The data engineering landscape is undergoing a seismic shift. As organizations accelerate AI adoption, traditional, code-heavy methods are struggling to keep pace. Building and maintaining reliable data pipelines has become a major bottleneck complex, time consuming, and resource intensive.
The modern enterprise needs a more intuitive, intelligent, and automated approach, one that allows both Data Engineering Teams and Business Analysts/Users to engage with data seamlessly.
Enter the Intelligent Data Engineering Agent (iDEA), Tiger Analytics’ AI-powered conversational platform for end-to-end data management on the Databricks Lakehouse.
The Core Pain: Slow Time-to-Insight
Despite advances in cloud data platforms, enterprises continue to face critical challenges:
- The Code Barrier: Most data workflows rely heavily on centralized technical teams, creating bottlenecks. Non-technical users struggle to navigate different tools for data management.
- Pipeline Lag: Building reliable data pipelines is a manual, weeks-long process. Data consumers cannot quickly access or transform data for analytics.
- The Trust Gap: Lack of consistent standardization, governance, and observability leads to inconsistent data quality and a pervasive lack of trust in data-driven decisions.
These pain points result in slower time-to-insight, reduced agility, and a widening disconnect between business and data teams.
iDEA: AI-Driven Empowerment for All Users
iDEA is an Intelligent Data Engineering Agent that transforms data management by leveraging a conversational interface to orchestrate and automate the entire data lifecycle.
Through a simple conversational interface, iDEA allows users to simply “ask” for data operations. This delivers dual benefits:
Advantages for Data Engineering Personnel:
- Standardization of Data Management: Enforces consistent, auditable, and governed data pipelines by embedding best practices into every step, including In-Built Auditing, Logging, and Alerting features.
- MCP-Powered Agentic Orchestration: Implements MCP to seamlessly orchestrate tools and data sources using natural language prompts, simplifying complex deployments.
- Cloud-Scale Deployability: Uses cloud-native and open-source technologies for scalability and elasticity, supporting multi-environment (Dev/Test/Prod) setups.
Advantages for Business Analysts & Business Users:
- Generative AI Automation: Enables users to access and analyze datasets by asking in simple Natural language, eliminating the need to write complex SQL or PySpark code.
- Prompt-Driven End-to-End Workflow: A simple user prompt initiates the full data lifecycle, from Exploratory Data Analysis to automated Ingestion/Transformation.
- Instant Insight Generation: Translates NLQ into visualizations instantly. Users type questions and receive the corresponding analysis and chart.
- Semantic Data Discovery: Support Semantic Data Discovery and simplify data search, reinforcing the semantic nature of data being proposed.
The Power of Conversational Data Engineering
By eliminating the need for manual coding, iDEA democratizes data engineering. Data Engineers, Business analysts, domain experts, and data scientists can all interact with the Databricks Lakehouse intuitively, while governance and standards remain intact.
This paradigm shift allows organizations to:
- Accelerate time-to-insight by automating routine engineering tasks.
- Enhance collaboration between business and technical users.
- Improve governance and data trust through consistent, trackable pipelines.
Realizing the Future of Agentic Data Management
iDEA represents a new class of AI-driven solutions where agentic systems can act autonomously, interpret intent, and execute actions responsibly.
As part of Tiger’s broader vision for the AI-driven enterprise, iDEA seamlessly integrates with Tiger’s modernization accelerator (IDX) and Intelligent Data Quality framework (IDQ), creating a unified, intelligent data ecosystem.
Together, these platforms enable enterprises to move from “data management” to “data empowerment,” a shift that unlocks true agility and innovation.
Business Impact
Early adopters of iDEA have achieved measurable results:
- Up to 50% reduction in engineering effort through automation.
- 30-40% faster pipeline deployment via AI-guided orchestration.
- Enhanced data reliability through built-in governance and observability.
Practical Business Case: Accelerating Sales Insights
iDEA’s value is best demonstrated through its ability to solve real-world data bottlenecks for different user roles:
| Role | Problem/Goal | iDEA Action (Conversational) | Outcome |
| Business Analyst | Needs an urgent analysis of last quarter’s sales to identify top-performing manufacturers and formats in the Western region. | “Show me the sales revenue by region for Seasonal Chocolate and Lindt for the last quarter.” | Instantly generates the required analysis and visualization directly from the Lakehouse, enabling rapid decision-making. |
| Data Engineer | Must onboard Sales data feed from OLTP System containing sales transaction data and ensure it meets compliance standards. | “Ingest Sales data from Sales DB, after performing Exploratory Data Analysis.” | The agent automatically configures the data acquisition pipeline, performs ingestion contract validation, cutting deployment time from weeks to minutes. |
| Domain Expert | Needs to find all available product and customer datasets related to the ‘Retail’ domain. | “Find all entities in the ‘retail’ domain that contain ‘product’ or ‘customer’ metadata.” | Leverages the Semantic Search to simplify data discovery, and context for trust in the dataset. |
The Tiger Vision: Engineering with Intelligence
At Tiger Analytics, the mission is clear: to enable enterprises to realize the full potential of their data by bringing together AI, automation, and human expertise.
With iDEA, Tiger is redefining what it means to engineer data. No longer a manual, code-intensive process, it becomes an intelligent, conversational, and collaborative experience.
The Intelligent Data Engineering Agent is more than a copilot; it’s the future of AI-driven data engineering, designed to accelerate how modern enterprises build, manage, and trust their data.
