Open lakehouses for federal-scale data.

Overview: why federal agencies are moving to lakehouses

The federal data landscape is fracturing under the weight of legacy patterns. Most civilian agencies still carry some combination of a legacy Oracle Exadata warehouse, an aging Cloudera cluster, a disconnected Hadoop environment nobody remembers architecting, and three or four SaaS BI subscriptions that each hold partial copies of the same data. Every new analytic question triggers another data copy, another pipeline, another governance gap. The lakehouse pattern — warehouse-grade compute over open-format data on object storage — collapses that mess.

A lakehouse is not a data lake with a new marketing coat. The core technical innovation is the open table format: Apache Iceberg, Delta Lake, and Apache Hudi each add a metadata layer over Parquet files that gives ACID transactions, time travel, schema evolution, partition evolution, and concurrent readers and writers — all on plain object storage like S3 GovCloud or Azure Data Lake Storage Gen2. That turns a pile of Parquet files into a real database table that Spark, Trino, Snowflake, BigQuery, DuckDB, and Flink can all read without ETL copies.

For federal agencies, the consequences are substantial. Storage costs collapse (object storage is five to ten times cheaper than warehouse storage). Vendor lock-in collapses (the data is in an open format, portable by definition). ML pipelines stop needing a separate feature store in a different cloud. Cross-agency data sharing becomes a governance change instead of an engineering project. And the entire stack runs inside FedRAMP High and DoD IL4/IL5 boundaries when properly architected.

Precision Federal designs, builds, and operates these lakehouses for federal missions. Below is the complete capability surface.

Our technical stack

We do not pitch a single vendor. We build on the stack the mission needs. A full inventory of what we deploy:

• Object storage: Amazon S3 GovCloud (US-East, US-West), Azure Data Lake Storage Gen2 (Azure Gov, Azure Gov Secret), Google Cloud Storage (Assured Workloads), MinIO for on-prem and air-gapped deployments.
• Open table formats: Apache Iceberg (our default for engine neutrality), Delta Lake (on Databricks), Apache Hudi (for heavy upsert and streaming).
• File formats: Parquet, ORC, Avro, GeoParquet, Lance for AI-native workloads.
• Compute engines: Apache Spark, Databricks Runtime, Trino, Presto, DuckDB, Ray, Dask, Apache Flink.
• Lakehouse platforms: Databricks on AWS GovCloud and Azure Government, AWS EMR, AWS Athena (Iceberg), Starburst Galaxy on Gov clouds.
• Catalogs: Unity Catalog (Databricks), AWS Glue Data Catalog, AWS Lake Formation, Apache Polaris, Project Nessie, Hive Metastore (legacy).
• Streaming: Kafka, MSK, Kinesis, Event Hubs, Pulsar. See streaming data.
• Orchestration: Airflow, Dagster, Prefect, Databricks Workflows. See ETL / ELT.
• Transformation: dbt, SQLMesh, Spark SQL, Delta Live Tables.
• ML on lake: Databricks Mosaic AI, MLflow, Ray, Spark MLlib, PyTorch, TensorFlow, Feast feature store, Databricks Feature Store.
• Governance & lineage: OpenLineage, DataHub, Collibra, Atlan, Unity Catalog, Immuta, Privacera.
• Quality: Great Expectations, Soda, Monte Carlo patterns, dbt tests.
• Geospatial: Apache Sedona, H3, GeoParquet, Databricks Mosaic, PostGIS for serving.
• Search / retrieval: OpenSearch (FedRAMP), Elasticsearch, vector indexes (pgvector, LanceDB, Databricks Vector Search) for RAG over lake content.

Federal use cases

The lakehouse pattern reshapes workloads across agencies. Specific examples we have architected, shipped, or are pursuing:

• SAMHSA behavioral health analytics (confirmed past performance): production ML on treatment admissions and survey data, governed, reproducible, documented for federal standards. Demonstrates the full pattern at mission scale.
• Army sustainment and readiness (pursuing): lakehouse consolidation of GCSS-Army, LIW, AESIP, and maintenance systems into a unified analytic layer. Depot throughput, parts demand forecasting, and unit readiness rollups from a single governed source.
• Navy fleet logistics (pursuing): ship-level maintenance, sparing, and availability analytics. Time-series at vessel scale with Iceberg hidden partitioning.
• FBI investigative analytics (pursuing): cross-case pattern detection over structured and unstructured evidence. Lakehouse enables joint SQL and ML workloads without copying data out of the bureau's boundary.
• CDC / HHS public health surveillance: syndromic surveillance, outbreak detection, and treatment capacity modeling on governed public health data.
• NASA earth observation and mission telemetry: imagery catalogs, satellite telemetry, and mission science data in GeoParquet and Iceberg, queryable with Sedona and DuckDB.
• Treasury and IRS improper payment detection: cross-program joins over obligations, disbursements, and beneficiary data to surface duplicate and fraudulent payment patterns.
• NSF research data infrastructure: cross-directorate research data commons built on Iceberg for engine-neutral researcher access.
• DOE scientific computing: petabyte-scale experiment data organized for reproducible analysis with Parquet and Iceberg.
• DHS immigration and enforcement analytics: case-level analytics over TECS, ENFORCE, and ATS with row-level CUI controls.

Reference architectures

Architecture 1: Iceberg on AWS GovCloud with Trino and Spark

S3 GovCloud buckets organized by sensitivity (public / CUI / PII), each bucket a separate KMS customer-managed key. AWS Glue Data Catalog or Apache Polaris as the Iceberg catalog. Amazon EMR Serverless for Spark jobs, Trino on EKS for interactive SQL, Athena for ad-hoc analyst queries. AWS Lake Formation enforcing row- and column-level policy. Airflow on MWAA for orchestration. dbt for transformation. OpenLineage shipping to DataHub. All within a single VPC with PrivateLink endpoints, no public egress. Agency SSO via ICAM or Okta Federal. Audit logs to CloudTrail and the agency SIEM.

Fits: civilian agencies on AWS, cost-sensitive workloads, teams that want engine optionality and long-term openness.

Architecture 2: Databricks on Azure Government with Unity Catalog

Azure Data Lake Storage Gen2 with hierarchical namespace, sensitivity containers, and Azure Key Vault customer-managed keys. Databricks on Azure Government with Unity Catalog as the governance plane. Delta Lake as the storage format. Databricks Workflows for orchestration, Delta Live Tables for declarative pipelines, Mosaic AI for ML, Databricks SQL for BI. Power BI via Private Link for Section 508-conformant dashboards. Microsoft Purview for enterprise governance. All within Azure Gov IL5 boundary; Entra ID Gov for identity; logs to Sentinel.

Fits: DoD components, M365 GCC High agencies, teams that want the most integrated lakehouse UX.

Architecture 3: Air-gapped / on-prem Iceberg with MinIO and Trino

Some IC and DoD workloads cannot touch the commercial cloud. MinIO provides S3-compatible object storage on-prem. Iceberg tables written by Spark on Kubernetes (OpenShift or Rancher). Trino cluster on the same Kubernetes for SQL. Hive Metastore or Apache Polaris for catalog. Dagster for orchestration. Grafana for observability. Full lakehouse semantics, zero cloud dependency. Ideal for classified enclaves and tactical edge.

Delivery methodology

How a Precision Federal lakehouse engagement runs:

1. Discovery (weeks 1-3): source inventory, sensitivity classification, workload profiling, ATO landscape, cost baseline, consumer inventory. Output: a mission-data map nobody inside the agency has had before.
2. Design (weeks 3-6): engine selection, partition strategy, governance plane, network topology, identity integration, CI/CD pipeline, IaC scaffolding. Security controls mapped to NIST 800-53. Cost model with workload projections.
3. Build (weeks 6-14): Terraform infrastructure, Spark / dbt pipelines, Iceberg or Delta schemas, governance policies, monitoring, documentation. CI/CD with SCA, SAST, and SBOM generation.
4. ATO support (weeks 10-16, parallel): system security plan input, control narrative drafting, continuous-monitoring plan, penetration test support. We hand the authorization package to the agency ISSO ready for review.
5. Operate & evolve (ongoing): monitoring, cost reviews, schema evolution, performance tuning, continuous ATO evidence. SRE-grade ops if the engagement includes site reliability engineering.

Engagement models

• SBIR Phase I fixed-price: 6-9 month prototype, typical topic ceiling $150K-$250K. Lakehouse-focused SBIR scopes fit comfortably.
• SBIR Phase II fixed-price: 18-24 month production build, $1M-$2M. Full lakehouse with ATO support.
• Fixed-price prototype (non-SBIR): 90-day focused build on a defined scope. Typical range $75K-$300K.
• Time & materials task order: subbed under a prime on GSA MAS, CIO-SP4, Alliant 2, or agency BPA. Labor-category driven.
• OTA prototype: via Tradewind, Defense Innovation Unit, or consortia we participate in. Faster than FAR.
• Subcontractor to a prime: we bring the lakehouse specialty to a prime who owns the vehicle and ATO.
• Direct BPA / IDIQ task order: once vehicle access is in place.

Maturity model: where does your agency sit?

• Level 1 — Pilot: one dataset, one team, ad-hoc governance. Good for proving the pattern.
• Level 2 — Program: one program office, multiple datasets, formal governance, CI/CD, ATO inherited from cloud foundation.
• Level 3 — Enterprise: multi-program, cross-directorate, central catalog, enforced classification, SLOs on data freshness and query latency.
• Level 4 — Mission-integrated: real-time streams, ML workloads, serving endpoints feeding operational systems. Error budgets and SRE posture.
• Level 5 — Continuously monitored: ongoing ATO, automated evidence collection, federated sharing to partner agencies via Delta Sharing or Iceberg REST. Zero manual audit work.

Deliverables catalog

• Terraform / Bicep infrastructure-as-code repositories.
• Spark and dbt project repositories with tests and documentation.
• Iceberg / Delta table schemas with partition strategy rationale.
• Unity Catalog or Lake Formation policy-as-code.
• CI/CD pipelines with SAST, SCA, SBOM, and policy validation gates.
• OpenLineage lineage capture configured across Spark and dbt.
• Data dictionaries auto-generated from dbt docs and Iceberg metadata.
• NIST 800-53 control narratives (AC, AU, SC, SI families in particular).
• System Security Plan input suitable for ISSO intake.
• Runbooks for compaction, schema evolution, backfill, and incident response.
• Cost dashboards and monthly optimization reports.
• Training materials for the agency's own data engineers.

Technology comparison: Iceberg vs Delta vs Hudi

• Iceberg: engine-neutral. Read from Spark, Trino, Snowflake, BigQuery BigLake, DuckDB, Flink. Strong partition evolution, branching, and tagging. REST catalog protocol enables cross-org sharing. Default choice when the agency wants maximum portability.
• Delta Lake: tightest integration with Databricks and Spark. Strong tooling, change data feed, liquid clustering. Other engines read Delta but with fewer features. Default when Databricks is the primary compute.
• Hudi: streaming-first. Merge-on-read for heavy upsert workloads, record-level indexing. More operational complexity but unmatched for CDC-heavy pipelines into a lake.

Federal compliance mapping

A lakehouse touches a broad swath of the NIST 800-53 Rev 5 control catalog. Key families we address directly:

• AC (Access Control): AC-2 through AC-24. Row- and column-level policies, role-based access, federated identity, session management, attribute-based controls via Lake Formation or Unity Catalog.
• AU (Audit): AU-2, AU-6, AU-12, AU-16. Query-level audit logs shipped to the agency SIEM, cross-service correlation.
• CM (Configuration Management): CM-2, CM-6, CM-7. Infrastructure-as-code baselines, policy-as-code enforcement, drift detection.
• IA (Identification & Authentication): IA-2, IA-5, IA-8. MFA, PIV/CAC integration, federated SSO.
• SC (System & Communications Protection): SC-7, SC-8, SC-12, SC-13, SC-28. PrivateLink/Private Endpoint, TLS 1.3, FIPS 140-2/3 modules, customer-managed KMS.
• SI (System & Information Integrity): SI-4, SI-7. Continuous monitoring, integrity checks on Iceberg metadata.
• RA (Risk Assessment): RA-5 vulnerability scanning, container image scanning in CI/CD.

The cloud foundation (AWS GovCloud, Azure Gov, GCP Assured Workloads) is FedRAMP High authorized; the lakehouse layer inherits that baseline and adds workload-specific controls. Databricks and Snowflake each carry their own FedRAMP authorizations that further inherit upward.

Sample technical approach: consolidating an agency Cloudera cluster into Iceberg on GovCloud

A common scenario: the agency has a 300-node Cloudera cluster purchased in 2016, costing $2.4M per year in support plus hardware refresh, holding 1.8 petabytes of Hive-managed Parquet across 42 critical datasets. Query latencies are minutes. Only three people still know how to administer the cluster.

Our approach: provision S3 GovCloud buckets with CMK encryption and sensitivity-tagged prefixes. Stand up AWS Glue as the Iceberg catalog. Use distcp and Spark to migrate Parquet files to S3 with minimal reshaping; re-register as Iceberg tables with schema evolution enabled. Profile existing Hive queries and rewrite the hot ones for Trino on EKS. Build dbt marts for BI consumers. Wire OpenLineage into Spark and dbt. Ship audit logs to Splunk. Cut over read traffic incrementally while dual-running for 60 days, reconciling query results. Decommission the Cloudera cluster once parity is proven. Result: 70-80 percent cost reduction, sub-second interactive queries on hot tables, governance uplift from zero to Unity-Catalog-grade.

Past performance

Related capabilities, agencies, and insights

Lakehouses rarely ship alone. See also data warehousing, data engineering, streaming data, ETL / ELT, data governance, machine learning, and observability. For agency-specific pursuit detail see SAMHSA, Army, Navy, FBI, and HHS. Contract vehicles: SBIR, OTA, GSA MAS. Related insights: Iceberg vs Delta for federal, Databricks on Azure Government, Migrating Cloudera to a federal lakehouse.