Explore how AceMQ engineers solve complex messaging challenges across RabbitMQ, Kafka, Redis, IBM MQ, and 40+ more technologies.
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Replacing fragile SQL-trigger-based ingestion with a reliable event-driven architecture for plant-floor data movement and low-latency operations.
Improving RabbitMQ reliability, queue behavior, and operational guidance for a payment system processing over 200 production changes weekly.
Troubleshooting cluster failover, partition handling, and quorum queue issues in a high-stakes aviation operational environment.
Standardizing RabbitMQ deployment and training staff while migrating infrastructure from VMware to Nutanix.
Reducing manual error-queue operations by improving retry handling, dead-lettering, and downstream flow management across RabbitMQ, BizTalk, and D365.
Migration to supported RabbitMQ versions with managed services, standardization, compliance posture, and Tanzu commercial licensing.
Resolving weekly RabbitMQ crashes, optimizing for 300,000+ connected devices, and architecting horizontal scaling strategy.
Enterprise-grade RabbitMQ support with code-level remediation and patch management for regulated production environments.
Resolving federation failures causing pipeline monitoring delays in critical SCADA infrastructure.
Resolving leader election bugs and planning migration from Windows to Linux for a physical security platform.
Independent architecture and performance review of RabbitMQ, Kafka, and Redis for an online trading platform.
Ongoing enterprise RabbitMQ support and advisory for a global leader in industrial digital reality solutions.
Enterprise messaging support for a leading health savings account and benefits administration platform.
RabbitMQ support and advisory for defense electronics and communications systems.
Enterprise messaging support for one of the world's largest online gaming software providers.
Rapid troubleshooting of production incidents including stuck queues, publish failures, cluster instability, and performance bottlenecks.
Moving from community support risk to enterprise-backed support with patch access, compliance posture, and procurement guidance.
Moving from unsupported versions to supported LTS or enterprise versions with compatibility validation and rollback design.
Hardening RabbitMQ in Kubernetes environments with StatefulSet tuning, quorum queue optimization, storage isolation, and memory/network configuration.
Throughput, latency, and resource utilization optimization including queue design, publisher confirms, replication settings, and concurrency tuning.
Improving availability posture with cluster design, partition handling, cross-AZ guidance, DR planning, and quorum strategy.
Decoupling legacy ERP and file-based processes with API enablement, middleware design, and asynchronous event flows.
Support and compliance alignment for healthcare, finance, government, and defense with supported releases, audit posture, and vendor-backed escalation.
Improving observability with Prometheus, Grafana, alerting, queue visibility, disk/memory thresholds, and retry metrics.
Reducing manual intervention in failed message processing with DLX design, poison message control, and retry orchestration.
Short, focused engagement to understand risk, review architecture, identify findings, and define a prioritized roadmap.
Ongoing operational support and expert escalation with portal-based support, advisory sessions, ticketing, and recurring health checks.
Moving from older or more rigid middleware to RabbitMQ patterns with architecture transition planning, interoperability, and phased cutover.
Aligning RabbitMQ with long-term platform strategy through installation design, operational model changes, and automation planning.
Faster customer onboarding with centralized support collaboration, documentation upload, ticket workflows, and engagement tracking.
Implementing a comprehensive CVE patching and compliance strategy across 10,000+ RabbitMQ deployments running end-of-life versions, with real-time vulnerability monitoring and phased upgrade planning.
Implementing a CVE patching strategy and compliance framework across thousands of on-premises RabbitMQ deployments, with tiered SLA support and quarterly health checks.
Remediating critical RabbitMQ federation failures and implementing CVE patching across SCADA pipeline infrastructure where delays trigger mandatory shutdowns.
Developing a CVE patching strategy and upgrade path for RabbitMQ 3.12 deployments in a regulated medical certification environment, evaluating community 4.x versus commercial 3.13 LTS options.
Implementing a blue-green deployment strategy for RabbitMQ CVE patching and version migration, integrated with middleware upgrades and Active Directory authentication.
Providing private CVE patching and remediation for legacy RabbitMQ versions across regulated industrial automation environments where forced upgrades are infeasible.
AceMQ diagnosed Redis connection timeout issues causing service disruptions in a legacy fintech platform being modernized, identifying client-side resource exhaustion as the root cause and delivering a remediation plan for high-concurrency caching.
AceMQ advised a UK-based technology firm on Redis Enterprise licensing compliance and architecture validation as they migrated high-volume messaging infrastructure to OpenShift, handling millions of messages daily alongside RabbitMQ.
AceMQ expanded its enterprise support agreement with global quantitative trading firm DRW to include Redis caching alongside RabbitMQ, providing L3 escalation support across the full messaging and caching technology stack.
AceMQ's Redis Health and Architecture Assessment identifies cluster vulnerabilities, performance bottlenecks, and optimization opportunities before they become production incidents — delivering a prioritized remediation roadmap.
AceMQ helped a financial services provider architect Redis as a reusable enterprise data layer supporting session management, caching, and real-time data processing across millions of daily transactions.
AceMQ supported the integration of Redis as a modern caching layer during the migration of a legacy banking application from monolithic architecture to microservices, resolving concurrency and timeout issues during the transition.
American National Insurance replaced a Kafka-based CDC pipeline with a standalone Debezium + RabbitMQ architecture, simplifying operations while maintaining SQL Server change capture with improved message routing flexibility.
AceMQ developed a multi-technology compliance strategy covering CVE patching for Kafka alongside RabbitMQ and IBM MQ deployments, creating a unified vulnerability management approach across the entire enterprise messaging stack.
AceMQ provided ongoing support for PagoNXT's high-throughput payments infrastructure running Kafka and RabbitMQ at 2,500 transactions per second, including load testing validation and architecture optimization.
AceMQ's Kafka assessment service evaluates streaming architecture health, identifies operational risks, and provides a structured migration or modernization roadmap for enterprises running or evaluating Kafka.
AceMQ advises enterprises transitioning IBM MQ workloads to modern messaging platforms, routing workloads to Kafka for event streaming or RabbitMQ for transactional messaging based on specific use case requirements.
AceMQ led the phased migration of American National Insurance's IBM MQ infrastructure to RabbitMQ, including legacy code refactoring and HIPAA-compliant data handling across Windows and mainframe queue environments.
AceMQ and a global technology partner developed a joint motion for displacing IBM MQ in enterprise accounts, leveraging RabbitMQ as a cost-effective open-source alternative with AceMQ's commercial support model.
AceMQ helps financial services organizations replace costly IBM MQ deployments with RabbitMQ, providing the commercial support and SLA guarantees that regulated industries require while dramatically reducing messaging infrastructure costs.
AceMQ provided a support model and SLA comparison for a government agency evaluating alternatives to IBM MQ support, including 48-hour critical patch response commitments and compliance documentation capabilities.
AceMQ's IBM MQ TCO assessment quantifies licensing, operational, and risk costs of IBM MQ deployments and presents a structured comparison against open-source alternatives with AceMQ commercial support.
FIMC is migrating from Azure Service Bus to a 3-node RabbitMQ cluster for improved compliance control and disaster recovery, handling 1,300 msg/sec with 200KB payloads and warm schema replication DR.
AceMQ designed the migration architecture for Woodmen Life Insurance moving 66 applications from self-managed RabbitMQ to Azure Service Bus, including Azure Managed Grafana observability and a co-ownership migration model.
Bank Vrede, dissatisfied with Microsoft Azure Service Bus performance and flexibility, engaged AceMQ to assess migration to RabbitMQ, comparing total cost, operational control, and messaging capability for banking workloads.
Fortior Solutions selected RabbitMQ over Azure Service Bus for a federal contracting application requiring FIPS-compliant messaging transport, with AceMQ providing FIPS configuration guidance and compliance documentation.
AceMQ's Azure Service Bus comparison assessment helps organizations evaluate whether managed Azure messaging is cost-effective versus self-managed RabbitMQ with commercial support, covering both Standard and Premium tier economics.
A global financial institution upgraded over 1,000 Spring and Java applications in a single 24-hour window using AceMQ's deterministic Spring upgrade process, achieving significant CPU and memory reductions through Broadcom's commercial Spring support.
AceMQ provides day-zero CVE patch access for Spring Framework through Broadcom's commercial Spring subscription, enabling retail and banking organizations to address critical Spring security vulnerabilities immediately upon disclosure.
AceMQ delivers Spring Framework security compliance programs for government agencies, meeting 48-hour critical patch SLA requirements through Broadcom commercial Spring support with FIPS compliance and audit documentation.
AceMQ supports security technology companies using Spring Framework with RabbitMQ for IoT messaging, providing expertise across the Spring–RabbitMQ integration layer and commercial support for both technologies under a single engagement.
AceMQ helps enterprise software organizations quantify and manage the CVE risk exposure created by running community Spring Framework without a commercial support agreement, transitioning them to Broadcom commercial support.
Emergency remediation of an Elasticsearch cluster that would not return to green after a data node failure, with replicas blocked by disk watermarks.
Ongoing 24/7 support for an Elasticsearch estate suffering repeated parent circuit breaker trips and long garbage collection pauses under aggregation load.
Assessment of an oversharded Elasticsearch cluster where cluster-state size and pending task queues were driving master instability.
Consulting engagement to design index lifecycle management, data tiering, and snapshot policy for a regulated Elasticsearch estate.
Emergency recovery of an OpenSearch domain where snapshot restores were failing partway through and leaving indices in a red state.
24/7 enterprise support for OpenSearch clusters carrying regulated search and audit workloads, including security plugin and upgrade coverage.
Assessment of the technical and licensing implications of moving a large Elasticsearch estate to OpenSearch, including client and plugin compatibility.
Consulting engagement to design tenant isolation, index strategy, and query governance for OpenSearch serving thousands of customer tenants.
Remediation of a Grafana deployment that became unusable during incidents, with dashboards timing out exactly when engineers needed them most.
Ongoing support for Grafana unified alerting, notification routing, and datasource reliability across an operational monitoring estate.
Assessment of a sprawling Grafana dashboard estate to identify duplication, broken panels, and the small set of dashboards anyone actually uses.
Consulting engagement to consolidate fragmented metrics, logs, and traces onto a single Grafana-based observability layer with consistent labeling.
Emergency remediation of Prometheus servers being OOM-killed repeatedly after a deployment introduced an unbounded label.
Ongoing support for large Prometheus instances where restarts caused extended monitoring blind spots due to slow write-ahead log replay.
Assessment of a Prometheus federation topology that had grown past its limits, causing gaps and duplicated data across sites.
Consulting engagement to design multi-year Prometheus metric retention with downsampling and object storage, replacing oversized local disks.
Remediation of intermittent Datadog telemetry gaps traced to agent buffering, container lifecycle, and network egress behavior on the customer's own infrastructure.
Ongoing support for a Datadog monitor estate producing more alerts than the on-call rotation could meaningfully act on.
Assessment of APM instrumentation coverage and trace completeness across a service estate where distributed traces kept breaking at service boundaries.
Consulting engagement to govern custom metric cardinality, log ingest, and trace volume so observability spend tracks value instead of accident.
Remediation of a Splunk ingest pipeline where forwarder queues backed up and security events arrived hours late during peak periods.
Ongoing support for a Splunk environment where scheduled searches skipped, ad-hoc searches queued, and analysts blamed the platform.
Assessment of an index and sourcetype design that had grown organically, driving poor search performance and unmanageable retention rules.
Consulting engagement to reduce Splunk daily ingest volume through filtering, routing, and tiering without losing security or compliance coverage.
Remediation of application latency and memory growth traced to APM agent configuration on the customer's own JVM and container estate.
Ongoing support for distributed tracing across a mixed New Relic and OpenTelemetry estate where traces broke at instrumentation boundaries.
Assessment of which services, dependencies, and code paths are genuinely covered by APM instrumentation versus assumed to be.
Consulting engagement to govern ingested telemetry volume and user allocation so observability spend reflects operational value.
Remediation of an ELK pipeline where Logstash back-pressure stalled Beats agents and left log gaps across the fleet.
Ongoing support across the full ELK ingest path — Beats, Logstash, ingest pipelines, and index templates — with 24/7 coverage.
Assessment of log volume, field-level utility, and retention across an ELK estate where storage growth had outpaced any plan for it.
Consulting engagement to redesign an ELK ingest architecture around buffered queues, ingest node pipelines, and schema standardization.
Emergency remediation of a Memcached tier evicting hot keys while reporting free memory, traced to slab class allocation.
Ongoing support for a Memcached tier prone to thundering-herd database load after node changes and cache expiry cliffs.
Assessment of Memcached sizing, key distribution, and hit rate to determine whether adding capacity would actually help.
Consulting engagement to design multi-region Memcached topology, key namespacing, and invalidation strategy for a latency-sensitive platform.
Diagnosing and resolving application stalls caused by a working set that outgrew the WiredTiger cache, pushing the server into continuous eviction pressure.
Ongoing support for replica sets where a short oplog window was forcing repeated full initial syncs of secondaries during nightly batch loads.
Redesigning a monotonically increasing shard key that concentrated all inserts on one shard and produced jumbo chunks that would not split.
Structured review of schema design, index efficiency, replica set topology, and backup recoverability ahead of a major workload increase.
Emergency intervention on a database approaching transaction ID wraparound because autovacuum could not keep pace with the largest tables.
Ongoing support for a cluster where an abandoned logical replication slot repeatedly filled the WAL volume and threatened to halt the primary.
Designing an automated failover architecture with quorum-based leader election, synchronous replication policy, and tested recovery procedures.
Independent assessment of query performance, index health, table and index bloat, and connection management for a cluster with degrading response times.
Resolving replica lag that grew to hours during batch windows because single-threaded apply could not keep pace with large multi-row transactions.
Ongoing support for deadlocks and lock wait timeouts under booking concurrency, including history list growth from long-running transactions.
Planning and executing a major version upgrade including character set migration and online schema changes on large tables using gh-ost.
Review of replication topology, failover readiness, backup recoverability, and configuration drift across a MySQL estate that had grown organically.
Resolving read timeouts caused by tombstone accumulation on queue-like partitions where deletes outpaced compaction and gc_grace_seconds.
Ongoing support for anti-entropy repair that never completed within gc_grace_seconds, leaving the cluster exposed to deleted data resurrecting.
Redesigning partition keys and clustering order to eliminate unbounded partitions and remove secondary index queries that were hitting every node.
Assessment covering topology, replication and consistency configuration, JVM and garbage collection behavior, compaction health, and growth headroom.
Resolving ingestion failures where frequent small inserts produced parts faster than background merges could retire them, tripping the parts limit.
Ongoing support for ReplicatedMergeTree clusters covering replication queue stalls, memory limit failures on large queries, and mutation backlogs.
Redesigning ORDER BY keys, partitioning, codecs, and materialized views so dashboard queries read a small fraction of the data instead of full scans.
Assessment of shard and replica topology, storage tiering, query concurrency limits, and merge behavior ahead of a significant data volume increase.
Resolving Kafka supervisor lag caused by task slot exhaustion, oversized ingestion tasks, and handoff failures to deep storage.
Ongoing support for broker timeouts and unpredictable query latency driven by segment sizing, cache behavior, and processing thread contention.
Redesigning segment granularity, partitioning, and auto-compaction policy so segment counts stay bounded as historical data accumulates.
Assessment of historical tiering, retention rules, and replication factors to align infrastructure cost with how data is actually queried over time.
Eliminating periodic multi-hundred-millisecond latency spikes traced to RDB snapshot fork stalls amplified by transparent huge pages.
Ongoing support for instances where the eviction policy did not match how the keyspace was used, causing session data to be evicted under memory pressure.
Planning a migration from vertically scaled standalone Redis to Cluster mode, including hash tag design and remediation of cross-slot operations.
Assessment of persistence configuration, replication topology, and failover behavior against the durability the workloads actually require.
Resolving pipeline runtime blowouts caused by queries spilling to remote storage on undersized warehouses while concurrent jobs queued behind them.
Ongoing support for Snowpipe, stream, and task failures including stale streams past their retention window and silent partial-load conditions.
Restructuring warehouse sizing, auto-suspend policy, and workload isolation to bring credit consumption in line with the work actually being done.
Assessment of clustering keys, micro-partition pruning, and table design on large tables where queries had begun scanning most of the data.
Right-sizing Databricks compute by moving scheduled work off all-purpose clusters and tightening autoscaling, instance selection, and idle timeouts.
Migrating off the legacy Hive metastore to Unity Catalog with external location mapping, table upgrades, and a grant model that survives audit.
Fixing Delta tables where streaming writes and over-partitioning have produced millions of tiny files, stalling reads and vacuum operations.
Named-engineer support for production Databricks job failures — driver OOM, spot reclamation, library conflicts, and workflow retry storms.
Making predicates, aggregates, and joins execute at the source connector instead of pulling full tables into Trino workers.
Evaluating whether a federated query layer will actually work against a given set of source systems before the platform is committed to.
Stopping worker crashes caused by unbounded joins, missing spill configuration, and memory limits that do not match the concurrency the cluster actually sees.
Named-engineer support for Starburst and Trino latency regressions, connector failures, and concurrency problems in production.
Fixing nightly jobs where a handful of skewed keys concentrate data onto a few executors and drive repeated out-of-memory failures.
Resolving jobs that fail after a dimension table grows past the broadcast threshold and the optimizer keeps trying to broadcast it anyway.
Reducing shuffle write volume and disk spill on nightly batch jobs so the processing window fits inside the reporting deadline.
Profiling a Spark estate to find over-provisioned jobs, redundant pipelines, and workloads better served by something other than Spark.
Moving off an aging Hadoop cluster to object storage and open table formats, with Hive, MapReduce, and Oozie workloads translated rather than lifted.
Establishing what is actually running on a Hadoop cluster, what it costs to keep, and what a defensible migration sequence and timeline look like.
Relieving NameNode heap pressure and long GC pauses caused by small-file sprawl across HDFS, before the cluster loses its metadata service.
Named-engineer support for YARN queue starvation, container allocation failures, and NodeManager instability on production Hadoop clusters.
Resolving checkpoint timeouts under backpressure where RocksDB state has grown past what the configured checkpoint interval can absorb.
Diagnosing event-time windows that stop firing because a single idle source partition holds the watermark back across the whole job.
Designing state backend, key partitioning, and rescaling strategy for large-state Flink jobs that must restart without hours of downtime.
Evaluating whether a proposed streaming workload belongs on Flink, and what the exactly-once, state, and operational requirements will really cost.
Migrating from Kafka to Redpanda with client compatibility testing, ACL and schema registry translation, and a staged cutover per topic.
Designing tiered storage for long retention so historical data lives in object storage without local disk dictating how long you can keep it.
Resolving produce and consume latency spikes traced to Raft leadership imbalance, disk saturation, and partition distribution across brokers.
Named-engineer 24/7 support for Redpanda clusters covering node recovery, consumer lag incidents, upgrades, and client-side failures.
Resolving cluster-wide write latency caused by bookie journal and ledger device contention, and restoring write quorum headroom.
Diagnosing producers blocked by backlog quota enforcement when a slow or abandoned subscription prevents the backlog from clearing.
Designing tenant, namespace, and policy structure so independent teams can share a Pulsar cluster without interfering with each other.
Sizing brokers, bookies, and metadata for a Pulsar deployment against real throughput, retention, and durability requirements.
Restoring scheduling on Airflow deployments where zombie tasks hold executor slots and pools until nothing new gets queued.
Fixing scheduler delay caused by DAG files that make network or database calls at parse time, blocking every DAG in the deployment.
Moving from Celery to the Kubernetes executor, or the reverse, with a sizing model and deployment design that matches the workload profile.
Reviewing an Airflow deployment for reliability, DAG authoring practice, secrets handling, and upgrade readiness before it becomes unmaintainable.
Recovering NiFi nodes where the content repository has filled because a backpressured downstream processor has no queue limits in front of it.
Resolving nodes disconnecting from a NiFi cluster under load due to heartbeat timeouts, GC pauses, and ZooKeeper coordination failures.
Restructuring sprawling NiFi canvases into versioned, parameterized, testable flows with a promotion path across environments.
Assessing a NiFi estate for throughput headroom, provenance and audit coverage, security posture, and which flows belong on NiFi at all.
Diagnosing an Airbyte connection that kept reporting success while the destination table quietly went stale after an upstream schema change.
Stopping an Airbyte connection that kept dropping out of incremental mode and re-running full refreshes against a large table every night.
A structured review of an Airbyte estate that had grown organically — auditing connector versions, sync modes, state handling, and failure visibility.
Taking a proof-of-concept Airbyte install to a production-grade deployment with proper isolation, secrets handling, resource limits, and recovery procedures.
Planning and executing a staged move off Pentaho Data Integration onto a modern ELT stack, without a big-bang cutover of hundreds of transformations.
Reconstructing lineage and documentation for an undocumented estate of legacy Kettle transformations before anyone attempts to change or replace them.
Ongoing support for a Pentaho Data Integration estate that must keep running reliably while a longer-term replacement is planned.
Resolving Carte slave server instability where long-running clustered transformations hung, leaked memory, and left orphaned carte sessions.
Tracing intermittent 502s at the Kong gateway to misconfigured active health checks and stale DNS resolution of upstream service names.
Fixing rate limits that allowed several times the configured quota because the plugin was using the local counter policy across a multi-node gateway.
Designing a Kong topology for a company consolidating several ad-hoc API entry points, including control plane separation and environment promotion.
Measuring where request latency is actually spent inside the Kong plugin chain, and which plugins are worth their cost.
Debugging proxy-level latency and policy execution problems in Apigee that sit outside what the platform vendor's support will investigate.
Correcting Apigee quota and spike arrest configuration that was rejecting legitimate traffic while letting genuine bursts through to backends.
Planning and executing a migration from Apigee Edge to Apigee X, including the policy, networking, and analytics differences that break naive lift-and-shift.
Assessing a sprawling Apigee proxy estate and designing a shared flow architecture that removes duplicated policy logic across hundreds of proxies.
Resolving out-of-memory failures in a Mule application that buffered entire multi-hundred-megabyte payloads because no repeatable streaming strategy was configured.
Investigating recurring CloudHub worker restarts under memory pressure and the application-level causes behind them.
An honest evaluation of which MuleSoft integrations justify their licensing cost, and a staged migration path for the ones that do not.
Inventorying and evaluating a MuleSoft estate for reliability, error handling, and reuse before committing to either investment or migration.
Fixing throttling policies that applied inconsistently across WSO2 gateway nodes, letting some consumers far exceed their subscription tier.
Resolving registry database deadlocks under concurrent load that intermittently froze WSO2 API deployment and gateway startup.
Planning a multi-version WSO2 API Manager upgrade including registry migration, API redeployment, and identity integration changes.
Reviewing a WSO2 deployment's topology, database layout, high availability posture, and gateway sizing against its actual traffic profile.
Resolving containers repeatedly OOMKilled because the JVM and Node runtimes inside them were sizing heap against host memory rather than the cgroup limit.
Stopping recurring build agent and node outages caused by unpruned Docker build cache, dangling images, and orphaned volumes filling the filesystem.
Restructuring Dockerfiles and CI caching so builds reuse layers properly, cutting pipeline time and image size across a large service estate.
Assessing and hardening container images and runtime configuration — non-root execution, read-only filesystems, and secrets that had been baked into layers.
Breaking a redrive loop where SQS messages were reprocessed indefinitely because the queue visibility timeout was shorter than the Lambda function timeout.
Diagnosing VPC-attached Lambda invocation failures caused by ENI and subnet IP exhaustion during scale-out, and the connection handling that made it worse.
Measuring memory, duration, and concurrency across a large Lambda estate to right-size functions that were provisioned by guesswork.
Designing event-source mapping, batching, ordering, and failure handling for a Lambda estate that had grown without a consistent event architecture.
Diagnosing functions that silently stopped triggering after a storage account connectivity change — a dependency the runtime has but the application code never mentions.
Recovering Durable Functions orchestrations stuck mid-flight because non-deterministic orchestrator code broke replay after a deployment.
Reviewing hosting plan choice, instance scaling behavior, and cold start impact across an Azure Functions estate to align cost with actual workload shape.
Designing trigger selection, concurrency control, and failure handling for an Azure Functions estate integrating messaging and event streams.
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