Engineering · Migration and modernization · Data modernization services
Data Modernization: Off Legacy Licences, End-of-Life Versions and Scaling Ceilings, Without Downtime
MinervaDB data modernization moves production estates from commercial RDBMS, proprietary warehouses, unsupported versions and batch ETL onto open-source and cloud-native platforms selected by measurement. Every migration runs as a dual-run CDC cutover with independently verified data parity and a rollback path that stays live until post-cutover validation passes. The engineers who plan the wave are the engineers on the bridge during the switch, and the same team operates the platform afterwards under 24×7 SLAs.
01 · Why data modernization
Legacy data platforms fail on economics before they fail on technology
Most data modernization programmes start from a measurable signal, not from a strategy slide. We quantify that signal first, then design the smallest safe change that removes it.
Licence and support economics
Per-core commercial licensing compounds with every scale-out event, and support renewals are priced against the vendor's list, not your utilisation. A data modernization business case is built from your actual renewal and support invoices, cloud bills and the engineering hours the platform consumes, never from vendor list price.
End-of-life version risk
Workloads still on MySQL 5.7 (unsupported since October 2023), MySQL 8.0 (30 April 2026), PostgreSQL 12 and 13, or SQL Server 2016 (extended support ended 14 July 2026) run without security fixes. PostgreSQL 14 follows on 12 November 2026. Version currency is treated as a first-class migration with the same gates as a platform change.
Scaling ceilings
Vertical scaling ends at the largest instance you can buy, and the case for data modernization shows up first in telemetry: buffer-pool hit ratio falling, checkpoint and flush pressure, lock waits climbing with concurrency, p99 diverging from p50. We re-architect for horizontal growth through read routing, partitioning, sharding and columnar offload, each change justified by those numbers.
Operational drag
Nightly batch windows, manual failovers and backups nobody has restored from accumulate risk quietly. After data modernization the estate runs streaming CDC, automated HA (Patroni, Group Replication, Always On), timed restore drills and SLOs with error budgets, so reliability is a measured quantity rather than an assumption.
02 · Migration paths
From legacy estate to open, cloud-native data platform
Every data modernization engagement maps each source class to a target selected by measurement. Candidate targets are benchmarked against your captured workload traces before a single table is committed.
Figure 1. Data modernization paths: each source class passes through one controlled migration engine, and the source platform remains the rollback target until validation passes.
Commercial RDBMS to PostgreSQL
Schema, PL/SQL, T-SQL and application-layer conversion to PostgreSQL 17 or 18, using Oracle-compatibility layers only where they shorten the path without creating a new dependency. Waves are planned from object inventory and stored-procedure complexity scoring, and Oracle 19c's Premier Support horizon (December 2029) is used to sequence, not to postpone. PostgreSQL consulting →
Warehouse to ClickHouse
Re-platform Teradata, Vertica, Netezza, Redshift, Snowflake and BigQuery workloads onto open-source ClickHouse 26.x for sub-second analytics at open-source cost. MergeTree schema engineering, Kafka ingestion and the dual-run reporting cutover are delivered through ChistaDATA, our dedicated ClickHouse practice. ChistaDATA ClickHouse services →
On-premises to cloud DBaaS
Right-sized moves to RDS, Aurora, Azure Database, Cloud SQL and AlloyDB with FinOps guardrails built into the target design. Instance class, storage tier, IOPS provisioning and reserved-capacity decisions come from your own utilisation telemetry, not from the provider's sizing calculator. Cloud FinOps →
Version and platform currency
Major-version upgrades run as migrations: replication-based blue-green upgrades, regression capture and replay, optimizer-plan comparison before and after, and a rehearsed fallback to the previous major. MySQL 8.0 to 8.4 LTS or 9.7 LTS, PostgreSQL 12–14 to 17 or 18, MariaDB 10.x to 11.8 or 12.3 LTS. MySQL consulting →
Batch ETL to streaming CDC
Replace overnight batch windows with Debezium and Kafka 4.x change streams feeding analytics, search indexes and caches in near real time. The same pipeline is later reused as the change-capture layer for zero-downtime cutovers, so the data modernization investment pays twice. Kafka support →
Databases on Kubernetes
Operator-managed PostgreSQL and MySQL where your platform team already runs Kubernetes, with storage-class, anti-affinity and backup design done properly, and an honest answer when a managed service is the better fit for the workload. PostgreSQL on Kubernetes →
03 · Version currency in data modernization
End-of-life exposure across the estate, and where each workload lands
Unsupported versions are the most common trigger for a data modernization programme and the least forgiving, because a CVE does not wait for a budget cycle. The table below is the working view we build for every estate.
Figure 2. Version currency in a data modernization programme: end-of-life exposure by engine, the LTS or major release each workload is upgraded to, and the upgrade method used. Dates verified on 28 September 2026 against the vendors' lifecycle pages.
| Engine | Current release we target | Upgrade method | Pre-flight evidence |
|---|---|---|---|
| PostgreSQL | 18.6, or 17 where extensions lag | Logical replication blue-green with reverse subscription; pg_upgrade --link for in-place with a snapshot fallback | pg_stat_statements baseline, extension compatibility matrix, pg_amcheck on the target |
| MySQL | 8.4 LTS or 9.7 LTS | Replica-first upgrade, Group Replication rolling, ProxySQL traffic switch | mysqlcheck --check-upgrade, performance_schema digest baseline, deprecated-syntax scan |
| MariaDB | 11.8 LTS or 12.3 LTS | Galera rolling upgrade with wsrep_* status checks between nodes | Galera flow-control and cert-failure counters, optimizer-switch diff |
| SQL Server | 2022 or 2025, or a staged exit to PostgreSQL | Always On availability group rolling upgrade; Query Store plan forcing during compatibility-level change | Query Store regression report, sys.dm_os_wait_stats baseline, agent-job inventory |
| Oracle | 19c or 26ai, or PostgreSQL where licensing no longer earns its keep | Data Guard rolling upgrade; ora2pg and AWS SCT for the exit path | AWR baseline, SQL Plan Management baselines, PL/SQL complexity score |
| MongoDB | 8.x | Replica-set rolling upgrade, one feature-compatibility-version step per major | Profiler and explain("executionStats") baseline, driver compatibility matrix |
| Kafka | 4.3 (KRaft) | ZooKeeper-to-KRaft migration, then rolling broker upgrade | Consumer-lag baseline, ISR and under-replicated partition counts, client protocol versions |
Release and end-of-life dates above are from the vendors' published lifecycle policies, for example the PostgreSQL versioning policy. Confirm the exact minor release at engagement start; minors move monthly.
04 · Data modernization method
Seven phases, a verification gate at every transition, rollback held open throughout
Nothing irreversible happens until parity is proven. The cutover is rehearsed in staging before it runs against production, and blast radius is written down before any change touches a live system.
Figure 3. The seven-phase data modernization lifecycle. Each gate has a named pass criterion, and the rollback path for each phase is documented before the phase begins.
Assessment before commitment
Object inventory, SQL-dialect divergence scan, stored-procedure complexity scoring and workload trace capture (pg_stat_statements, performance_schema, Query Store, AWR, system.query_log) produce a wave plan with per-workload effort estimates before any migration work is sold or started. Nothing in this phase touches production; it needs read-only catalog access and telemetry.
Parity proven by query
Row counts, per-chunk checksums, aggregate parity queries on the tables finance signs off on, and replayed production traffic compared on p95 and p99 latency. Plan differences on the top statements are reviewed by hand, because an index that exists on both sides can still be chosen differently. A wave ships only when its verification suite is green.
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 every write intact, following a procedure that was rehearsed before go-live. The source is decommissioned only after written sign-off, never as a side effect of the cutover.
05 · Target selection
Targets are selected by measurement, per workload, and "stay where you are" is a valid answer
A data modernization target is a hypothesis until it has run your captured workload at production concurrency. We test the hypothesis before the business case is written, not after the migration is half done.
Figure 4. Data modernization target selection by measurement: workload traces, catalog inventory and cost evidence feed a benchmark harness and a three-year TCO model; the decision names the metric that justifies it.
Workload traces are captured over a window long enough to include month-end, batch and reporting peaks, typically two to four weeks. Candidate targets are provisioned at the proposed size and the captured statements are replayed at production concurrency, recording p50, p95 and p99 latency, throughput, lock waits and error rate per candidate. Failure injection follows: node loss, forced failover and a timed restore, so the HA and DR posture is measured rather than assumed.
The three-year TCO model prices licensing, infrastructure, support, engineering effort and the migration itself for each candidate, with sensitivity on growth and reserved-capacity assumptions. It is built to survive a finance review as well as an engineering one.
The decision is made per workload, not per estate. A transactional system with heavy PL/SQL may move to PostgreSQL in a later wave than the reporting workload that leaves it, and a workload whose measured cost and risk are already acceptable stays put. MinervaDB holds no reseller agreements, so a recommendation to remain on a platform we do not sell services for, or to move to one, carries no commercial penalty for us.
Every recommendation in the data modernization plan cites the metric and the catalog or system table behind it, so your team can verify the reasoning independently during the programme and after we leave.
06 · Zero-downtime data modernization cutover
Dual-run CDC architecture for near-zero-downtime data modernization
Production cutovers follow one pattern: an initial consistent snapshot, continuous change data capture to keep the target current, independent parity verification on both sides, then a short, rehearsed traffic switch with reverse replication held open as the fallback.
Figure 5. Dual-run CDC for data modernization: the target is proven equivalent under production change volume before any traffic moves; reverse replication protects the observation window after cutover.
Before the switch, replication lag is driven to zero under production write volume and held there for an agreed number of hours. Connection draining is sequenced through the proxy layer (PgBouncer, ProxySQL, HAProxy or a cloud load balancer) one pool at a time, 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.
Change capture is chosen per path: PostgreSQL logical replication for PostgreSQL-to-PostgreSQL and major-version upgrades; Debezium 3.x on Kafka for heterogeneous moves and for feeding ClickHouse; AWS DMS where the source is Oracle or SQL Server and the target is an AWS managed service. Each tool's operational limits, such as LOB handling, DDL propagation gaps and sequence or identity synchronisation, are written into the runbook rather than discovered during the window.
Parity verification runs independently of the replication tool, because a CDC pipeline reporting zero lag is not evidence that the data is equal. The suite below is scheduled through the dual-run window and its results are attached to the cutover go/no-go record.
-- per-chunk checksum, PostgreSQL side (same statement on both engines)
SELECT
(id / 100000) AS chunk_id,
COUNT(*) AS row_count,
MD5(STRING_AGG(
CONCAT_WS('|', id, customer_id, amount, updated_at)
, ',' ORDER BY id)) AS chunk_hash
FROM orders
WHERE id BETWEEN ${CHUNK_LOW} AND ${CHUNK_HIGH}
GROUP BY chunk_id
ORDER BY chunk_id;
-- aggregate parity on the table finance signs off on
SELECT DATE_TRUNC('day', created_at) AS d,
COUNT(*) AS n,
SUM(amount) AS total,
MIN(id), MAX(id)
FROM invoices
WHERE created_at >= NOW() - INTERVAL '35 days'
GROUP BY d ORDER BY d; Hash inputs are normalised for type and collation differences before comparison, timestamps are compared at the source's precision, and any chunk that drifts is re-copied and re-checked before the window opens.
07 · Warehouse re-platforming
Warehouse to ClickHouse through a Kafka dual-run, retired only after analysts sign off
Analytics data modernization is where the economics move fastest and where a result that is "almost the same" is unacceptable. The reporting layer reads both engines until parity is signed off, and only then is the legacy warehouse retired.
Figure 6. Warehouse-to-ClickHouse data modernization: OLTP change streams and event producers land in Kafka, ClickHouse consumes alongside the unchanged batch loads, and the reporting suite dual-runs until result parity is proven.
Schema engineering, not a lift-and-shift
ClickHouse data modernization starts with MergeTree sort keys, partitioning and index granularity designed from the captured query log, not copied from the warehouse DDL. Projections and materialized views replace the aggregate tables the old ETL maintained, and EXPLAIN PIPELINE plus system.query_log are the evidence for every design choice.
Ingestion built for replay
Kafka retention is sized so the whole dual-run window can be replayed into ClickHouse after a schema correction. Exactly-once sinks and idempotent inserts on ReplicatedMergeTree mean a replay produces the same rows, which is what makes the parity signature stable.
Decommission gate
The legacy warehouse is retired only after result parity across the reporting suite, p95 latency and ingestion throughput within SLO on ClickHouse, a passed restore drill on the target and a verified Kafka replay. ChistaDATA operates the platform afterwards under 24×7 SLAs. ChistaDATA →
08 · Data modernization platform coverage
One data modernization practice across the whole database estate
Modernization programmes rarely involve a single engine. MinervaDB engineers work across OLTP, analytics, document, in-memory and streaming platforms, so one wave plan can cover the estate and one team is accountable for it.
PostgreSQL
The default landing zone for commercial RDBMS exits: Patroni HA, PgBouncer pooling, Citus for horizontal scale, PostGIS, pgvector and TimescaleDB, on PostgreSQL 17 and 18. PostgreSQL engineering →
MySQL and MariaDB
Version currency to MySQL 8.4 or 9.7 LTS and MariaDB 11.8 or 12.3 LTS, Group Replication and Galera topologies, ProxySQL routing, online schema change at scale with gh-ost and pt-online-schema-change. MariaDB engineering →
SQL Server
Always On availability groups, upgrades to 2022 and 2025, Azure SQL migrations, and staged exits to PostgreSQL where per-core licensing no longer earns its keep. SQL Server engineering →
MongoDB
Replica-set and shard-key data modernization, WiredTiger tuning, upgrades to 8.x, and Atlas versus self-managed decisions grounded in workload telemetry rather than preference. MongoDB engineering →
ClickHouse and real-time analytics
Warehouse re-platforming, MergeTree schema engineering, Kafka ingestion and ClickHouse Keeper design through ChistaDATA, our full-stack ClickHouse practice. ChistaDATA →
Cloud and lakehouse platforms
AWS, Azure and Google Cloud data platform engineering, Databricks and Snowflake architecture, Iceberg and Delta table formats, with FinOps discipline on every target. Cloud data platforms →
09 · Data modernization 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 programme. Every deliverable is a working document your team keeps, whether or not MinervaDB executes the migration.
Estate inventory and dependency map
Schemas, objects, stored procedures, scheduled jobs, linked servers, extensions 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 behaviour such as locking semantics, implicit conversions, NULL ordering and optimizer hints. Migration effort estimates are built from this report, not from object counts.
Target architecture and TCO comparison
Candidate 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 and 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 and communications plan
Every wave ships with a minute-by-minute 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.
Tooling we operate daily
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 currency work; Kafka-based pipelines for ClickHouse. Each tool's limits are written into the wave 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 the source and the target platform.
10 · Data modernization engagement models
Assess, migrate, operate: engage for one stage or the whole programme
Teams run the data modernization plan themselves, bring MinervaDB in for the highest-risk waves only, or hand over the whole programme. The deliverables are execution-ready either way.
Data modernization assessment
Fixed scope, typically measured in weeks and sized by estate complexity. Requires read-only catalog access and telemetry; makes no change to production. Returns the inventory, divergence report, TCO comparison, wave plan and risk register described above.
Data modernization waves
Execution of one or more migration waves under the seven-phase method, with named senior engineers on the cutover bridge, the parity suite run and recorded, and reverse replication held through the observation window. Sold per wave against the assessment's estimate.
Operate after go-live
Platforms move into MinervaDB 24×7 consultative support or remote DBA operations with defined SLAs (S1 15 min, S2 12 h, S3 24 h, S4 48 h), monitoring, backup and DR drills, capacity planning and quarterly architecture reviews. The team that migrated the platform stays accountable for how it runs. 24×7 consultative support →
Vendor-neutral by principle
No reseller agreements bias target selection. When a workload is better served staying where it is, or moving to a platform we do not 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, AWR, system.query_log.
Depth across the estate
Principal-level engineers across PostgreSQL, MySQL, MariaDB, SQL Server, Oracle, MongoDB, ClickHouse, Cassandra, Redis, Valkey, Kafka and the major cloud DBaaS platforms, trusted by 900+ enterprises.
Knowledge transfer by default
Data modernization runbooks, SOPs and the parity suite are versioned and handed back, and your team is trained as we go, so the organisation ends the programme stronger rather than dependent.
11 · FAQ
Data modernization questions we are asked most
Short answers to the questions that come up in the first call. Anything not covered here is answered in the assessment scoping conversation.
How do you migrate a production database without downtime?
Source and target run in parallel under change data capture: PostgreSQL logical replication, Debezium on Kafka, AWS DMS or engine-native tooling, chosen per path. Parity is verified independently of the replication tool with row counts, per-chunk checksums, aggregate parity queries and replayed production statements compared on p95 and p99 latency. Application traffic then switches in a short, rehearsed window, read replicas first, with reverse replication held open as the rollback path through the observation period.
Which data modernization paths does MinervaDB support?
Commercial RDBMS (Oracle, SQL Server, Db2) to PostgreSQL; end-of-life MySQL, MariaDB and PostgreSQL versions to current LTS or major releases; proprietary and cloud warehouses (Teradata, Vertica, Netezza, Redshift, Snowflake, BigQuery) to ClickHouse through ChistaDATA; on-premises estates to AWS, Azure and Google Cloud managed services; batch ETL to streaming CDC on Kafka; and operator-managed databases on Kubernetes where that is the right fit.
How long does a data modernization project take?
Duration follows measured inventory: schema and stored-procedure complexity, data volume, SQL-dialect divergence and application coupling. The assessment produces a wave plan with per-workload estimates before migration work begins, so scope is established from catalog evidence rather than from a target date. Waves are sized so that each one can be rolled back independently.
Is MinervaDB tied to any database vendor?
No. Target selection is anchored in workload measurement and a three-year TCO model, and we recommend against a technology when it is the wrong fit, including technologies we sell services for. Staying on the current platform is a valid outcome of the assessment.
What happens after cutover?
Reverse replication stays live through an agreed observation window, and the source is decommissioned only after written sign-off. Platforms then move into MinervaDB 24x7 consultative support or remote DBA operations: SLO definition, monitoring, backup and disaster-recovery drills, capacity planning and quarterly architecture reviews, under the standard severity targets of S1 15 minutes, S2 12 hours, S3 24 hours and S4 48 hours.
How long does a data modernization assessment run, and what access does it need?
It is a fixed-scope engagement, typically measured in weeks and sized by estate complexity: number of schemas, stored-procedure volume and application coupling. It requires read-only catalog access and workload telemetry such as pg_stat_statements, performance_schema, Query Store or AWR. No change is made to production systems during the assessment.
Can our own team execute the migration from your plan?
Yes. The wave plan, divergence report, parity suite, cutover runbook and rollback procedures are execution-ready documents. Teams run them independently, engage MinervaDB for the highest-risk waves only, or hand over the whole programme; the deliverables are the same in each case.
How do you handle stored procedures and engine-specific SQL?
The SQL-dialect divergence report scores every object: mechanical translation, re-engineering, or application change where a query depends on locking semantics, implicit conversions, NULL ordering or optimizer hints. Conversion tooling such as ora2pg and AWS SCT handles the mechanical share; the remainder is re-engineered by hand, unit-tested against captured inputs and outputs, and included in the query-replay comparison before the wave is allowed to cut over.
Start data modernization with an assessment, not a commitment
A data modernization assessment quantifies your licence exposure, version risk and scaling headroom, and returns a wave plan you can execute with us or without us. Bring the renewal invoice, the version inventory and the slow-query log to the first call.