Everything we deploy, in plain terms

Elastic compute, on Apache Spark

Spark handles the batch and streaming workloads that make a lakehouse useful — joining, aggregating, and reshaping data at whatever scale your business runs at. We deploy it so compute scales independently of storage: idle clusters cost nothing, and a quarter-end spike doesn't require re-architecting anything.

We configure structured streaming for near-real-time pipelines alongside scheduled batch jobs, autoscaling policies tuned to your workload shape, and resource pools so one noisy job can't starve the rest of the cluster.

Security: encryption, Kerberos & Ranger

Security is designed in at deployment time, not added after an audit finding. Three layers work together:

Encryption in transit and at rest

TLS is enforced between every service in the cluster, and data at rest is encrypted on disk or in object storage. This is the baseline most compliance frameworks (SOC 2, HIPAA, PCI-DSS) expect before anything else is evaluated.

Kerberos authentication

Every service — Spark, the metastore, notebook servers, BI endpoints — authenticates through a single Kerberos realm. No shared passwords, no ad hoc service accounts, and mutual authentication between services by default.

Ranger authorization

Apache Ranger enforces fine-grained, centrally managed access policies — down to the row and column. Finance sees finance data; a support analyst sees a masked customer table. Every access decision is logged for audit.

// Example Ranger row-filter policy (illustrative)
{
  "policyType": "row-filter",
  "resource": { "database": "sales", "table": "orders" },
  "rowFilter": "region = current_user_region()",
  "appliesTo": ["role:regional_analyst"]
}

Orchestration with Apache Airflow

Every pipeline is a DAG: scheduled, retried on failure, alerting on breach, and visible in one place. We deploy Airflow with sensible defaults — SLAs on critical DAGs, isolated worker pools per team, and secrets pulled from a managed vault rather than hard-coded into pipeline code.

# Simplified DAG shape (illustrative)
with DAG("daily_orders_pipeline", schedule="0 3 * * *", catchup=False):
    ingest >> validate >> transform_spark >> publish_to_catalog

Automated data quality checks

Expectation-based validation runs as part of every pipeline, not as a separate audit weeks later. Row counts, null thresholds, referential checks, and distribution drift are tested before a batch is allowed to publish. Failures quarantine the batch and alert the owning team instead of silently reaching a dashboard.

Catalog & governance, powered by DataHub

DataHub is the system of record for what data exists, where it came from, and who's responsible for it. We connect it to ingest metadata directly from Spark, Airflow, and Ranger, so lineage and access policy stay in sync with what's actually running — not a spreadsheet someone forgot to update.

• Search & discovery: every table, column, and dashboard is findable, with descriptions and ownership attached.
• End-to-end lineage: trace any field from source system through every transformation to the dashboard it feeds.
• Business glossary: shared definitions of terms like "active customer," enforced across teams.
• Audit-ready history: who accessed what, and when — pulled directly from Ranger's access logs.

Collaborative notebooks, with an MCP server

Notebooks run on a shared, multi-user workspace with real-time collaborative editing — no more emailing .ipynb files around. Alongside that, we deploy an MCP (Model Context Protocol) server in front of the notebook environment, so AI agents such as Claude can query, transform, and analyze data directly through natural language — scoped to exactly the same Ranger-enforced permissions as the human using them.

That last part matters: an AI agent connected through MCP never sees more than the person it's acting on behalf of is authorized to see.

BI connectivity via Thrift/JDBC

A standard Thrift (HiveServer2) endpoint means Tableau, Power BI, Looker, and Superset connect the same way they always have, over JDBC or ODBC, authenticated through the same Kerberos realm. No rip-and-replace of the BI layer your teams already know.