MinervaDB Database Consulting

Vendor-neutral · Engineer-led · 24×7×365

MinervaDB: Database Consulting, 24×7 Support and Remote DBA for Every Major Engine

Covina, California · Delivery operations in Bengaluru · Engineers in 46 cities

MinervaDB is a full-stack database infrastructure firm trusted by more than 900 enterprises to design, build, operate and optimize mission-critical data systems across PostgreSQL, MySQL, MariaDB, Microsoft SQL Server, Oracle, IBM Db2, MongoDB, SAP HANA, ClickHouse, Trino, Apache Cassandra, Redis, Valkey, Milvus and every major cloud DBaaS. One engineering team owns the estate end to end.

Talk to a principal architectSee the engine matrix
MinervaDB operating model: database engines mapped to engineering disciplines and measurable outcomes
900+Enterprises served globally
46Cities where our engineers work
15 minS1 response target, 24×7×365
200+Years of combined leadership experience

Why enterprises bring MinervaDB into the database estate

Most enterprises now run five or more database engines across self-managed hosts, Kubernetes and two or three cloud providers. Each engine has its own failure modes, its own telemetry and usually a vendor with something to sell. The result is an estate that nobody sees whole. MinervaDB exists to be the one engineering team that does.

What changes when MinervaDB takes responsibility for the estate:

  • One accountable team across every engine, instead of a separate contract and escalation path per vendor
  • Recommendations anchored to measurements from pg_stat_statements, performance_schema, Query Store, AWR or system.query_log, never to opinion
  • Senior engineers on the incident bridge, not a first-line queue that forwards tickets
  • A stated blast radius and rollback path for every change to a running database
  • Advice that includes when not to use an engine, including engines we support

MinervaDB brings more than fifteen years of company depth to this work, and our leadership has over 200 years of combined experience across database vendors, internet-scale operators and enterprise platform teams.

MinervaDB four disciplines: architecture, engineering, operations and analytics

The MinervaDB engine matrix: fourteen databases and Kafka, one standard of depth

Every engine below is delivered by engineers who run it in production, with the same runbook structure, the same measurement discipline and the same escalation path. Each linked row opens the dedicated MinervaDB service page for that engine.

Engine What we engineer and operate Typical fit
PostgreSQL Autovacuum and bloat control, wraparound prevention, logical and streaming replication, PgBouncer, Patroni HA, Citus sharding, PostGIS, pgvector, TimescaleDB OLTP and HTAP
MySQL InnoDB tuning, Group Replication, ProxySQL routing, online schema change, HeatWave assessments OLTP at scale
MariaDB Galera Cluster, MaxScale, ColumnStore, MyRocks, migrations in both directions between MySQL and MariaDB Open-source RDBMS
Microsoft SQL Server Always On Availability Groups, optimizer and index tuning, Query Store analysis, Azure SQL migrations Enterprise OLTP
Oracle Database Exadata, RAC, Data Guard, RMAN, AWR and ASH analysis, license cost optimization, Oracle-to-PostgreSQL modernization Enterprise OLTP and ERP
IBM Db2 Db2 LUW and Db2 for z/OS, HADR, pureScale, data sharing, mainframe offload and modernization Mainframe and enterprise
MongoDB Shard-key design, replica sets, WiredTiger cache tuning, aggregation pipelines, MongoDB Atlas Document store
SAP HANA Column store and delta merge, memory management, HANA System Replication, scale-out topologies In-memory ERP
ClickHouse MergeTree schema engineering, projections, ClickHouse Keeper, sharding and replication, delivered with ChistaDATA Columnar OLAP
Trino Connector design, federated query tuning, resource groups, lakehouse access over Iceberg and Hive catalogs Federated SQL
Apache Cassandra Query-first data modeling, compaction strategy, repair, multi-datacenter replication Wide-column
Redis Cluster and Sentinel, RDB and AOF trade-offs, memory and eviction engineering In-memory KV
Valkey Redis-to-Valkey migration, cluster operations, BSD-licensed caching and session stores Open KV fork
Milvus Index selection, collection sizing and vector search for RAG and recommendation workloads Vector search
Apache Kafka Broker and partition sizing, replication, Kafka Connect and change-data-capture pipelines Event streaming

ClickHouse and real-time analytics engagements are delivered with our sister company ChistaDATA. Oracle and Db2 exit programs are covered on the data modernization page, and every NoSQL engine is summarized on NoSQL database support.

Three ways to work with MinervaDB

Most customers start with one model and add another as trust builds. All three use the same engineers, the same documentation standard and the same severity definitions.

MinervaDB database consulting: health checks, audits, architecture reviews and migration plans

Database consulting

Fixed-scope health checks, performance audits, architecture reviews and migration plans. You receive a versioned report with the evidence behind every finding and a prioritized fix list.

Database consulting

MinervaDB 24x7 database support severity levels S1 to S4

24×7 consultative support

A subscription for production incidents and advisory work, with severity-based response targets and senior engineers who already know your topology when the pager goes off.

24×7 database support

MinervaDB remote DBA and managed database services

Remote DBA and managed services

We operate the estate: monitoring, patching, upgrades, backups with restore drills, capacity and cost reviews, and quarterly service reviews with your team.

Remote DBA services

Cloud DBaaS on AWS, Azure and Google Cloud, without the lock-in

Managed database services remove host administration. They do not remove sizing, parameter tuning, connection management, backup verification or cost discipline, and their abstractions can quietly cap performance or inflate the bill. MinervaDB brings the same depth to managed platforms that it brings to self-hosted ones, and keeps the architecture portable.

  • Amazon Web Services: Amazon RDS, Aurora (PostgreSQL- and MySQL-compatible), DynamoDB, ElastiCache, MemoryDB, Keyspaces and Amazon Redshift
  • Microsoft Azure: Azure SQL Database, Azure Database for PostgreSQL and MySQL, Cosmos DB and Azure Cache for Redis
  • Google Cloud: Cloud SQL, AlloyDB, Spanner, Bigtable, Memorystore and BigQuery
  • Vendor clouds and data platforms: MongoDB Atlas, Confluent, DataStax Astra, ClickHouse Cloud, Redis Cloud, Oracle MySQL HeatWave, Snowflake and Databricks

On managed platforms we measure with the provider’s own instruments: Performance Insights and Enhanced Monitoring on RDS and Aurora, Query Store on Azure SQL, Query Insights on Cloud SQL and AlloyDB, and billing exports for database FinOps. Whether your strategy is single-cloud, multi-cloud, hybrid or repatriation to your own hardware, the guidance stays evidence-based.

MinervaDB cloud DBaaS coverage across AWS, Microsoft Azure, Google Cloud and vendor clouds

Six engineering disciplines behind every MinervaDB engagement

Each discipline has a named evidence source. If a recommendation cannot point to a metric, a catalog view or a drill result, it does not go into a MinervaDB report.

01

Performance engineering

Execution-plan analysis, index and schema design, wait-event profiling, connection pooling and configuration tuned to the workload you actually run.

Evidence: EXPLAIN ANALYZE, pg_stat_statements, performance_schema, Query Store, AWR, system.query_log

02

Scalability architecture

Partitioning, read replicas, sharding and capacity models built from measured growth curves, so the next order of magnitude is planned rather than discovered.

Evidence: pg_stat_database, InnoDB metrics, MongoDB serverStatus, system.parts

03

High availability and DR

Patroni, Group Replication, Galera, Always On, Data Guard, Db2 HADR, HANA System Replication and ClickHouse Keeper, with RPO and RTO proven by drills.

Evidence: replication lag, failover drill timings, restore test logs

04

Data reliability engineering

SLOs and error budgets, alert tuning, runbook automation, backup validation and point-in-time recovery for systems that cannot lose data.

Evidence: SLO burn rate, backup verification reports, incident timelines

05

Security and compliance

Encryption in transit and at rest, least-privilege RBAC, audit logging and evidence packs for GDPR, HIPAA, SOC 2, PCI DSS and DPDP reviews.

Evidence: role and grant audits, audit-log coverage, TLS configuration scans

06

Database FinOps

Right-sizing, reserved capacity, storage tiering and license optimization across RDS, Azure SQL, Cloud SQL, Oracle and SQL Server estates.

Evidence: billing exports, utilization percentiles, license entitlement maps

Related pages: database SRE, database security services and database design and performance consulting.

The first query of a PostgreSQL performance engagement

Before any index is added or parameter changed, we establish where server time goes. On PostgreSQL that starts with pg_stat_statements, ranked by total execution time rather than mean, because the query that runs a million times at 4 ms usually matters more than the report that runs once at 40 seconds.

-- PostgreSQL 13+: where does the server actually spend its time?
SELECT
    queryid,
    calls,
    ROUND(total_exec_time::numeric, 1)      AS total_ms,
    ROUND(mean_exec_time::numeric, 2)       AS mean_ms,
    shared_blks_hit,
    shared_blks_read,
    ROUND(100.0 * total_exec_time
          / SUM(total_exec_time) OVER (), 1) AS pct_of_total
FROM pg_stat_statements
ORDER BY total_exec_time DESC
LIMIT 20;

Column names shown are for PostgreSQL 13 and later; the extension must be listed in shared_preload_libraries.

The same question on ClickHouse

On ClickHouse the equivalent evidence lives in system.query_log. Grouping by normalized_query_hash collapses literal values so one bad query shape does not hide behind thousands of distinct statements, and p99 exposes the tail that dashboards feel first.

-- ClickHouse: slowest query shapes in the last 24 hours
SELECT
    normalized_query_hash,
    count()                                   AS runs,
    quantile(0.99)(query_duration_ms)          AS p99_ms,
    formatReadableSize(sum(read_bytes))       AS bytes_read,
    any(query)                                 AS sample_query
FROM system.query_log
WHERE type = 'QueryFinish'
    AND event_time >= now() - INTERVAL 1 DAY
GROUP BY normalized_query_hash
ORDER BY p99_ms DESC
LIMIT 20;

Requires query logging enabled (the default in current releases). Run on each replica, or through clusterAllReplicas, for a full view.

The open-source toolchain MinervaDB runs in production

MinervaDB’s expertise is rooted in the open-source database community, and our independence is rooted in refusing to be captured by any single vendor. These are the tools our engineers deploy, operate and recover with every week.

Layer Tools we operate
High availability Patroni, MySQL Group Replication, Galera Cluster, MaxScale, SQL Server Always On, Oracle Data Guard, Db2 HADR, HANA System Replication, Redis Sentinel, ClickHouse Keeper
Pooling and routing PgBouncer, ProxySQL, HAProxy, MaxScale read/write split
Backup and recovery pgBackRest, WAL-G, Percona XtraBackup, Oracle RMAN, native HANA and Db2 backup, clickhouse-backup
Scale-out Citus, MongoDB sharded clusters, ClickHouse distributed tables, Cassandra multi-DC rings
Extensions PostGIS, pgvector, TimescaleDB, pg_stat_statements, auto_explain
Streaming and CDC Apache Kafka, Kafka Connect, Debezium, Confluent
Platform Kubernetes and database operators, Linux kernel and filesystem tuning
Observability PMM, Prometheus exporters, Grafana, engine-native views and cloud monitoring APIs

Tool choice follows the estate, not habit. A three-node PostgreSQL cluster on virtual machines, a CloudNativePG deployment on Kubernetes and an Aurora cluster each get a different high-availability and backup answer, and we document why.

Database migration and modernization without drama

Migrations fail on the details nobody measured: a collation difference that reorders results, a sequence that was never reset, a stored procedure that silently depends on implicit type conversion, a cutover window sized on the average day instead of the worst one. MinervaDB plans every migration around those details.

  • Assessment first: object inventory, code conversion effort, data volume and change rate, and a written go or no-go before any contract for the build
  • Parallel run with row-count and checksum validation between source and target, so correctness is proven with data rather than asserted
  • Cutover rehearsed at least once against production-sized data, with the rollback path tested, timed and signed off
  • Common paths: Oracle and Db2 to PostgreSQL, on-premises to Amazon RDS, Aurora, Azure SQL or Cloud SQL, MySQL to MariaDB and back, and warehouse moves into ClickHouse, BigQuery or Snowflake

See data modernization for Oracle and Db2 exit programs and Amazon RDS support for cloud landing zones.

Data analytics, data engineering and AI on database-grade foundations

Analytics and AI programs fail at the data layer far more often than at the model. MinervaDB applies the same reliability standards to pipelines, warehouses and vector stores that it applies to transactional databases.

  • Analytics platforms and data engineering: real-time and batch pipelines on ClickHouse, Snowflake, BigQuery, Redshift, Databricks and Trino, with Kafka and change-data-capture ingestion
  • Production machine learning: feature stores with point-in-time correctness, model registries with promotion gates and drift monitoring across Vertex AI, SageMaker, Azure ML, Databricks and Kubernetes (MLOps consulting)
  • Private and in-VPC generative AI: retrieval-augmented generation on pgvector and Milvus, with GDPR, DPDP and HIPAA-class governance (enterprise GenAI)
  • Decision intelligence and managed operations: governed metrics, Customer 360 and 24×7 run of data and AI platforms (decision intelligence)

The full practice is described on the data science and AI consulting page.

MinervaDB data science and AI practice: analytics platforms, production ML, private GenAI and managed operations

24×7 support severity levels and response targets

Severity is agreed with you when a ticket opens, and the escalation matrix is fixed during onboarding so nobody negotiates contacts in the middle of an outage.

Severity Definition Response target
S1 Critical Production down, data at risk or no workaround available 15 minutes, 24×7×365
S2 High Production degraded or a major function impaired 12 hours
S3 Medium Non-critical defect with a workaround in place 24 hours
S4 Low Question, advisory request or scheduled change 48 hours

Need help right now? Use contact support or call +1 (844) 588-7287.

How a MinervaDB engagement typically starts

Nothing is changed in the first two weeks. Discovery and a measured baseline come first, so every later recommendation can be compared against a known starting point.

MinervaDB onboarding sequence: discovery, baseline, findings and steady state

Vendor-neutral means we will tell you when not to use an engine

MinervaDB takes no referral fees and resells no licenses, so the recommendation can follow the workload. In practice that means advice like this:

  • ClickHouse is the wrong home for high-concurrency, single-row update OLTP; keep that on PostgreSQL or MySQL and feed ClickHouse through CDC.
  • Cassandra rewards query-first modeling and punishes ad-hoc joins; if the access patterns are not known yet, a relational engine is the safer start.
  • An Oracle or Db2 exit only pays when license and support savings exceed the migration and retesting cost; we model both before recommending either.
  • A managed service is not automatically cheaper; at steady high utilization, reserved or self-managed capacity often wins, and we show the numbers.

The same independence applies to engines we support every day. If the evidence says the current platform is fine and the problem is a missing index, that is what the MinervaDB report will say.

MinervaDB frequently asked questions

What does MinervaDB do?

MinervaDB is a vendor-neutral, full-stack database infrastructure firm. We provide database consulting, 24×7 consultative support, remote DBA and managed services across architecture, engineering, operations and analytics, for self-managed, Kubernetes and cloud DBaaS estates.

Which database engines does MinervaDB support?

PostgreSQL, MySQL, MariaDB, Microsoft SQL Server, Oracle Database, IBM Db2 (LUW and z/OS), MongoDB, SAP HANA, ClickHouse, Trino, Apache Cassandra, Redis, Valkey and Milvus, plus Apache Kafka and the managed database services of AWS, Microsoft Azure, Google Cloud, MongoDB Atlas, Confluent, DataStax Astra, ClickHouse Cloud, Redis Cloud, Snowflake and Databricks.

What is included in the MinervaDB remote DBA service?

Remote DBA covers 24×7 monitoring and alerting, incident response, patching and version upgrades, backup and restore verification, performance tuning, capacity reviews, security hardening and a documented escalation path. The exact scope, hours and change windows are agreed in writing during onboarding.

What are the MinervaDB 24×7 support response times?

Response targets are set by severity: S1 critical in 15 minutes, S2 high in 12 hours, S3 medium in 24 hours and S4 low in 48 hours. S1 coverage runs 24×7×365.

Does MinervaDB support Amazon RDS, Aurora, Azure SQL and Google Cloud SQL?

Yes. We architect, migrate, tune and operate Amazon RDS, Aurora, DynamoDB, ElastiCache, MemoryDB, Keyspaces and Redshift; Azure SQL Database, Azure Database for PostgreSQL and MySQL, Cosmos DB and Azure Cache for Redis; and Cloud SQL, AlloyDB, Spanner, Bigtable, Memorystore and BigQuery.

Is MinervaDB tied to any database vendor?

No. MinervaDB takes no referral fees and resells no licenses. Our recommendations follow the workload, and we will advise against an engine, including one we support, when it is the wrong fit.

How is MinervaDB related to ChistaDATA?

ChistaDATA is our sister company for ClickHouse and real-time analytics. ClickHouse engagements are delivered by ChistaDATA engineers; every other engine is delivered by MinervaDB, so customers with mixed estates still work with one leadership team.

How do we start an engagement with MinervaDB?

Book a call with a principal architect. We agree on scope and access, then run discovery and a measured baseline before any change is proposed. Every change to a running database is documented with its blast radius and rollback path.

Further reading: official engine documentation

Our engineers work from primary sources. These are the references we cite most often in runbooks and reports.

Guidance on this page is general. Test every change in a non-production environment first, and maintain a verified backup and disaster-recovery posture before applying changes to production.

Talk to a MinervaDB principal architect

Tell us which engines you run, where they are hosted and what is keeping you up at night. We will tell you what we would measure first, and whether you need us at all.

Book an appointmentExplore database consulting
Phone+1 (844) 588-7287
Emailcontact@minervadb.com
Headquarters440 N Barranca Ave #9718, Covina, CA 91723