MinervaDB · Engineering
Data Modernization Services
Move off legacy licenses, end-of-life versions and scaling ceilings to open-source and cloud-native data platforms. Every migration is engineered for zero downtime, verified data parity and a rollback path at every stage.
Why Modernize
Legacy data platforms fail on economics before they fail on technology
Most data modernization programs begin with a measurable signal: a license renewal that no longer clears finance review, an end-of-life notice on a version running revenue workloads, or p99 latency that hardware upgrades have stopped fixing. We quantify those signals first, then design the smallest safe change that removes them.
License & support economics
Per-core commercial licensing compounds with every scale-out event. Moving to PostgreSQL, MySQL or ClickHouse converts license spend into engineering investment, and we build that case from your actual renewal and support invoices rather than vendor list price.
End-of-life version risk
Workloads still on MySQL 5.7 (EOL October 2023) or PostgreSQL 12 (EOL November 2024) run without security patches. We plan upgrades to current LTS releases such as MySQL 8.4 and PostgreSQL 16+, with tested upgrade and rollback procedures.
Scaling ceilings
Vertical scaling ends at the largest instance you can buy. We re-architect for horizontal growth through read routing, partitioning, sharding and columnar analytics offload, each change justified by workload telemetry: buffer-pool hit ratios, checkpoint pressure, lock waits, p95/p99 latency.
Operational drag
Batch ETL windows, manual failovers and untested backups accumulate risk quietly. A modernized estate runs streaming CDC, automated HA (Patroni, Group Replication, Always On) and quarterly restore drills, with SLOs and error budgets that make reliability measurable.
Modernization Paths
From legacy estate to open, cloud-native data platform
Every data modernization engagement maps each source platform to a destination selected by measurement. We benchmark candidate targets against your workload traces before committing a single table.
Commercial RDBMS → PostgreSQL
Schema, PL/SQL and application-layer conversion to PostgreSQL, using Oracle-compatibility options where they shorten the path. Waves are planned from object inventory and stored-procedure complexity scoring.
PostgreSQL consulting →Warehouse → ClickHouse
Re-platform proprietary and cloud warehouses onto open-source ClickHouse for sub-second analytics at a fraction of per-query cost. Delivered through ChistaDATA, our dedicated ClickHouse practice.
ChistaDATA ClickHouse services →On-prem → Cloud DBaaS
Right-sized moves to RDS, Aurora, Azure Database, Cloud SQL and AlloyDB, with FinOps guardrails on the target design. Instance class, storage tier and reserved-capacity decisions come from your own utilization telemetry.
Cloud FinOps →Version & platform currency
Major-version upgrades treated as first-class migrations: replication-based blue-green upgrades, regression capture and replay, optimizer-plan comparison before and after.
MySQL consulting →Batch ETL → streaming CDC
Replace overnight batch windows with Debezium/Kafka change streams feeding analytics, search and caches in near real time. The same pipeline later powers zero-downtime cutovers.
Kafka support →Databases on Kubernetes
Operator-managed PostgreSQL and MySQL where your platform team already runs Kubernetes, including clear guidance on when a managed service is the better fit.
PostgreSQL on Kubernetes →Methodology
Verification gates and rollback paths at every phase
Each phase ends with a verification gate and keeps a rollback path open. Nothing irreversible happens until parity is proven, and the cutover is rehearsed in staging before it runs against production. Blast radius is documented before any change touches a production system.
Assessment before commitment
Object inventory, SQL-dialect divergence scan, stored-procedure complexity scoring and workload trace capture (pg_stat_statements, performance_schema, query store) produce a wave plan with per-workload effort estimates before any migration work is sold or started.
Parity proven by query
Row counts, per-chunk checksums, aggregate parity queries and replayed production traffic compared on p95/p99 latency. A wave ships only when its verification suite passes.
Rollback stays live until validation passes
Reverse replication from target back to source stays active through the post-cutover observation window. If a regression surfaces, traffic returns to the source with data intact, following a procedure rehearsed before go-live.
Zero-Downtime Cutover
Dual-run CDC architecture for near-zero-downtime migration
Our production cutovers follow one pattern: an initial consistent snapshot, continuous change-data-capture to keep the target current, independent parity verification, then a short, rehearsed traffic switch. Reverse replication stays open as the fallback.
Before the switch, replication lag is driven to zero under production write volume, connection draining is sequenced through the proxy layer, and application health checks gate each traffic increment. For read-heavy estates we cut over read replicas first, which proves the target under real query load while writes still land on the source. Warehouse re-platforming gets the same treatment: the reporting layer dual-runs against both engines until analysts sign off on result parity, and only then is the legacy warehouse retired.
Platform Coverage
One practice across the full database estate
Modernization programs rarely involve a single engine. MinervaDB engineers work across OLTP, analytics, document, in-memory and streaming platforms, so a single wave plan can cover the whole estate.
PostgreSQL
The default landing zone for commercial RDBMS exits: HA with Patroni, horizontal scale with Citus, connection pooling with PgBouncer, extensions from PostGIS to pgvector.
PostgreSQL engineering →MySQL & MariaDB
Version currency to MySQL 8.4 LTS, Group Replication and Galera topologies, ProxySQL routing, online schema change at scale.
MariaDB engineering →SQL Server
Always On availability groups, Azure SQL migrations, and staged exits to PostgreSQL where licensing no longer earns its keep.
SQL Server engineering →MongoDB
Replica-set and shard-key modernization, WiredTiger tuning, and Atlas vs self-managed decisions grounded in workload telemetry.
MongoDB engineering →ClickHouse & real-time analytics
Warehouse re-platforming, MergeTree schema engineering and Kafka ingestion pipelines through ChistaDATA, our full-stack ClickHouse practice.
ChistaDATA →Cloud & lakehouse platforms
AWS, Azure and Google Cloud data platform engineering, plus Databricks and Snowflake architecture, with FinOps discipline on every target.
Cloud data platforms →Why MinervaDB
Vendor-neutral engineering that stays accountable after go-live
Vendor-neutral by principle
We hold no reseller agreements that bias target selection. When a workload is better served staying where it is, or moving to a platform we don't sell services for, that is the recommendation you get.
Recommendations name their evidence
Every recommendation cites the metric or system table that justifies it: pg_stat_statements, performance_schema, query store, system.query_log. Your team can verify the reasoning independently, during the project and after we leave.
Depth across the whole estate
Principal-level engineers across PostgreSQL, MySQL, MariaDB, SQL Server, MongoDB, ClickHouse, Cassandra, Redis, Kafka and the major cloud DBaaS platforms. One practice for a heterogeneous estate, trusted by 900+ enterprises.
We operate what we migrate
After go-live, platforms move into 24×7 consultative support or remote DBA operations with defined SLAs, restore drills and quarterly architecture reviews. The team that migrated the platform stays accountable for how it runs.
Assessment Deliverables
Inside a MinervaDB data modernization assessment
The assessment is a fixed-scope engagement that converts a legacy estate into a costed, sequenced data modernization program. Every deliverable is a working document your team keeps, whether or not MinervaDB executes the migration.
Estate inventory & dependency map
Schemas, objects, stored procedures, scheduled jobs, linked servers and the applications that touch them, extracted from catalog views and traffic captures rather than questionnaires. Dependency edges decide wave boundaries, so no application is cut over before every database it reads from is ready.
SQL-dialect divergence report
A per-object conversion difficulty score: which procedures translate mechanically, which need re-engineering, and which application queries rely on engine-specific behavior such as locking semantics, implicit conversions and optimizer hints. Migration effort estimates are built from this report.
Target architecture & TCO comparison
Candidate target designs benchmarked against captured workload traces, with three-year cost models covering licensing, infrastructure, support and engineering effort, so the business case stands up in front of finance as well as engineering.
Wave plan, risk register & rollback strategy
Workloads sequenced by risk and dependency, each wave with its verification suite, cutover window and tested rollback procedure. The register names each risk's blast radius and mitigation before any production change is scheduled.
Cutover runbook & communications plan
Every wave ships with a minute-by-minute cutover runbook: pre-flight verification queries, the traffic switch sequence, health-check gates with named owners, abort criteria, and the rollback procedure rehearsed against staging. Stakeholder communications are pre-drafted for both a clean go-live and a rollback, so nobody is drafting status updates under pressure.
Migration tooling we operate daily
Migration programs run on proven tooling, selected per path: ora2pg and AWS SCT for schema conversion out of Oracle; AWS DMS, Debezium and native logical replication for continuous data movement; pgloader for bulk loads into PostgreSQL; pt-online-schema-change and gh-ost for online DDL during MySQL version currency work; and Kafka-based pipelines for warehouse re-platforming into ClickHouse. The wave plan documents each tool's operational limits, such as replication lag budgets, LOB handling and DDL propagation gaps, so cutover engineering accounts for them from day one.
Verification tooling gets the same rigor: per-chunk checksum comparison, aggregate parity queries scheduled through the dual-run window, and production query replay against the target with p95/p99 latency compared side by side before any cutover window is booked.
FAQ
Frequently asked questions
How do you migrate a production database without downtime?
Which migration paths does MinervaDB support?
How long does a database modernization project take?
Is MinervaDB tied to any database vendor?
What happens after cutover?
How long does a data modernization assessment run?
Can our own team execute the migration from your plan?
Standing engineering caveat: every procedure referenced on this page is validated in a staging environment against production-representative data before it is applied to a production system, and every data modernization plan includes a tested disaster-recovery posture for both source and target platforms.
Next Step
Start with an assessment
A data modernization assessment quantifies your license exposure, version risk and scaling headroom, and returns a wave plan you can execute with us or without us.