26+ Databases, One Expert Team
Database Services
JusDB provides enterprise multi-engine database consulting, 24/7 remote DBA, performance tuning, and zero-downtime migrations across 26+ database platforms (relational, NoSQL, distributed SQL, real-time OLAP, and CDC). Senior Database Reliability Engineers maintain a contractual 99.99% uptime SLA and P1 under 15-minute response, delivering 40%–80% latency reductions under SOC 2 operational standards.
One expert team for 26+ database technologies. From MySQL performance tuning to StarRocks real-time analytics — consulting, remote DBA, migration, and 24/7 managed services. We've managed 1000+ production instances across fintech, logistics, e-commerce, and SaaS.
Proven in Production
Why Teams Choose JusDB for Database Management
One team for every database
No juggling three vendors for MySQL, MongoDB, and StarRocks. One team, one Slack channel, one SLA — across your entire database tier.
SRE discipline, not just DBA firefighting
We define SLOs, track error budgets, run blameless postmortems, and automate toil. Not just keeping databases alive — engineering their reliability.
We pay for ourselves
60% average cloud cost savings through right-sizing, reserved instance planning, and query optimization. The audit alone typically exceeds our engagement fee.
Productive in week one
Structured fleet discovery, configuration audit, and query profiling methodology. No 3-month ramp-up — we find savings and fixes in the first week.
Information Gain · High-Consequence Reliability
Multi-Engine Operational Failure Modes
Modern polyglot data architectures introduce distributed failure surfaces between transactional engines, caches, and analytical sinks. Here is how our Principal SREs resolve high-consequence failure states:
Heterogeneous CDC Pipeline Lag & Replica Split-Brain
Log-based CDC pipelines (Debezium, AWS DMS, Flink) experience unhandled schema evolutions or network partitions. Binlog position desynchronization leads to undetected silent data drift between transactional OLTP and downstream analytics.
We implement automated cryptographic checksum validation, heartbeat table monitoring, and bi-directional CDC reverse-sync failbacks for 100% data consistency.
Cross-Engine Connection Pooler Starvation
Sudden microservice burst traffic exhausts backend database connection pools (PgBouncer, ProxySQL). Thread contention and unindexed locking queries cause queue head-of-line blocking, escalating to estate-wide cascading 504 gateway timeouts.
We implement transaction-mode connection pooling, strict query statement timeouts, and intelligent query routing proxies that shed non-essential reads under heavy load.
Silent Backup Restore Degradation & Unverified PITR
Backup cron jobs generate physical snapshots without automated restore testing. When catastrophe strikes, corrupted WAL/binlog streams or incompatible catalog schemas cause recovery attempts to fail, turning a 15-minute outage into hours of data loss.
We deploy continuous automated sandbox restore verification (pgBackRest, Barman, XtraBackup) validating point-in-time recovery down to exact transaction timestamps.
Our Principal SREs execute read-only telemetry queries to inspect connection states, lock queues, and buffer memory hit rates across live production engines:
SELECT state,
count(*) AS connections,
round(100.0 * sum(blks_hit) / nullif(sum(blks_hit + blks_read), 0), 2) AS cache_hit_ratio
FROM pg_stat_activity, pg_stat_database
WHERE pg_stat_database.datname = current_database()
GROUP BY state;SELECT variable_name, variable_value FROM performance_schema.global_status WHERE variable_name IN ( 'Threads_running', 'Threads_connected', 'Innodb_buffer_pool_read_requests', 'Innodb_buffer_pool_reads' );
Relational Databases
Battle-tested relational database services for mission-critical transactional workloads. From MySQL query optimization to PostgreSQL HA clusters — we handle the database layer so your engineering team can ship product.
MySQL / Percona
InnoDB buffer pool optimization, query profiling with pt-query-digest, Galera and InnoDB Cluster HA, ProxySQL load balancing, zero-downtime 5.7→8.0 upgrades. We manage MySQL fleets from 5 to 500+ instances.
PostgreSQL
Streaming replication, PgBouncer connection pooling, Patroni automated HA, WAL-G continuous backups, logical replication for zero-downtime migrations. Extensions: pgvector, TimescaleDB, PostGIS.
Microsoft SQL Server
Always On Availability Groups, Query Store performance analysis, execution plan optimization, SQL Server to Azure SQL migration, SSRS/SSIS management, 24/7 monitoring and incident response.
MariaDB
MariaDB Server and Enterprise optimization, Galera Cluster multi-master replication, MaxScale intelligent proxy routing, ColumnStore for analytics workloads, migration from MySQL.
TiDB
HTAP workload optimization balancing OLTP and OLAP on a single cluster. Horizontal scaling strategy, TiKV storage layer tuning, TiFlash columnar engine configuration, TiCDC change data capture.
NoSQL & Distributed
High-performance NoSQL databases for workloads that demand sub-millisecond latency, horizontal scalability, and multi-datacenter replication. We architect, deploy, and manage your distributed data layer.
MongoDB
Replica set architecture, sharded cluster deployment and rebalancing, Atlas-to-self-managed migration, change streams for event-driven applications, index optimization for read-heavy workloads.
Cassandra
Multi-datacenter replication topology design, repair scheduling with Reaper, compaction strategy optimization (STCS/LCS/TWCS), tombstone management, DataStax OpsCenter integration.
Aerospike
Sub-millisecond latency at scale for real-time decisioning, ad-tech bidding, and fraud detection. Memory and SSD storage tier optimization, cross-datacenter replication (XDR), capacity planning.
Redis / Valkey
High availability cluster architecture with Redis Sentinel or Cluster mode. ElastiCache and MemoryDB optimization, caching strategy design, memory fragmentation analysis, persistence tuning (RDB/AOF).
ScyllaDB
High-performance Cassandra-compatible NoSQL delivering 10x throughput at lower latency. Migration from Cassandra to ScyllaDB, shard-per-core architecture tuning, Scylla Manager and Scylla Monitoring setup.
Amazon DynamoDB
Partition key and sort key design to avoid hot partitions, Global Tables multi-region replication, RCU/WCU capacity planning and auto-scaling, DAX caching layer, Streams + Lambda for event-driven pipelines.
Azure Cosmos DB
RU/s sizing and cost optimization, multi-API workloads (SQL, Mongo, Cassandra, Gremlin, Table), tunable consistency levels, multi-master multi-region writes, integrated vector search for AI/ML.
Dragonfly
Modern multi-threaded in-memory data store with full Redis and Memcached compatibility. Delivers 25x higher throughput, sub-millisecond latencies under heavy write load, and hardware-efficient memory density.
YugabyteDB
Distributed SQL with PostgreSQL-wire compatibility. Multi-region active-active deployments, yb-voyager migration from Oracle/PostgreSQL, YCQL for Cassandra workload consolidation, Yugabyte Aeon managed cloud.
CockroachDB
Distributed SQL with Google Spanner architecture and PostgreSQL wire-compatibility. Multi-region active-active tables with serializable consistency guarantees, zero-downtime online schema changes, and MOLT migrations.
Neo4j
Property graph database for connected data: fraud detection, recommendation engines, knowledge graphs. Cypher query optimisation, causal clustering, Graph Data Science library, and AuraDB managed service.
Search & Analytics
Real-time analytical databases and search engines for dashboards, log analytics, AI/ML vector search, and user-facing analytics. We optimize for sub-second query performance on billions of rows.
Elasticsearch / OpenSearch
Log analytics pipeline architecture (ELK/EFK), security hardening with role-based access control, snapshot and restore automation, index lifecycle management, cluster scaling for multi-TB deployments.
StarRocks
Real-time OLAP with vectorized execution engine, data lakehouse architecture with Apache Iceberg integration, materialized view strategy, compaction tuning, Prometheus/Grafana observability.
ClickHouse
High-speed columnar analytics for time-series, event data, and real-time dashboards. MergeTree engine optimization, partition strategy, materialized views, distributed query tuning across shards.
Apache Pinot
Real-time OLAP for user-facing analytics with sub-second latency. Star-tree indexing, upsert support for mutable data, Kafka ingestion pipeline, segment management and tiered storage.
Apache Druid
Real-time analytics database engineered for fast slice-and-dice queries on massive streaming and batch datasets. Kafka/Kinesis stream ingestion, native compaction, tiered historical storage, and Druid SQL tuning.
TimescaleDB
PostgreSQL extension purpose-built for time-series data. Hypertable partitioning, columnar compression achieving 90%+ storage reduction, continuous aggregates for real-time dashboards, multi-node scaling.
InfluxDB
Purpose-built time-series database for IoT, metrics, and high-frequency sensor telemetry. Apache Arrow/DataFusion engine optimization in InfluxDB 3.0, retention policy configuration, Telegraf agent pipelines, and high-cardinality tuning.
pgvector
Vector similarity search inside PostgreSQL for AI/ML applications. HNSW and IVFFlat index tuning, embedding pipeline optimization, hybrid search combining full-text and vector queries.
CDC & Data Replication
Change Data Capture solutions for real-time data replication, event streaming, and data lake ingestion. We design, deploy, and manage CDC pipelines that keep your systems in sync without impacting source database performance.
Debezium
Log-based CDC for MySQL, PostgreSQL, MongoDB, and SQL Server via Kafka Connect. Schema registry integration with Avro/Protobuf, exactly-once delivery semantics, connector monitoring and alerting.
AWS DMS
Managed database migration with minimal downtime. Homogeneous and heterogeneous migrations, continuous replication for hybrid cloud, Schema Conversion Tool (SCT) for cross-engine migrations.
Flink CDC
Real-time event streams from databases into data lakes and warehouses. Exactly-once processing semantics, Apache Iceberg and Delta Lake sink connectors, stateful stream transformations.
Apache SeaTunnel
High-performance distributed data integration supporting 100+ connectors. Unified batch and streaming architecture, CDC from MySQL/PostgreSQL/MongoDB, data lake and warehouse sinks.
Comparative Matrix · Multi-Engine Operations
How JusDB Multi-Engine SRE compares to alternative models.
Managing modern data stacks requires cross-engine fluency across relational, document, caching, time-series, and analytical OLAP tiers. Here is how JusDB compares to cloud support tiers, single-engine boutiques, and in-house generalist teams.
| Service Dimension | JusDB Multi-Engine SRE | Cloud Providers (AWS/GCP) | Siloed Single-Engine Boutiques | In-House Generalist Teams |
|---|---|---|---|---|
| Multi-Engine Estate Coverage | Unified SRE coverage across 26+ relational, NoSQL, distributed SQL, OLAP, and CDC technologies under a single contractual SLA | Siloed per-service support (RDS vs DynamoDB vs Redshift); zero cross-engine architectural orchestration or unified on-call | Narrow single-technology specialization (e.g. Postgres-only or Mongo-only), forcing clients to juggle multiple disjointed vendors | Developers forced to context-switch across multiple unfamiliar database paradigms without specialized internal DBRE depth |
| 24/7 SLA & Incident Escalation | Contractual P1 < 15 min, P2 < 1 hour response backed by named Principal Database SREs with root-cause incident remediation | Tier-1 ticketing queue with 4-8 hour response targets; relies on generic documentation and customer self-troubleshooting | Business-hours coverage or offshore call centers without guaranteed senior engineer response for complex off-hours outages | Exhausted on-call generalists woken up for unfamiliar alerts, leading to alert fatigue and prolonged production downtime |
| Cross-Engine Observability & Forensics | Deep engine-native telemetry (Performance Schema, pg_stat_statements, WiredTiger cache, LSM compactions) with proactive alert tuning | Coarse cloud infrastructure graphs (CPU, IOPS, free storage) with zero visibility into table locks, bloat, or execution plan drift | Proprietary dashboards limited strictly to their supported engine, lacking end-to-end distributed system correlation | Generic APM host metrics that signal high database CPU without revealing the offending query, index, or transaction lockup |
| Zero-Downtime Maintenance & Online DDL | 100% non-blocking schema migrations (gh-ost, pg_repack), online failovers, and minor/major version upgrades without downtime | Maintenance windows frequently impose brief downtime or read-only failovers; DDL modifications lock tables by default | Varying tooling depending on engine maturity; often requires scheduling late-night maintenance maintenance windows | High risk of issuing blocking DDL statements during peak production, causing cascading application pool connection lockups |
| Cloud FinOps & Infrastructure Optimization | Systematic memory, buffer pool, storage tiering, and index optimization yielding average 60% reductions in cloud compute spend | Incentivized to recommend vertical upscaling to larger instance sizes rather than optimizing underlying SQL and storage efficiency | Focuses strictly on engine queries without analyzing cloud compute reservation, EBS IOPS costs, or data egress economics | Cloud bills continuously grow as unindexed queries force engineers to incrementally provision larger cloud instances |
| Security, Ephemeral Access & Compliance | Audited ephemeral bastion access with zero persistent credentials; full ISO 27001, SOC 2 Type II, HIPAA, and PCI DSS compliance | Broad Cloud IAM policies with expansive cloud tenant permissions across your infrastructure | Varying security hygiene; often relies on shared long-lived credentials, Slack snippets, or unmonitored bastion access | Widespread developer access with shared database credentials and unlogged manual production queries |
Frequently Asked Questions
Which databases do you support?
MySQL, PostgreSQL, MongoDB, Cassandra, Aerospike, Redis/Valkey, Dragonfly, StarRocks, ClickHouse, Elasticsearch/OpenSearch, TiDB, SQL Server, ScyllaDB, MariaDB, CockroachDB, YugabyteDB, Azure Cosmos DB, Amazon DynamoDB, Neo4j, Apache Druid, Apache Pinot, TimescaleDB, InfluxDB, and pgvector. We also manage CDC tools: Debezium, AWS DMS, Flink CDC, and Apache SeaTunnel.
Do you support multiple databases simultaneously?
Yes — this is our most common setup. Most clients run 2-5 different database engines. We provide a single team with cross-database expertise, unified observability, and one SLA for the entire database tier.
What's the difference between /databases and /services?
The /databases hub organizes our expertise by database technology. The /services page organizes by service type (Remote DBA, Performance Tuning, etc.). Same team, same expertise — different entry points depending on how you think about your needs.
Can you manage databases across multiple clouds?
Yes. We manage databases on AWS (RDS, Aurora, DynamoDB, ElastiCache), GCP (Cloud SQL, AlloyDB, BigQuery), Azure (Azure SQL, Cosmos DB), and on-premise. Our observability stack works regardless of hosting.
Which Database Do You Need Help With?
Tell us your stack and we'll match you with the right database expert — free initial assessment included.