Expert insights on data solutions, database administration, data engineering, and DevOps
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.