Most batch pipelines aren’t broken — they’re just slowly getting worse. A job that ran in 40 minutes last year now takes four hours, costs three times more in compute, and nobody wants to be the one who touches it. When it fails at 2am, the morning dashboards are quietly wrong, and finance usually finds out before engineering does.
We rebuild batch on Spark, Databricks, AWS Glue and Airflow with the unglamorous things done properly: partitioning and file sizing that match how you actually query, incremental loads instead of nightly full refreshes, idempotent jobs that are safe to re-run, and alerts that tell you which table is stale rather than “a DAG failed.” The usual result is the same data, a smaller cloud bill, and a load window that finishes before your team logs in.
Key Features
Incremental & CDC Loads
Delta / Iceberg Tables
Airflow Orchestration
Spark & Databricks Tuning
Idempotent Re-Runs
Automated Backfills
Data Quality Gates
Cost & Runtime Monitoring
Key Advantages
A Compute Bill You Can Explain
Right-sized clusters, spot and autoscaling policies, and cost tracked per pipeline — not one lump cloud invoice nobody can decompose.
Safe to Re-Run
Idempotent, checkpointed jobs. A 2am failure becomes a retry, not an incident bridge.
A Load Window That Actually Fits
Jobs tuned to finish before business hours — not during them, and not into them.
Bad Data Gets Quarantined, Not Averaged In
Schema and quality checks at ingest, so a broken upstream feed fails loudly instead of silently skewing your reports.
Backfills Without Fear
Replay months of history on demand without corrupting the tables your dashboards are reading right now.
Table Formats Built to Last
Delta and Iceberg give you ACID updates, time travel, and schema evolution — so an upstream column change doesn't take down your warehouse.
Trusted by Innovators Across Industries
The data engineering work happens underneath: pipelines that are clean, tested, and production-grade, so the AI layer on top actually holds up. That's the difference between a chatbot that impresses in a demo and an agent your team can put into a real workflow.
What Our Clients Say
Trusted by global enterprises and fast-growing startups to deliver reliable, scalable, and intelligent data solutions.
Frequently Asked Questions
Here are some of the most common questions we receive from businesses exploring our solutions.
What kind of data do you work with?
We handle structured, semi-structured, and unstructured data from various sources, including databases, APIs, files, IoT devices, and streaming platforms.
Can you work in our cloud environment?
Yes, with our Bring Your Own Cloud (BYOC) model, we build and manage data pipelines securely within your existing cloud infrastructure.
What visualizations or reports can you deliver?
We create interactive dashboards and visual reports using tools like Power BI, Tableau, or custom-built frontends tailored to your KPIs.
Is your service scalable as our data grows?
Yes, all our solutions are cloud-native and built to scale with your data volume, user base, and business complexity.
See Our Work in Action
Watch how we transform raw data pipelines into actionable dashboards, with AI agents enabling real-time insights.
AI Agents & LLM Apps Built to Run in Production
Most AI agents fail quietly - on stale data, broken pipelines, or retrieval that returns the wrong context. GKCodeLabs builds AI agents, RAG systems, and LLM applications on data infrastructure we engineer ourselves - so what you ship holds up under real traffic, not just demos.