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DataLAB by Snaplytics
Transform your workflow.
DataLAB is a data analytics platform that helps teams connect to data, shape it into trusted datasets, and turn repeatable analysis into easy governed workflows.
Built desktop-first with a analyst-focused approach, DataLAB brings data management, data exploration, testing, ML, import and export workflows into one practical environment for data analysts.
DataLAB workspaceQuery, control, test, model
Built for teams stuck between spreadsheets and engineering queues
Finance teamsAudit teamsOperations analystsData-heavy SMBsAnalyst-led departments
Three Steps to Analytical Control
Connect the data, automate the work, standardize the result
The platform journey is simple: get the right data under control, let analysts work faster, then turn the best workflows into repeatable operating assets.
Step 1
Plug into the data you already have
Load spreadsheets, files, PDFs, SQL sources, and warehouse extracts into one controlled workspace with context, metadata, and lineage.
Step 2
Inject analyst automation
Use GUI workflows, SQL, and SnapQL to clean, join, transform, test, model, and rerun the same process without hand-built glue code.
Step 3
Standardize what works
Turn repeatable analysis into financial tests, reusable pipelines, model runs, exports, and team workflows that can improve every cycle.
Unify and Optimize
Enhance the systems teams already use
DataLAB is designed to modernize practical business analytics without forcing a full platform replacement. It gives analysts stronger control over the files, exports, and databases they already depend on.
Dataset managerTrusted data starts here
Break down data silos
Bring disconnected files, operational exports, and database tables into a workspace analysts can actually inspect and control.
Activate spreadsheet and report data
Move everyday business data out of fragile files and into reusable datasets, shaped transformations, and auditable results.
Model the business context
Track datasets, relationships, row counts, columns, mappings, and analytical context so work does not disappear after one review.
Make testing repeatable
Package journal testing, reconciliation, payroll checks, revenue analysis, and anomaly review into consistent workflows.
Analytics and AI
Move from prepared data to advanced analysis
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, not only data scientists.
Forecasting and anomaly detection
Move from cleaned datasets into predictive workflows that help teams spot patterns, outliers, and emerging risks.
Diagnostics and explainability
Keep model outputs inspectable with evaluation, experiment tracking, feature review, and clear result handoff.
SnapQL automation
Give power users a compact language for queries, transformations, pipelines, and domain-specific analytical commands.
AutoML studioTrain, compare, explain
Analyst Operating Layer
A workflow fabric for business data
DataLAB connects data preparation, domain testing, advanced analytics, and SnapQL automation into one analyst-centered operating layer.
- Connect siloed files and databases into a trusted workspace
- Preserve dataset context, lineage, relationships, and mappings
- Run transformations, SQL, SnapQL, ML, and financial tests together
- Package repeatable workflows for review, export, and future automation
- Support desktop-first control with a web platform path
Dataset Management
SQL and SnapQL
Data Preparation
Financial Testing
Reconciliation
AutoML
Forecasting
Anomaly Detection
Pipelines
Export Workflows
Web Workspaces
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 build models without leaving the workflow
Data surfaces
Works across the data analysts already touch
CSVExcelPDF tablesSQL databasesParquetSnapQL
From messy data to repeatable decisions.
The best next step is a focused pilot around one real workflow, one real dataset, and one measurable bottleneck your team wants to remove.