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Big Data Migration

Leave legacy big data platforms without the big-bang risk.

Hadoop clusters and appliance warehouses are reaching end of support, and their costs keep rising. We migrate data, workloads and users to a modern lakehouse in planned waves, so the business keeps running and every number is reconciled before the old platform is switched off.

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Sound familiar?

The problems we solve

  • End-of-life platforms

    Hardware refreshes, support renewals and scarce skills for Hadoop and appliance warehouses.

  • Petabytes and thousands of jobs

    Data volumes and job counts that make a manual migration impractical.

  • Fear of disruption

    Reports, regulatory filings and downstream systems that cannot afford a failed cutover.

  • Unknown dependencies

    Undocumented consumers, shadow extracts and hard-coded paths scattered across the estate.

Outcomes

What you get

  • Legacy hardware and support contracts retired
  • Workloads running faster on elastic cloud compute
  • Every migrated dataset reconciled and signed off
  • A platform ready for machine learning and generative AI

Approach

How we deliver it

  1. 1

    Assess

    Discover data, jobs, users and dependencies; size the estate and build the business case.

  2. 2

    Plan waves

    Group workloads into low-risk migration waves with clear success criteria for each.

  3. 3

    Migrate and convert

    Bulk and incremental data transfer, HiveQL, Spark and SQL conversion, security and access mapping.

  4. 4

    Validate in parallel

    Run old and new side by side; automated reconciliation proves parity.

  5. 5

    Cut over and retire

    Switch consumers wave by wave with rollback plans, then decommission the source.

Deliverables

What we hand over

Working software, tests and documentation in your repositories and cloud accounts.

Technologies

  • Cloudera
  • Hadoop HDFS
  • Hive
  • Teradata
  • Netezza
  • Oracle Exadata
  • Databricks
  • Snowflake
  • BigQuery
  • Microsoft Fabric
  • AWS DataSync
  • Azure Data Box
Migration assessment
Inventory, total cost of ownership comparison, target architecture and wave plan.
Data transfer
Secure bulk transfer of historical data plus CDC to keep targets in sync until cutover.
Workload conversion
Hive, Impala, Pig, Spark and warehouse SQL converted and optimized for the target platform.
Reconciliation and testing
Automated parity checks on counts, aggregates and samples, with sign-off reports.
Cutover runbooks
Step-by-step plans, rollback criteria and hypercare support for each wave.
Decommissioning
Safe shutdown of legacy clusters and archival of anything that must be retained.

Use cases

Where this makes a difference

  • Cloudera Hadoop to Databricks

    HDFS data and Hive/Spark jobs moved to Delta Lake with Unity Catalog governance.

  • Teradata to Snowflake or BigQuery

    Enterprise warehouse, BTEQ scripts and BI semantic layer migrated with full reconciliation.

  • Netezza and Exadata exit

    Appliance workloads re-platformed onto elastic cloud warehouses before hardware renewal.

FAQ

Common questions

Lift-and-shift or re-architect?

Usually a mix. Stable workloads move with minimal change; workloads with clear value from the new platform are re-architected. The wave plan makes that decision per workload.

How long do both platforms run in parallel?

Typically two to six weeks per wave, long enough to reconcile a full business cycle such as a month-end close.

What about historical data we rarely use?

We tier it: frequently used data goes to the lakehouse, cold data to low-cost archive storage that remains queryable.

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Tell us where your data is today.

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