Mapping and rules
Agreed entity mappings, transformations, handling of missing values, and acceptance rules.
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Data Migration Sprint
Move the data. Keep the meaning.
Turn a risky data move into a repeatable, verifiable process. Mappings, dry runs, reconciliation, and a cutover plan for one agreed migration.
Teams replacing a legacy tool or consolidating business data
Sound familiar?
Critical information is trapped in an old database or spreadsheets.
A new platform needs trustworthy data before launch.
Nobody can prove that an import is complete or correct.
What you take away
Deliverables you
can actually use.
The starting package covers one source, one target, and up to five agreed entities. We confirm the exact boundary together.
Repeatable migration
Versioned extraction and loading steps for the scoped data, with error reporting.
Dry run and reconciliation
A trial migration and a report comparing record counts, relationships, and agreed business totals.
Cutover playbook
The sequence, ownership, validation checkpoints, and recovery approach for the final move.
A practical guide
Make a data move repeatable and verifiable.
A successful migration preserves the meaning of the data as well as moving the records. Start with a small set of entities, agree their rules with a data owner, and prove that a trial move produces a result the business can recognize.
How to do it yourself
First, profile a representative source sample. Look for missing identifiers, inconsistent formats, duplicate records, and broken relationships. Identify which fields carry business meaning, including status, dates, currency, and units. Record the questions that need an owner’s decision before transformation begins.
Create a mapping from the source to the target. For each field, describe the conversion, default, and rejection behavior. Agree how identifiers and relationships will be preserved. Keep ambiguous records visible rather than silently inventing a value that makes the import pass.
Build a repeatable extraction and loading process against a test destination. Keep the source unchanged and record each run’s inputs, version, and result. Define what happens if the run stops halfway through or is repeated, so a restart does not quietly duplicate data.
Reconcile the trial result at several levels. Compare record counts, relationships, meaningful totals, and selected examples. Then exercise a business workflow against the migrated data. Matching counts alone will not reveal a swapped unit, an incorrect status, or a broken relationship.
Prepare the final sequence with the data owner. Include changes arriving during the move, any required pause in writes, acceptance checks, and the recovery decision. Rehearse that sequence and preserve the results. Production cutover should follow the agreed scope and an explicit readiness decision.
How we approach the work
Our plan draws on SQL, .NET integration, and business data modeling. Experience with product, order, and operational data helps us ask what a record represents and how it is used. We treat transformation rules as business decisions that must be reviewed, rather than incidental details in an import script.
We define the scoped mappings, implement the repeatable migration, and review a dry run with the data owner. The handover includes reconciliation evidence, rejected-record handling, and a cutover playbook. Your team gets a process it can inspect and rerun under defined conditions.
The tools behind the work
SQL queries help profile and reconcile the data. C# or PowerShell can coordinate versioned extraction and loading where they fit the systems involved. For applications using Entity Framework Core, migrations manage schema changes; the data transfer and its reconciliation still need their own explicit checks.
SDD, Specs Driven Development, links migration requirements to mapping specifications, exceptional cases, and validation results. We use it to preserve decisions such as how a missing value is treated and which evidence the data owner needs before accepting the move.
Explore the tools
Ready to begin
What we need
from your team.
- Representative source data and target schema
- A data owner to approve mapping decisions
- Authorized access and a test destination
A few useful answers
Before we start.
Do you handle Access, Excel, or legacy SQL data?
Yes, subject to inspecting the source and confirming access. The package starts with explicit mappings and a dry run so that business meaning is checked before a final move.
How is the engagement priced?
The indicative estimate starts at €7,500, excluding VAT, for the stated package boundary. After a fit conversation, you receive a fixed quote with the exact deliverables, fee, and schedule. Work starts after acceptance; changes are quoted separately.
What happens after delivery?
You receive the working files, findings, and a handover session. Your team can take the next steps, or we can scope a follow-on engagement together.
Let’s talk about Data Migration Sprint
Your next step
starts here.
Tell us what needs to change.
We’ll review your request and come back with a practical next step. No commitment required.
Prefer email? dev@roggemans.com ↗Discuss this package
A little context helps us make the conversation useful.
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