Core service
Give your data room to grow
When one database carries transactions, reports and every new feature, growth turns into contention and long batch windows. We redesign the data layer and move it without downtime.
- Duration
- 6 to 16 weeks
- Model
- Design and staged migration
- Output
- Data model and zero-downtime migration
Signs the data layer has become the bottleneck
These usually appear together. They point to a data model and a storage layout that were right for an earlier stage of the business.
- Month-end reports slow down the live system, so heavy queries are scheduled for the middle of the night.
- One table has grown so large that adding an index or a column needs a maintenance window.
- Every service reads and writes the same schema, and a change in one place breaks another.
- Read replicas were added, but users still see stale or inconsistent data right after they write.
- Nobody is sure which system is the source of truth for customer or order data.
- A database upgrade or a cloud move keeps being postponed because nobody can estimate the downtime.
- Storage and licence costs grow every quarter while most of the data is rarely read.
The model first, the technology second
Data problems are often treated as capacity problems. A bigger instance or another replica buys a few months, but the pressure returns because the shape of the data has not changed: transactional and analytical workloads still share tables, and ownership of each dataset is still unclear.
We start from the model: which data belongs to which part of the business, how consistent it needs to be, how it is read and how it grows. Only then do we choose the layout, whether that means partitioning, separate stores for reporting, caching, or splitting ownership between services.
Migration is designed as carefully as the target. Data moves in stages with dual writes, backfills and verification, and every step has a way back. The system keeps serving users throughout.
Scope
Included
- Analysis of the current data model and access patterns
- Target data model and ownership map
- Partitioning or sharding plan
- Separation of reporting and transactional workloads
- Caching strategy
- Zero-downtime migration procedure with rollback steps
- Data verification and reconciliation checks
- Capacity and cost projection for the target layout
Not included
- Building dashboards and BI reports
- Data science and machine learning models
- Day-to-day operation of your databases after handover
- Licence negotiations with database vendors
How the work runs
- Weeks 1 to 2Map the data. Schemas, query logs, growth trends and data ownership are analysed. The heaviest queries and the riskiest tables are identified by measurement, not by impression.
- Weeks 3 to 5Design the target. The target model, the partitioning plan and the responsibility of each store are designed and written down as decision records. Capacity and cost are projected for the expected growth.
- Week 6 onwardMigrate in stages. Data moves table by table or domain by domain, with dual writes and backfills. Each stage is verified by reconciliation checks before traffic is switched.
- Final weeksHand over. Runbooks, monitoring for the new layout and the migration record are handed over to your team, together with the measurements taken before and after.
What you receive
Every output stays in your repositories and your accounts.
Who this service is not for
- Systems where the data fits comfortably and queries are fast. Adding partitions or new stores too early adds cost without benefit.
- Teams looking for a reporting dashboard. We design the data layer that feeds reporting; building the reports is a separate job.
- Migrations that must happen in a single weekend with no verification. We move data in stages because a failed big-bang migration is the most expensive outcome.
- Projects that want a new database product chosen before the problem is understood.
Frequently asked questions
Can the migration really happen without downtime?
In most cases, yes. We design the move so that the old and new layouts run side by side and traffic is switched only after verification. Where a short pause cannot be avoided, for example the final cutover of a small dataset, it is planned and measured in advance.
Do we have to change databases?
Not necessarily. Many problems are solved inside the database you already run, with partitioning, better indexing or a separate path for reporting. A new store is recommended only when the model calls for it.
Which databases do you work with?
Mainly PostgreSQL, MongoDB and Redis, plus Kafka where data moves as events. For other engines we tell you in the first call whether we know them well enough.
Who runs the migration, you or our team?
Usually both. We design and lead the migration, your team runs parts of it with us, and by the end your engineers can operate the new layout on their own.
What happens if a stage fails?
Every stage has a rollback path that is tested before the stage starts. Traffic returns to the old layout, and the stage is repeated once the cause is fixed.
Start with a short technical call
Thirty minutes. You describe your data and where it hurts, and we tell you whether the fix is in the model, the layout or the migration.