Eliminate complex data silos and accelerate real-time business intelligence. Weproztech delivers enterprise-grade Databricks consulting services—combining Medallion Delta Lake architecture, Apache Spark data engineering, Unity Catalog centralized governance, and MLflow-driven MLOps to turn fragmented enterprise data into scalable, high-return business assets.
Consolidate disjointed data warehouses and lakes into an open, high-performance Lakehouse platform supporting BI, streaming, and machine learning from a single source of truth.
Construct resilient ETL/ELT pipelines using Delta Live Tables (DLT) and Apache Spark to deliver clean, business-ready Bronze, Silver, and Gold datasets without manual backfills.
Enforce cross-cloud access control, end-to-end data lineage, column/row masking, and compliance tracking across workspaces through Unity Catalog.
End-to-end modern data architecture designed for speed, governance, and tangible business ROI.
Consolidate fragmented databases into a unified Databricks Lakehouse on AWS, Azure, or GCP. Optimize Photon execution engines and Serverless SQL endpoints for high-concurrency BI queries.
Build scalable batch and real-time streaming pipelines using PySpark, Delta Lake, and Delta Live Tables (DLT) with automated schema enforcement and data quality validations.
Create production-ready environments for MLflow model tracking, Feature Stores, Mosaic AI serving, and enterprise Retrieval-Augmented Generation (RAG) grounded in Lakehouse data.
Establish centralized metadata management, granular role-based permissions, automated audit logs, and Delta Sharing distribution across external partner ecosystems.
AI models are only as effective as the data foundation beneath them. We turn historical records into real-time operational foresight.
Orchestrate vector embeddings, Delta Lake search indexes, and custom LLM reasoning copilots grounded in your proprietary business documentation.
Implement structured model registries, automated hyperparameter tracking, model serving endpoints, and continuous retraining loops for production ML.
Build real-time customer churn predictors, batch-expiry forecasts, and dynamic inventory replenishment algorithms directly on Databricks clusters.
Enforce cluster right-sizing policies, auto-termination limits, and spot instance execution strategies that cut idle compute spend by up to 50%.
Delivering high-throughput data reliability across multi-cloud environments.
Faster Pipeline Ingestion
Lower Compute Costs
Production Pipeline Uptime
Unified Unity Catalog Governance
Measurable transformation stories replacing brittle ETL systems with reliable Lakehouse foundations.
Modernized fragmented database silos across educational networks into a centralized Databricks Lakehouse, standardizing reporting and real-time performance tracking.
Engineered streaming analytics pipelines using Apache Spark and Delta Live Tables to transform millions of raw clickstream logs into actionable product KPIs in real time.
Discuss your data migration roadmaps, pipeline bottlenecks, or Lakehouse governance requirements with our senior data engineers.