We transform siloed, fragmented enterprise data into unified intelligence, engineering real-time analytics pipelines, data governance frameworks, and semantic data layers that make your data a strategic asset.
Modern enterprises are drowning in data but starving for insight. The problem isn't volume; it's fragmentation, inconsistency, and governance gaps that prevent data from flowing where it's needed.
We architect data intelligence platforms on the open lakehouse standard, combining batch and streaming processing with business-friendly semantic layers and automated data quality enforcement. The result: trusted data that analysts, data scientists, and AI systems can use with confidence.
Key differentiator: We treat data as a product, with SLAs, owners, consumers, and quality guarantees enforced by code, not documentation.
A breakdown of the specific tools, patterns, and practices we bring to every data intelligence engagement.
Apache Iceberg + Delta Lake open table formats on S3/ADLS/GCS with time-travel, schema evolution, and ACID transactions. Unified batch + streaming with Flink Iceberg sink. Compaction strategies, partition pruning, and Z-ordering for petabyte-scale performance.
Apache Flink for stateful, exactly-once streaming with windowed aggregations, CEP (Complex Event Processing), and watermark management. Kafka Streams for lightweight topology design. ksqlDB for SQL-based stream processing without a separate cluster.
Modern ELT with dbt Core v1.8, featuring modular SQL models, incremental strategies, cross-database macros, Jinja templating, and automated testing (source freshness, schema, data). dbt Semantic Layer for business-level metric definitions shared across all BI tools. dbt Cloud for CI/CD pipeline automation.
Apache Atlas for metadata lineage and classification, DataHub for lineage graphs and usage analytics. Column-level encryption, row-level security, data masking policies. GDPR/CCPA data residency enforcement. Automated PII discovery with Microsoft Purview integrated across cloud data stores.
Great Expectations for declarative data quality suites with automated alerting. Soda Core for scheduled profiling and anomaly threshold configuration. dbt tests for schema and referential integrity enforcement. Monte Carlo for end-to-end observability with anomaly detection using statistical baselines across 300+ checks.
Power BI Premium Gen2, Tableau, and Superset connected to semantic layers for consistent metrics. Cube.dev pre-aggregations for sub-second dashboard response times. Row-level security synced from Active Directory groups. Embedded analytics via React components for custom portal experiences.
Every engagement begins with a comprehensive data landscape assessment before any architecture decision is made. We map what exists, what's trusted, and what business decisions it should support.
Our delivery teams include data engineers, analytics engineers, data governance specialists, and platform architects working in 2-week sprints with bi-weekly data quality checkpoint reviews.
Catalog existing data sources, assess quality and freshness, and interview data consumers across business units. Produce a data maturity scorecard covering completeness, timeliness, consistency, and governance. Deliver a prioritized modernization roadmap with effort estimates and business value scores.
Provision the open lakehouse with Iceberg/Delta on your cloud (AWS S3+Glue, Azure ADLS+Unity Catalog, GCP GCS+BigLake). Migrate highest-priority tables with full historical data. Establish CI/CD for data pipelines with automated schema validation, integration tests, and promotion gates from dev to prod.
Build Kafka + Flink ingestion pipelines for operational events with exactly-once semantics and dead-letter queue handling. Implement dbt transformation layer with automated testing gates on every pull request. Configure Airflow DAGs with SLA monitoring, alerting, and automatic retry logic with exponential backoff.
Deploy DataHub with automatic lineage scraping from Spark, dbt, and Airflow metadata. Implement Great Expectations test suites on all critical datasets with Slack alerting on failures. Define data contracts with SLAs, ownership assignments, and deprecation workflows enforced through pull request automation.
Deploy dbt Semantic Layer with Cube.dev caching for sub-second BI query response. Train business analysts on Power BI/Tableau connected to the governed semantic layer. Implement usage analytics with DataHub to track dataset adoption, identify unused assets, and surface popular metrics to new users.
Concrete examples of how data intelligence platforms are delivering measurable value across industries.
Modernized a federal agency's COBOL-based financial reporting system to a real-time Iceberg lakehouse on AWS GovCloud. Flink ingestion pipelines pull from 12 operational systems. dbt transformed 4TB/day with 200+ automated quality checks. Financial report generation time collapsed from 3 days to 15 minutes, enabling daily instead of monthly executive reporting cycles.
99% faster financial reportingBuilt Kafka + Flink pipeline processing 2M events/second for a large retail platform. Real-time dbt aggregations feed a Pinecone product embedding index for personalized recommendations. Customer behavior signals (page views, cart additions, purchase history) are incorporated within 30 seconds of occurrence, driving significant uplift in recommendation-attributed revenue.
$12M attributed revenue increaseMigrated a 12-hospital health system from a centralized Oracle data warehouse to a federated Delta Lake lakehouse architecture. Domain teams in clinical, finance, and operations each own their data products. DataHub lineage tracking provides auditable data flow graphs proving HIPAA compliance for every downstream report and analytics workload.
HIPAA audit time: 3 weeks → 2 daysImplemented a Flink-based real-time risk calculation engine ingesting market data feeds, trade events, and reference data from 8 upstream systems. Monte Carlo monitors 300+ data quality checks with automated escalation to risk desk on violations. The platform calculates portfolio VaR and stress scenarios in near real-time, replacing an overnight batch process.
Real-time risk calculations in <200msStart with a Data Architecture Assessment: we'll audit your current data landscape and deliver a modernization roadmap in 2 weeks.