24/7 Apache Spark Support with a 15-Minute Emergency SLA

Our Spark support covers 24/7 emergency incident response with a 15-minute SLA, root cause analysis on every P1, performance tuning for partitioning and shuffle behavior, pipeline architecture review, cluster sizing and cost optimization, and migration or upgrade support. Every ticket reaches a named senior engineer with no tier-1 triage layer.

Yes, this is one of the most common Spark issues we see. We start with Spark UI stage detail to check for partition skew and broadcast join sizing, since those cause the large majority of executor OOM incidents that don't trace back to a genuine data volume increase.

Yes. The core Spark engine issues — skew, shuffle spill, memory tuning — are consistent across managed platforms; what differs is the platform-specific autoscaling and cluster policy layer, which we also cover for each of the major managed offerings.

Yes, for open source Spark on your own infrastructure. As an extension of the support contract, AceMQ runs managed Spark operations on Kubernetes, YARN or standalone clusters in your account: monitoring the cluster and the jobs on it, holding the pager, Spark and JVM patching, version upgrades rehearsed against real jobs, capacity and cost reviews, and the event logs and history server your engineers debug from. Your data engineers keep writing jobs; keeping the cluster under them healthy becomes our job.

Databricks and Amazon EMR are managed Spark platforms: they provision and scale the clusters and ship their own packaged Spark runtimes, Databricks Runtime and EMR releases. They are a strong choice when you want a managed notebook and job platform or are standardised on AWS. Neither tunes your jobs, so skew, shuffle spill and memory settings stay with your team. AceMQ's managed operations suit estates that keep open source Spark on their own Kubernetes or Hadoop clusters, for cost, version control or data that has to stay on-premises. Our support covers Spark on Databricks, EMR and Dataproc as well.

Yes. We support both legacy Hadoop clusters and modern Spark deployments, which means we plan migrations from people who understand what's actually being left behind — not just consultants who only know the destination platform.

P1 emergencies — jobs failing cluster-wide, a pipeline that stopped producing output — get a 15-minute first response with a senior engineer joining a live bridge. P2 critical issues are 1 hour, P3 high-priority 4 hours, and P4 standard questions next business day. These are contractual targets.

Yes. We support the current Spark release and the five end-of-life release lines before it for both batch ETL and Structured Streaming workloads, and because we also support Flink, we bring the same event-time and state-management thinking to Spark streaming jobs that we apply to dedicated streaming engines.

Apache Spark is an open source unified engine for large-scale data processing from the Apache Software Foundation. One Spark cluster runs batch data processing on Spark Core, SQL queries and data analytics with Spark SQL, stream processing with Structured Streaming, machine learning with MLlib and graph processing with GraphX, through APIs in Python with PySpark, Scala, Java and SQL. It replaced Hadoop MapReduce for most workloads and still reads from HDFS and Hive alongside object storage. Our support covers all of it: the Spark architecture of driver, executors and cluster manager on Kubernetes, YARN or standalone; the Spark applications themselves, including their data sources, partitioning and memory configuration; and the platforms that host them, Databricks, EMR and Dataproc, where the vendor supports the runtime and nobody supports the job.

The usual causes are skewed joins that send most of the data to a few executors, broadcast tables that outgrew the threshold, shuffle spill to slow disks, thousands of small output files, missing statistics, and Structured Streaming state that grows without a watermark. Diagnosis starts in the Spark UI and event logs, and the fix is usually in the job or its configuration rather than the cluster. A critical outage, meaning a production pipeline failed inside its window or a streaming job is falling behind, gets a senior engineer on a live call within 15 minutes, around the clock, with a written root cause afterwards. Upgrades to Spark 4.x, which requires newer Java and turns on ANSI SQL mode by default, are planned and rehearsed against real jobs before production.

Yes. AceMQ works on Apache Spark in two ways: ad hoc engagements for a defined piece of work, such as a health check, an upgrade or an incident, or an annual partner engagement that covers 24/7 support, proactive reviews and named engineers. Many teams start ad hoc and move to an annual partnership once the first engagement is done.

Every AceMQ proposal is tailored to your requirements: your Apache Spark environment, the SLA you need, the hours of cover and the scope of work. Most contracts run $24,000–72,000 a year, depending on the SLA, the scope and how Apache Spark is deployed, with aggressive discounts when several technologies share one contract. Request a quote and AceMQ replies within 1 hour.