What this story illustrates is something we see consistently in enterprise AI and data engineering engagements: the bottleneck is rarely the AI capability. It’s the data infrastructure underneath it. When the foundation can’t breathe, no amount of machine learning fixes the problem upstream.
And once the architecture is right, the numbers tend to speak plainly. Seventy million transactions processed daily. Compliance no longer a fire drill. That’s what data modernization services actually look like when executed with intent rather than just aspiration.
Unlocking scalable education insights with 30+ new data sources
The edtech space has a particular challenge. The data it needs to serve learners well is often distributed across dozens of disconnected systems, from early childhood assessment platforms to district-level reporting tools. Integrating those sources isn’t a technical nicety. It’s the whole point.
This client came in with a platform that couldn’t ingest new data sources quickly enough to keep pace with the product roadmap. Transformation and governance were manual, slow, and brittle.
The modernization effort brought in AWS, Kafka, Databricks, and MWAA to rebuild the entire data pipeline from ingestion through to governance. The result was a platform capable of absorbing more than 30 additional data sources, each integrated with proper transformation logic and quality controls built in.
But the more interesting outcome wasn’t the number of sources. It was what the architecture became: genuinely agile. The ability to add new data inputs without re-engineering the core stack is precisely what digital transformation with AI demands, because it means the platform grows with the business rather than constraining it.
For companies in data-intensive verticals like education and healthcare, this kind of scalability isn’t optional infrastructure investment. It’s competitive positioning. Organizations that can act on real-time, multi-source data will make better product decisions faster, and that gap compounds over time.
35% efficiency boost with AI-powered data lake for global supply chain optimization
Supply chain data is notoriously fragmented. Warehouses run on different systems. Temperature, energy, inventory, and shipment data live in separate stacks with limited interoperability. For a company specializing in temperature-controlled distribution, that fragmentation isn’t just inconvenient. It’s a risk.
This client’s data estate reflected that reality. Visibility across warehouses was limited, and without automated quality controls, the data that did exist couldn’t be fully trusted. Decision-making slowed and efficiency suffered.
Our approach consolidated warehouse data into a modern Cloudera Data Lake, migrating ETL workloads from Apache NiFi and Pulsar to Databricks and Kafka. The team layered in automated governance mechanisms using Spark, Hive, Airflow, and Impala, creating a system that didn’t just collect data but actively validated and structured it for use.
The 35% operating efficiency gain is real, but it’s almost secondary to what it represents. Real-time visibility into shipment tracking, energy consumption, and warehouse optimization means this company now makes decisions from a live picture of its operations rather than a retrospective one. That’s a fundamentally different operating posture.
For enterprises in logistics, manufacturing, or any sector where physical operations generate continuous data streams, the question isn’t whether to build this capability. It’s how to build it without creating yet another fragmented layer. The architecture decisions that made this transformation work are what separate a successful data engineering and modernization engagement from one that stalls at the pilot stage.
Cutting 30% costs by migrating from Teradata to AWS for a leading energy provider
Legacy EDW infrastructure is a familiar constraint for enterprise IT and data leaders. Teradata and Informatica environments were built for a different era of analytics, and while they’ve served their purpose, the cost-to-capability ratio has shifted decisively.
This energy and utilities client was running on-premise with Informatica and Teradata at the core. The costs were high, scalability was limited, and the architecture simply couldn’t support the self-service analytics and streaming capabilities that modern operational decisions require.
The migration to AWS and Databricks wasn’t a like-for-like lift and shift. We re-architected the environment entirely, replacing the rigid existing structure with a single cloud platform capable of handling heterogeneous data at scale. Advanced analytics and streaming use cases that were previously out of reach became standard capabilities.
The 30% reduction in storage cost matters. But the more durable outcome is what the organization can now do that it couldn’t before. Self-service analytics changes the relationship between data teams and business units. When analysts can query data directly rather than waiting for IT-mediated reports, the speed of insight generation increases in ways that compound across every function.
This is the pattern we see in successful cloud modernization strategy engagements: the cost savings fund the business case, but the capability gains drive long-term value.
Driving $17M revenue growth with AI-powered analytics for faster go-to-market
Most data modernization efforts are framed around cost reduction or operational efficiency. This one is different. It’s framed around revenue.
A financial services firm specializing in debt resolution and credit risk management had a straightforward problem: it couldn’t share data with B2B customers in real time. That limitation meant slower decisions, constrained collaboration, and missed commercial opportunities. The business case for change was explicit.
The solution was a greenfield data lakehouse built on Databricks, on Azure cloud, designed from the ground up to support self-service business intelligence and ML model deployment. Real-time data sharing with customers became not just possible but central to the platform’s architecture.
The revenue impact, over $17 million, followed directly from that capability. When customers can access live data and act on it, the value of the partnership increases. Credit risk decisions get faster and debt resolution outcomes improve. The 3.7x return on investment reflects the commercial logic of building data infrastructure as a product rather than a cost center.
This is one of the more compelling cases for generative AI data solutions and modern data lakehouse architecture: when the platform enables customers to do their jobs better, it creates a stickiness that no sales motion can replicate.
Accelerating ETL code conversion with AI-driven automation
Legacy SAS environments are one of the most common modernization challenges in financial services. The scripts work. The institutional knowledge is embedded in them. But SAS talent is scarce, performance at scale is limited, and the governance model doesn’t map cleanly onto modern data platform expectations.
This client needed to modernize 450+ ETL scripts, a task that done manually would take years and carry significant risk. We met that challenge directly with an LLM-powered code conversion accelerator that automated the translation from SAS to Databricks PySpark.
The interesting part isn’t the automation itself. It’s the validation layer built around it. Automated accuracy checks and a human-in-the-loop review process ensured that converted code didn’t just run but ran correctly, a distinction that matters enormously in regulated environments where output accuracy isn’t negotiable.
The result: 450+ scripts converted within a year, with measurable improvements in performance, feature rollout speed, and maintainability. The new codebase also draws on a readily available PySpark talent pool rather than the shrinking SAS community.
This is what responsible AI automation looks like in practice. Not replacing human judgment but compressing the time and effort required to deliver accurate outcomes at scale. For data and engineering leaders managing large legacy ETL estates, how to validate AI-generated code at volume is one of the most consequential design decisions in the entire process.
Enhancing scalability and efficiency with R-based ETL modernization
700 unique SAS scripts. That’s the scale of the technical debt this edtech company carried into its modernization effort. Each script represented a piece of business logic, a data transformation rule, a reporting dependency. Converting them wasn’t purely a technical exercise. It was a knowledge transfer challenge wrapped in an engineering problem.
The migration target was an R-based ETL environment on Azure and Databricks, a choice driven by the practical availability of R talent in the education data science ecosystem. The team didn’t just convert scripts. They refactored them into reusable R packages and PySpark jobs, implementing parameterized scripts for straightforward tuning rather than one-off customization.
The outcomes are specific and measurable: a 40% reduction in file processing time, a two-year transformation window for 700+ scripts, and a maintainable codebase that won’t create a talent dependency problem down the line.
Reusability deserves emphasis here. One of the persistent failures in legacy modernization is building a new system that replicates the old system’s brittleness. Parameterized, reusable code components are the antidote to that pattern, because the next time the business needs to add a data source or change a processing rule, the platform adapts rather than resists.
For organizations in education technology, research, or any domain with large estates of analytical scripts, this transformation model, combining AI-assisted conversion with disciplined software engineering practices, offers a more honest picture of what AI-driven data transformation actually requires to succeed.
