Data Federation ROI: 35% Cost Savings Explained

Most organizations have accumulated years of data platforms, pipelines, and infrastructure layers that were added incrementally as needs evolved. The result is a landscape defined by data silos, duplicated processing, unnecessary data movement, and rising costs. These inefficiencies grow each year as more analytics and AI workloads shift to cloud environments.

Scalytics Federated provides a different approach. Instead of moving all data into a central engine, the platform brings computation to the data across the systems an organization already operates. Existing platforms such as Postgres, Spark, Flink, or local compute nodes are unified into a single analytical layer. This reduces data transfers, avoids redundant pipelines, and uses distributed resources more efficiently.

Organizations adopt Scalytics Federated to reduce operational overhead and extend the lifetime of their current data stack while accelerating AI and analytics.

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Capital expenditure: reusing existing systems instead of building new ones

A recent customer evaluation compared traditional single-engine execution against a federated data processing model using Scalytics Federated. In their previous setup, workloads were consolidated into standalone Spark clusters. After deployment, Scalytics Federated acted as the federated access and execution layer, orchestrating Spark only when necessary and offloading other parts of the workflow to systems better suited for them.

The organization evaluated three representative workload families:

• Text analytics (word statistics, inverted index construction)
• Analytical queries (aggregations and joins across multiple sources)
• Machine learning (SGD, K-Means, cross-community PageRank)

The comparison was run on common AWS instance types to reflect realistic operational environments. Under the same 8-hour daily usage pattern, the customer observed time and cost reductions due to two factors:

1. Scalytics Federated reduced unnecessary data transfers by operating directly on Postgres and HDFS before passing the minimal required data to Spark.
2. The optimizer identified execution paths that combined engines efficiently, rather than forcing all workloads onto one cluster.

In this configuration, the organization observed annualized savings exceeding their previous infrastructure costs by a substantial margin. The results underscore that performance is not just determined by the speed of a single engine but by choosing the right engine for each stage of a workflow.

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Operating expenditure: reducing the complexity of analytics operations

Many data teams grow large not because they are running high analytical volume but because managing multi-platform infrastructure manually is costly. Teams are often required to maintain pipelines, monitor systems, tune clusters, and compensate for legacy architectures that were never designed to work together.

Before adopting Scalytics Federated, our customer maintained a Spark-centric architecture for multiple analytics and AI projects. The operational model required a team structure similar to:

• Backend engineers
• Platform specialists
• Data scientists and analysts
• Project managers and coordinators

By integrating Scalytics Federated, their architecture became significantly simpler: execution could be pushed to the right backend automatically, pipelines no longer needed custom orchestration, and the operational overhead of managing a single-engine bottleneck was removed.

The customer reported that the required team size for the same number of projects was reduced by half. This does not mean fewer initiatives get completed. In fact, the organization reallocated capacity to additional AI and analytics projects. Scalytics Federated improved productivity by reducing low-value maintenance work and eliminating recurring manual migrations between engines.

The downstream effect was lower OpEx and the ability to execute more projects in parallel using the same headcount.

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Extending the value of existing investments

Many organizations have already invested in Hadoop, Spark, commercial distributions, and cloud services. Those investments often remain underutilized because workloads tend to gravitate to one “dominant” engine, even when another system would be more efficient.

Scalytics Federated extends the life and value of these investments by making them work together. Instead of decommissioning legacy platforms or overprovisioning a single engine, organizations can use the strengths of each system without forcing a full refresh of their architecture.

Across customer deployments, this architectural reuse has led to meaningful reductions in both OpEx and CapEx. Savings are typically reinvested into new initiatives, allowing organizations to increase their analytical output without expanding budget.

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Summary

Scalytics Federated enables organizations to modernize analytics without rebuilding everything from scratch. By unifying existing data platforms into a federated execution layer, it reduces data movement, optimizes resource usage, and lowers both infrastructure and operational costs.

Across benchmarking scenarios and real-world deployments, the platform has shown that intelligent cross-platform execution delivers measurable performance gains while reducing the cost and complexity associated with traditional single-engine architectures.

Scalytics Federated turns existing data platforms into a coordinated analytical fabric and unlocks more value from the systems enterprises already operate.