North India

Data Warehouse Migration across Punjab

Moving warehouses without losing trust in the numbers, reconciled row by row, cut over in stages. Covering every district and PIN code in Punjab.

Districts
22
PIN codes
527
Cities mapped
22

Data Warehouse Migration in Punjab

SQL dialects differ in ways that quietly change results, especially around nulls, dates and rounding. We document every behavioural difference rather than assuming equivalence.

Punjab runs on agriculture and agri-machinery, textiles and hosiery, sports goods, light engineering and food processing, agri supply chains and SME manufacturing, where the practical win is workflow automation rather than frontier models. Where data warehouse migration earns its budget here usually follows directly from that mix.

We start from the constraint, not the capability, what the system must never do, who signs off, and what happens when it is wrong. Six weeks to something running in production, not six quarters to a strategy document.

ਸਤ ਸ੍ਰੀ ਅਕਾਲ , Sat Sri Akaal. We work in Punjabi and English across Punjab.

Punjab coverage

State / UT
Punjab
Region
North India
Districts covered
22
PIN codes covered
527
Cities mapped
22
Working languages
Punjabi, English

What is included

  • Inventory of every table, job and downstream consumer
  • Query translation with behaviour differences documented
  • Row-level and aggregate reconciliation between old and new
  • Dual running until the numbers agree
  • Staged cutover by consumer group
  • Cost model comparing before and after

Questions

Do you cover all of Punjab?

Yes, all 22 districts and 527 PIN codes. Delivery is remote-first, so coverage is genuinely statewide rather than limited to the cities we happen to have offices in.

Which Punjab sectors do you work with most?

Across Punjab the economy leans towards agriculture and agri-machinery, textiles and hosiery, sports goods, light engineering, food processing. Agri supply chains and SME manufacturing, where the practical win is workflow automation rather than frontier models.

How do you avoid breaking reports?

Row-level and aggregate reconciliation between old and new, plus dual running until the numbers agree. Consumers move in stages, never all at once.

Which warehouse should we move to?

It depends on workload and existing cloud. We model cost against your real query patterns rather than list pricing, and sometimes the answer is to stay.

How long does it take?

Driven by the number of downstream consumers far more than data volume. The inventory in week one gives a realistic estimate.

Data Warehouse Migration in Punjab

Covering all 22 districts. Tell us what you are trying to change.

Or email bd@dtrasglobal.com · call +91 74118 77878