At a Glance

Industry: Upstream Oil & Gas

Goal: Replace a decentralized set of siloed operational databases with a single governed data lakehouse on Databricks and AWS that gives engineering, operations, and finance teams one trusted, continuously updated view of well and production data across a mixed cloud and on-premises environment.

Services & Technologies

OpsGuru Services: Data Strategy & Advisory, Data Platform Engineering, Data Migration, Data Governance, Analytics Enablement

Tech Stack

• Lakehouse Storage & Processing: Databricks on AWS, Amazon S3, Delta Lake, Apache Spark

• Ingestion & Change Data Capture: Delta Live Tables, API integrations, Site-to-Site VPN

• Governance & Access Control: Unity Catalog

• Compute & Cost Optimization: Amazon EC2 (c6i, m7g), Spot Instances, Databricks SQL Serverless

• Analytics & Visualization: TIBCO Spotfire, Power BI, Amazon QuickSight

Outcomes

• 16x Faster Data Pipelines: Well and production data now refreshes in minutes instead of overnight batch runs, so planning decisions use numbers from today, not yesterday.

• One Governed Source of Truth: Engineering, operations, and finance now query the same access-controlled dataset instead of reconciling numbers across multiple disconnected systems.

• 130+ Self-Service Users: Teams pull their own reports directly from the platform instead of waiting on manual exports.

• 40% Lower Compute Costs: Rightsized instances and Spot capacity cut the cost of running batch workloads without slowing them down.

• A Foundation for What's Next: The platform is built to support AI and machine learning use cases as the business is ready for them.

Running Wells on Yesterday's Numbers

Oil and gas operators run on data that has to be both accurate and current. Well performance, equipment health, and financial reporting all depend on numbers that reflect what is happening in the field right now, not what happened last week. Across the industry, legacy systems and disconnected data silos routinely get in the way of that. Investment in digital transformation and cloud-native architecture across oil and gas is accelerating quickly, with the digital transformation market for the sector projected to grow from roughly USD 72 billion in 2026 to nearly USD 125 billion by 2031, according to Mordor Intelligence. Operators that solve their data problem first are the ones positioned to take advantage of that shift.

The operator at the center of this story runs more than 5,000 wells producing 38,000 barrels of light crude oil per day. Its business depends on tracking well performance, equipment status, and financial data continuously, then feeding that information back into planning and production decisions. Doing that well requires what is known as a data lakehouse: a single platform that combines the flexible, low-cost storage of a data lake with the structure, security, and reliability of a traditional data warehouse, so teams get one trusted place to find and analyze information instead of piecing it together from multiple systems.

The operator's wells were producing consistently, and the business itself was running well. What had not kept pace was the data infrastructure behind it. Operational and financial data lived across a mix of Oracle, SQL Server, and MySQL systems, some hosted in the cloud and some still running on premises, alongside unstructured documents and imagery stored on file shares. Each system worked for its own purpose, but none of them talked to each other in a way that gave the business one consistent view of what was happening across the well portfolio.

As the business grew, that gap became harder to work around. Teams needed continuously updated, trustworthy data to make faster well planning and production decisions, delivered through a governed platform that could hold up to financial and regulatory scrutiny. The operator's existing environment could not support that on its own, particularly as data volumes and reporting demands increased. That gap in capability, not appetite, is what led the operator to bring in OpsGuru to design and build a new data platform from the ground up.

Where Production Data Lived in Five Different Places

Before OpsGuru got involved, getting a straight answer about well or production performance meant checking several different systems and hoping they agreed.

• Data Spread Across Disconnected Systems: Operational and financial data lived in Oracle, SQL Server, and MySQL systems split across cloud and on-premises infrastructure, with unstructured documents and imagery stored separately on file shares. Because these systems were not built to work together, pulling a complete picture of a single well meant manually cross-referencing multiple sources, and there was no single system anyone could point to as the source of truth.

• Pipelines That Broke Under Their Own Weight: The ETL processes that moved data between systems, short for extract, transform, and load, were not built to handle the data volumes the business had grown into. When jobs failed under load, the team's workaround was to delete older history from the warehouse just to keep the pipelines running, trading long-term visibility for short-term stability.

• Manual Entry and Inconsistent Metadata Undermined Trust: Data entered by hand, combined with a lack of validation and inconsistent metadata standards, meant errors could slip into reports without anyone catching them. Internal governance surveys reflected the problem directly, scoring data policy clarity at just 2.4 out of 5, a sign that even the people closest to the data were not confident in it.

• Compliance Exposure From Inconsistent Data Handling: Sensitive and financial data was not handled consistently across the operator's systems, which created regulatory exposure the business could not fully quantify or control. Without a single governance model, there was no reliable way to prove who had accessed what data, or when.

A Governed Lakehouse and Self-Service Analytics on Databricks and AWS

Phase 1: Validating the Architecture and Compute Model

Before writing any code, OpsGuru's team assessed the operator's existing data flows and validated the core architectural bet behind the engagement: that a lakehouse built on Databricks and AWS could support both real-time ingestion and strict governance without requiring a full data warehouse rebuild. The team benchmarked different AWS instance families to confirm that ingestion workloads and processing workloads could each run on hardware suited to their specific demands rather than a one-size-fits-all setup, which mattered for both performance and cost. With the architecture and compute model validated, the team moved forward with confidence that the platform could hit both the reliability and cost targets the operator needed.

Phase 2: The Lakehouse, Governance Layer, and Analytics Delivery

• The Data Lakehouse: Built on Databricks running on AWS, using Amazon S3 for storage and Delta Lake as the underlying data format, the lakehouse was structured using what is known as a medallion architecture: raw data lands in a Bronze layer, gets cleaned and standardized in a Silver layer, and is organized for business use in a Gold layer. Combined with Delta Lake's transaction guarantees and version history, the team could always trace a number back to its source and roll back to an earlier version of the data if something went wrong.

• Unified Data Ingestion: OpsGuru used Delta Live Tables to stream ongoing changes from the Oracle and SQL Server systems into the lakehouse as they happened, a method known as change data capture. Historical data was migrated in bulk, and the two data centers connected to the platform over a redundant Site-to-Site VPN so ingestion kept working even if one connection dropped.

• Governance by Design: Unity Catalog gave the operator a single place to manage who could access what data and to track where every dataset came from. Automated quality checks quarantined records that failed validation before they reached downstream reports, sensitive fields were masked, and data retention followed rules aligned with Sarbanes-Oxley requirements.

• Right-Sized, Cost-Aware Compute: Based on the benchmarking done in Phase 1, ingestion workloads ran on AWS's c6i instance family and processing workloads ran on m7g instances, with Spot capacity used for batch jobs that could tolerate interruption. Databricks SQL Serverless handled analytics queries without the operator needing to manage or pay for compute that sat idle between jobs.

• Analytics in the Tools Teams Already Used: Rather than asking teams to learn a new interface, OpsGuru connected the governed lakehouse to TIBCO Spotfire, Power BI, and Amazon QuickSight, the same tools different teams were already using. Everyone kept their preferred way of working, but every dashboard now pulled from the same governed data.

Phase 3: Proving the Platform and Handing It Over

With the lakehouse built, OpsGuru ran the platform against the targets that mattered most to the business: 95 percent or better data quality accuracy, 99.5 percent platform availability, and average data access times under five minutes. The team resolved issues surfaced during testing, documented the architecture and governance model, and trained the operator's staff to manage and extend the platform themselves. By the end of the engagement, the operator's own team could onboard new data sources, manage access permissions, and troubleshoot pipeline issues without needing to call OpsGuru first.

The Outcome: Production Decisions Built on Today's Data

What changed shows up at two levels: what the platform itself can now do technically, and what that makes possible for the business day to day.

• What Changed Under the Hood

• ETL Runtime Dropped From 8 Hours to 30 Minutes: The pipelines that used to take most of a workday to run now finish in half an hour, a 16x improvement driven by switching from batch processing to Delta Live Tables' change data capture streaming. In practice, that means data reflects what happened in the field that morning, not what happened before the previous night's batch job ran.

• Compute Costs Down 40 Percent: Moving fault-tolerant batch workloads to Spot Instances and matching instance types to the specific demands of ingestion versus processing cut compute spending on those workloads by 40 percent. The savings came without giving up performance, because the right-sizing was based on actual benchmarked workload behavior rather than a default instance size.

• A Single Governed Access Model Through Unity Catalog: Every dataset in the lakehouse now has traceable lineage and a consistent access control policy, instead of each source system enforcing its own rules or none at all. That matters because it gives the operator a defensible, audit-backed answer any time a regulator or auditor asks who could see sensitive data and when.

• Data Quality and Availability Targets Built Into the Platform: The platform was built to hit 95 percent or better data quality accuracy and 99.5 percent availability, with average data access times under five minutes. Those targets exist because a governed platform is only useful if people can trust it and reach it when they need it, not just in theory.

What It Means for the Business

• 130+ People Get Their Own Answers: Engineering, operations, and finance staff can now pull governed reports directly from Spotfire, Power BI, or QuickSight instead of waiting on someone else to run a report or reconcile numbers by hand. That shift frees up time that used to go into manual reporting so people can spend it acting on the data instead.

• Engineering Time Goes Back to Engineering Work: With pipelines that run reliably and do not need constant manual reconciliation, the engineering time that used to go into keeping the lights on is now available for higher-value work. Storage duplication and inefficiency dropped too, once standardized formats and automated tiering replaced storage decisions made system by system.

• Well Planning Runs on Current Numbers: Because data refreshes continuously instead of on a weekly cycle, planning and production decisions are made against what is happening right now in the field, not what happened a week ago. That is the difference between reacting to a problem and catching it early.

• A Foundation for AI and Advanced Analytics: The governance model, data quality standards, and lineage tracking built into the platform were designed to support AI and machine learning use cases, not just today's dashboards. As the operator is ready to take on predictive maintenance, production optimization, or other AI-driven use cases, the data foundation is already in place.