Product overview
One governed foundation for data engineering, analytics and AI.
24 slides · English · Interactive scenarios
View presentation
01 / Overview
Your data. Your AI. One governed platform.
Unify data engineering, warehousing, analytics, and AI on an enterprise data platform with governance built into every workflow.
How can Genellipse help?
Genellipse plans a focused MVP within 6 weeks. Agree the outcome, interfaces, access, client reviewers, and acceptance criteria at the start. Use the delivered evidence to decide the next stage.
ILLUSTRATIVE WORKFLOW
- 01
Connect & engineer
- 02
Analyze & build AI
- 03
Govern & operate
Confirm the capabilities and configuration appropriate to your requirements.
01 / 07
GENEDATA · PRODUCT AND ROLE SHOWCASES
One platform. Every data discipline.
Explore the product overview and five detailed role showcases. Follow complex data from ingestion and modelling to analytics, AI and reliable operations, with security at every handoff.
Explore GeneData capabilities
Data Engineer
Reliable pipelines for complex sources, with data contracts, recovery and shared lineage.
24 slides · English · Interactive scenarios
View presentation
Data Analyst
Trusted metrics and explainable decisions, built on shared definitions and source evidence.
23 slides · English · Interactive scenarios
View presentation
Data Scientist
Reproducible models, rigorous evaluation and controlled business impact.
24 slides · English · Interactive scenarios
View presentation
Data Modeler
Shared models, historical context and dependable data contracts that support business change.
23 slides · English · Interactive scenarios
View presentation
Database Engineer
Dependable data services through safe modernisation, workload control and tested recovery.
25 slides · English · Interactive scenarios
View presentationGeneData
A shared foundation for data engineering, analytics, and AI.
GeneData covers the enterprise data lifecycle: connect sources, build and run pipelines, query a warehouse, define shared metrics, and support data science and AI. Catalog, lineage, access, and operational controls keep the work connected as it passes between teams.
EXPLORE GENEDATA
Built around
the work that matters.
GeneData connects the work of data engineers, analysts, data scientists, and business teams. Bring pipelines, SQL, business intelligence, machine learning, and shared knowledge together, with a common catalog, lineage, access controls, and audit history. Connect the systems you already use and expand from your first use case.
- Data & technology leaders
- Data engineers
- Analysts & data scientists
- Data stewards
Data engineering & warehousing
Connect databases, cloud services, applications, and files. Build batch and streaming pipelines, manage data quality, and query governed data through a SQL warehouse.
Analytics, data science & AI
Work with shared metrics, dashboards, and reports. Develop models and AI workflows with tools for evaluation, approvals, and operational oversight.
Catalog, governance & control
Discover data and business knowledge in a shared catalog. Follow lineage, manage permissions, and keep ownership, policies, and evidence connected to the work.
INSIDE GENEDATA
Explore the layers of the data platform.
Explore the capabilities behind the platform, the work each supports, and the details to examine with your team.
GeneFlow data engineering
Connect sources and build batch, streaming, and change-data-capture pipelines. Visual design and code-based transformation share an execution graph, with dependencies and quality assertions attached to the work.
Explore technical details
- Coordinate ingestion, transformation, and orchestration
- Support backfills, retries, and pipeline monitoring
SQL warehouse & open storage
Cortex SQL brings analytical querying to a warehouse that separates storage from compute. The platform describes support for Iceberg, Delta, and Parquet so data can be used across compatible tools.
Explore technical details
- Serve reporting and exploratory SQL workloads
- Review workload isolation, history, and query cost
Catalog, knowledge & lineage
Bring technical metadata, business definitions, ownership, and lineage into a shared discovery layer. Give teams a way to understand what a dataset means and how an upstream change may affect its consumers.
Explore technical details
- Find relevant data and accountable owners
- Follow information from source through transformation
Analytics & business intelligence
Build analysis, dashboards, recurring reports, and briefings around shared metrics. Connect the result to the definitions and sources that support it so users can investigate a change in the numbers.
Explore technical details
- Align recurring reporting with consistent definitions
- Support both exploration and repeatable business reviews
Data science & governed AI
Use the data foundation for analytical models, experiments, and AI workflows. Connect model and agent activity with the relevant datasets, evaluation expectations, and review responsibilities.
Explore technical details
- Give analysts, scientists, and actuaries a shared data basis
- Connect knowledge and data with controlled AI workflows
Governance & platform operations
Manage access, ownership, approvals, and operational evidence across the data lifecycle. Review freshness, quality, workload health, usage, and cost alongside the business uses they support.
Explore technical details
- Keep policies and permissions attached to the work
- Establish operating ownership for reliable data products
A WORKFLOW IN PRACTICE
Follow a source-to-decision workflow.
An illustrative example: a team needs a repeatable operational report and a trusted dataset for further analysis. Follow both the data and its ownership through the workflow.
Connect & define
Select the operational sources, identify the owners, and agree the business definitions and access requirements for the dataset.
Build & validate
Create the ingestion and transformation pipeline. Attach quality rules and establish how failed checks or late inputs will be handled.
Analyze & share
Publish the governed output for queries, dashboards, or model work. Keep metric definitions and lineage available to the people using the result.
Operate & improve
Monitor freshness, pipeline health, access, and cost. Review the impact of source or logic changes before extending the data product to new uses.
YOUR EXISTING ENVIRONMENT
Plan the connections
that matter.
GeneData is positioned to connect existing databases, applications, cloud platforms, files, and event streams. Begin with the sources and consumers required for one data product. Assess connector fit, network access, source load, table formats, and ownership before deciding what to retain, connect, or migrate.
FROM CAPABILITY TO APPLICATION
Where GeneData
fits into the work.
When pipelines, reporting, models, and governance live in separate tools, teams spend time reconciling definitions and reconstructing how an answer was produced. GeneData brings these activities into a shared environment so data can move from source to business use with its ownership and context intact.
Build reusable data products
Bring operational sources into a defined pipeline, apply quality checks, and publish a dataset with an owner and a documented meaning. Give downstream analysts and model builders a common starting point.
Connect reporting with its sources
Create analysis and dashboards around shared business definitions. Follow lineage when an input changes so the team can understand the effect on a metric, report, or decision.
Prepare data for AI
Connect governed datasets and business knowledge to model and agent workflows. Keep access, evaluation, and review requirements in view as a use case moves beyond experimentation.
PLAN A USEFUL EVALUATION
Start with a workflow
your team knows.
Bring a representative example, the people who own it, and the requirements it must meet. Review the whole path from input to output, including exceptions and handoffs.
EVALUATE WITH CONFIDENCE
MVP delivery within 6 weeks.
Genellipse plans a focused MVP within 6 weeks. Agree the outcome, interfaces, authorized access, client reviewers, and acceptance criteria before delivery begins.
COMMON QUESTIONS
Details that help
you decide.
Explore the scope, practical considerations, and a useful starting point for your team.
What kind of platform is GeneData?
GeneData is an enterprise data and AI platform. It brings data engineering, a SQL warehouse, a shared catalog, business intelligence, data science, and governed AI workflows together. It serves the broader data lifecycle, from connecting sources to using information in operational decisions.
Does adopting GeneData require replacing every existing system?
The platform is designed to connect existing databases, applications, files, cloud environments, and data platforms. A useful starting point is one source-to-decision workflow. Migration scope and any systems to retain should be agreed during architecture planning.
Where should an evaluation begin?
Choose a recurring business question whose data is currently difficult to assemble or trust. Trace the required sources, define the output, identify who can access it, and agree how you will assess data freshness, quality, and usability.
TAKE A CLOSER LOOK
Explore GeneData.
See the product, explore its capabilities, and find your next step.