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Data Warehousing and Business Intelligence for Oracle Database Administrators (DBAs)

Introduction

Data warehousing and business intelligence (BI) play a crucial role in organizations by providing insights, analytics, and decision support. As an Oracle DBA, understanding best practices for designing, managing, and optimizing data warehouses is essential. In this whitepaper, we will explore data warehousing concepts, ETL processes, and techniques for efficient data handling.

Understanding Data Warehousing

Purpose of Data Warehousing

  • Data warehouses store historical data for analysis and reporting.
  • Support decision-making processes by providing a consolidated view.

Transactional Databases vs. Data Warehouses

  • Transactional databases optimize for write operations.
  • Data warehouses optimize for read-heavy analytical queries.

Designing an Effective Data Warehouse

Star Schema vs. Snowflake Schema

  • Star Schema: Simple, denormalized structure with a central fact table and dimension tables.
  • Snowflake Schema: Normalized structure with additional dimension hierarchies.

Dimensional Modeling and Fact Tables

  • Use dimensions (e.g., time, product, geography) for slicing and dicing data.
  • Fact tables store measures (e.g., sales, revenue) and connect to dimensions.

Aggregations and Summary Tables

  • Precompute aggregations to improve query performance.
  • Create summary tables for common queries.

Data Extraction, Transformation, and Loading (ETL)

ETL Process

  1. Extraction: Retrieve data from source systems (e.g., OLTP databases, flat files).
  2. Transformation: Clean, validate, and transform data (e.g., data type conversions, calculations).
  3. Loading: Load data into the data warehouse.

Oracle Data Integrator (ODI) and Oracle Warehouse Builder (OWB)

  • ODI: ETL tool for data integration and transformation.
  • OWB: Oracle’s legacy ETL tool (deprecated but still in use).

Best Practices for Efficient ETL Workflows

  • Use bulk loading techniques (e.g., SQL*Loader, external tables).
  • Parallelize ETL processes.
  • Monitor and optimize performance.

Optimizing Query Performance

Indexing Strategies

  • Bitmap indexes for low cardinality columns.
  • B-tree indexes for high cardinality columns.
  • Function-based indexes for derived data.

Partitioning Large Tables

  • Range partitioning by date or numeric ranges.
  • List partitioning by specific values.
  • Hash partitioning for even distribution.

Materialized Views and Query Rewrite

  • Create materialized views for precomputed results.
  • Enable query rewrite to use materialized views automatically.

Conclusion

Data warehousing and business intelligence are essential for informed decision-making. As an Oracle DBA, focus on efficient design, ETL processes, and query optimization. Collaborate with data architects and analysts to create a robust data warehouse that meets business needs.

Background

A large automotive plant operator has been using a custom application framework to build an application integral to their operations. Originally built using JDK8 and JBoss4 against an Oracle database, the application received periodic updates, with the last major update in 2020/2021. In 2024, the client and Performance One Data Solutions, a division of Ross Group Inc., embarked on a joint effort to upgrade the technology stack to enhance performance, security, and maintainability.

Objectives

The primary goals of the project were:

  1. Upgrade the Custom Application Framework from JDK8 to JDK21.
  2. Upgrade the jQuery integration to release 3.7.1.
  3. Upgrade the application to run on Wildfly 30 from Wildfly 11.
  4. Migrate the database backend from Oracle 12 to Microsoft SQL Server 2020.

Challenges and Solutions

Framework and Database Migration:

  • Challenge: The application framework was vetted against Microsoft SQL Server but was not deployed in a major application.
  • Solution: Performance One refactored the framework to conform to Java 9+ standards and regression-tested it against Microsoft SQL Server 2020.

Application Refactoring:

  • Challenge: Refactoring the client application to compile and run using the new tech stack.
  • Solution: Performance One took a copy of the client application and refactored it to compile and launch using JDK21, Wildfly 30, and Microsoft SQL Server.

Data Migration:

  • Challenge: Migrating data from Oracle to Microsoft SQL Server.
  • Solution: Performance One provided migrated data and created installation scripts to upgrade the client’s Microsoft SQL Server.

Report Conversion:

  • Challenge: Converting BIRT and Crystal reports against Microsoft SQL Server.
  • Solution: Performance One converted the reports and ensured they worked with Microsoft SQL Server.

Project Timeline

Initial Milestones:

    1. Complete Framework upgrade to JDK21/Wildfly 30.
    2. Provide migrated data from Oracle to Microsoft SQL Server.
    3. Regression test the application framework against Microsoft SQL Server 2020.
    4. Refactor the client’s application to compile and launch using the new tech stack.
    5. Create installation data script for Microsoft SQL Server compliance.
    6. Regression test the application installation processes.
    7. Make recommendations for unused application features.
    8. Validate and upgrade application code.

Additional Tasks

  • Database Procedures Conversion: Performance One converted the client’s database procedures, functions, and views to Microsoft SQL Server.
  • Report Migrations: Ongoing conversion of BIRT and Crystal report development.
  • Application Enhancements: Upgrading client’s application screens for jQuery 3.7.1, file attachment uploads, and integration with scanner devices using web sockets.
  • Server Maintenance: Wildfly server maintenance, release builds, and deployment handling, with a transition plan for client resources post-go-live.

Conclusion

Performance One Data Solutions partnered with the client to upgrade the technology stack, ensuring improved performance and maintainability. The collaboration between Performance One and the client team demonstrated effective problem-solving and adaptability, setting a strong foundation for future upgrades and enhancements.

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