Migrate Hadoop to Databricks to reduce costs & increase productivity
Migrating Hadoop to a modern cloud data platform can be complex. Databricks provides a unified foundation to simplify AI and machine learning projects and streamline analytics processes. The collaborative Databricks Spark-based platform brings together all your data and Machine Learning workloads for faster decision-making.
Gain faster insights with Hadoop to Databricks Migration
Simpler and Faster Migration
Modernizing your Hadoop workloads on Azure Databricks unlocks the benefits of cost-efficiency and provides your organization with business-critical insights, a unified experience, and unmatched security and flexibility so you are prepared for whatever may come tomorrow.
WinWire is helping customers move their Hadoop workloads onto a unified Apache Spark-based analytics platform called Azure Databricks. Our Migration as a Service (MaaS) framework helps organizations adopt the right Azure data analytics services to eliminate the high CAPEX and deliver fresh insights to your business.
Hadoop to Databricks Migration Journey
Analysis of the current Hadoop infra including environment sizing, prioritization, workload inventory, complexities, dependencies, and risks.
Azure services setup for the pilot, data migration approach in terms of network, security and define best practices for workload scenarios.
For the identified scope, migrate model, automate migration, determine patterns for improvement, incorporate learnings & finalize the migration plan.
Subject-priority based waves of migration execution of data, model, workloads & interface objects of Hadoop Setup metadata driven data lake ingestion process.
How we accelerate Hadoop to Databricks migration?
Results You Can Expect
How does Databricks benefit my organization?
- Faster time to market
- Cost savings from retiring legacy infrastructure
- Unlock new data science opportunities
- Improved data team productivity
Discover how our accelerator helps you perform Hadoop migration to Databricks, faster.
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