24/7 Apache Spark Support
Keep Your Spark Workloads Running at Peak Performance
We are Open source Code Contributor
Zero-Day Vulnerability Fixes
Critical Vulnerability Assessment
Roadmap & Recommendations
SLA-Backed Technical Support
Zero-Day Vulnerability Fixes
Critical Vulnerability Assessment
Roadmap & Recommendations
SLA-Backed Technical Support
Apache Spark Support Service Built Around Your Compliance Requirements.
En(AI)blingTM Success for Industry Leaders
Apache Spark Support Packages in the USA
Every plan is carefully curated around your cluster's scale, your compliance requirements, and your acceptable resolution window. Choose the coverage that fits your reality.
Standard
Advanced
Platinum
What Ksolves Dedicated Apache Spark Support Service Has Delivered for Organisations Like Yours
Most Spark environments that come to us are broken in the same ways - jobs running 10x longer than they should, executors dying on skewed joins, engineers spending more time reading GC logs than building pipelines. Here is what changes after Ksolves takes over.
99.99%
SLA Maintained
SLA Maintained
Ksolves maintains 99.99% uptime across Spark client environments through proactive executor health monitoring, automated job recovery, and incident response that resolves before the business notices.
40%
Lower TCO
Lower TCO
From executor right-sizing and dynamic resource allocation to eliminating redundant shuffle operations, Ksolves reduces total cost of ownership by 40% without touching job performance or reliability.
98%
Contract Renewal Rate
Contract Renewal Rate
98% of clients running Spark with Ksolves renew. Why? Because OOM errors stopped, jobs run on time, and the team stopped getting paged at 2am.
30
Min Turnaround Time
Min Turnaround Time
When a Spark cluster goes down in production, Ksolves engineers are diagnosing the DAG within 30 minutes, keeping pipelines running and data teams unblocked.
Apache Spark Support Services Across Your Full Data Processing Lifecycle.
Spark is not just a cluster you stand up; it is a distributed system that needs constant tuning as data volumes grow, job complexity increases, and new workloads land in production. One Apache Spark support company across your entire lifecycle means the context never gets lost.
24/7 Managed Spark Operations
Most Spark failures do not announce themselves because they show up as slow stages, climbing GC times, and executors that quietly disappear before anyone notices. Our engineers watch the signals before they become incidents.
- Performance optimization across executor sizing, DAG analysis, and GC overhead reduction
- 24x7 monitoring and issue resolution across driver, executor, and cluster node health
- Deployment and scaling across on-premises, Kubernetes, AWS EMR, Azure HDInsight, and GCP Dataproc
- Automated backup and disaster recovery against defined recovery time objectives
- Resource usage reports covering job performance, throughput, and cluster utilisation
Spark Applications Engineered for Production Load
A Spark application that passes testing and falls apart under real data volumes is the most common environment we inherit because most pipelines are built for demo conditions, not production skew.
- Custom development in PySpark, Scala, Java, and Spark SQL for production-scale data volumes
- Integration with big data ecosystems covering Kafka, Hadoop, Hive, Delta Lake, and Cassandra
- Spark Structured Streaming pipelines for real-time data ingestion and transformation
- MLlib integration for machine learning pipelines within existing Spark environments
- PySpark development and AQE-enabled optimisation for analytics engineering teams
Apache Spark Upgrades Without the Risk
Spark 3.x changed AQE behaviour, join strategy selection, and shuffle handling in ways that are not always visible until a workload hits production data and starts producing results no one can explain.
- Spark 3.5 to 4.x migration covering code refactoring, API changes, and performance validation across all active workloads
- Hadoop MapReduce to Apache Spark migration with full performance benchmarking
- Spark on Kubernetes deployment covering executor pods and dynamic resource allocation
- Performance tuning and load balancing across JVM heap, shuffle partitions, and executor cores
- Post-upgrade DAG-level regression testing across all Spark applications and pipelines
Secure by Design. Compliant by Default
Spark's default configuration is not secure because column-level access control, execution layer encryption, and audit logging across job runs are not enabled out of the box, and in regulated environments that gap is a compliance failure waiting to be found.
- Data security and compliance covering Kerberos, LDAP, and SSL/TLS across cluster nodes
- Role-based access control and column-level encryption for data at rest and in transit
- Spark job audit logging and lineage tracking for GDPR, HIPAA, and SOC 2 traceability
- Compliance reporting and audit trail documentation for ISO 27001 and SOC 2 Type II
- Security patching and CVE-driven vulnerability management across Spark and JVM components
Architecture Guidance From Certified Spark Experts
Most Spark performance problems are architectural because the wrong partition strategy, missing broadcast hints, and shuffle partitions left at the default 200 create compounding issues that no amount of memory increase will solve on their own.
- Spark architecture review covering DAG design, partition strategy, and shuffle minimisation
- Capacity planning and scaling roadmap built around workload profiles and volume growth
- Developer enablement covering PySpark, Spark SQL, AQE configuration, and MLlib workflows
- Spark 3.x and 4.0 migration readiness assessment covering API and shuffle behaviour changes
- Incident post-mortem and RCA workshops across OOM failures, shuffle spills, and straggler tasks
Through the Client's Lens
Why is Ksolves a Trusted Choice of Global Teams for Apache Spark Support?
From troubleshooting to optimization, Ksolves is a trusted name when it comes to Apache support experts, offering tailored support for smooth data integration and performance. Here’s why:
90%
Client Retention Rate
750+
Projects Successfully
Delivered
NSE & BSE
Publicly Listed
Company
600+
Workforce and still
growing
350+
Certifications
200+
Happy Clients
150K+
Support Hours
Completed
Industries We Help Scale with Apache Spark
A Spark OOM in a fintech fraud detection job and a Spark OOM in a retail demand forecasting job carry different consequences, different resolution windows, and different compliance implications. Our Apache Spark support service is built around that difference.
Telecom
Telecom networks run Spark across subscriber analytics, network event processing, and anomaly detection pipelines where a single executor failure creates gaps in data feeding billing and fraud systems simultaneously.
Healthcare
Healthcare data teams depend on Spark for patient data processing and HIPAA-regulated ML workloads where the distance between a misconfigured executor and a compliance violation is one bad setting.
E-Commerce
Having supported e-commerce Spark environments at scale, we keep personalisation engines, demand forecasting pipelines, and recommendation workloads running without shuffle failures disrupting the customer experience.
Fintech
In fintech, a straggler task on a fraud detection job is not a performance inconvenience — it is a risk event. We manage Spark environments where processing speed and data accuracy carry equal weight.
Entertainment
Entertainment platforms process petabytes of engagement data daily. We support Spark environments running recommendation algorithms and content analytics pipelines that cannot afford data lag at release time.
Manufacturing
With hands-on manufacturing data experience, we run Spark support coverage across predictive maintenance models, sensor analytics, and supply chain intelligence built on continuous high-frequency operational data.
Retail
Retail Spark environments fail most often on skewed join keys during peak traffic. We manage partition strategy, AQE configuration, and executor tuning so demand forecasting and loyalty pipelines run clean.
Banking & Financial Services
Banking institutions run Spark across transaction analytics, credit risk models, and regulatory pipelines where job accuracy and full audit traceability are equally non-negotiable and equally audited.
Logistics & Supply Chain
We run support coverage for route optimisation models, warehouse analytics, and shipment tracking across distributed environments.
Technology & SaaS
For SaaS companies, Spark reliability directly determines product SLA. We support environments where dynamic resource allocation, AQE tuning, and Delta Lake optimisation define whether the platform meets its commitments.
Ksolves on Spark: Insights by the Industry Leaders
Every piece below comes from engineers who support NiFi environments daily. If it is on this list, it is because someone on the Ksolves team has dealt with it in production.
Apache Spark Problems We Have Solved. In Production. At Scale.
Every case study below started with a Spark environment that was costing more, running slower, or failing more often than the team could explain. Check out the impact of production-grade Spark engineering delivered right. By Ksolves!
Spark Bulk Data Processing Engine
Challenge
A client needed to process large volumes of deeply nested JSON streams, but their Java-based microservices were too slow and required extensive code changes for each new data type.
Solution
Built a metadata-driven Spark processing engine integrated with Kafka, enabling new JSON types to be onboarded via configuration files with no code changes.
3X
Faster Processing vs Legacy System
Spark-Based JSON Data Mapping
Challenge
A client processing 10,000 records per minute from 30-40 entity JSON files had no scalable way to map nested data to database tables without heavy code changes.
Solution
Deployed a multi-node Spark cluster on Kubernetes with metadata-driven mapping files, enabling instant JSON-to-database mapping without code modifications.
Zero
Code Changes for New JSON Types
Spark Query Engine for Financial ETL
Challenge
A finance client struggled to run ETL on large datasets and lacked Spark expertise internally, blocking the team from deriving insights without developer dependency.
Solution
Deployed a custom Spark Query Processing Engine driven by configuration files, letting non-technical users run transformations without writing Spark code.
Config-Driven
ETL Without Spark Expertise
Real-Time Burst Fraud Detection Pipeline
Challenge
A Telco processing 5 billion daily events had no real-time system to detect burst fraud, leaving campaign budgets exposed to bots generating up to 150,000 events per second.
Solution
Built a Kafka and Spark Structured Streaming pipeline with 30-second tumbling windows and watermarking to detect and suppress fraud within the same processing cycle.
30 Sec
Fraud Suppression Window
Confluent to Open-Source Kafka Migration
Challenge
Confluent enterprise licensing consumed 60% of a telecom analytics firm's data platform budget, with no flexibility to scale without compounding cost.
Solution
Migrated 50+ topics across 12 applications to open-source Kafka using MirrorMaker 2, with phased cutover, TLS/SASL security, and zero code changes to any application.
60%
Annual Licensing Cost Reduction
Spark Bulk Data Processing Engine
Challenge
A client needed to process large volumes of deeply nested JSON streams, but their Java-based microservices were too slow and required extensive code changes for each new data type.
Solution
Built a metadata-driven Spark processing engine integrated with Kafka, enabling new JSON types to be onboarded via configuration files with no code changes.
3X
Faster Processing vs Legacy System
Spark-Based JSON Data Mapping
Challenge
A client processing 10,000 records per minute from 30-40 entity JSON files had no scalable way to map nested data to database tables without heavy code changes.
Solution
Deployed a multi-node Spark cluster on Kubernetes with metadata-driven mapping files, enabling instant JSON-to-database mapping without code modifications.
Zero
Code Changes for New JSON Types
Spark Query Engine for Financial ETL
Challenge
A finance client struggled to run ETL on large datasets and lacked Spark expertise internally, blocking the team from deriving insights without developer dependency.
Solution
Deployed a custom Spark Query Processing Engine driven by configuration files, letting non-technical users run transformations without writing Spark code.
Config-Driven
ETL Without Spark Expertise
Real-Time Burst Fraud Detection Pipeline
Challenge
A Telco processing 5 billion daily events had no real-time system to detect burst fraud, leaving campaign budgets exposed to bots generating up to 150,000 events per second.
Solution
Built a Kafka and Spark Structured Streaming pipeline with 30-second tumbling windows and watermarking to detect and suppress fraud within the same processing cycle.
30 Sec
Fraud Suppression Window
Confluent to Open-Source Kafka Migration
Challenge
Confluent enterprise licensing consumed 60% of a telecom analytics firm's data platform budget, with no flexibility to scale without compounding cost.
Solution
Migrated 50+ topics across 12 applications to open-source Kafka using MirrorMaker 2, with phased cutover, TLS/SASL security, and zero code changes to any application.
60%
Annual Licensing Cost Reduction
Frequently Asked Questions
Everything you need to know before choosing a Apache Spark Support Partner
Ksolves Apache Spark support service covers the full cluster lifecycle including deployment, executor tuning, DAG optimisation, 24×7 monitoring, version upgrades, custom application development, and integration with Kafka, Delta Lake, and Hadoop. All plans include SLA-backed response times, risk assessment reports, and future release roadmap monitoring.
Spark 3.5 reached end-of-life in April 2026, which means no further security patches or bug fixes from the Apache community. Migration to Spark 4.x involves code refactoring for deprecated APIs, validation of existing workloads against the new execution model, and performance testing to confirm throughput is maintained or improved post-migration. Ksolves manages the full transition with zero downtime and full regression coverage across all active pipelines.
Ksolves engineers diagnose executor OOM errors at the task level using Spark UI stage metrics, identifying oversized partitions, GC overhead, shuffle spill to disk, and broadcast variables exceeding driver memory limits. Resolution covers executor memory reconfiguration, spark.memory.fraction tuning, and partition rebalancing based on actual workload data.
Ksolves identifies skewed partitions through task duration variance in the Spark UI stage view, applies salting strategies for hot keys, enables AQE skew join handling, and tunes spark.sql.shuffle.partitions based on actual data cardinality rather than the default 200 that performs poorly on most real-world workloads.
Yes. Ksolves manages end-to-end Hadoop MapReduce to Spark migrations covering workload compatibility assessment, job rewriting in PySpark or Scala, shuffle behaviour validation, and full performance benchmarking post-migration. Zero-downtime migration is standard across all Apache Spark support service tiers.
Critical severity incidents receive a 30-minute acknowledgement and a 2-hour resolution target across all Apache Spark support service plans, contractually defined in your SLA. Platinum customers get a dedicated escalation path with 24×7 access to a named Spark engineer.
Yes. Ksolves supports Spark on Kubernetes across on-premises clusters and cloud environments including AWS EMR, Azure HDInsight, and GCP Dataproc, covering executor pod configuration, dynamic resource allocation, and cluster autoscaling for production workloads.
Databricks requires a vendor subscription and runtime lock-in. Ksolves provides standalone Apache Spark support covering open-source Spark 3.x and 4.0, Delta Lake environments, and Kubernetes-native clusters without requiring a Databricks subscription or migrating existing workloads to their platform.
Slow Spark jobs typically trace to data skew creating oversized partitions, excessive shuffle between stages, GC pressure from undersized executor heaps, or spark.sql.shuffle.partitions misconfiguration. Ksolves engineers diagnose at the DAG level and configure AQE to handle skew and partition coalescing dynamically going forward.
