Power BI & Tableau Dashboards
Delivers focused dashboard implementations on Power BI and Tableau from data model through published workspace with the governance and performance work needed to make them stick. The outcome is dashboards that load fast tell the truth and stay useful after go-live.
Everything included under this practice line.
Data model design in Power BI using star schema proper relationships and calculated columns versus measures decisions
DAX authoring for time intelligence ratios running totals and complex filter contexts
Tableau data source design including extracts versus live connections published data sources and row-level security
Performance tuning covering query folding aggregations incremental refresh and visual reduction on slow dashboards
Deployment pipelines using Power BI workspaces and deployment pipelines or Tableau projects and content migration
Row-level and object-level security wired to Azure Entra ID Okta or the enterprise directory
Custom visuals calculated field libraries and reusable template design for consistent look and behavior
Migration between platforms or from legacy tools such as SSRS Cognos or BusinessObjects
The stack we reach for.
What the business gets, measured.
- Dashboards that render in seconds instead of timing out on the board meeting
- Lower licensing cost through right-sized capacity and workspace consolidation
- Reliable refreshes with fewer support tickets for stale or broken visuals
- Trust in executive numbers because security and row-level filtering behave correctly
- Faster delivery of new dashboards from a reusable template and component library
The specialists behind this practice line.
Power BI and Tableau specialists lead the dashboard build and performance work because platform-specific knowledge of DAX VizQL and refresh internals is where most dashboards go wrong. Data modelers shape the underlying semantic layer so the front end is not compensating for a bad model.
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.
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.
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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