Lakehouse architecture, Spark pipelines, and ML on the Databricks platform.
Fremen Consulting implements Databricks lakehouse platforms — Delta Lake architecture, Spark ETL pipelines, MLflow model lifecycle, Unity Catalog governance, and integration with BI tools for enterprise analytics and ML teams.
Problems we solve for businesses like yours
Separate data warehouse and data lake systems create duplicate pipelines, inconsistent metrics, and BI tools querying stale or conflicting data sources.
Data scientists train models in notebooks without MLflow tracking or deployment pipelines — models stay in research while business value goes unrealized.
Unmanaged access to sensitive datasets without Unity Catalog or column-level security creates compliance risk and audit failures.
Solutions tailored to your industry and growth goals
Delta Lake medallion architecture (bronze/silver/gold), Spark pipeline design, and dbt integration for governed analytics-ready datasets.
Experiment tracking, model registry, batch and real-time inference endpoints, and CI/CD for ML model promotion to production.
Data lineage, fine-grained access control, audit logging, and cross-workspace sharing for enterprise data governance requirements.
Technologies and platforms we work with in this space
Measurable outcomes from projects in this space
Migrated legacy Hadoop and Redshift workloads to Databricks lakehouse, reducing analytics pipeline runtime by about 60% and unifying batch and streaming.
Clear answers to common questions in this industry
We design lakehouse architecture, build Spark ETL pipelines, implement MLflow for model lifecycle, configure Unity Catalog governance, and integrate Databricks with BI tools and data sources.
A lakehouse combines data lake storage flexibility with data warehouse ACID transactions and performance using Delta Lake on Databricks — enabling both BI analytics and ML on one platform.
Yes. We assess workload compatibility, plan incremental migration, rebuild pipelines in Spark, and validate data quality throughout the transition.
Yes. We set up MLflow experiment tracking, model registry, and deployment pipelines so data science teams can reproducibly train, compare, and promote models to production.
Initial lakehouse setup with first pipelines takes eight to twelve weeks. Enterprise-wide platform with governance typically takes four to nine months.
Tell us about your business and goals. We will recommend the right approach for your industry, timeline, and budget.