Ather Energy: Enterprise Data Lake on BigQuery
Replaced hand-assembled spreadsheet reporting with a governed BigQuery data lake, so executive reporting became a by-product of an automated pipeline.
Sector Electric mobility manufacturing (India)·Service lines Data Platform Engineering·Stack Google Cloud · BigQuery · SQL · Python
The Challenge
Reporting ran on spreadsheets
Ather Energy's core operational reporting was assembled by hand across Google Sheets, pulled from multiple systems with no single source of truth.
Numbers didn't reconcile
Different teams produced different figures from the same underlying data, so time was spent arguing about which version was right rather than acting on it.
Decisions waited on the reporting cycle
Leadership decisions depended on manual reporting cycles that were often stale by the time they reached the people making the call.
Our Approach
Re-architect the data flow, not the spreadsheets.
The obvious move would have been to automate the existing spreadsheets in place. We did not. TechTrapture re-architected the underlying data flow end to end, from source system to executive dashboard, so that reporting became a by-product of a governed pipeline rather than a manual exercise repeated every cycle.
Automating a broken process makes it faster and equally untrustworthy. Rebuilding the flow underneath it is what makes the numbers reconcile.
What We Built
An enterprise data lake on Google BigQuery, in three layers.
IngestionAutomated consolidation
Automated pipelines consolidate Ather's operational source systems into BigQuery, eliminating manual, spreadsheet-based data collection entirely.
TransformationOne consistent structure
A modelled transformation layer cleanses, reconciles and standardises raw data into a single consistent, trustworthy structure. This is the layer that resolves the reconciliation problem at its source rather than in the dashboard.
ReportingA governed single source
Curated, analytics-ready datasets power executive and operational reporting, so every dashboard draws from one governed source instead of team-by-team manual assembly.
The Outcome
| Before | After |
|---|---|
| Data assembled by hand across Google Sheets | Automated ingestion-to-reporting pipeline |
| Numbers reconciled differently team by team | One governed source behind every dashboard |
| Reporting delayed and often stale on arrival | Timely, trusted data flow |
| Each new analysis started from scratch | A governed foundation further work builds on directly |
Before
Data assembled by hand across Google Sheets
After
Automated ingestion-to-reporting pipeline
Before
Numbers reconciled differently team by team
After
One governed source behind every dashboard
Before
Reporting delayed and often stale on arrival
After
Timely, trusted data flow
Before
Each new analysis started from scratch
After
A governed foundation further work builds on directly
- BigQuery lakehouse architecture·
- Enterprise-scale ingestion and modelling·
- Governed data platforms
What It Runs On
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