Data Warehousing
Designs and implements the central analytical store where cleaned modeled and governed data lives for reporting BI and cross-functional analysis. The outcome is one place the business can query with confidence instead of arguing about which spreadsheet has the right number.
Everything included under this practice line.
Warehouse platform selection based on workload profile concurrency cost model and existing ecosystem fit
Dimensional modeling using Kimball star schemas Data Vault or wide denormalized marts depending on use case
Slowly changing dimension handling surrogate key strategy and historical accuracy requirements
Semantic layer definition so metrics like revenue active customer or margin mean the same thing across tools
Workload management warehouse sizing and query optimization to keep costs predictable under growth
Role-based access control row-level security and masking for sensitive attributes
Migration paths from legacy on-prem warehouses such as Teradata Netezza or Oracle Exadata to cloud-native platforms
Cost governance including query attribution chargeback reporting and idle-compute suspension
The stack we reach for.
What the business gets, measured.
- Consistent definitions of core business metrics across finance sales operations and product
- Predictable attributable analytics spend instead of a runaway monthly bill
- Faster onboarding for new analysts because the model is documented and queryable
- Reduced dependency on brittle legacy warehouse licenses and hardware
- Cleaner audit trail for financial and regulatory reporting
The specialists behind this practice line.
Data warehouse architects and dimensional modelers own the schema and semantic layer partnering with finance revenue and operations leaders to nail down metric definitions. Cloud and FinOps specialists tune the platform for concurrency and cost once the model is in place.
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.
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.
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