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DataLAB / Analyst operating systemIngest → Manage → Transform → Predict → Automate
Make data work.
Deep analytics. Less complexity.
All the analytical depth your team needs, none of the complexity. Data management, SQL, ML, pipelines, and AI. One platform for ALL analytical work.
Local lake manager
SQL + SnapQL
ML experiments
Agentic analytics
Full Lifecycle
Run the whole analytics lifecycle in DataLAB
Ingest
Bring data in from files, folders, spreadsheets, databases, and analytical extracts.
Prepare
Clean, join, reshape, enrich, and validate data without breaking the analytical flow.
Analyse
Explore data, write SQL, investigate patterns, validate rules, and build analytical logic.
Model
Train, compare, tune, explain, and score machine-learning models when the workflow needs them.
Govern
Keep datasets, metadata, relationships, validation, lineage, and outputs visible and controlled.
Automate
Save the workflow, rerun it on new data, schedule repeatable processes, and build from it again.
AI + DataLAB
Ask AI. Execute in DataLAB. Keep the evidence.
Turn natural-language requests into real analytical work. An approved AI client uses DataLAB tools to discover data, run analysis, and build workflows while every action and result stays visible to the analyst.
01Find the right data
02Run governed tools
03Review visible results
Live AI clientAI directs. DataLAB executes.
Data Control
A lake manager built for working analysts
DataLAB gives teams a managed place for files, extracts, tables, context, fields, relationships, and results before analysis becomes another spreadsheet maze.
Dataset managerTrusted data starts here
Lake and dataset manager
Move beyond loose files with managed datasets, contexts, metadata, relationships, schemas, columns, and persistent analytical storage.
SnapQL automation
Turn repeatable analysis into named pipelines, exports, validations, model runs, and controlled business workflows.
AI analyst execution
Give AI clients a safer operating layer with tool access, workspace context, visible results, and user-controlled execution boundaries.
Transformation and validation
Clean, join, reshape, and enrich data through visual transformation steps, then hold it to validation rules that flag exceptions before analysis.
Machine Learning and AI
From prepared data to models, experiments, and AI-assisted work
Once data is controlled, DataLAB helps analysts apply ML, forecasting, anomaly detection, diagnostics, and automation without moving into a separate notebook stack.
No-code ML and AutoML
Train, compare, and evaluate models from guided workflows built for analysts and data-heavy business teams.
Forecasting and anomaly detection
Move from controlled datasets into predictive workflows that help teams spot patterns, outliers, and emerging risks.
Experiment tracking
Keep model outputs inspectable with evaluation, run history, feature review, and clear result handoff.
AI-assisted operations
Let approved AI clients run DataLAB queries and pipelines while keeping the workspace, outputs, and evidence visible.
AutoML studioTrain, compare, explain
Analyst Operating Layer
The workbench underneath reliable AI analytics
DataLAB connects data preparation, transformation, advanced analytics, and SnapQL automation into one analyst-centered operating layer.
- Register and govern files, folders, lake-backed datasets, and SQL sources
- Preserve dataset context, lineage, relationships, mappings, and fields
- Run transformations, SQL, SnapQL, ML, and validation rules together
- Package repeatable workflows for review, export, automation, and AI execution
- Support desktop-first control with a server-ready workspace path
Dataset Management
Lake Operations
SQL and SnapQL
Pipeline Automation
AI Client Tools
AutoML
Experiment Tracking
Forecasting
Anomaly Detection
Data Validation
Transformations
Export Workflows
Transformation Pipelines
From raw extracts to review-ready outputs
One connected workflow takes a dataset from raw import to a controlled result: shape the population, apply repeatable transformation steps, validate the output, and hand off results the team can trust.
- Map and control large source populations
- Chain filters, joins, derivations, and validation steps
- Review exceptions and export repeatable outputs
Transformation pipelineConfigure, transform, review
Data surfaces
Works with the data sources analysts and AI clients already use
CSV and foldersExcelPDF tablesSQL databasesParquetLake tablesSnapQLAI clients