Newt Global’s Data Modernization Acceleration Platform (DMAP) is an enterprise-grade best-in-class customizable platform that's powered with the machine learning capabilities of Generative AI and GitHub Copilot. DMAP accelerates Oracle and application migration to Azure Database for PostgreSQL, saving time and effort up to 90%.

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DMAP features

DB assessment at scale: Assess the migration complexity and analyze conversion statistics and conversion error details of multiple schemas concurrently, with actionable insights for DB storage objects, code objects, and schema conversion effort.

App assessment at scale: Assess multiple applications simultaneously to determine the type of SQL statements embedded in application code such as procedure calls, static queries vs dynamic queries with variables, and queries in conditional IF ELSE blocks.

Schema conversion: Automatically convert 80 percent to 100 percent of storage and code objects in database.

Application conversion: Automatically convert up to 90 percent of Oracle PL/SQL code in application.

• Analyze the codebase for dead code that is never executed in a running program.
• Assess whether a Java application requires a JRE version upgrade to meet the minimum version necessary for optimal functionality on Azure.

Database performance analysis: Evaluate Oracle database performance analysis based on Automatic Workload Repository trend to identify bottlenecks and inefficiencies that can impact its speed and responsiveness.

• Insights on CPU/memory utilization, read/write IOPS and throughput IO statistics, session and logon details, top resource intensive SQLs and large table analysis
• Recommended Azure Database for PostgreSQL solution

Data migration: Simultaneously migrate data from multiple databases and schemas through a highly optimized process to minimize downtime.

Schema validation: Verify that the target database schema accurately represents the structure, relationships, and constraints of the data being migrated. It aims to assist in the identification and rectification of schema-related issues, ensuring data integrity and optimal database performance.

Data validation: Confirm the accuracy of the migrated and transformed data in the target. It aims to assist in resolving data-related issues, ensuring accuracy, completeness, and consistency in the target.

Advanced analytics:

• Insights on migration complexity and database/schema/application migration analysis
• Data migration approach and estimate
• Target Azure architecture
• Right-sized Azure SKU recommendations to run similar workloads on Azure.
• Summary of Azure investment (ACR) required

• Summary of one-time migration effort required for schema conversion, data migration, and database and application modernization
• TCO and ROI analysis showing estimated savings and break-even point

DMAP benefits