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Accelerate Insight Across Microsoft Fabric with Azure Databricks
Azure Databricks delivers performance, scale, and elasticity for data-driven businesses —often as part of a broader Microsoft Fabric strategy. Korcomptenz helps you harness it for analytics, ML, and GenAI—streamlining pipelines, governance, and cost. Azure Databricks integrates seamlessly with Azure Storage, Azure SQL Database, and Azure Machine Learning Studio to quickly build and deploy secure, end-to-end analytics and AI solutions.
Microsoft Azure Partner with Data & AI Expertise
As an Azure Certified Partner, we provide expert insight into why to use Azure Databricks in enterprise-class data and AI environments, maintaining optimal performance, governance, and compliance.
Data engineering
Design and operate high-performance data pipelines to process and change massive amounts of structured and unstructured data.
Machine learning
Streamline model development with collaborative notebooks, automated machine learning, and native Azure Machine Learning integration.
Driving Intelligent Outcomes with Azure Databricks
Superior Price-Performance for SQL Workloads
Achieve faster, cost-effective SQL performance with the Photon engine and AI-optimized queries: Spark, Python, and SQL runtimes power ETL, BI, and AI workloads.
Proven at Enterprise Scale
Trusted by 10,000+ global enterprises, including 60% of the Fortune 500, to run AI and data workloads seamlessly across 44+ Azure regions with reliability.
Seamless, Native Azure Integration
Launch instantly via Azure Portal with built-in security and governance through Entra ID, Unity Catalog and connect to Microsoft Fabric OneLake to share data across engineering, analytics, and BI teams. Enable collaboration across your data teams.
Core Capabilities of Azure Databricks
Our Azure Databricks and Microsoft Fabric Solutions & Services
Azure Databricks Consulting Services
Professional guidance on strategy, governance, and architecture designed for your business objectives. Utilize Azure Databricks services to speed up deployment and optimize ROI throughout the analytics lifecycle.
Data Engineering on Azure Databricks
Deploy and design high-performance Spark-based pipelines with Delta Lake and Lakeflow. These Azure Databricks services support efficient ingestion, transformation, orchestration, and robust analytics at cloud scale.
Machine Learning & AI Solutions
Create strong ML and AI workflows on collaborative notebooks with support for MLflow, automated training, and effortless Azure ML integration for productive, scalable intelligence capabilities.
Real-Time Data Streaming and Analytics
Facilitate real-time insights through structured streaming, event ingestion, and materialized views in Azure Databricks. Provide live dashboards, anomaly detection, and quick operational intelligence.
Custom Dashboards & BI Integrations
Bake SQL analytics and BI tools like Power BI into customized, interactive dashboards. Use lakehouse data to drive actionable business user insights.
End-to-End Azure Databricks Implementation
Manage end-to-end deployment life cycle, from workspace provisioning and MLOps establishment to governance, monitoring, and optimization, guaranteeing compliant, scalable, and enterprise-ready analytics platforms.
Azure Databricks Migration Services
Migration from Apache Spark / Hadoop to Azure Databricks
Effective Azure Databricks migration moves Spark or Hadoop workloads to Delta Lake with minimal refactoring, taking advantage of Photon and native Spark compatibility to enhance scalability, performance, and governance.
Lift-and-Shift from Legacy Data Warehouses
Move existing data warehouses to a new lakehouse in a lift‑and‑shift approach. Accelerated lift‑and‑shift migration provides faster time‑to‑value and continuous analytics for unified datasets.
Replatforming Current Azure Data Workloads
Update current Azure-based ETL, SQL, and analytics platforms by replatforming to a single unified Azure Databricks solution. Improve efficiency, streamline architecture, and lower technical debt.
Performance Tuning Post‑Migration
Balance clusters and workloads after migration through Photon, Delta Lake caching, partitioning, query profiling, auto-scaling, and cluster pools to enhance throughput and decrease latency.
Industry-specific Use Cases
Manufacturing
Stream IoT sensor data through Azure Databricks to predict equipment failure, streamline maintenance schedules, and gain visibility into the supply chain for enhanced uptime and productivity.
Industry-specific Use Cases
Designed for Azure with Native Governance and Compliance
Azure Databricks is commonly adopted as part of a unified Microsoft Fabric and Azure data platform—using the best engine for each workload while keeping data governed and shareable via OneLake.
Connect your Azure datastore ecosystem securely with dedicated connectors for high-speed access and simplified management. Azure Databricks service facilitates easy Unity Catalog setup, enforcing strong identity, access control, and compliance, while providing you with centralized control and simplified operational governance across your Azure data landscape .
Our End‑to‑End Process for Azure Databricks Delivery
Discovery & Use Case Identification
Work with stakeholders to evaluate existing systems, establish high impact analytics and AI use cases, and set priorities around opportunities that fit strategic objectives.
Architecture Planning & Cloud Readiness
Review infrastructure readiness, plan lakehouse architecture, choose best‑in‑class cluster and storage configurations, and lay out governance and security patterns.
Azure Databricks Setup & Integration
Provide workspaces, set up clusters and jobs, integrate with Azure services (Storage, ADLS, ML), and define secure identity and access controls.
Data Pipelines, ML Models & Dashboards
Build data pipelines on Spark, train ML models in notebooks and MLflow, and create interactive dashboards for actionable insights.
Testing, Optimization & Deployment
Verify performance, implement tuning (Photon engine, caching, partitioning), automate CI/CD pipelines using Terraform, Azure DevOps, or GitHub Actions before production in full.
Managed Support & Knowledge Transfer
Offer continuous managed care, monitoring, and platform health checks, as well as team training and documentation to facilitate self-sufficiency.
The Korcomptenz Advantage in Azure Databricks Implementation
01
Microsoft Azure Partner with Data & AI Expertise
As an Azure partner, we guide Databricks adoption for enterprise data and AI with performance, governance, and compliance.
02
Proven Experience with Spark, ML
Our team proves success with Spark, MLflow, and Synapse—delivering scalable, low-latency ETL, advanced analytics, and cost-efficient Databricks benefits.
03
Tailor-Made Business-Focused Solutions
We design tailored architectures and workflows, applying Azure Databricks migration best practices and accelerators for scalable, high-impact outcomes.
04
Accelerated Time-to-Value through Frameworks
Use our Databricks-ready frameworks and reusable templates to accelerate setup, reduce risk, and deploy insights and AI fast.
Our Azure Databricks Success Stories
Flooring CX Re-imagined Through Smart Integration
Secure, personalized UX streamlines search, comparison, and ordering.
Advanced Website Features for Customer Delight
Flooring Giant Elevates UX with Kentico Xperience.
Frequently Asked Questions (FAQs)
An open, horizontally scaled platform integrating Spark-based data processing, analytics, and ML pipelines for unified data, AI, and business intelligence solutions at scale.
Azure Databricks provides deep Spark optimizations, live collaborative notebooks, and autoscaling clusters, as opposed to Synapse's mainly SQL-based data warehouse features.
Yes, it is easy to move existing Apache Spark jobs with minimal refactoring, embracing Delta Lake and Databricks Runtime for best performance and compatibility.
Photon is Apache Spark re-coded in C++ and offers a high-performance query engine that can help speed up your time to insights and lower your overall cost per workload.
Delta Lake is an optimized storage layer that offers the basis for storing tables and data in Azure Databricks.
