Real-Time Analytics
Builds streaming ingestion processing and serving layers so decisions can be made on data that is seconds old instead of hours old. The outcome is operational visibility and automated response where latency actually changes the business result such as fraud logistics personalization or plant telemetry.
Everything included under this practice line.
Streaming ingestion from event sources IoT devices application logs and change data capture off transactional databases
Stream processing topology design covering windowing joins watermarking and exactly-once semantics
Materialized view and streaming warehouse patterns for low-latency serving to dashboards and applications
Real-time feature stores feeding online ML models for scoring personalization or risk decisions
Backpressure replay and dead-letter handling so the pipeline degrades gracefully under load or bad data
Alerting and anomaly detection on live streams rather than after the batch job runs
Cost and latency budgeting per use case so real-time is used where it earns its keep and batch where it does not
Reference architectures for lambda kappa or streaming-only patterns based on consistency requirements
The stack we reach for.
What the business gets, measured.
- Fraud and abuse caught while the transaction is still in flight rather than in a next-day report
- Operational teams acting on live signals instead of a batch view of the past
- Better customer experience through in-session personalization and inventory accuracy
- Reduced downtime through real-time alerting on telemetry from equipment and services
- New revenue paths unlocked where the product depends on live data such as trading logistics or gaming
The specialists behind this practice line.
Streaming data engineers own the pipeline topology and processing guarantees since correctness under partial failure is where these systems earn or lose trust. Site reliability and platform specialists sit alongside them for capacity backpressure and on-call design with domain experts defining what live actually means for their use case.
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.
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.
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