SERVICES · DATA ENGINEERING

Data Engineering & Modernization

Pipelines, lakehouse platforms and migrations built to be tested, versioned and observed. We move data from wherever it lives today into a platform that keeps analytics and AI fed — reliably, at any scale, without a big-bang cutover.
OVERVIEW

Pipelines that break?
Let’s build ones that don’t.

XEqualTo designs, builds and runs the engineering layer of your data estate. Whether you’re migrating SQL Server or Oracle to Snowflake or Databricks, replacing hand-written ETL with dbt and Airflow, or adding streaming ingestion for the workloads that can’t wait for a nightly batch, we deliver a pragmatic plan that balances speed with reliability.
The outcome is a platform your teams can trust: a single source of truth, pipelines that fail loudly and rarely, and data that arrives when the business needs it — not the morning after.
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WHERE YOU ARE
WHERE YOU LAND
Siloed sources across teams and tools
Hand-written ETL nobody wants to touch
Nightly batch, stale by morning
Pipelines that fail silently
Legacy licence and hardware cost
OUR CAPABILITIES

Everything a modern
data platform needs.

Warehouse & lakehouse migration

Tables, procedures and business logic moved from SQL Server, Oracle or Hadoop to Snowflake or Databricks — reconciled end to end, with old and new running in parallel until every number matches.

Lakehouse platform build

Ingestion, storage, modelling and consumption layers designed for ELT and separated storage/compute — not legacy schemas lifted as-is.

ETL/ELT pipelines with dbt & Airflow

Pipelines that are tested, versioned and orchestrated as code, with CI on every change and real-time ingestion where the business actually needs it.

Integration & lineage

APIs, SaaS sources, files and event streams landed into one model — with lineage so every downstream number traces back to its source.

Security built into the platform

Role-based access, encryption at rest and in transit, PII handling and monitoring — set up during the build, not after an audit.
OUR APPROACH

Assess, build,
harden, operate.

Every engineering engagement starts by measuring what exists and ends with a platform we can run for you or hand over cleanly. Assessment first, then phased delivery by domain, then governance and operations built in rather than bolted on.
01
Weeks 1–3

Assess

Inventory sources, pipelines and consumers. Measure data quality, run times and cost. Map the pain points — broken jobs, slow queries, unknown lineage — and produce a prioritised readiness plan. Everything after this is planned against what we find here.
Source inventoryPipeline auditQuality scorecardReadiness report
02
Phased by domain

Build

Migrate tables and procedures, redesign models for ELT and dbt, build orchestration in Airflow, and add streaming ingestion where needed. Old and new run side by side until every number reconciles and the business signs off on each slice.
Schema & procedure migrationdbt modelsAirflow orchestrationStreaming ingestion
03
Built in, not bolted on

Harden

Add tests, lineage, a data catalog, role-based access control, encryption and monitoring — so the platform is governed from day one and every pipeline failure is caught before a dashboard goes wrong.
Data testsLineageCatalogRBACMonitoring
04
Ongoing

Operate

SRE-style runbooks, on-call, cost guardrails and continuous tuning. The platform keeps getting faster and cheaper after go-live instead of drifting — run by us, or handed over to your team with the documentation to match.
RunbooksOn-callCost guardrailsPerformance tuning
TECH STACK WE USE

Built on the platforms
your data already trusts.

Snowflake, Databricks, dbt, Airflow and the major clouds — our engineers work across them daily, so your pipelines land on proven ground.
AWS
Databricks
Microsoft Azure
Snowflake
Google Cloud
Power BI
Tableau
dbt
QUESTIONS

Things people ask
before we start building.

Ready for a free platform assessment? Talk to us directly — we would rather answer it properly than guess at it.
Contact us
Most engagements start from SQL Server, Oracle, on-prem Hadoop or a tangle of SaaS sources, and land on Snowflake or Databricks. We also handle mixed estates where part of the data already lives in the cloud.
No. We phase delivery by domain or workload, run old and new pipelines in parallel with reconciled numbers, and retire legacy only once the business signs off on each slice.
Only where it pays off. Hand-written or fragile jobs are rewritten as dbt models and orchestrated in Airflow; tools that work and are well understood are kept and integrated rather than ripped out.
We add streaming ingestion — Kafka, Kinesis, CDC — only for the workloads that genuinely need it, and keep the rest on efficient batch. Most estates need far less real-time than they think.
Either us, under a managed DataOps arrangement, or your team. In both cases you get runbooks, tests, lineage and documentation — nothing lives only in someone’s head.
With a free platform assessment: we inventory sources and pipelines, measure quality and cost, and give you a scoped, prioritised plan within two to three weeks.

Ready for a free
platform assessment?

We’ll come back with an engineering roadmap and a cost estimate for your stack. No commitment beyond the conversation.

Contact us
Precision in Every Data Point
Newtown, Kolkata
One office, every time zone
connect@xequalto.com
+91 9147391433
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