Custom big data services
Our big data engineers have built data platforms processing petabytes of data for Fortune 500 companies. They design architectures that are cost-effective, performant, and maintainable at any scale....
We build data lakes on S3, ADLS, or GCS with proper partitioning, cataloging, and governance for cost-effective storage and fast query performance.
We use Kafka, Flink, and Spark Streaming to build event-driven architectures that process millions of events per second with sub-second latency.
We design modern data warehouse architectures with columnar storage, automatic scaling, and cost-optimized compute for faster analytics at lower cost.
We implement modern ELT patterns using dbt, Airflow, and Fivetran with built-in data quality checks, lineage tracking, and error handling.
We build analytics platforms using Tableau, Looker, or Power BI connected to optimized data models that enable business users to explore data independently.
We help organizations decentralize data ownership while maintaining governance, quality, and discoverability through federated data platforms.
"They built a data platform that processes 1 billion events daily. Our analytics team went from waiting days for reports to getting real-time insights."
Andrew Kim
CDO, LogiTrack
Case Studies
Real results from real projects. See how we've delivered transformative big data solutions.
Built a Data Platform Processing 1 Billion Events Daily
Designed a streaming architecture on Kafka and Flink powering real-time fleet tracking and route optimization.
Migrated a 500TB Data Warehouse to Snowflake
Modernized data infrastructure reducing query times from hours to seconds while cutting costs by 50%.
Implemented a Data Mesh for a Fortune 500 Company
Decentralized data ownership across 15 domains while maintaining enterprise-wide governance and quality standards.
Tools & Technologies
We combine industry-standard frameworks with modern tooling and proven internal processes to accelerate delivery.
Processing
- Apache Spark
- Flink
- Kafka
- Beam
Storage
- Snowflake
- BigQuery
- Redshift
- Delta Lake
Orchestration
- Airflow
- Dagster
- Prefect
- dbt
Analytics
- Tableau
- Looker
- Power BI
- Metabase
Frequently Asked Questions
Have more questions? Talk to an expert — we're happy to help.
A data lake stores raw data in any format at low cost. A data warehouse stores processed, structured data optimized for analytics. Modern architectures often combine both in a 'lakehouse' pattern.
We implement automated quality checks using Great Expectations, dbt tests, and custom validation rules that run on every pipeline execution, catching issues before they propagate.
Yes. We build streaming architectures using Kafka and Flink that process millions of events per second with exactly-once semantics and sub-second latency.
We implement data catalogs, access controls, lineage tracking, PII detection, and compliance policies that ensure data is discoverable, trustworthy, and used responsibly.
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