Azure Databricks
Unified Analytics for Data Engineering, ML, and Streaming
Wat wij doen
Azure Databricks is the industry-leading unified data analytics platform, combining Apache Spark with collaborative notebooks, MLflow, and Delta Lake. We implement Databricks environments that accelerate your data engineering, machine learning, and streaming workloads — at any scale.
Geschikt voor
Data engineering and ML teams who need scalable distributed compute without managing Spark infrastructure themselves
Veelvoorkomende toepassingen
Delta Lakehouse Implementation
Design and build a Delta Lake architecture with ACID transactions, schema evolution, and time travel for reliable analytics.
Large-Scale ETL/ELT
Replace slow on-premise ETL tools with Spark-based distributed processing pipelines that scale automatically with your data volume.
ML Feature Engineering
Build feature stores and transformation pipelines that feed your machine learning models with clean, consistent training data.
MLflow Model Tracking
Implement end-to-end ML lifecycle management: experiment tracking, model registry, and automated deployment with MLflow.
Real-Time Streaming
Process event streams from Kafka, Event Hubs, or IoT Hub using Spark Structured Streaming with sub-second latency.
Unity Catalog Governance
Implement Unity Catalog for fine-grained access control, lineage tracking, and cross-workspace data discovery.
Hoe wij werken
Workload Design
Map your use cases to the right Databricks features: Delta Live Tables, MLflow, or Structured Streaming.
Workspace Setup
Deploy Databricks workspace with VNet injection, Unity Catalog, and cluster policies — secure and cost-governed from day one.
Pipeline Development
Build notebooks, jobs, and DLT pipelines in Python/Scala. Implement testing and CI/CD with Databricks Asset Bundles.
Production Handover
Performance tuning, cost optimisation, runbook, and team enablement so your engineers can operate it independently.
Wat u ontvangt
- Databricks workspace with Unity Catalog and VNet injection
- Delta Lake architecture with bronze/silver/gold medallion layers
- Spark pipelines with automated testing and CI/CD
- MLflow tracking server with model registry
- Cluster policies and cost governance rules
- Full documentation and source code ownership
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