What data access do you need from us?

Read only access to a sample of your source data is enough to start. A few thousand records from each source system gives us enough to test transformation logic, schema handling, and load behavior. We sign an NDA before any data is shared.

Which pipeline technologies do you work with?

We work with Postgres, MySQL, BigQuery, Snowflake, Redshift, Kafka, Kinesis, dbt, Airbyte, and custom ETL. The POC uses the technology your team will actually maintain, not a tool we prefer.

Can you test a pipeline we already started building?

Yes. If you have a partially built pipeline, we can review it, run it against real data, identify where it breaks, and deliver a report on what needs to change before the full build. This is often faster than starting from scratch.

How is this different from just writing a data spec?

A spec documents what should happen. A POC proves what does happen with your actual data. Real schemas have edge cases, nulls, encoding issues, and volume spikes that never appear in documentation. The POC surfaces those before they become production incidents.

What happens if the pipeline architecture does not work?

The report explains exactly what failed and why, then recommends an alternative approach. Discovering the architecture needs to change during a 7 to 10 day POC is far less costly than discovering it 3 months into a full build.

Data Pipeline POC

Prove Your Data Pipeline
Works Before You Build It

Data pipeline architecture decisions made on paper often fall apart at real volume, schema complexity, or latency requirements. A data pipeline POC runs your actual data through the architecture in 7 to 10 days so you know it works before you build the full system.

7 to 10 day delivery

Fixed price

Full source code

Get Your Fixed-Price Estimate or book a 15-min fit call→

What This POC Proves

Eight critical questions answered with real data and measured performance numbers before you commit to a full pipeline build.

Ingestion throughput: confirm your pipeline handles peak volume without lag or data loss

Transform logic: prove complex transformation rules produce correct output on real data

Latency: measure end to end time from source event to queryable record at realistic volume

Schema evolution: test whether the pipeline handles changing source schemas without breaking

Data quality: validate deduplication, null handling, and type coercion before they hit production

Failure recovery: confirm dead letter queues and retry logic behave correctly when upstream breaks

Cost at scale: model infrastructure cost at 10x volume based on measured resource consumption

Query performance: verify downstream analytics respond within acceptable time at realistic row counts

Pipeline Types We Test

Each pipeline type has different architecture decisions, failure modes, and performance characteristics worth validating early.

ETL Pipeline

Extract from source systems, transform with complex business rules, and load into a target database or warehouse. We test your transformation logic against real records and measure row accuracy before you build the full scheduled pipeline.

Real Time Streaming

Kafka, Kinesis, or Pub/Sub based pipelines that process events as they arrive. We build a working consumer, run it at representative volume, and measure P95 latency so you know the architecture handles your throughput requirements.

Data Warehouse Load

Prove that your staging tables, dimension models, and fact table loads behave correctly with real data. We test incremental and full load patterns, measure query time on representative datasets, and document partition and clustering decisions.

Analytics and Reporting

Connect raw source data to a working analytics layer. We build the data model, run sample queries, and measure dashboard load time so your BI team knows the foundation is solid before you commit to the full warehouse design.

Build Timeline

Seven to ten days from data access to a working pipeline and a written performance report.

Day 1

Architecture review and data access

Map source systems, target systems, and transformation requirements. Access sample data. Define the success criteria: throughput, latency, accuracy threshold.

Days 2 to 4

Pipeline build and first run

Build the ingestion, transformation, and load layers. Run end to end with sample data. Log errors, edge cases, and schema issues encountered.

Days 5 to 7

Volume testing and tuning

Run at representative volume. Measure throughput, latency, and resource consumption. Tune batch sizes, parallelism, and indexing.

Days 8 to 10

Report and delivery

Document findings: what worked, what failed, measured performance numbers, and recommended production architecture with estimated infrastructure cost.