Transforming Legacy systems into Modern Data Infrastructure
In today's data-driven world, organizations are increasingly recognizing the importance of modernizing their data infrastructures. Cloud Data modernization involves updating and transforming legacy systems to leverage the power and capabilities of modern databases.
Generative AI (Gen AI) plays a crucial role in this transformation, offering innovative solutions for various phases of cloud data modernization. This blog explores how Mastek is leveraging Generative AI for various phases of Cloud Data Modernization like Data Discovery, Code Conversion, Data Migration, And Testing.
Data Discovery of Legacy Databases
The first step in any cloud data modernization project is to understand the existing Data landscape. Data Discovery involves identifying, cataloging, and understanding the data stored in legacy databases. Generative AI can significantly enhance this phase by automating the process of data profiling and cataloging. AI algorithms can help us build sophisticated programs which can scan through massive volumes of data, identifying patterns, relationships, and quality metrics. This not only saves time but also ensures a more accurate and comprehensive understanding of the legacy data environment.
It can assess legacy databases to identify data types, formats, and relationships. This reduces manual effort and accelerates the identification of relevant datasets that organizations can leverage.
This level of insight is invaluable for planning the subsequent phases of enterprise cloud data modernization.
Technical Insights:
- 1. Automated Metadata Extraction & Documentation: Generative AI models can help to analyze database schemas, stored procedures, and even code comments to automatically extract metadata and generate documentation, including:
- 2. Data Lineage: Tracing data flow from origin to destination, unveiling complex transformations.
- 3. Data Dictionaries: Defining data element meanings and structures, enhancing comprehension.
- 4. Relationship Discovery: Identifying relationships between tables and columns, uncovering hidden dependencies.
- 5. Semantic Understanding & Natural Language Search: Generative AI enables natural language queries, allowing users to search for data based on meaning rather than technical names. This significantly speeds up Data Discovery and empowers business users.
- 6. Data Profiling & Anomaly Detection: Generative AI analyzes data to identify patterns, outliers, and data quality issues. This understanding is crucial for preparing data for migration and ensuring its integrity.
We at Mastek understand the importance of Data Discovery in terms of strategizing, planning and executing the projects on time. We are developing a Data Discovery solution with the help of Generative AI to precisely understand the Data assets, Security assets etc. of the customer and help them through their Cloud Data Modernization journey.
Code Conversion from Legacy Databases
One of the most challenging aspects of enterprise data modernization is converting the code from legacy databases to modern platforms. Traditional databases like SQL Server, Oracle and Hadoop etc. often have complex stored procedures, triggers, and custom scripts that need to be translated into the syntax and functionality of modern databases such as Snowflake, Databricks PySpark, and DBT.
Generative AI can streamline this process by automating code conversion. Using machine learning models trained on a vast corpus of database scripts, AI can accurately translate legacy SQL code into the equivalent code for modern platforms. This not only reduces the risk of human error but also accelerates the conversion process. Additionally, Generative AI can optimize the converted code for performance, ensuring that the modernized system operates efficiently.
Technical Insights:
- 1. SQL Dialect Conversion: Translates SQL queries between dialects (e.g., T-SQL to Snowflake SQL), automating a traditional manual and error-prone process.
- 2. Stored Procedure Conversion: Converts stored procedures to equivalent implementations on the target platform, considering differences in syntax and functionality. For instance, a T-SQL stored procedure can be converted to a Snowflake stored procedure or a PySpark function.
- 3. ETL Pipeline Conversion: Transforms legacy ETL processes to modern data pipelines using tools like DBT, Databricks Delta Live Tables, or Snowflake Snowpipe.
- 4. Redshift to dbt Transformation: Streamlines the conversion of Redshift code to DBT, allowing for modular and maintainable data transformations.
Below are a few advantages of using Mastek’s Generative AI powered Code Conversion Solution:
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1. Automated SQL Translation: Generative AI models can translate SQL queries from legacy databases to the syntax and features of modern databases, ensuring compatibility and performance optimization.
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2. Code Refactoring: Generative AI can refactor legacy code to align with best practices and modern coding standards, improving maintainability and scalability.
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3. Conversion to Modern Frameworks: For example, converting Redshift SQL to DBT (Data Build Tool) models, Generative AI can automate the creation of DBT models, reducing manual effort and errors.
Data Migration
Once the code is converted, the next phase involves migrating the data from legacy databases to modern platforms. Data migration is a critical step that requires careful planning and execution to avoid data loss and ensure data integrity. Generative AI assists in various aspects of data migration, from data extraction to data loading. We can write optimized scripts with the help of Generative AI tools like Amazon Q, Ana AI etc. which can help us to migrate legacy database to new databases in a full and incremental manner.
This code can automate the extraction of data from legacy systems and load the data to new platforms. Generative AI can also handle schema mapping and data transformation tasks depending on client needs, ensuring that the data is correctly formatted and compatible with the target system. This reduces the manual effort involved in data migration and minimizes the risk of errors.
Technical Insights:
1. Data Mapping & Transformation: Automates the generation of data mapping rules between source and target databases, handling schema and data type differences.
2. Data Quality Enforcement: Identifies and corrects data quality issues during migration, ensuring data integrity in the new environment.
3. Performance Optimization: Optimizes the migration process by considering factors like network bandwidth and database capacity.
Testing
Testing is a vital phase in the enterprise data modernization process, ensuring that the migrated data and converted code function as expected in the new environment. In Mastek we are leveraging Generative AI to enhance testing by automating test case generation, execution, and validation.
With the help of tools like Amazon Q, Ana AI or Github Copilot we can automatically generate test cases based on the data structures and usage patterns identified during the Data Discovery phase. These test cases can cover a wide range of scenarios, including edge cases and performance benchmarks. We are using Generative AI tools to
- Generate comprehensive test plans
- Create test data sets
- Develop validation scripts
- Generate test scenarios
Technical Insights:
1. Automated Test Case Generation: Generates test cases based on schema, data lineage, and business rules, ensuring comprehensive coverage.
2. Data Validation & Reconciliation: Compares source and target data, identifying discrepancies and ensuring accuracy.
3. Performance Testing: Simulates realistic workloads to evaluate the modernized platform's performance.
By leveraging Generative AI for testing, Customers can achieve higher test coverage, faster execution, and more reliable results. This ensures that the modernized system meets the required standards of quality and performance.
The Mastek Advantage
Mastek’s cloud modernization services are carefully thought and structured for integrating Generative AI. By leveraging Generative AI effectively across different phases, Mastek can accelerate their client’s modernization initiatives while maintaining quality and reducing risks.
Below are some of the benefits of using Generative AI for Enterprise Data Modernization
- Use Generative AI to analyze existing data documentation
- Analyze current system architecture
- Recommend modern data architecture patterns
- Generate data migration roadmaps
- Analyze legacy code and stored procedures
- Identify optimization opportunities
- Generate phased migration plans
- Generate data transformation rules
- Generate comprehensive test plans
- Create test data sets
- Develop validation scripts
- Generate test scenarios
Conclusion
The journey of data modernization is complex and multifaceted but harnessing the power of Generative AI can significantly simplify and accelerate the process. From Data Discovery and code conversion to data migration and testing, Generative AI offers innovative solutions that enhance efficiency, accuracy, and reliability.
By embracing AI-driven tools and techniques, organizations can successfully transform their legacy systems into modern data infrastructures, unlocking new opportunities for innovation and growth.
