Why Data Lakes Fail
Many organizations build data lakes that become data swamps within months. Data gets dumped in without cataloging, governance, or quality checks. Users cannot find what they need. Data scientists spend most of their time cleaning and locating data instead of building models. And the platform team ends up managing a growing mess of unprocessed ingestion pipelines and ungoverned storage.
A well-architected data lake platform solves these problems at the architecture level.
What We Build
- Ingestion pipelines — Batch and streaming ingestion from databases, APIs, file systems, event streams, and SaaS applications. Schema-on-read with cataloging so data is discoverable from day one.
- Data catalog and governance — Automated cataloging with metadata, lineage, access controls, and data quality rules. Users search for data like they search for documents.
- AI-ready data preparation — Feature stores, training data pipelines, and labeled datasets prepared for ML workloads. Data is structured for the models that will consume it.
- Cost-optimized storage — Tiering, lifecycle policies, and compression that keep storage costs proportional to value. Hot, warm, and cold data managed automatically.
- Security and compliance — Encryption, access governance, audit logging, and data residency controls. Built for regulated environments.
- Platform operations — Monitoring, alerting, cost tracking, and ongoing optimization so the platform stays healthy and cost-effective.
Platforms
We build on AWS (S3, Glue, Athena, Lake Formation), Azure (ADLS, Synapse, Purview), and GCP (GCS, BigQuery, Dataplex). The architecture is tailored to your existing cloud investment and team skills.