Transform your data into your strategic asset with
tcgmcube™ – powered Data Lakehouse
The AI & analytics foundation for all data types I Powerful semantics for better contextualization
Why Modern Enterprises Need a Data Lakehouse
In the era of big data, advanced analytics, and AI, the need for efficient data management systems becomes critical. Traditional data warehousing and data lake architectures have their limitations, particularly in navigating through diverse and voluminous datasets, making it extremely difficult for users to get to relevant, contextualized data. There are challenges around data accessibility and data integrity, as well as significant collaboration bottlenecks.
The data lakehouse, which integrates the best features of both data lakes and data warehouses and adds a semantic layer for contextualization, emerges as a compelling solution. The data lakehouse enables dashboarding, traditional AI, generative AI, and AI-based applications on accessible and transparent data.
Leveraging our end-to-end AI platform, tcgmcube, organizations can create robust data lakehouses, with the aim to streamline data management by integrating various data processing and analytics needs into one architecture. This approach helps avoid redundancies and inconsistencies in data, accelerates analysis throughput, and minimizes costs, helping enterprises unlock the full potential of their data ecosystems with AI-driven insights and unified governance.
Leveraging our end-to-end AI platform, tcgmcube, organizations can create robust data lakehouses, with the aim to streamline data management by integrating various data processing and analytics needs into one architecture. This approach helps avoid redundancies and inconsistencies in data, accelerates analysis throughput, and minimizes costs, helping enterprises unlock the full potential of their data ecosystems with AI-driven insights and unified governance.
Why Modern Enterprises Need a Data Lakehouse
In the era of big data, advanced analytics, and AI, the need for efficient data management systems becomes critical. Traditional data warehousing and data lake architectures have their limitations, particularly in navigating through diverse and voluminous datasets, making it extremely difficult for users to get to relevant, contextualized data. There are challenges around data accessibility and data integrity, as well as significant collaboration bottlenecks.
The data lakehouse, which integrates the best features of both data lakes and data warehouses and adds a semantic layer for contextualization, emerges as a compelling solution. The data lakehouse enables dashboarding, traditional AI, generative AI, and AI-based applications on accessible and transparent data.
Leveraging our end-to-end AI platform, tcgmcube, organizations can create robust data lakehouses, with the aim to streamline data management by integrating various data processing and analytics needs into one architecture. This approach helps avoid redundancies and inconsistencies in data, accelerates analysis throughput, and minimizes costs, helping enterprises unlock the full potential of their data ecosystems with AI-driven insights and unified governance.
The data lakehouse, which integrates the best features of both data lakes and data warehouses and adds a semantic layer for contextualization, emerges as a compelling solution. The data lakehouse enables dashboarding, traditional AI, generative AI, and AI-based applications on accessible and transparent data.
Leveraging our end-to-end AI platform, tcgmcube, organizations can create robust data lakehouses, with the aim to streamline data management by integrating various data processing and analytics needs into one architecture. This approach helps avoid redundancies and inconsistencies in data, accelerates analysis throughput, and minimizes costs, helping enterprises unlock the full potential of their data ecosystems with AI-driven insights and unified governance.
Key Benefits
Presents a transformative approach to data management and helps foster a data-driven culture across the organization.
Improved Data Accessibility:
Facilitates actionable insights and analytics by ensuring that users have easy access to the right data at the right time through the right user interface.
Seamless Collaboration:
Enables teams to work together more effectively by providing a shared view of data across the organization.
Enhanced Analysis Integrity:
Enhances analysis integrity with better data management practices, version control, and semantic consistencies.
Core components of a holistic data lakehouse strategy
Comprehensive Architecture
- AI capabilities and data management on the same platform managed by common platform services
- Distributed, fault-tolerant, and cloud-native architecture
- Cloud-agnostic platform that can make native cloud calls
- Highly Interoperable – complement existing ecosystems
- Modular architecture- each module can scale dynamically
Features that make it “Easy to Get Data In”
- Streamlined data ingestion with pre-built connectors to various source systems and instruments.
- Support for both real-time and batch data ingestion, ensuring flexibility and efficiency.
- Enhanced ingestion process by utilizing semantic definitions for better contextualization.
- Cohesive and interconnected representation using knowledge graphs to integrate the data.
Features that make it “Easy to Get Data Out”
- Business metadata management powered by knowledge graphs, providing ontology management and knowledge modeling capabilities.
- Adherence to FAIR (Findable, Accessible, Interoperable, and Reusable) data principles
- Enhanced data understanding and usability through rich domain-context powered by Knowledge graphs.
- Use of contextualized semantic business terms for analytics, enabling efficient querying in natural language and easy interpretation of contextual responses.
tcgmcube: taking the Data Lakehouse to the next level
The platform tcgmcube provides advanced analytics and AI capabilities and data management on the same platform managed by common platform services. This makes it an extremely powerful platform for implementing the lakehouse and deploying analytical and AI applications on top of the lakehouse.
Data Ingestion
Data Storage
Data Leverage
Data Governance
Data Ingestion
The Data Ingestion Layer of tcgmcube comes with pre-built standard connectors to various source systems & instruments.
- Ingestion of structured, semi-structured, and unstructured data
- Options for real-time, near real-time, and batch ingestion
- Support for dynamic data pipelines
- Options for data transformation at various stages (ETL as well as ELT)
- Overlay of a semantic layer
Data Storage
The data storage layer comes with robust data management features. It leverages ontology and knowledge modeling capabilities, making it “easy to get data out” and has the following layers:
- Base data layer for source data processing, providing features to validate and catalogue the raw data
- Analytic Persistence layer with processed datasets for optimizing analytical queries and AI – driven processes
- Semantic Persistence Layer with contextualized data taxonomy through knowledge graphs
Data Leverage
The analysis and visualization features of the platform are powered by the semantic layer that makes it “easy to get data out” for analysis needs, providing options for specifying deep ontologies for domain contextualization. This block provides:
- Traditional AI at scale with a wide assortment of statistical, ML, DL, and optimization algorithms.
- Comprehensive Gen – AI algorithms covering traditional LLM and multimodal LLM RAG models for fast information retrieval and traceability.
- Insights dissemination options include dashboards with easy business user self-service, operational reports, and low-code “upgrade safe custom screen painting”. These leverage the semantic layer for data interpretation and reporting.
- Action dissemination options – provides inputs to automated operational processes such as alerts, recommendations, action triggers, etc.
Data Governance
The Platform Services & Governance layer helps implement data management & governance practices for data quality, security, & compliance. Features include:
- Role-based Access Control and fine-grained access policies (row, column, and object-level access control)
- Data Encryption at rest and in transit
- Audit Logs for all data access and processing activities.
- Layered Security: Security can be defined at various levels- cluster, index, document, and field
- Metadata Management powered by knowledge graphs
Resources
Get Ahead with tcgmcube Data Lakehouse
Transform your data into your strategic asset
