Integrate and prepare
Connect approved enterprise sources through batch or streaming patterns suited to the workload. Validate freshness, schema, and quality before a dataset is treated as reliable.
HYBRID-NATIVE DATA PLATFORM
i-Lakehouse
The Hybrid-Native
Intelligent Data Platform
Connect DataOps, AI/ML and governed data across cloud, on-premise and edge.
Explore the platformONE CONNECTED FOUNDATION
DATA
AI
CLOUD
ON-PREMISEHYBRIDEDGE
THE DATA PROBLEM
One foundation for usable enterprise data.
i-Lakehouse is Turbo AI's hybrid-native data platform concept for data engineering, analytics, and AI teams. It addresses fragmented sources, inconsistent quality, and disconnected workloads by bringing integration, DataOps, governance, and AI/ML access into a shared architectural approach.
The goal is not to move every dataset to one place. It is to give important data products clear ownership, traceable transformations, and controlled access across cloud, hybrid, on-premise, and edge environments where those choices are appropriate.
01 / UNIFIED INTELLIGENCE
Data, AI and cloud.
Working as one.
DATA+AI / ML+CLOUDINTELLIGENCE
OPERATING ARCHITECTURE
From sources to governed use.
Operate with DataOps
Version pipelines, document transformations, monitor failures, and assign data-product ownership. A repeatable process helps teams correct broken inputs before downstream analysis is affected.
Support AI and analytics
Make governed data available to analytics and AI/ML workloads with consistent definitions. Model evaluation and application decisions remain separate from the storage foundation.
Govern across environments
Apply access, lineage, retention, and security controls wherever the data runs. Hybrid placement is a design choice shaped by latency, privacy, operational cost, and existing systems.
02 / DEPLOY ANYWHERE
Built for your environment.
Cloud, hybrid, on-premise and edge.
i-Lakehouse ONE PLATFORM
01Cloud
02Hybrid
03On-premise
04Edge
03 / OPEN DATA FOUNDATION
Choice stays open.
Open standards support a flexible data foundation and reduce vendor lock-in.
01Apache Iceberg
02Delta Lake
03Hudi
04 / REAL-TIME INTELLIGENCE
From streams to insight.
Streaming and analytics technologies in one ecosystem.
KafkaStreamingSpark / Flink / TrinoAnalytics / AI
CONCEPTUAL TECHNOLOGY FLOW
05 / THE OPERATING FOUNDATION
Built for enterprise data work.
DataOps
Bring data operations together.
AI / ML
Connect data to intelligent workloads.
Governance
Keep control close to the data.
06 / TRUST BY DESIGN
Know where data goes.
Keep it governed.
SECURITY
LINEAGE
COMPLIANCE
ENTERPRISE APPLICATION
Built around real data products.
Operational analytics
Combine source events and reference data into a timely view with documented definitions. Teams can inspect why a metric changed and how recently its inputs arrived.
AI-ready knowledge
Prepare permitted documents and records for retrieval-based assistants. Ownership, access boundaries, and lineage help keep generated answers grounded in approved information.
Hybrid data access
Workloads can be placed according to data locality, latency, and governance needs. The architecture supports evaluating cloud, on-premise, and edge patterns without asserting that every deployment is identical.
Adoption path
Start with one bounded data product: identify users, sources, quality checks, access rules, and a measurable decision. Expand only when operations and ownership are established.
OUTCOMES AND DIFFERENTIATION
Open data, governed operation, deployment choice.
i-Lakehouse is designed to help teams create reusable data products with clearer quality, lineage, ownership, and access. That can reduce repeated integration work, improve confidence in analytics, and give AI applications a more dependable source foundation.
Its differentiator is the relationship among DataOps, AI/ML, governance, and hybrid-native deployment. Open table formats support data portability, while the operating model keeps security, lineage, and compliance close to the data. Actual architecture and integrations are validated for each environment.
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Read insightFREQUENTLY ASKED QUESTIONS
i-Lakehouse questions
The essentials of its hybrid-native, open data approach.
What is i-Lakehouse?
i-Lakehouse is a hybrid-native data platform that brings DataOps, AI/ML and cloud workloads into one connected foundation.
Where can i-Lakehouse operate?
i-Lakehouse supports cloud, hybrid, on-premise and edge environments.
Which open data formats are included?
Its open data foundation highlights Apache Iceberg, Delta Lake and Hudi, alongside security, lineage and compliance controls.
Who is i-Lakehouse for?
It is positioned for data-platform, analytics, and AI teams that need to connect enterprise sources while retaining governance and deployment choice.
Does every workload have to move at once?
No. A practical adoption path starts with a defined data product, maps its source and access rules, and expands as quality and operating ownership are established.
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