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DataLAB by Snaplytics
The analytics platform for teams that need SQL, data operations, and deeper workflow control.
DataLAB is first and foremost a mature desktop application for finance teams, auditors, analysts, and data-heavy business units. A web experience exists today as an early MVP, but the desktop product remains the primary production surface.
40+
ML algorithms
129
Financial tests
Desktop-first
Primary delivery model
Engagement-led
Finance and audit workflows
Current productMain workspace
Capabilities
What DataLAB actually covers today
Broad workflow coverage today, with mature depth on desktop and an early web MVP that is still being shaped through real-world feedback.
SQL analytics
Query files and connected data through SQL workflows with joins, CTEs, window functions, and practical export paths.
Dataset operations
Manage loaded datasets with metadata, contexts, row counts, column detail, and persisted parquet storage built into the product flow.
Machine learning
Work with 40+ algorithms, AutoML, hyperparameter tuning, evaluation tools, and experiment tracking from the same environment.
Financial analytics
Run reconciliation, journal testing, anomaly review, and broader finance-focused analytical workflows inside the same platform.
Pipelines and automation
Build repeatable ETL and analytical workflows through the pipeline layer rather than recreating every process manually.
Desktop-first delivery
Use the mature desktop product for real production work today, while the web experience continues as an early MVP for evaluation, feedback, and product shaping.
Inside The Product
Four views into the current platform
A look at workflow depth across the product - not only output, but how DataLAB is actually used day to day.
Main workspaceQuery, inspect, and export
Dataset managerOrganize loaded data
AutoML studioCompare models faster
Financial suiteReview risk-heavy financial records
Example Workflows
The product stays close to real analytical tasks
SQL and product workflows are both part of the story. The examples below show how teams move from data work to practical output.
Query operational data quickly
Use SQL to explore business data, aggregate operational records, and move toward export, validation, or further workflow steps.
- ? Join multiple files or connected datasets
- ? Work with CTEs, window functions, and aggregations
- ? Push results toward export or downstream review
analytics.sql
SELECT entity,
DATE_TRUNC('month', CAST(posting_date AS DATE)) AS month,
SUM(debit_amount) - SUM(credit_amount) AS net_movement,
COUNT(*) AS journal_lines
FROM general_ledger
GROUP BY entity, DATE_TRUNC('month', CAST(posting_date AS DATE))
ORDER BY month DESC;How It Works
Simple architecture, practical output
1
Load data
Files or connected sources
2
Work in DataLAB
Query, test, model, export
3
Execution core
Analysis and orchestration
4
Results
DataFrames, exports, model outputs
Connectors
Connect through files or databases
DataLAB supports a mix of file-based and connected workflows, which is part of why it fits both analytics and finance-heavy teams.
CSV
File
Excel (.xlsx)
File
Parquet
File
JSON
File
SQL Server
Database
PostgreSQL
Database
MySQL
Database
Oracle
Database
SQLite
Database
Want to see whether DataLAB fits your workflow?
The best evaluation is a focused walkthrough around your actual datasets, your team structure, and the process bottlenecks you want to remove.