Data Engineering
Database systems and pipelines built for speed to insight
TrendLab Analytics designs database systems and data pipelines for small and mid-size businesses with one measure of success: how fast a new piece of data becomes a reported insight. Database design, ETL and ELT pipelines, and migrations from spreadsheets and legacy systems to PostgreSQL, SQL Server, Snowflake or BigQuery, all built to feed reporting and dashboards directly instead of another export. Based in Atlanta, Georgia and serving clients nationwide, the practice is led by a principal consultant with a decade of production SQL across Snowflake, BigQuery, Teradata and PostgreSQL and 17 years in healthcare and financial services analytics, where clean, auditable data is the job.
Who this is for
- •Companies whose "database" is a folder of spreadsheets and a shared drive
- •Teams stuck on an aging Access, on-prem SQL or vendor system that nobody wants to touch
- •Businesses adding dashboards or AI that need a reliable, documented data foundation first
What you get
- •Data models and schemas designed around how your business actually reports
- •Automated pipelines that load, validate and transform data on a schedule, with alerts on failure and the reporting layer wired in at the end so insight is minutes behind the source, not days
- •Ingestion of complex and unstructured sources (documents, logs, exports, APIs) into clean, queryable, AI-ready tables
- •Agentic data pipelines: agents that pull, clean, validate and report on new data without a human in the loop, with checks that stop bad data at the door
- •A semantic and metrics layer so every dashboard and every AI agent uses the same definitions: one single source of truth
- •Migrations from legacy systems and spreadsheets with reconciliation reports proving nothing was lost
- •Query optimization and indexing for reports that currently take minutes
- •Documentation, data dictionary and access controls
Results from similar work
500+
users on one reporting foundation
Warehouse and pipeline work behind an executive dashboard suite serving hundreds of end users.
70%
faster reporting after re-platforming
Claims and membership data pipelines rebuilt in Python and SQL.
How it works
- 1.Discovery call to inventory sources, volumes and the reports that depend on them
- 2.Target design and migration plan with a cutover checklist
- 3.Build and test with parallel runs against the old system
- 4.Cutover, reconciliation sign-off and hand-off
Timeline
Small migrations and pipelines take 2 to 4 weeks. Multi-system migrations are phased over 1 to 3 months.
Pricing
Fixed-scope quotes per phase after the discovery call. Cloud or licensing costs are separate and estimated up front.
Tools
Snowflake · BigQuery · Teradata · PostgreSQL · SQL Server · Python · dbt-style transformations · Airflow and scheduled jobs · AWS
Data Engineering questions answered
Straight answers. If yours is not here, the discovery call is free.
Can you migrate us off Access or an old on-prem database?
Yes. We plan the target schema, migrate with reconciliation reports, run both systems in parallel, and cut over when the numbers match.
Will our reports break during a migration?
That is what the parallel run is for. Existing reports are pointed at the new system only after they reconcile.
Do we need a cloud data warehouse?
Not always. Many small businesses are well served by a managed Postgres or SQL Server. We recommend the smallest platform that meets your reporting and growth needs.
How much does data engineering work cost?
Projects are quoted as fixed-scope phases after a free discovery call, with cloud and licensing costs estimated separately.
Do you work outside Atlanta?
Yes. We are Atlanta-based and work with clients across the United States remotely.
Start with a free 30-minute call
Tell us the process, report or dataset that is costing you the most time. You leave with a clear next step, whether or not that is us.
Other services: Business Process Automation · Analytics & Dashboard Reporting · AI & Machine Learning · Agentic AI & Context Engineering