Why Nekt exists
Most teams reach a point where data starts to spread across multiple systems:- SaaS tools and APIs
- spreadsheets and exports
- BI tools and dashboards
- internal databases
- AI tools and agents
- inconsistent KPIs across tools and teams
- no historical record of how data changes over time
- manual data consolidation with no audit trail
- ungoverned access — no control over who sees what
- data silos that block collaboration and AI adoption
Core modules
Nekt is built around a set of modules that move data from source to activation.Sources
Connect data from 100+ systems including SaaS platforms, databases, and APIs — no manual API management required.
Catalog
Browse and manage all your tables, schemas, and data structure in a lakehouse architecture.
Explorer
Query your data with SQL or use the AI-powered SQL Assistant to generate queries from natural language.
Queries
Write SQL to transform and consolidate datasets into new tables ready for dashboards, reports, and AI.
Notebooks
Run Python or PySpark code for advanced processing, enrichment, or automation on a schedule.
Histories
Track how records change over time with a built-in SCD Type 2 template.
Destinations
Deliver processed data to analytics platforms, internal systems, and communication tools automatically.
Integrations
Connect to BI tools, APIs, and AI agents — Looker Studio, Power BI, Grafana, MCP, and more.
Who uses Nekt
Nekt is used by teams that want to move faster with data without building a full internal data platform. This includes:- data teams that need reliable infrastructure
- revenue and operations teams that depend on analytics
- product teams building AI-powered workflows
- companies that want structured data without heavy engineering effort
Getting started
The fastest way to understand Nekt is to connect your first data source. From there you can:- extract data from your systems
- explore it in the Catalog
- build Queries with SQL or Notebooks with Python
- activate the results in dashboards, automations, or AI agents