Expert insights on data solutions, database administration, data engineering, and DevOps
IT/OT convergence stalls on the org chart, not the tech. OT (Operational Technology) keeps the field running; IT (Information Technology) runs the business. Why they split, what convergence really means, and a read-only historian path.
You don't need an MDM platform for one clean list of wells. Build per-source well dimensions and conform them into a master with a surrogate key.
How we centralized a fragmented upstream data estate spanning 20+ vendor systems into one master well table with resolved well identity.
Moving fast has real value, especially in upstream. But there's a predictable inflection point where the systems built for speed become the thing slowing you down. The earlier you build with handoff in mind, the cheaper the inflection is. Here's what 'built for handoff' actually means and how AI-assisted development changes the calculus.
The discipline that made medallion architecture the default in business intelligence matters more for operational data, not less. The source data is messier, the consumers move faster, and the blast radius of bad data is larger. Here's how bronze, silver, and gold should actually look when the input is SCADA and historian data.
Most SCADA ingestion programs fail because they try to boil the ocean. A phased approach that proves a repeatable pattern on the cleanest asset first is almost always faster end-to-end. Here's a realistic sequencing guide for someone who has been told they own SCADA ingestion and is trying to figure out where to start.
Most data teams treat SCADA and OT (Operational Technology) data as a special case that lives outside the normal data stack. The same discipline that makes dbt valuable for business data is exactly what OT data is missing, and the consequences of getting it wrong are higher. Here's the case for medallion-on-dbt against OT data and the tests that catch real-world problems.
Every acquisition comes with somebody else's SCADA stack. After enough deals you have eight to twelve platforms, no common namespace, and a field team that lives in browser tabs. The real cost isn't licensing, it's the analytics you can't run and the integrations you keep rebuilding. Here's why and what to do about it without replacing the SCADA vendors.
Most operators are further along on SCADA-to-Snowflake than they realize architecturally, and closer to the edge than they realize operationally. The gap between a pipeline that runs and a pipeline you can trust is observability, error handling, ownership, and recovery. Here's where the fragility actually lives and how to harden what you already have without starting over.
Full-refresh ETL against a vendor-hosted SCADA source is the easy default and the wrong one. Here's why backfills push pipelines toward full refresh, what it actually costs the source, and the layered incremental pattern (overlap window, updated_at pass, reconciliation, planned deep pulls) that gets the same correctness without the call from the vendor.
Most operators are sitting on years of SCADA history that never made it into their analytical systems. The data is rich, the integration is unglamorous. Here's why the gap persists, what a real SCADA-to-warehouse pipeline looks like, and the volume, downsampling, and reconciliation decisions you have to make on the way.
Field tickets, run tickets, JIBs, and service invoices still arrive at most mid-size operators as paper or PDFs. Here's why the digitization gap has persisted, what a real ingestion pipeline looks like, and where LLM-assisted extraction earns its keep versus where it just creates silent errors in close.
Autonomous systems in oil and gas only deliver their full value when they're built on clean, standardized data. Operators treating data standardization as a core competitive capability, not an afterthought, get measurably better results from automated drilling, predictive maintenance, and remote field operations.
What does a real PPDM ingestion pipeline actually look like? Here's the architecture: Airflow DAGs pulling from OCC and other source systems, landing normalized data into PPDM-aligned PostgreSQL tables, and DuckDB handling the analytical layer on top. Design decisions, common mistakes, and what a working monthly cycle looks like.
Every PPDM project eventually asks which database to build on. The answer is mostly driven by practical constraints, not religious preference. Here's the decision framework: when SQL Server is the right call, when PostgreSQL makes more sense, and where DuckDB fits in the stack.
Most mid-size operators don't have a dedicated data team and don't have the budget to build one. That doesn't mean governance has to wait. Here's the minimum viable version: five domains, five owners, five pages, and the discipline to keep them current.
Land and production almost never agree on the first try, and the reconciliation eats more analyst time than any other problem in upstream data. Here's why it's genuinely hard, where the disagreements come from, and how to actually solve it instead of papering over it every month.
The industry standardized the barrel in 1866 and saved itself a century of disputes. The lack of standardization in upstream data is the most expensive data problem most operators don't realize they have, and the bill comes due in the diligence room. Part three of the 42 Gallons series.
Nobody runs a producing field on the honor system. Every barrel is measured, gauged, and accounted for. Your data deserves the same. Part two of the 42 Gallons series covers clean ingestion, end-to-end governance, and what it really means to be divestiture-ready from day one rather than scrambling six weeks before close.
The oil industry has known what's in every barrel since the 1860s. The same can't be said about most operators' data. Part one of a three-part series on treating data with the same discipline the industry has applied to the physical product for 150 years. Lineage, provenance, and governance built in rather than bolted on.
Most failed PPDM implementations fail for the same reasons, and none of them are about the model itself. Here's the pattern we keep seeing, why it happens, and how to restart a stalled implementation without throwing out the work that was already done.
PPDM gets pitched as either the answer to upstream data problems or as a thousand-table beast nobody implements. Both takes miss the point. Here's an honest look at what adopting the model actually buys you, what it doesn't, and how to scope an implementation that finishes.
Every operator has a data quality story that ends with 'and then we gave up.' Upstream data is genuinely hard, and the usual frameworks don't quite fit. Here's a practical look at what actually goes wrong and how to start making progress without trying to fix everything at once.
Most Oklahoma operators are still pulling Oklahoma Corporation Commission data by hand every month. There's no technical reason for that. Here's what a proper OCC ingestion pipeline looks like, and what it takes to get one running.
Most mid-size upstream operators are running on spreadsheets. That's not a failure. But there's a point where it starts costing real time and money. Here's a phased, realistic path from Excel to a proper data stack without the six-figure platform purchase.
We put our entire SDLC in git. Requirements, decisions, task assignments, everything. Then we cancelled standup. Nobody complained. OK, I complained, which is apparently how you get assigned the blog post about it.
Every data initiative starts the same way: pick a platform, centralize everything, and wait for insights to emerge. Years later, the data engineers are busy and the business has nothing to show for it. Here's why the default answer keeps failing and what to do instead.
A practical reference for data pipeline patterns: loading strategies, slowly changing dimensions, change data capture, Lambda, Kappa, and Medallion architecture, reliability fundamentals like idempotency and atomic swap, and orchestration patterns.
It's time someone said the quiet part loud. The database industry has been gatekeeping these advanced techniques for years. We're pulling back the curtain on ten tips that will revolutionize the way you manage data.
The hyperscale data platform pitch is compelling, but it was designed for a different customer. Here is an honest look at where open source tools like PostgreSQL, DuckDB, Airflow, and dbt outperform proprietary platforms for most Oklahoma organizations, and when the proprietary option is actually the right call.
A practical breakdown of infrastructure automation tools for data engineering workloads: OpenTofu, Pulumi, Ansible, CloudFormation, CDK, and Crossplane. Covers on-premise and cloud tradeoffs, team fit, and honest recommendations for Oklahoma businesses.
Stop coupling DAGs by time or ExternalTaskSensor. Airflow's dataset scheduling lets you wire pipelines together through the data they produce and consume, so the right DAGs run at the right time without the fragility.
Cloud-first isn't always the right answer, especially in Oklahoma. Here's an honest breakdown of data warehouse options for small and mid-size businesses that need something that actually works without a runaway bill.
Oklahoma energy companies are sitting on enormous amounts of data spread across systems that were never designed to talk to each other. PPDM gives you a standard. Data engineering makes it actually work.
We've seen a trend of small-to-mid size Oklahoma businesses outgrowing their data setup. Here's how to tell if it's time to bring in a real data engineer.
If your Airflow Variables, Connections, and secrets only exist in the UI or someone's memory, you don't have a config strategy, you have a time-bomb. Here's how to actually fix that.
Learn how to build portable, testable data pipelines by containerizing your ETL logic and using Airflow purely as a scheduler, keeping your code independent from any specific orchestration tool.
Stop fighting for inbound VPN access. Put your Airflow workers where the data lives and let them call home.
A practical guide to building your first AI batch processing pipeline. From identifying the right problem to architecture patterns and common pitfalls to avoid.
100% automation isn't the goal. Learn how to build hybrid systems where AI handles the bulk and humans handle exceptions, achieving better results than either alone.
AI is expensive for chatbots. Batch processing has different economics. Learn about batch API discounts, model selection, and when AI costs less than human labor.
Every organization has decades of data trapped in formats machines couldn't understand. LLMs change that. Here's how AI solves the dirty data problem traditional automation couldn't crack.
The AI bubble question is everywhere. Valuations are stretched. Skepticism is warranted. But bubbles burst speculation, not value. Here's what's real and why you shouldn't sleep on it.
Forget the tech debt. These strategic shifts change how your organization uses data, and they start with conversations, not code.
Data engineering and platform development often benefit from outsourcing or hybrid staffing. Learn when to hire consultants vs. full-time engineers for your organization.
Data quality isn't a one-time fix, learn how to execute, measure, and evolve your data strategy to drive real business impact while staying ahead of change.
Your data strategy will fail without the right culture. Learn how to build executive buy-in, drive data literacy across your organization, implement governance that enables rather than restricts, and overcome resistance to change.
Learn how to build a production-ready CI/CD pipeline using GitHub Actions, Docker, and Alembic migrations, with automated deployment to Hetzner Cloud. This comprehensive guide covers everything from containerization to zero-downtime deployments.
Transform your data strategy assessment into an actionable roadmap. Learn how to prioritize initiatives based on business value, break down data silos, set meaningful success metrics, and build a flexible 12-18 month plan that drives real results.
Most organizations claim to have a data strategy, but few can explain it. Learn the three critical reasons data strategies fail and discover the five foundational elements needed to build a strategy that aligns with business objectives and drives real value.
Transform database maintenance expertise into a $1.2M+ consulting practice through assessment-driven business development. Learn proven methodologies for generating recurring revenue, building client partnerships, and scaling maintenance consulting services.
Achieve 67% better query performance in 90% less maintenance time through fragmentation-based optimization. Master intelligent index maintenance that improves database performance rather than disrupting business operations.
Move beyond green checkmark syndrome to professional backup strategies that ensure recovery capability. Learn intelligent backup automation, verification techniques, Availability Group integration, and cloud optimization that prevented a $2.8M HIPAA violation.
Discover Ola Hallengren's free, open-source maintenance solution—the industry standard trusted by millions of databases worldwide. Transform your SQL Server maintenance from business liability into competitive advantage through intelligent backup, integrity, and index automation.
Discover how outdated SQL Server maintenance practices cost businesses millions in lost revenue, regulatory penalties, and competitive disadvantage. Learn why default maintenance plans fail in modern 24/7 business environments and what professional-grade alternatives exist.
Learn how to build a disaster recovery consulting practice that generates recurring revenue. Transform your expertise into a sustainable business that helps organizations build true resilience.
Discover how cloud-native disaster recovery can reduce costs while improving capabilities. Learn to leverage cloud technologies for resilient, cost-effective business continuity solutions.
Compare SQL Server disaster recovery technologies and learn how to choose the right approach for your requirements. From Always On to Log Shipping, master the tools that ensure business continuity.
Discover why most backup strategies create false confidence and learn how to build verification systems that ensure your backups will work when you need them most.
Learn how to align recovery objectives with business requirements to avoid million-dollar misunderstandings. Master RTO and RPO calculations that drive cost-effective disaster recovery strategies.
Discover the 47 critical assessment points that determine whether your organization will survive or succumb during disaster scenarios. Move beyond backup confidence syndrome to proven resilience.
The dangerous belief that having backups equals having disaster recovery is costing organizations millions. Learn why most disaster recovery plans fail and how to build true resilience that becomes a competitive advantage.
A practical, copy-pasteable introduction to Git covering installation, configuration, core commands, repository management, remote connections, and branching basics for beginners.
Master the essential maintenance tasks that keep SQL Server running at peak performance. From index maintenance to statistics updates, learn the practices that prevent problems before they start.
Implement bulletproof high availability and disaster recovery solutions for SQL Server. From Always On Availability Groups to failover clustering, ensure your databases are always accessible.
Set up comprehensive monitoring and alerting for your SQL Server environment. Learn to identify issues before they become problems and maintain optimal database health.
Transform your sluggish SQL Server into a high-performance machine. Learn proven techniques for query optimization, index management, and system configuration that deliver real results.
Master the art of SQL Server backup and recovery. From simple backup strategies to complex disaster recovery scenarios, learn how to protect your data and ensure business continuity.
Deep dive into SQL Server authentication modes, user management, and role-based security. Learn how to implement robust security measures that protect your data while maintaining operational efficiency.
Why Your Database is Silently Costing You Money. Every day, SQL Server environments across the globe are hemorrhaging money. Not from catastrophic failures or security breaches—though those happen too—but from the silent killers: inefficient configurations, forgotten security gaps, and performance bottlenecks that compound over time.
This guide takes an existing Meltano proof-of-concept and elevates it by using a true data source and target database. We will be working with public REST API endpoints as an extractor, and we will use PostgreSQL as a loader.
Containerize a Meltano EL pipeline with Docker to get a reproducible, self-contained workflow that produces a JSONL artifact.
Your comprehensive resource for migrating to AWS RDS SQL Server, implementing bulletproof security, and mastering cloud database operations. Covers migration strategies, DMS, backup and recovery, SSIS/SSRS configuration, security fundamentals, and operational best practices.