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DataLAB by Snaplytics
Make data analysis seamless, from raw data to insights with DataLAB.
DataLAB brings dataset management, SQL, SnapQL pipelines, machine learning, finance workflows, and governed AI execution into one practical workspace.
Built desktop-first and server-ready for teams that need more than dashboards, but do not want the cost and complexity of a full enterprise data stack.
Lake manager
SQL + SnapQL
ML experiments
AI client ready
DataLAB workspaceQuery, control, test, model
Built for the shift from BI dashboards to governed AI execution
Agentic analyticsGoverned AI executionRight-sized data platformsSQL-first automationFinance and audit readiness
Why Now
Teams need an operating layer between raw data and AI
The market is moving toward AI-assisted analytics, but the hard part is still data access, workflow control, repeatability, and trust. DataLAB is built for that layer.
Dashboards are not enough
Modern teams need to ask questions, prepare data, validate logic, run workflows, and hand off trusted results without stitching together a fragile toolchain.
AI needs a governed workbench
AI agents are useful only when they can reach approved data, use known tools, run auditable steps, and leave results where analysts can inspect them.
SMEs need power without platform sprawl
Mid-size teams want serious data capability, but not the cost and operating burden of assembling a full enterprise data stack from scratch.
How DataLAB Works
Control the data, automate the work, let AI execute safely
DataLAB turns messy business data into a governed workspace where analysts and approved AI clients can query, validate, model, and repeat work.
Step 1
Control the data foundation
Register files, folders, database extracts, and lake-backed datasets into one workspace with context, fields, row counts, lineage, and ownership.
Step 2
Automate the analyst workflow
Use GUI workflows, SQL, and SnapQL to clean, join, validate, test, model, and rerun the same process without rebuilding it manually.
Step 3
Let AI execute safely
Connect AI clients to governed DataLAB tools so approved assistants can inspect data, run queries, launch pipelines, and return visible results.
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, row counts, 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.
Finance and audit workflows
Run journal testing, reconciliation, payroll review, revenue analysis, anomaly detection, and audit-ready exception workflows.
Analytics 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, domain testing, 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 financial tests 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
Reconciliation
Journal Testing
Export Workflows
How Teams Use DataLAB
Practical workflows, not disconnected features
The strongest use cases start where analysts already feel friction: month-end work, audit testing, dataset cleanup, repeatable review, and model-assisted analysis.
WorkflowFinance teams control month-end data
WorkflowAuditors run standardized analytical review
WorkflowAnalysts automate repeat work with AI support
Data surfaces
Works with the data sources analysts and AI clients already use
CSV and foldersExcelPDF tablesSQL databasesParquetLake tablesSnapQLAI clients
Turn messy data into insights in minutes.
The best next step is a focused pilot around one real workflow, one real dataset, and one measurable bottleneck your team wants to remove.