Data platform migration

Move to a modern data platform — without stopping the business.

From on-prem Hadoop, Oracle, SQL Server or legacy warehouses to a modern cloud lakehouse — GB to petabyte scale, one function at a time, with no downtime.

Plan your migration See case studies

The goal

Why move to a modern data platform.

Legacy platforms struggle to scale, miss SLAs and cannot keep up with what analytics and AI now demand.

Cloud flexibility

Scale up and down with the business, and pay only for what you use.

Modern AI capabilities

ML, GenAI and agents built directly on the platform, next to the data.

Advanced warehouse features

Lakehouse storage, real-time ingestion, built-in governance and data sharing.

Cost optimisation

No idle hardware or licences; compute that runs only when jobs do.

Key principles

How we migrate without risk.

Assess what exists

Inventory every source, pipeline, table and report, then map dependencies before anything moves.

One function at a time

Migrate step by step, business function by function, so each move is small, tested and reversible.

No downtime

Old and new platforms run in parallel and are reconciled; users switch over only once the numbers match.

Efficient history load

A fast, one-time bulk move of historical data, followed by continuous incremental sync.

Case studies

Migrations we have delivered, across stacks.

Large finance conglomerate · 5 lending businesses

Disparate sources to an Azure Databricks lakehouse, for regulatory reporting

Disparate source systemsADF · Azure Databricks · Delta Lake · Unity Catalog

A lender whose largest businesses are vehicle and personal loans. We migrated data from disparate sources across its five lines of business to Azure Databricks, and built the pipelines behind its regulatory reporting.

Multi-TB

medallion lakehouse

1,000s

ETL jobs running smoothly

On time

reliable regulatory reports

NBFC · Financial services

On-prem Hadoop to serverless AWS

Hadoop · Hive · OozieSpark · EMR · Step Functions · Lambda

A growing on-prem cluster was missing data SLAs. Hive ETL was lifted and shifted to Spark SQL with our no-code Elastic Data Platform, onto pay-per-use serverless AWS.

2.5h→40m

daily processing

2×

lower cost

4×

faster migration

City council · Public sector

Oracle to Snowflake, in near real time

Oracle 19cKafka · Snowflake

1.2 billion records from HCM and ERP apps moved to a Snowflake reporting warehouse with a custom Kafka-based pipeline: full first load, then incremental sync.

1.2B

records moved

10k/s

rows throughput

<10 min

data freshness

Telecom data solutions provider

Proprietary stack to a petabyte-scale open-source lake

Proprietary hardware & softwareHadoop · Spark · REST query layer

A usage-records platform serving Tier-1 telecoms was missing retrieval SLAs as 4G drove data to multi-petabyte scale. We moved it to an open-source big data lake on commodity hardware.

~10×

faster queries

Multi-PB

data scale

Days→min

large queries

AdTech platform

MapReduce to Spark, Avro to Parquet

MapReduce · AvroSpark · Parquet · CI/CD

For a platform serving 70 billion ad impressions a day on a 600-node cluster, we migrated transformation workflows to Spark, moved storage to Parquet and automated deployment.

2×

workflow performance

12 TB

data per day

Less

storage footprint

Planning a move to a modern data platform? Start with an assessment.

We assess your current platform and lay out a function-by-function migration plan with no downtime.