Capability
Data Engineering across India
The pipelines, warehouse and contracts that make everything else possible, tested, monitored and documented.
- Industries
- 30
- Stack options
- 9
- Typical first release
- 6 weeks
What data engineering means when we build it
Orqent Labs builds the unglamorous layer properly, ingestion, modelling, quality and lineage, because everything above it inherits whatever we get wrong here.
We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.
Multi-model by default, so a provider outage is a routing decision rather than an incident. Six weeks to something running in production, not six quarters to a strategy document.
What is included
- Source system audit and ingestion design
- Incremental pipelines with change data capture
- Dimensional models your analysts can actually query
- Data quality tests that fail loudly
- Lineage and documentation generated from the code
- Cost monitoring on warehouse spend
Who this is for
We usually work with data leaders, analytics managers, CTOs and BI teams, the people who own the outcome rather than the tooling decision.
Data Engineering by industry
Each sector changes the constraints, regulation, systems of record, and what a wrong answer costs.
- Data Engineering for Healthcare & HospitalsDPDP Act 2023
- Data Engineering for Pharmaceuticals & Life SciencesCDSCO
- Data Engineering for Financial ServicesRBI guidelines
- Data Engineering for InsuranceIRDAI regulations
- Data Engineering for Legal ServicesBar Council rules
- Data Engineering for ManufacturingISO 9001
- Data Engineering for Logistics & Supply Chaine-way bill compliance
- Data Engineering for Retailconsumer protection rules
- Data Engineering for Education & EdTechDPDP Act 2023
- Data Engineering for Government & Public SectorDPDP Act 2023
- Data Engineering for Energy & UtilitiesCEA regulations
- Data Engineering for SaaS & TechnologySOC 2
- Data Engineering for Media & Entertainmentcopyright law
- Data Engineering for Real Estate & Construction TechRERA compliance
- Data Engineering for TelecommunicationsTRAI regulations
- Data Engineering for Agriculture & AgritechFSSAI standards
- Data Engineering for HospitalityFSSAI for food service
- Data Engineering for AutomotiveAIS standards
- Data Engineering for Construction & Infrastructurebuilding codes
- Data Engineering for Professional Servicesprofessional body standards
- Data Engineering for Nonprofit & DevelopmentFCRA compliance
- Data Engineering for Travel & Tourismtourism ministry guidelines
- Data Engineering for BankingRBI master directions
- Data Engineering for E-commerceconsumer protection e-commerce rules
- Data Engineering for Recruitment & HR Techlabour laws
- Data Engineering for Mining & MetalsDGMS safety regulations
- Data Engineering for Textiles & Apparelexport documentation requirements
- Data Engineering for Chemicals & Process IndustryPESO licensing
- Data Engineering for AviationDGCA regulations
- Data Engineering for Defence & Aerospacesecurity clearance requirements
Data Engineering, stack options
We pick per workload. Each page states the honest trade-off.
- Data Engineering with PostgreSQLdata
- Data Engineering with Supabaseplatform
- Data Engineering with Snowflakedata
- Data Engineering with Databricksdata
- Data Engineering with Pythonframework
- Data Engineering with dbtdata
- Data Engineering with Apache Airflowdata
- Data Engineering with Apache Kafkadata
- Data Engineering with TypeScriptframework
Data Engineering across India
Delivered remotely from our hubs, the city page tells you the nearest one and the realistic kickoff time.
Questions we get asked
Which warehouse do you recommend?
It depends on your volume, team and existing cloud. Postgres carries far more workloads than people expect; Snowflake, BigQuery and Databricks earn their cost at genuine scale.
Can you work with our existing stack?
Yes. Rebuilding a working stack is rarely the right call. We usually extend and stabilise what exists rather than starting over.
How do you handle data quality?
Tests that run on every pipeline execution and fail loudly, plus lineage so a bad number can be traced to its source in minutes rather than days.
Considering data engineering?
Tell us the workflow and the constraint. We will tell you honestly whether it is worth building.
Or email bd@dtrasglobal.com · call +91 74118 77878