One source of truth Real-time when it matters
Pipelines that do not require a rescue rota. Dashboards leaders actually open.
What we do here.
Data platforms are as good as the operators who trust them. We build lakehouses warehouses and streaming pipelines that get opened at 9am by the people who need them. Every pipeline has an owner every dashboard has a purpose and every schema change has a migration path.
Every capability that ships behind a senior owner.
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.
ETL/ELT Pipelines
Ingestion pipelines that pull from APIs databases and files then land clean data in the warehouse. We prefer ELT with dbt where the warehouse can handle it and stream CDC where batch windows hurt.
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.
A four-step delivery method.
Model
Dimensional modeling that survives the next 5 years of schema drift not the next sprint.
Ingest
CDC where it fits batch where it doesn't. Schema enforcement at the boundary.
Serve
Warehouse for analytics lakehouse for ML streaming for real-time. Pick the one that matches the question.
Trust
Data quality monitors freshness SLAs and lineage that operators can read.
What clients measure.
- One canonical answer to any business question
- Dashboards refreshed in seconds not overnight
- Data quality visible before it breaks the report
- Schema changes shipped safely with contract tests
- Analytics cost predictable and traceable
The tools we reach for first.
Compose several into a program.
Digital Transformation
Modernizing what runs the business without freezing what pays the bills.
Cloud & DevOps
Infra you can reason about at 3am priced against what you actually use.
Artificial Intelligence
RAG grounded in your data. Evaluations that catch regressions. Guardrails that hold.
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