
Traditionally, data analytics took time. Users needed help from data teams to overcome their company’s fragmentation challenges. Data engineers had to develop extract, transform, and load (ETL) and extract, load, and transform (ELT) pipelines to copy, prepare, and load it into a new dataset.
This time-consuming development process extended the span between business questions and insights, slowing the company’s decision-making. The proliferation of data warehousing is symptomatic of a company’s struggles to shorten time to insight.
Data federation empowers end users with real-time access to data across the organization. Leaving data at the source and creating a virtual data consumption layer makes pipelines and interim datasets unnecessary. Users no longer need help from data teams. They can run queries themselves using the analytical and business intelligence tools they already know.
Democratized, real-time access speeds time to insight and makes decision-making more effective.
2. Data integration
Machine learning algorithms and artificial intelligence applications can yield the most profound business insights. However, data scientists can only produce innovative data products with ready access and reliable data quality. When domains and proprietary systems silo data, it takes enormous effort by the data teams to extract, cleanse, and prepare the large datasets data scientists require.
By unifying the organization’s disparate data sources behind a data consumption layer, data federation streamlines the integration of large datasets. Scientists can quickly run queries as they iteratively explore subsets of the company’s vast data stores. With a better understanding of the data landscape, data scientists can present engineers with more refined requirements for integrating exponentially larger datasets.
3. Data federation reduces costs
Fragmented data infrastructures make data analytics more expensive. Companies must invest extra storage capacity to support interim datasets and new databases. Data warehouses promise to consolidate the data that matters, but the old data sources always seem to stay.
Less visible, but just as important, companies must accept lower productivity from their data teams. Developing and maintaining data pipelines is time-consuming and limits how accessible data teams are to the rest of the organization.
Data federation reduces these costs. Leaving data at the source avoids the dedicated databases and the proliferation of data warehouses that drive escalating storage costs.
Additional savings arrive indirectly when federation frees data teams from less productive tasks. They no longer maintain catalogs of ETL and ELT pipelines. Data democratization means engineers are not distracted by simple query requests. As a result, data teams have more time to support complex projects that can drive business innovation.
4. Data federation increases scalability
One reason for the escalating costs of big data analytics is the just-in-case investment companies must make to ensure the storage and compute capacity is there when analysts need it. Underutilized capacity ties up cash the company could allocate to more productive uses.
Data federation leverages cloud economics to decouple storage and compute. IT departments can plan for steady growth in storage capacity and develop a data infrastructure with the optimal balance of performance, productivity, and cost.
Rather than over-investing to manage variations in compute demand, data federation lets companies scale on-demand compute capacity.
5. Flexibility through federated data
Fragmented data infrastructures are brittle and resistant to change. For instance, any glitch in a data migration project could disrupt operations for days. The reason for this inflexibility is the way data use cases are inextricably linked to data infrastructure. Companies design data products around how each source stores and structures data. A change at the source ripples through these dependencies in unanticipated ways.
By abstracting sources within a data consumption layer, federation eliminates these dependencies. Changes at the source happen transparently to business users.
For example, most users will never know when a migration project moves data from an on-premises system to the cloud. One day, their queries in the federated consumption layer pull data from the old system. The next day, their queries pull data from the new system.
The differences between data federation and a data lake
Data federation and data lakes are different solutions to similar challenges. They both make data more accessible for analysis and exploration but do it in different ways.
Data federation does not move or copy the raw data. Instead, it focuses on virtualizing multiple data sources and providing a unified view through its abstracted consumption layer.
Data lakes ingest large volumes of raw data to support analysis and exploration. However, data lakes do not necessarily replace the original sources. They become another element in an enterprise’s growing storage infrastructure.
The differences between data federation and virtualization
Although the terms may seem interchangeable, federation and virtualization are not identical. Federated data requires virtualization, but virtualized data is not necessarily federated.
Data virtualization is an overarching concept that encompasses federation and other data management capabilities. Virtualization abstracts the complexity of an underlying source or sources to simplify access.
Data federation is specifically the virtualization of multiple data sources. Creating a data consumption layer makes pulling data from different locations within the same query easier.
Related reading: Data Federation and Data Virtualization Never Worked in the Past But Now it’s Different
Data federation examples
Starburst is a data federation solution that will virtualize your company’s disparate data sources within a single point of access. Seamless integration with each source and advanced query optimizations compress time to insight and optimize your data infrastructure.
Here are five game-changing features of data federation with Starburst:
1. Integration of Disparate Data Sources
Starburst erases the silos that separate your users from your data by offering connectors to more than fifty enterprise-class relational databases, data warehouses, data lakes, cloud storage platforms, and other data systems.
With seamless access to every source, your engineers can explore datasets without the time-consuming moves that sap productivity and undermine security. Data engineers can quickly lay the groundwork early in a new project to reduce the risk of more expensive changes later.
2. Querying Across Multiple Sources
Starburst’s virtualized data consumption layer gives business intelligence analysts and other users direct access to every data source. They use the tools they already know to write SQL-based federated queries that consolidate high-quality data from multiple sources.
Democratizing real-time data access lets analysts generate business insights faster to help executives make more informed decisions.
3. Query Optimization and Performance
Powered by the open-source Trino query engine, Starburst delivers enhanced performance features that supercharge your queries:
- Dynamic filtering: reduce loads on networks and data sources.
- Query pushdown: push queries or parts of queries into the data source for optimal performance.
- Cached views: speedy access to frequently-viewed data.
- Cost-based optimization: each query uses the most efficient join enumerations and distributions.
Taken together, these and other features in the Starburst distributed analytics platform deliver a performant and cost-effective means for unifying your data infrastructure.
4. Data Security and Data Governance
While Starburst democratizes access to data across your organization, the platform’s security and governance capabilities ensure that access is authorized.
Multiple authentication options combined with role-based and attribute-based authorization policies limit users to data their jobs justify. Fine-grained controls let you manage access at the table, row, and column levels.
Since Starburst’s federated platform leaves data at the source, you avoid data duplication security risks. End-to-end encryption protects all data in transit.
Data logging and real-time monitoring improve compliance and enforcement of your data governance policies.
5. Scalability
Starburst evolves with your data workloads as they scale from gigabytes to petabytes.
Autoscaling and graceful shutdown capabilities allow you to manage clusters without impacting queries.
Fault-tolerant execution ensures cluster failures do not impact long-running workloads.
