ETL/ELT Pipelines
Builds the extraction transformation and load flows that move data from source systems into the warehouse or lake in a controlled testable and observable way. The outcome is data that arrives on time in the right shape with a clear record of what happened to it.
Everything included under this practice line.
Pattern selection between ETL and ELT based on source constraints transformation complexity and warehouse compute economics
Incremental load design using change data capture watermarks or partition-based reloads instead of full refreshes
Idempotent transformation logic so pipelines can safely rerun without duplicating or corrupting data
Testing framework covering unit tests on transformations source freshness tests and row-level assertions
Error handling dead-letter routing and retry policies for transient source failures
Environment separation with dev staging and production branches tied to CI/CD
Backfill strategy for historical loads and schema changes without downtime for consumers
Cost and runtime monitoring per pipeline so slow or expensive jobs are visible before they become a bill shock
The stack we reach for.
What the business gets, measured.
- Reports refresh on schedule instead of missing the morning deadline
- Fewer late-night pages because failures are caught routed and retried automatically
- Lower warehouse compute cost through incremental loads and smart scheduling
- Faster delivery of new data sources because the framework is reusable
- Confidence that reprocessing history will not create duplicates or silent gaps
The specialists behind this practice line.
Analytics engineers own the transformation logic and testing while integration specialists handle the source-side extraction quirks unique to each system such as SAP Salesforce or NetSuite. DevOps engineers wire the pipelines into CI/CD and monitoring.
Compose several capabilities into one engagement.
Data Engineering
We build the pipelines schemas and orchestration that move data from source systems into something a query engine can actually use. Airflow or Dagster dbt for transforms and tests that fail loudly.
Data Warehousing
Snowflake BigQuery or Redshift stood up with sensible cost controls and a modeled semantic layer. We separate storage from compute size warehouses per workload and keep query costs off the CFO's radar.
Data Lake Solutions
Lakehouse setups on S3 ADLS or GCS using Iceberg Delta or Hudi. Partition layouts and compaction jobs tuned so Trino Spark and Athena all read the same tables without stepping on each other.
Business Intelligence
Metric layers governed dashboards and self-serve access for the people who actually need the numbers. We define metrics once in code so finance product and ops stop arguing about whose revenue figure is right.
Power BI & Tableau Dashboards
Dashboards built in Power BI or Tableau that load fast and don't fall over when someone adds a filter. DAX and LOD expressions written by people who've debugged them at 2am.
Real-Time Analytics
Streaming pipelines on Kafka or Kinesis feeding ClickHouse Pinot or Druid for sub-second queries. Useful when a nightly batch is too slow and ops needs to see what happened five seconds ago.
Let's talk
Book your free consultation with an AUERON engineer
One senior engineer will respond within one business day.
Prefer email? hello@aueron.in