Data Lake Solutions
Establishes a scalable store for raw semi-structured and unstructured data alongside the governance and access patterns that make it useful rather than a swamp. The outcome is a single place to land any data cheaply and query it directly when needed for analytics ML or archival.
Everything included under this practice line.
Lakehouse architecture design using open table formats such as Delta Lake Apache Iceberg or Apache Hudi
Zone layout for raw curated and consumption data with clear promotion rules between them
File layout partitioning and compaction strategy so query performance does not degrade as volume grows
Metadata catalog and discovery layer so analysts can find and understand what is in the lake
Access control down to file table column and row level integrated with the enterprise identity provider
Unstructured data handling for images PDFs audio and logs including feature extraction pipelines
Query engine selection across Trino Presto Athena or Spark SQL based on workload shape
Lifecycle policies for tiering cold data to cheaper storage without breaking downstream queries
The stack we reach for.
What the business gets, measured.
- Cheap storage for data that has future value but no immediate consumer
- One platform serving both BI and machine learning workloads instead of duplicated copies
- Faster experimentation because data scientists can access raw sources directly under governance
- Reduced vendor lock-in through open table formats and standard query engines
- Compliance-ready retention and deletion controls for regulated data classes
The specialists behind this practice line.
Lakehouse architects define the storage layout and table format choices working with security and identity specialists to wire in fine-grained access control. ML and analytics practitioners are looped in early so the lake actually serves the workloads it is meant to.
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.
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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