CASE STUDY 003
From Raw Data to Conversational Business Intelligence
Building an AI-Powered Analytics Platform with Google BigQuery, DeTLeng MCP Server, OpenAI & FastAPI
Industry
E-Commerce Analytics
Technology
Google BigQuery • FastAPI • OpenAI • MCP • Python
Status
✔ Completed
Difficulty
Advanced
Estimated Reading Time
25–30 Minutes
Executive Summary
This case study documents the complete implementation of an AI-powered Conversational Business Intelligence platform developed by DeTLeng. The project demonstrates how a real-world e-commerce dataset can be transformed into a production-ready analytics solution by combining Data Engineering, Business Intelligence, Artificial Intelligence, and cloud technologies.
Rather than building another dashboard, the objective was to enable business users to ask questions in natural language while receiving trusted answers generated directly from analytical business models stored in Google BigQuery. The implementation follows a layered Data Engineering approach consisting of Raw, Staging, and Analytics layers, followed by a custom-built DeTLeng MCP Server, FastAPI backend, OpenAI integration, and a live AI chatbot deployed on the web.
This project reflects DeTLeng's engineering philosophy:
Artificial Intelligence becomes truly valuable only when it is built upon trusted Data Engineering.
Project Overview
Dataset Name
Brazilian E-Commerce Public Dataset by Olist
The project began with the publicly available Brazilian Olist E-Commerce Dataset, which contains nine CSV files representing customers, orders, products, payments, sellers, reviews, and geographical information. Instead of using the dataset directly for reporting, the complete Data Engineering lifecycle was implemented.
The CSV files were imported into Google BigQuery, organized into Raw, Staging, and Analytics layers, and transformed into reusable business models using SQL. Once the Analytics Layer was complete, a custom DeTLeng MCP Server was developed to expose approved Business Intelligence tools. A FastAPI backend and OpenAI integration were then added, allowing users to interact with the analytics layer through a conversational AI chatbot.
The final solution demonstrates how enterprise data can be transformed into an Intelligent Business System that combines trusted analytics with natural language interaction.
Business Challenge
Most organizations already have dashboards, reports, and large amounts of business data. However, business managers often depend on technical teams to answer even simple analytical questions. Every request requires SQL queries, dashboard modifications, or manual reporting, slowing down decision-making.
The challenge was not to build another reporting system but to remove the dependency between business users and technical teams. At the same time, security, governance, and data quality had to be preserved. Allowing an AI model to access raw operational data directly was never considered a reliable solution.
Objective:
Build a trusted Data Engineering foundation first, then enable Artificial Intelligence to consume only validated analytical models.
Project Objectives
The project was designed to demonstrate a complete implementation of an AI-powered Business Intelligence platform. The objectives included importing a real-world dataset into Google BigQuery, designing a layered Data Engineering architecture, building reusable analytical models, developing a custom MCP Server, integrating OpenAI through FastAPI, deploying the application on Render, and delivering a production-ready conversational analytics experience.
Import a real-world dataset into Google BigQuery.
Design a layered Data Engineering architecture.
Build reusable analytical business models.
Develop a custom DeTLeng MCP Server.
Integrate OpenAI through FastAPI.
Deploy the application on Render and deliver a production-ready conversational analytics experience.
Technology Stack
The implementation combines Google BigQuery for cloud data warehousing, SQL for analytical modelling, Python for backend development, FastAPI for API services, OpenAI for natural language understanding, a custom DeTLeng MCP Server for secure Business Intelligence access, GitHub for version control, Render for deployment, and HTML, CSS, and JavaScript for the web interface.
Google BigQuery
Cloud Data Warehouse
SQL
Analytical Modelling
Python
Backend Development
FastAPI
API Services
OpenAI
Natural Language Understanding
DeTLeng MCP Server
Secure Business Intelligence Access
GitHub
Version Control
Render
Application Deployment
HTML • CSS • JavaScript
Web Interface
Solution Architecture
The solution follows a layered architecture where raw business data is first transformed into analytical business models before Artificial Intelligence is introduced. The chatbot never communicates directly with operational tables. Instead, every business question passes through the DeTLeng MCP Server, which invokes approved Business Intelligence tools against the Analytics Layer inside Google BigQuery.
Raw Data
➜
Analytics Layer
➜
DeTLeng MCP Server
➜
AI Chatbot
📄
CSV Files
📥
Raw Layer
⚙️
Staging Layer
📊
Analytics Layer
🛡️
DeTLeng MCP Server
⚡
FastAPI
🤖
OpenAI
💬
AI Chatbot
Implementation Phase 01
Dataset Selection
The implementation started by downloading the Brazilian Olist E-Commerce Dataset from Kaggle. The dataset consists of nine CSV files representing the complete lifecycle of an e-commerce business, including customers, orders, products, payments, reviews, sellers, and geographical information.
Before importing any data, each dataset was studied to understand its business purpose, relationships, and analytical value. This business understanding formed the foundation for the entire Data Engineering process.
Implementation Phase 02
Google Cloud & BigQuery Setup
The implementation started by creating a dedicated Google Cloud project, followed by configuring Google BigQuery as the central cloud data warehouse. Separate datasets were designed to support the Raw, Staging, and Analytics layers.
☁️
IAM permissions, authentication, and service accounts were configured to enable secure access for backend services while maintaining proper governance throughout the implementation.
Figure 1.
Google Cloud Console showing the dedicated project with separate
Raw,
Staging, and
Analytics datasets configured inside
Google BigQuery.
This environment serves as the foundation for the complete Data Engineering and Conversational Business Intelligence implementation.
Implementation Phase 03
Building the Raw Layer
The nine CSV files were imported into the Raw Layer without any transformations. This layer serves as the system of record and preserves the original source data exactly as received.
Imported CSV Files
cs003_olist_raw
cs003_olist_raw_category_translation
cs003_olist_raw_customers
cs003_olist_raw_geolocation
cs003_olist_raw_order_items
cs003_olist_raw_orders
cs003_olist_raw_payments
cs003_olist_raw_products
cs003_olist_raw_reviews
cs003_olist_raw_sellers
Keeping an untouched copy of the imported data provides traceability, simplifies debugging, and supports future transformation requirements.
Implementation Phase 04
Building the Staging Layer
The Staging Layer was developed to clean, standardize, and prepare the raw datasets for analytical processing. Data types were corrected, inconsistent values were standardized, duplicate records were reviewed, and column naming conventions were unified.
Data Cleaning
Raw datasets were cleaned to remove inconsistencies and prepare reliable source data.
Standardization
Data types, naming conventions, and inconsistent values were standardized across all datasets.
Validation
Duplicate records were reviewed and quality checks were applied before analytics processing.
Engineering Foundation
This layer separates technical data preparation from business analytics, creating cleaner and more maintainable transformation logic.
Raw Layer preserves data.
Staging Layer prepares data.
Analytics Layer creates business intelligence.
Staging Layer prepares data.
Analytics Layer creates business intelligence.
Raw ➜ Staging Engineering Work
📂 Raw Layer
Raw Layer was never modified. It preserves the original imported datasets exactly as received.
⚙️ Staging Layer
Every transformation, validation, cleaning, and standardization was performed inside the Staging Layer before analytical modelling.
📄 stg_category_translation
Purpose
Map Portuguese product categories into readable English categories.
Engineering Work
- Prepared category translation lookup
- Identified missing mappings
- Maintained standard category naming
👤 stg_customers
Purpose
Standardize customer master data.
Engineering Work
- Corrected data types
- Validated null values
- Reviewed duplicate records
- Applied standard column naming
- Cleaned customer location fields
🌍 stg_geolocation
Purpose
Prepare geographical data for analytical processing.
Engineering Work
- Validated latitude and longitude
- Reviewed duplicate ZIP coordinates
- Standardized state and city names
- Improved geographic consistency
📦 stg_order_items
Purpose
Prepare order line items for analytics.
Engineering Work
- Converted numeric fields
- Validated prices
- Standardized freight values
- Verified foreign keys
🛒 stg_orders
Purpose
Prepare business-ready orders.
Engineering Work
- Converted date columns
- Validated timestamps
- Standardized order status
- Validated delivery dates
💳 stg_payments
Purpose
Prepare payment transactions.
Engineering Work
- Standardized payment types
- Validated currency values
- Maintained numeric precision
- Reviewed installment values
📦 stg_products
Purpose
Clean and prepare product information.
Engineering Work
- Validated product dimensions
- Standardized weight fields
- Prepared category mapping
- Reviewed missing values
⭐ stg_reviews
Purpose
Prepare customer review data.
Engineering Work
- Validated review dates
- Checked rating consistency
- Reviewed duplicate reviews
- Validated review text quality
🏪 stg_sellers
Purpose
Prepare seller master data.
Engineering Work
- Cleaned seller location
- Reviewed duplicate records
- Normalized state values
- Applied standard naming
Implementation Phase 05 • Part 01
Building the Analytics Layer
The Analytics Layer transforms prepared data into business-ready analytical models. Unlike the Raw and Staging layers, this layer is specifically designed to support reporting, KPI calculations, dashboards, and conversational business intelligence. Instead of exposing operational tables, all Business Intelligence tools and AI services interact only with this governed analytical layer, ensuring trusted, secure, and consistent business insights.
Raw Layer
➜
Staging Layer
➜
Analytics Layer
Why BI & AI use this layer?
The Analytics Layer contains validated business models instead of raw operational data. This enables Business Intelligence dashboards, KPI reporting, and AI-powered conversational analytics to deliver trusted, governed, and business-ready insights without exposing the underlying source tables.
The Analytics Layer contains validated business models instead of raw operational data. This enables Business Intelligence dashboards, KPI reporting, and AI-powered conversational analytics to deliver trusted, governed, and business-ready insights without exposing the underlying source tables.
Implementation Phase 05 • Part 02
Dimension Tables
Dimension tables provide the business context required for reporting and analytics. They describe who, what, where, and when, allowing fact tables to be analyzed from multiple business perspectives. These reusable dimensions create a consistent and governed analytical model across the entire Business Intelligence platform.
dim_customers
Customer master information used for customer-based analytics and segmentation.
dim_dates
Calendar dimension supporting daily, monthly, quarterly, and yearly reporting.
dim_geography
Location hierarchy used for regional, city, and state level analysis.
dim_products
Product information supporting product categories and sales performance analysis.
dim_sellers
Seller master information used for supplier and seller performance reporting.
Implementation Phase 05 • Part 03
Fact Tables
Fact tables contain the measurable business events that drive reporting, KPI calculations, dashboards, and conversational analytics. Every important business metric is calculated from these tables, making them the foundation for trusted Business Intelligence and data-driven decision-making.
fact_delivery
Measures delivery performance, shipping timelines, and logistics efficiency.
fact_orders
Captures order activity, order status, and transaction volume.
fact_payments
Stores payment transactions, installments, and payment performance metrics.
fact_reviews
Tracks customer ratings, review scores, and customer satisfaction indicators.
fact_sales
Provides revenue, sales performance, profitability, and business KPI calculations.
Implementation Phase 06
Why We Stopped Before Building Dashboards
A traditional implementation would normally continue by building dashboards. Instead, DeTLeng introduced Artificial Intelligence directly on top of the Analytics Layer. This approach allows business users to ask natural language questions while the underlying analytical models continue to provide trusted and governed business information.
📊 Traditional Approach
Build dashboards first, then expect business users to search for answers manually.
➜
🤖 DeTLeng Approach
Connect Artificial Intelligence directly to the governed Analytics Layer and let users ask questions naturally.
The innovation was not replacing Business Intelligence.
Instead, Artificial Intelligence was introduced as a new conversational interface that works on top of trusted analytical models—preserving governance while making business insights instantly accessible through natural language.
Instead, Artificial Intelligence was introduced as a new conversational interface that works on top of trusted analytical models—preserving governance while making business insights instantly accessible through natural language.
Implementation Phase 07
Engineering the DeTLeng MCP Server
To prevent the AI model from generating uncontrolled SQL queries, a custom DeTLeng MCP Server was developed. The server exposes predefined Business Intelligence tools that execute validated analytical queries against Google BigQuery.
🛡 Secure Access
Artificial Intelligence never communicates directly with operational or analytical tables.
⚙️ Approved BI Tools
Only predefined Business Intelligence tools are exposed through the MCP Server.
📊 Trusted Analytics
Every response is generated from validated analytical models stored inside Google BigQuery.
Figure 3.
Live startup of the custom DeTLeng BigQuery MCP Server, showing the MCP service initialization and readiness to expose governed Business Intelligence tools. Instead of allowing unrestricted SQL generation, the server provides controlled access to validated analytical models, ensuring secure and trusted conversational analytics.
Implementation Phase 08
Building Business Intelligence Tools
Reusable Business Intelligence Tools were developed using Python. Each tool represents a specific business capability such as revenue analysis, customer statistics, order metrics, delivery performance, or product analytics. Instead of asking the AI to generate SQL dynamically, the assistant simply invokes these approved tools whenever business information is required.
Revenue Analysis
Retrieve trusted revenue metrics and financial performance directly from analytical models.
Customer Statistics
Analyze customer growth, segmentation, and business trends.
Order Metrics
Measure order volume, order status, and operational performance.
Delivery Performance
Evaluate logistics efficiency and delivery performance indicators.
Product Analytics
Generate product-level business insights from governed analytical datasets.
Implementation Phase 09
FastAPI Backend
A FastAPI backend was implemented to manage communication between the website, OpenAI, the DeTLeng MCP Server, and Google BigQuery. The backend performs request routing, authentication, tool execution, and response formatting before returning business-friendly answers to the user.
Website
➜
FastAPI
➜
MCP Server
➜
BigQuery
🔀 Request Routing
Directs every user request to the correct Business Intelligence service.
🔐 Authentication
Verifies secure communication between connected components.
🧰 Tool Execution
Invokes approved MCP Business Intelligence tools and processes responses.
📄 Response Formatting
Converts technical outputs into clear, business-friendly responses.
Implementation Phase 10
OpenAI Integration
OpenAI was integrated to provide natural language understanding and conversational responses. Every user question is first analyzed to determine whether it requires general knowledge or live business analytics. When business data is required, the request is automatically forwarded to the appropriate Business Intelligence tool before OpenAI generates the final business-friendly explanation.
User Question
➜
OpenAI Analysis
➜
BI Tool
➜
Business Answer
🧠 Intent Recognition
OpenAI understands the user's natural language question and determines the required action.
📊 Business Analytics
Analytical requests are securely routed to the appropriate approved Business Intelligence tool.
💡 Conversational Response
OpenAI transforms validated business results into clear, accurate, and easy-to-understand explanations.
Implementation Phase 11
Connecting Only the Analytics Layer
One of the most important engineering decisions in this implementation was ensuring that Artificial Intelligence never interacts directly with the Raw or Staging layers. Only the Analytics Layer is exposed through the DeTLeng MCP Server, guaranteeing that every response is based on governed, validated, and business-ready data.
Raw Layer
✖
Staging Layer
✖
Analytics Layer
✔
Artificial Intelligence
🚫 Protected Data Layers
Raw and Staging layers remain isolated from Artificial Intelligence, protecting operational data and engineering processes.
✅ Trusted Analytics Only
Only approved analytical models are exposed through the DeTLeng MCP Server for Business Intelligence and AI.
🛡 Secure Decision Making
Every AI response is generated from validated, governed, and business-ready information rather than raw operational records.
Implementation Phase 12
GitHub Repository Structure
The complete implementation was managed using GitHub, enabling version control, documentation, source code management, and deployment integration. The repository contains the backend services, MCP Server implementation, Business Intelligence tools, website components, and comprehensive technical documentation required to maintain and extend the platform.
📂 Documentation
- 01-architecture.md
- 02-business-tools.md
- 03-security.md
- 04-data-flow.md
- 06-deployment-guide.md
- 07-api-reference.md
- 09-design-principles.md
- 11-ai-mcp-roadmap.md
⚙️ Source Code
- server.py
- tools.py
- bigquery_client.py
- config.py
- system_prompt.md
- README.md
- requirements.txt
🧪 Testing & Quality
- test_bigquery.py
- test_client.py
- test_tools.py
- LICENSE
Figure 4.
GitHub repository containing the complete implementation of the platform, including architecture documentation, MCP Server source code, Business Intelligence tools, backend services, deployment configuration, testing modules, and engineering documentation. The repository serves as the central development workspace for version control, collaboration, and continuous platform evolution.
Figure 5.
A dedicated AI Knowledge Repository was created to manage the domain knowledge consumed by the backend services. The repository stores structured knowledge assets, including the detleng_knowledge.txt knowledge base, allowing the AI platform to maintain consistent organizational context while remaining independent from the analytical data warehouse. Separating business knowledge from analytical models improves maintainability, simplifies future updates, and enables the platform to evolve without affecting the underlying Business Intelligence architecture.
Engineering Beyond Source Code
The implementation extends beyond source code and backend services. A dedicated AI Knowledge Repository was engineered to manage domain-specific knowledge separately from analytical business models. This separation ensures that conversational intelligence, organizational knowledge, and Business Intelligence remain independently maintainable while working together as a unified AI platform.
The implementation extends beyond source code and backend services. A dedicated AI Knowledge Repository was engineered to manage domain-specific knowledge separately from analytical business models. This separation ensures that conversational intelligence, organizational knowledge, and Business Intelligence remain independently maintainable while working together as a unified AI platform.
Implementation Phase 13
Deployment on Render
The backend services were deployed to Render, where environment variables, API keys, and cloud configuration were managed securely. Deploying the backend to the cloud transformed the implementation from source code into a publicly accessible conversational analytics platform, enabling the DeTLeng Case Study website to communicate securely with OpenAI, the MCP Server, and Google BigQuery.
🌐
Website
➜
☁️
Render
➜
🤖
Backend APIs
🔐 Secure API Keys
Environment variables keep API keys and cloud credentials protected outside the source code.
🚀 Cloud Deployment
GitHub source code is automatically deployed as a live backend service running on the internet.
🌍 Public Access
Render exposes secure API endpoints that allow the website to communicate with backend services.
⚙️ Configuration
Cloud configuration, runtime settings, and deployment management are handled centrally.
Figure 6.
The backend services were deployed on Render, where cloud hosting, secure environment variables, deployment automation, and runtime configuration were managed. The deployment transformed the GitHub repository into a live backend service, enabling the DeTLeng Case Study website to communicate securely with OpenAI, the DeTLeng MCP Server, and Google BigQuery through publicly accessible API endpoints.
Implementation Phase 14
Technical Challenges
The project involved numerous engineering challenges, including Google Cloud IAM configuration, service account authentication, BigQuery connectivity, Render deployment, API communication, MCP integration, environment variable management, and backend debugging. Each challenge required careful analysis, testing, and refinement before a reliable production-ready solution could be achieved.
Cloud Security
IAM permissions, service accounts, and authentication had to be configured correctly for secure cloud access.
Platform Integration
BigQuery, Render, OpenAI, FastAPI, and the MCP Server had to communicate reliably as one platform.
Deployment & APIs
Deployment issues, API communication, runtime configuration, and environment variables required continuous testing.
Debugging
Backend behaviour, MCP integration, and production issues were resolved through systematic debugging and validation.
Implementation Phase 15
Live Demonstration
The completed implementation allows users to ask business questions directly from the DeTLeng Case Study website. Questions are processed through the FastAPI backend, securely routed via the DeTLeng MCP Server, executed against the Analytics Layer in Google BigQuery, and transformed into clear, business-friendly responses by OpenAI.
👤
User
➜
🌐
Website
➜
⚡
AI Platform
➜
💬
Business Answer
💡 Natural Questions
Business users interact using everyday language without writing SQL or navigating dashboards.
🛡 Trusted Analytics
Every response is generated from governed analytical models through approved Business Intelligence tools.
🚀 Interactive Experience
Enterprise analytics becomes conversational, immediate, and accessible directly from the web.
Figure 7.
The live DeTLeng Analytics AI Assistant running on the DeTLeng Case Study website. Users can ask business and analytics questions in natural language, while the platform securely processes each request through the FastAPI backend, the DeTLeng MCP Server, approved Business Intelligence tools, and the Analytics Layer in Google BigQuery before OpenAI delivers a trusted conversational response.
Project Results
Business Outcomes
The project successfully demonstrates how traditional Business Intelligence can be extended with Artificial Intelligence without sacrificing governance or analytical integrity. Business users can retrieve live insights using natural language, while organizations benefit from reusable analytical models, secure architecture, improved accessibility, and faster decision-making.
Conversational Analytics
Business users can obtain trusted insights simply by asking questions in natural language.
Trusted Governance
Artificial Intelligence operates only on validated analytical models, preserving security and data governance.
Reusable Intelligence
Business Intelligence models become reusable assets that support reporting, analytics, and AI applications.
Faster Decisions
Decision-makers receive business-ready answers instantly without relying on manual SQL queries or dashboard modifications.
Project Reflection
Lessons Learned
Reliable Artificial Intelligence begins with reliable Data Engineering. Building trusted analytical models before introducing AI significantly improves accuracy, governance, maintainability, and business confidence. This project also demonstrated that long-term success depends more on sound architecture and engineering decisions than on simply selecting modern technologies.
Architecture First
Strong architecture creates a foundation that remains scalable, maintainable, and reliable as systems evolve.
Trusted Data
Artificial Intelligence delivers better business outcomes when it consumes validated analytical models instead of raw operational data.
Engineering Over Hype
Sustainable AI solutions are built through disciplined engineering, governance, and thoughtful design—not by technology choices alone.
Looking Ahead
Future Roadmap
The current platform establishes a strong foundation for enterprise-grade Conversational Business Intelligence. Future enhancements will expand analytical capabilities, deepen AI integration, and extend the DeTLeng ecosystem with advanced knowledge retrieval, intelligent automation, and next-generation decision support technologies.
Advanced BI Integrations
Expand support for Power BI, Looker Studio, and additional enterprise reporting platforms.
Knowledge & RAG
Introduce enterprise knowledge bases, vector search, and Retrieval-Augmented Generation (RAG) for richer AI responses.
Multi-Agent AI
Develop collaborative AI agents capable of solving complex analytical and business workflows together.
DeTLeng Ecosystem
Extend AI-powered Business Intelligence across the broader DeTLeng ecosystem with reusable enterprise services.
About DeTLeng
Engineering Intelligent Business Systems
DeTLeng works on Data Engineering, Analytics Engineering, Business Intelligence, Artificial Intelligence, and Intelligent Business Systems. The focus is on building practical, well-engineered business solutions where trusted data becomes the foundation for meaningful insights and informed decision-making.
Data Engineering
Designing reliable data foundations that support analytics, reporting, and AI applications.
Business Intelligence
Transforming analytical data into information that helps organizations understand and improve their business.
Artificial Intelligence
Applying AI on top of trusted analytical models to deliver conversational and business-friendly experiences.
Integrated Solutions
Connecting data, analytics, cloud technologies, and AI into complete business platforms.
Closing Remarks
Final Conclusion
This implementation demonstrates that modern Business Intelligence is no longer limited to dashboards and static reports. By engineering a disciplined Data Engineering foundation, transforming raw business data into governed analytical models, and integrating those models with a custom DeTLeng MCP Server, FastAPI, and OpenAI, a traditional data warehouse was transformed into a conversational Business Intelligence platform.
The most important lesson from this project is simple. We did not build a chatbot and then connect it to data. We first engineered a trusted Data Engineering foundation by importing the Olist dataset into Google BigQuery, organizing it into Raw, Staging, and Analytics layers, and creating governed business models. Only after the Analytics Layer was complete did we introduce Artificial Intelligence. The chatbot intentionally connects only to the Analytics Layer through the DeTLeng MCP Server, ensuring that every response is accurate, consistent, secure, and business-ready.
The most important lesson from this project is simple. We did not build a chatbot and then connect it to data. We first engineered a trusted Data Engineering foundation by importing the Olist dataset into Google BigQuery, organizing it into Raw, Staging, and Analytics layers, and creating governed business models. Only after the Analytics Layer was complete did we introduce Artificial Intelligence. The chatbot intentionally connects only to the Analytics Layer through the DeTLeng MCP Server, ensuring that every response is accurate, consistent, secure, and business-ready.
Data Engineering First
Reliable analytics begin with disciplined Data Engineering and governed business models.
Artificial Intelligence Second
AI becomes valuable when it works on trusted analytical foundations instead of raw operational data.
Business Value Always
Every engineering decision was made to deliver reliable insights, stronger governance, and practical business outcomes.
Final Reflection
Technology continues to evolve, but one principle remains consistent: trusted business decisions depend on trusted data. This project reflects a practical engineering approach where Data Engineering, Business Intelligence, Artificial Intelligence, and cloud technologies work together to solve real business problems in a secure, maintainable, and meaningful way.
Data Engineering first. Artificial Intelligence second. Business value always.
Technology continues to evolve, but one principle remains consistent: trusted business decisions depend on trusted data. This project reflects a practical engineering approach where Data Engineering, Business Intelligence, Artificial Intelligence, and cloud technologies work together to solve real business problems in a secure, maintainable, and meaningful way.
Data Engineering first. Artificial Intelligence second. Business value always.
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