Building smart AI applications or large language model (LLM) embedded applications requires a strong foundation in platform upgrades, legacy application integration, and API services to ensure scalability, performance, and seamless user experiences.

Why AI Needs a Strong Tech Foundation?

• Platform Upgrades ensure scalability and efficiency for AI workloads
• Legacy Application Integration bridges old and new technologies, enabling smooth AI adoption
• API Services provide seamless connectivity between AI models and business applications

How are these key skills helping Sun Technologies Customers to build smart AI embedded applications:

1. Platform Upgrades Ensuring AI Apps Run on Modern, Scalable Infrastructure

AI-driven apps require high computational power, scalable architecture, and real-time processing capabilities. Without platform modernization, AI applications may suffer from performance bottlenecks.

Why Platform Upgrades Matter for AI

• AI workloads require modern cloud-native architectures (e.g., AWS, Azure, GCP) with auto-scaling capabilities.
• AI models need GPU and TPU acceleration for training and inference, which legacy systems may not support.
• Microservices-based architecture enhances AI deployment and reduces monolithic dependencies.
• Upgrading to serverless or containerized environments (e.g., Kubernetes, Docker) optimizes cost and scalability.

Example: Upgrading from an on-premise monolithic system to a cloud-native microservices architecture allows an AI-driven chatbot to process thousands of customer interactions per second without downtime.

2. Legacy Application Integration Bridges the Gap Between Old & New AI Capabilities

Many enterprises still rely on legacy applications that were not designed to support modern AI functionalities. Expertise in legacy integration is essential to enable AI capabilities without disrupting existing workflows.

Challenges & Solutions in Legacy AI Integration

• Data Silos: Legacy systems store data in inaccessible formats → Use ETL pipelines, APIs, and middleware to enable AI access.
• Outdated Infrastructure: AI models need real-time data processing, but legacy systems rely on batch processing → Implement event-driven architectures using Kafka or RabbitMQ.
• Security & Compliance Issues: AI needs access to sensitive legacy data → Implement secure API gateways and role-based access controls (RBAC).

Example: An insurance company using a 20-year-old claims processing system integrates an AI-based fraud detection engine via REST APIs and middleware, allowing real-time risk assessment while keeping the core system intact.

3. API Services Enable AI Models to Seamlessly Communicate Across Systems

AI applications must interact with multiple services, databases, and external platforms. APIs serve as the bridge between AI models and business applications, ensuring seamless data flow and automation.

Why API Services Are Crucial for AI Apps

• Real-time Data Exchange: AI models require real-time access to structured and unstructured data via APIs.
• Model Deployment & Access: AI models must be exposed as RESTful or GraphQL APIs for integration with front-end applications.
• Cross-Platform Compatibility: APIs allow AI functionalities to be embedded in web, mobile, IoT, and enterprise applications.
• Security & Governance: API management platforms (Apigee, Kong, AWS API Gateway) help in securing AI APIs and enforcing rate limits.

Example: A retail company uses an AI-powered recommendation engine via an API to deliver personalized product suggestions in its mobile app, website, and chatbot—all in real-time.

4. Key Technologies Required for AI-Driven Smart Apps

Without expertise in these areas, AI-powered applications may fail to scale, integrate, or perform efficiently.

Specialized testing skills involving integration of large-scale data warehouses with modern BI tools.

Below are some key testing skills required for integrating large-scale data warehouse with modern BI tools:

Skills Summary

Popular BI warehouses include (such as Snowflake, Oracle DW, Google BigQuery, Amazon Redshift) with modern BI and reporting tools (such as Tableau, Power BI, SAP Webi, Looker) to analyze vast amounts of data, generate insights, and drive decision-making.

1. End-to-End Data Pipeline Testing

Skillset:

• Understanding of ETL/ELT workflows (e.g., Informatica, Talend, dbt, Matillion)
• Ability to test source-to-target mappings
• Proficiency in SQL-based validation of transformations and aggregations

Key Tasks:

• Validate data ingestion, cleansing, enrichment, and transformation
• Ensure no data loss or duplicates during extraction and load
• Confirm accuracy of derived fields used in BI reports (e.g., loan default risk %)

2. Data Warehouse Performance & Query Optimization Testing

Skillset:

• Knowledge of partitioning, clustering, materialized views
• Ability to analyze query execution plans and test performance at scale
• Experience with concurrency and load testing

Key Tasks:

• Test performance of complex queries used by BI dashboards
• Validate response times under peak loads (e.g., fiscal year-end reporting)
• Ensure indexes or optimizations don’t affect data accuracy

3. BI Report & Visualization Testing

Skillset:

• Expertise in BI tools: Power BI, Tableau, SAP Webi, Qlik
• Familiarity with data modeling (star/snowflake schema, joins, measures)
• Test scripting for automated report validation (e.g., using Selenium, Playwright, Power BI REST APIs)

Key Tasks:

• Validate accuracy of metrics, KPIs, and filters in dashboards
• Perform regression testing after backend DW changes
• Ensure drill-downs, tooltips, and cross-filters work as expected

4. Test Automation & CI/CD Integration

Skillset:

• Ability to build automated test cases for data pipelines and reports
• Use of DataOps and DevOps tools (e.g., Jenkins, Git, Azure DevOps)
• Integration with BI test automation frameworks

Key Tasks:

• Create regression suites that auto-trigger on ETL or schema changes
• Automate validations for daily/weekly data refresh jobs
• Alerting for test failures in staging BI environments

5. Data Reconciliation and Regression Testing

Skillset:

• Tools for data comparison: QuerySurge, Informatica Data Validation, Python scripts
• Experience with delta and snapshot validation between old and new systems
• Ability to write custom reconciliation scripts

Key Tasks:

• Compare legacy and modern BI results (pre- and post-modernization)
• Validate row/column-level consistency across systems
• Detect broken joins, mismatches, or incorrect aggregations

6. Security & Access Testing for BI

Skillset:

• Understanding of role-based access controls (RBAC) in DW & BI tools
• Testing data masking/anonymization in non-prod environments
• Experience validating SSO, OAuth, and audit logging

Key Tasks:

• Validate data visibility based on roles (analyst vs exec)
• Test report security (e.g., financial vs HR data access)
• Ensure audit trails are captured correctly in the BI layer

7. Metadata & Lineage Testing

Skillset:

• Familiarity with data catalogs and lineage tools (e.g., Alation, Collibra, Azure Purview)
• Ability to test metadata consistency across layers
• Understanding of data tagging, glossary mapping, and traceability

Key Tasks:

• Ensure column names, data definitions, and descriptions are consistent
• Validate report fields map back to correct DW source
• Support data stewards with lineage validation for compliance

8. Compliance & Audit-Ready Testing

Skillset:

• Knowledge of federal compliance frameworks (FISMA, NIST, OMB A-123)
• Testing for 508 accessibility in BI tools
• Data retention and archival validation

Key Tasks:

• Ensure reporting data adheres to privacy and audit policies
• Validate that sensitive data is masked in exports or print views
• Confirm that historical data is preserved and retrievable

Get in touch with our application development and testing experts to get a custom design and test plan template for one of your BI + DW projects (e.g., Snowflake + Tableau in a lending environment). Our experts will help you evaluate the cost savings you can incur by incorporating Opensource tools like Playright alongside our in-house testing automation tool IntelliSWAUT. We will also help  evaluate tools like Informatica TDM, Delphix, K2View VS. custom open-source Test Data Management strategies that are best suited for your environment.