Data modernization has fast become the foundation for enterprise AI success. As organizations look to operationalize AI at scale, they must first address fragmented data ecosystems, legacy platforms, and governance gaps.
A recent global study found that 73% of respondents expect agentic AI to require increased data modernization investments. At Tiger Analytics, we’ve seen how legacy challenges play out across industries, from financial services to manufacturing, and retail to healthcare. The message is clear: data modernization must evolve beyond migration to become intelligent, automated, and AI-native. Modernization now means:
- Building a future-proof Lakehouse architecture on Databricks.
- Enabling governed self-service access for business and technical users alike.
- Embedding AI and Generative AI to automate end to end data lifecycle.
- Operating with end-to-end visibility, lineage, and observability.
Tiger Analytics’ IDX is designed precisely for this new reality – a modular, metadata-driven accelerator that leverages the full power of the Databricks and Generative AI to make modernization faster, smarter, and scalable.
IDX + Databricks: A Unified Foundation for Intelligent Modernization
Built on top of Databricks, Intelligent Data Express provides powerful self-service & low/no code capabilities (Web UI & Conversational Interface) for end-to-end data supply chain (from ingestion to insight generation), in a highly governed and scalable manner.
IDX was developed using our deep data engineering expertise and domain knowledge to serve as a comprehensive modernization framework that helps organizations:
- Hyper-accelerate Lakehouse implementation and migration.
- Improve governance and observability in the entire data lifecycle.
- Democratize access to data and data management operations through AI.
Some of its key features includes:
- Self-Service Platform Management: Empowers users across personas to use low/no-code capabilities to create and manage data pipelines through intuitive web-based and REST API interfaces.
- Standardized Data Lifecycle Workflows: Streamlines data extraction, ingestion, transformation, and enrichment with built-in auditing, logging, and alerting frameworks thereby ensuring best practices and consistency across use cases.
- Integrated Data Quality & Access Control: Applies automated data profiling, quality validation, and access-management policies through a unified governance framework.
- AI-Powered Natural Language Interaction: Allows business users to query and analyse data using simple English, eliminating the need to write SQL or PySpark code, and extends analytics to unstructured data through AI-driven processing.
- Accelerated Transformation Framework: Seamlessly converts existing SQL logic into executable transformation pipelines within minutes, reducing engineering effort.
- Metadata-Driven Discovery & Knowledge Graph: Leverages knowledge graphs and intelligent search for advanced Cataloging, metadata management, and data discovery.
- End-to-End Observability: Enables unified visibility across data, pipelines, infrastructure, and costs to minimize downtime, enhances operational efficiency, and optimizes Lakehouse spend.
- Cloud-Native Scalability: Built on Databricks and leading open-source technologies, IDX delivers scalability, elasticity, and modular deployments across Dev, Test, and Prod environments.
IDX Architecture Explained: Governed, Scalable Data Modernization on Databricks
IDX architecture is built as a multi-layered system that introduces an intelligent, AI-powered control plane on top of an Enterprise Data Lakehouse Platform.
- Foundational Data Platform: At its core, the architecture features a foundational Data Lakehouse on Databricks (with tools like DLT, Workflows, UC, Mosaic AI) and a Medallion Architecture (Bronze, Silver, Gold). This layer handles the complete data flow from ingestion (from RDBMS, APIs, etc.), through cleansing and transformation, to a consumption layer for analytics, Power BI, and ML.
- Reusable DataOps Frameworks and Microservices: A layer of reusable metadata-driven frameworks and microservices sits on top of the Lakehouse to manage DataOps and metadata (both technical and operational). There are more than 200 services that handle various data and pipeline management tasks such as pipeline instantiation, execution and monitor, schema discovery and STTM mapping, data merge, profiling data, receiving and setting asset details, managing DQ rules, applying transformation routines, enriching and registering data products, logging, monitoring & notification, Genie Interaction etc. – communicating via REST APIs or SQL.
- The Brain Layer: This is the cross-cutting layer that acts as the “brain” and powers intelligence in various platform operations, frameworks & experience layers. It’s an Agentic AI Layer built on an MCP architecture (exposing various capabilities of underlying frameworks and micro-services as Tools & Resources) and multiple agentic flows (used within the core data engineering processes).
- Multi-Interface Experience Layer: Users interact with the platform through two primary interfaces. First, a Self-Service Web UI (part of a Data Lake Management and Migration Workbench Application) provides a traditional graphical interface. Second, a Conversational Agent allows for natural language interaction. The Brain layer intercepts user requests from the Conversational Agent, uses Mosaic AI models and guardrails to understand intent, and orchestrates the MCP Servers to execute the correct sequence of microservices needed to fulfil the request.
- Integration Layer: IDX also integrates with other Tiger Accelerators on Databricks like iDEA (Intelligent Data Engineering Agent), Tiger Data Marketplace Accelerator, Tiger Entity Resolver, Tiger Modernization & Migration Workbench etc.
From Automation to Intelligence with GenAI in IDX
The key differentiator for IDX is that it goes beyond simply automating data engineering workflows to infuse GenAI intelligence across the lifecycle. In practical terms, IDX acts as a data engineering co-pilot, augmenting human expertise with AI-driven reasoning and contextual understanding. Some of its key GenAI-powered capabilities include:
- Source analysis & knowledge extraction: Automated profiling of structured sources plus information extraction from unstructured content (text, HTML, PDF) to infer schema, populate metadata, and curate knowledge graphs for harmonized datasets.
- Data quality inference & monitoring: AI-driven discovery of DQ rules (in-motion and at-rest) from profiling and usage logs, with autogenerated tests and monitoring hooks.
- Automated pipeline metadata generation: Generate pipeline configuration (ingest → transform → enrich → target) from requirements or NL prompts (powered by Tiger accelerator iDEA).
- Code conversion and optimization: Transpile legacy SQL/ETL logic to PySpark/Spark-SQL (Delta-native), applying Lakehouse best practices and runtime optimizations.
- Natural-language analytics and co-pilot experience: NLQ and conversational co-pilot capabilities for analysts and engineers to generate queries, transformation logic, and iterative pipeline refinements.
Impact and Proven Outcomes from Real-World Deployments
In our collaborations with multiple clients, we’ve observed that modernization delivers its real impact when automation, governance, and AI work as one system. IDX was built precisely for this convergence. Here is how it performs in real-world scenarios:
For an Asian InsurTech firm specializing in digital healthcare solutions, we built a unified data platform to automate Data and MLOps pipelines, enabling the delivery of advanced healthcare analytics products for insurance service providers.
A leading manufacturer of agricultural equipment partnered with us to implement a foundational Data Lakehouse with Azure Databricks to streamline data management, enable self-service analytics, and enhance data integration and governance.
We worked with a US-based medical device company to transform data processing capabilities through a modern Azure Lakehouse platform, achieving substantial cost reductions, faster processing times, and improved scalability.
Across all our partnerships, IDX has delivered measurable results, including:
- Over 40% faster greenfield Lakehouse setup and data product delivery.
- Time taken for pipeline development and deployment reduced from weeks to minutes, accelerating data lifecycle management with AI-powered DataOps and Self-service Efficiency.
- Standardized modernization patterns that simplify governance and lifecycle management, even in complex, data mesh-like environments.
- Future-ready Data & AI platforms grounded in industry-tested Lakehouse architecture.
Key Use Cases for IDX Across Industries
Modernization is only the start. IDX helps businesses operationalize intelligence, tuning lakehouse foundations to business domains to connect data to decision-making. Organizations are applying IDX across a range of modernization scenarios:
- Greenfield Data Lakehouse / Data Fabric / Data Mesh Implementation on Databricks for managing large volumes of multi-structured Data and building Analytical Data Products.
- Legacy DWH Migration to Next-Gen Data Lakehouse with IDX Data Migration and Code Conversion Capabilities.
- Augment existing Lakehouse with additional capabilities (Self-Serve Management / AI-powered Automation / Data Quality & Observability / Strengthen Governance etc.).
IDX also serves as the core foundation for building Domain Intelligence Platforms across industries, enabling scalable, AI-powered modernization and insight generation.
Intelligent Data Express (IDX) provides organizations with a guided, intelligent transformation and modernization engine. By layering GenAI into various aspects of Data Management, all grounded on Databricks’ modern stack, it helps enterprises modernize faster, democratize insight generation, govern without friction and evolve continuously.
