Demos

See DataLAB working on real analyst workflows.

Watch focused product walkthroughs covering the main DataLAB workspace, Import Manager, Dataset Manager, SnapQL, reconciliation, journal testing, payroll testing, and repeatable analytics workflows.

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What the demos prove

Import and manage

Load real business files, assign them to the workspace, and make them reusable.

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Query and automate

Use SQL and SnapQL pipelines when useful work needs to become repeatable.

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Test and reconcile

Turn finance populations into controlled matches, exceptions, and review queues.

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Model and score

Move from prepared datasets into guided ML and scored analytical outputs.

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Video Library

Product walkthroughs you can play directly here.

Each demo is embedded from YouTube so prospects can watch without leaving the Snaplytics website. The notes under each video are based on the internal recording scripts, so visitors know what business problem the demo is solving before they press play.

Demo 1

DataLAB Overview

Analytics for everyone, from raw files to managed workflows

A walkthrough of the DataLAB desktop workspace: importing demo data, browsing managed datasets, querying with SQL, opening financial tools, and showing how advanced analytics fits into the same analyst workflow.

Business takeaway

Viewers understand DataLAB as one environment for import, manage, transform, query, test, model, export, and automate, rather than another single-purpose BI, audit, or ML tool.

Watch for

Import Manager loading normal business files into a managed workspace
Dataset Manager turning raw files into reusable data assets
The platform story across SQL, financial testing, reconciliation, and ML

general_ledgertrial_balancerevenue_registercustomer_masterpayroll_registertime_attendance

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Demo 2

DataLAB Exploring Data with SnapQL

Queries, pipelines, reconciliation logic, and ML automation

A practical SnapQL walkthrough showing how SQL-oriented analysts can move from one-off inspection queries into repeatable validation, reconciliation, pipeline, and model-scoring workflows.

Business takeaway

Viewers see how SnapQL keeps SQL users in a familiar surface while giving them a path into repeatable monthly processes and automation.

Watch for

Ledger inspection and movement analysis on a 2.5M-row general ledger
GL to trial balance comparison logic
Pipeline examples using WITH steps for validation, reconciliation, and collections risk scoring

general_ledgertrial_balancerevenue_registercash_receiptscustomer_master

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Demo 3

DataLAB Financial Reconciliation

GL to trial balance matching with controlled exceptions

A finance-focused walkthrough of reconciling a full general ledger population to a trial balance, configuring matching logic, reviewing outputs, and turning reconciliation into an exception workflow.

Business takeaway

Viewers see reconciliation move from spreadsheet matching into a controlled workflow that separates clean matches from review-ready exceptions.

Watch for

Joining GL account/entity keys to trial balance account/entity keys
Comparing GL net movement to trial balance net balance
Reviewing reconciliation status instead of manually scanning raw files

general_ledgertrial_balance

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Demo 4

DataLAB Journal and Payroll Testing

Full-population testing on large finance and payroll datasets

A testing walkthrough showing standardized journal testing on a 2.5M-row ledger alongside payroll testing patterns for high-volume review populations.

Business takeaway

Viewers see how DataLAB turns heavy technical testing into a repeatable analyst workflow with mapped datasets, selected tests, progress feedback, and reviewable results.

Watch for

Journal testing against a realistic 2.5M-row general ledger
Payroll and attendance testing across large HR/finance populations
A standardized testing flow that creates review populations instead of manual spreadsheet hunts

general_ledgerpayroll_registertime_attendance

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Demo Flow

The story is bigger than individual features.

The videos are intentionally ordered around the DataLAB product logic: start with business files, make them managed datasets, ask questions, standardize testing, and automate once the workflow matters.

Load source files with Import Manager

Organize datasets in Dataset Manager

Query and automate through SQL/SnapQL

Run financial testing or reconciliation

Use Advanced Analytics when prediction adds value

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