Data Engineering Consulting: Data Pipelines From Raw Data to Ready Answers

Data ingestion automatically collects records from accounting software, CRM (customer relationship management) systems, ERP (enterprise resource planning) systems for finance and stock, and CSV files (comma-separated exports). Scheduled database requests, APIs (connections between programs), and file watchers collect each source.

ETL (extract, transform, load) cleans data before it is stored, which suits sensitive data or older on-premises setups.

ELT (extract, load, transform) loads raw data first and cleans it inside a cloud warehouse such as Snowflake or BigQuery.

If your applications already run on Amazon Web Services, it rarely makes sense to ship the data somewhere else to report on it. On AWS we use Amazon S3 as the landing zone: S3 is low-cost cloud file storage, and a data lake is simply an organized collection of raw files kept there before they are cleaned. AWS Glue is Amazon's service for cataloging those files and running the cleaning and transformation jobs. Clean data is then loaded into Amazon Redshift, Amazon's data warehouse, or into Amazon RDS when a regular relational database is enough.

Example use cases might include a logistics company whose dispatch and telematics apps were built on AWS, a software or e-commerce business with order data already in RDS, and a manufacturer collecting machine and quality files in S3. We keep jobs small and well-named, set up alerts through Amazon's monitoring tools, and document costs so the monthly AWS bill holds no surprises.

Before building, we agree how fresh the data needs to be, because hourly and nightly loads cost very different amounts on AWS. Every Glue job, S3 folder, and Redshift table we create is documented, tagged for cost tracking, and owned by your AWS account, so nothing depends on our access to keep running.

Beyond the cloud vendors' own tools, there is a widely used open toolkit that works on any of them. Python is a programming language that handles the odd jobs no connector covers: an awkward vendor API, a PDF report that must be read into rows, or forty differently shaped branch spreadsheets. dbt (data build tool) lets the cleaning rules be written as plain SQL files that are version-controlled, tested automatically, and documented, so the logic behind every number is visible instead of buried in someone's workbook. Airflow is a scheduler that runs each step in the right order, retries failures, and shows the whole pipeline as a picture anyone can follow. For very large data volumes, Spark spreads the work across many machines.

We reach for this toolkit when a company wants to avoid being tied to one cloud, already has developers who know Python, or needs tested, auditable transformation logic for regulated reporting in finance, healthcare, or government. It works alongside Snowflake, BigQuery, Redshift, Azure SQL, and PostgreSQL alike, and it serves Stages 1 and 2 of the pipeline, pulling data in and cleaning it up.

Everything is kept in version control, so every change to a cleaning rule is recorded with who made it and why.

We are based in Oklahoma City, work on-site in OKC and Tulsa when needed.