- Migration runbook
- Reconciliation report template — and the actual reconciliation numbers for your migration
- Performance benchmarks
- Cutover and rollback plan
Data Migration at Scale
Data migration you can prove didn't lose anything.
The scary part of a large migration isn't the volume — it's not knowing what you lost until a customer complains three months later.
We build Apache Spark pipelines that are idempotent and resumable — a failed run picks up where it stopped, it doesn't reprocess and duplicate — with row-level and aggregate reconciliation built in from day one, so every migrated dataset comes with proof, not just a completion message.
01 In practice
What this looks like in practice
01
Source/target schema mapping done explicitly, including the edge cases — nulls, encoding, timezones — that quietly corrupt naive migrations.
02
Idempotent, resumable Spark jobs — a crash mid-run is a retry, not a re-migration.
03
Row-level and aggregate reconciliation reports as a deliverable, not an afterthought.
04
Custom-conversion logic where source and target schemas don't map 1:1.
05
A cutover plan with a defined rollback window.
02 Deliverables
What you get
03 Proof
Migration, in production
Large-scale data migration with Apache Spark — 80% faster migration, zero data-loss incidents. Read the case study →
04 FAQ
Data migration — frequently asked questions
How do we know the migration didn't lose anything?
Because reconciliation is built into the pipeline rather than run as a spot check afterwards. Every migrated dataset comes with row-level and aggregate reconciliation between source and target, so you get proof rather than a completion message. The scary part of a large migration is not the volume — it is not knowing what you lost until a customer complains three months later.
What happens if a migration job fails halfway through?
It resumes. The jobs are idempotent and resumable, so a failed run picks up where it stopped instead of reprocessing from the beginning and duplicating rows. This is the difference between a crash being a retry and a crash being a re-migration — and it matters most on exactly the large datasets where a full restart is least affordable.
Which source and target systems do you migrate between?
Apache Spark is the engine, so the connectors are broad — relational databases, NoSQL stores, object storage and warehouses. We have run this pattern on end-of-life data stores specifically, including a Cassandra-to-PostgreSQL migration. Where source and target schemas do not map one-to-one, we build the custom conversion logic as part of the pipeline rather than pre-processing the data somewhere else.
What usually goes wrong in a naive migration?
The edge cases in the schema mapping, almost every time. Nulls that mean different things in each system, character encoding that silently mangles a subset of rows, and timezone handling that shifts timestamps by hours without erroring. None of these fail loudly — they produce a migration that looks successful and is quietly wrong, which is why we map them explicitly up front.
How long is the rollback window?
It is defined as part of the cutover plan, before cutover, based on how long you need to be confident in the target. The point is that the window is an agreed, documented thing with a tested procedure behind it, rather than an assumption that everything will be fine.
Talk to an architect about your migration.
Tell us what's moving, from where to where, and how much of it — we'll map a migration you can prove.