The Scale and Fragmentation of Enterprise Data
Global data creation reached an estimated 181 zettabytes in 2023. More than 80 percent of this data now resides across fragmented environments such as local file systems, cloud storage, data warehouses, operational databases, and edge systems. Most organizations cannot use the majority of their data. Industry surveys show that only 15 percent of companies utilize more than 70 percent of the information they already possess.
The traditional response to this challenge has been data consolidation. Enterprises attempted to centralize information into a single repository using ETL processes and data warehousing tools. While this approach supported reporting and analytics for many years, it introduced structural limitations. Consolidation increases cost, raises privacy concerns, and frequently generates new silos rather than eliminating them. Each new platform, cloud service, or application adds another integration workflow that rarely aligns with existing on premises systems. As a result, annual cloud and data movement costs continue to rise.
Data Consolidation: The Conventional Approach
Centralized data warehouses and data lakes remain common because they support high performance analytics. ETL processes pre compute transformations so that queries execute quickly. However, the tradeoff is that analytics often operates on stale information because preprocessing runs on scheduled intervals rather than on live data.
Enterprises rely on ETL engineering to manage regulatory controls and pipeline complexity. Skilled engineers tune extraction, transformation, and load processes to improve reliability and data quality. These pipelines are essential for many analytics workflows but become expensive to maintain as data sources grow in number and diversity.
Some organizations complement consolidation with data federation to avoid duplicating sensitive information and to reduce storage and transfer costs. While federation can provide a unified view of distributed data, it does not resolve the core issue of executing computation efficiently across heterogeneous environments.
Federated Data for Agility, Privacy, and AI Readiness
As AI driven applications require faster access to operational data, the limitations of consolidation become more visible. Federated data processing offers a more agile model by running computation where the data is stored. This reduces unnecessary data movement, supports real time insight generation, and maintains compliance across regions.
Modern federation platforms enable fast onboarding of new data sources, whether from a new SaaS application or an acquired business unit. Organizations can evaluate data through standard interfaces without manually stitching together complex integration flows.
Federation also simplifies compliance with data sovereignty rules. Many jurisdictions require specific categories of data to remain within regional boundaries. Traditional ETL pipelines designed for centralized warehousing often cannot satisfy these requirements without costly redesigns.
By combining distributed queries, local execution plans, and event driven pipelines, a federated model supports AI workloads that depend on near real time visibility without sacrificing governance.
Scalytics: Next Generation Federated ETL and In Situ Data Processing
Scalytics Connect provides a federated execution layer that replaces the cost and complexity associated with traditional consolidation. Instead of moving data into a central system, Scalytics processes data in situ and coordinates execution across existing databases, warehouses, lakes, and edge systems.
The platform enables organizations to extract, transform, and load data through a federated ETL model that does not require duplication or long distance transfers. Data pipelines execute locally on each connected environment, with only compliant intermediate results exchanged. This approach improves security, reduces cost, and minimizes operational overhead.
Why Scalytics Connect defines the next era of ETL integration
Unified access across distributed sources
Scalytics Connect integrates data from heterogeneous locations and formats, removing silos without moving data.
Real time insights
Federated ETL pipelines allow immediate access to the most recent data because no preprocessing or scheduled consolidation is required.
Privacy and compliance
Data remains under the control of its owner. Scalytics enforces regional constraints through federated execution rather than centralized storage.
Cost efficiency
By eliminating data duplication, long distance transfers, and deep ETL chains, Scalytics reduces the operational cost of data engineering. The platform manages thousands of dynamic pipelines efficiently across on premises and cloud systems.
Agility
New sources can be added quickly without rearchitecting existing flows. This removes the maintenance load associated with brittle ETL pipelines and inconsistent data copies.
Scalytics brings federated execution, federated ETL, and in situ processing into a single platform that enables enterprises to use their data without centralizing it. Organizations gain fast access to accurate information while maintaining control, compliance, and cost transparency.
About Scalytics
Scalytics Connect provides federated data processing across Spark, Flink, PostgreSQL, and cloud-native engines through a single abstraction layer. Our cost-based optimizer selects the right engine for each operation, reducing processing time while eliminating vendor lock-in.
Scalytics Copilot extends this foundation with private AI deployment—running LLMs, RAG pipelines, and ML workloads entirely within your security perimeter. Data stays where it lives. Models train where data resides. No extraction, no exposure, no third-party API dependencies.For organizations in healthcare, finance, and government, this architecture isn't optional—it's how you deploy AI while remaining compliant with HIPAA, GDPR, and DORA.Explore our open-source foundation: Scalytics Community Edition
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