24/7 Apache Flink Support
Keep Your Stream Processing Jobs Running at Full Speed
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 Flink Support That's Built to Meet the World's Strictest Data Standards
En(AI)blingTM Success for Industry Leaders
Flink Support Packages
Every plan is designed around a specific operational reality. Choose the one that matches how critical your Flink environment is and how fast you need us to move when something goes wrong.
Standard
Advanced
Platinum
What Ksolves Has Delivered for Organizations Running Apache Flink at Scale
Across fintech, telecom, logistics, and SaaS, enterprises running Apache Flink in production trust Ksolves AI-First approach to deliver stable operations, improved reliability, and scalability.
99.99%
SLA Maintained
SLA Maintained
Ksolves holds 99.99% uptime across client environments through proactive monitoring, auto-healing pipelines, and zero-drama incident response.
40%
Lower TCO
Lower TCO
From licensing audits to compute consolidation, Ksolves cuts total cost of ownership by 40%, without cutting corners on performance or reliability.
98%
Contract Renewal Rate
Contract Renewal Rate
We take pride in saying 98% of clients come back. Not because of lock-in, but because the work speaks for itself. That’s Ksolves Promise - on time, on budget, and exactly what was promised.
30 Min
Turnaround Time
Turnaround Time
Ksolves responds and resolves in under 30 minutes, keeping production running and teams unblocked.
End-to-End Apache Flink Support Services for Your Complete Stream Processing Lifecycle
Ksolves handles the full Apache Flink lifecycle from architecture and deployment to monitoring, security, and long-term operational support.
24/7 Flink Operations, Fully Managed
Your dedicated Flink ops team, available around the clock with SLA-backed response and zero distraction to your data team.
- Continuous cluster monitoring across YARN, Kubernetes, and standalone deployments
- Automated restart, failover handling, and checkpoint/savepoint management
- Exception classification, failure pattern detection, and restart alerting
- Monthly health review covering stability, backpressure trends, and capacity planning
- Architecture review hours for new pipelines, schema changes, and topology refactors
- Proactive upgrade readiness and breaking change advisory for each Flink release
Always-On Monitoring and Structured Diagnostics
Prometheus and Grafana are instrumented across every critical Flink signal, plus a one-time diagnostic report benchmarked against production best practices.
- Grafana dashboards covering checkpoint duration, backpressure, throughput, restart count, and task latency
- Per-operator backpressure detection using busyTimeMsPerSecond and idleTimeMsPerSecond metrics
- Kafka consumer lag monitoring with offset tracking and threshold-based alerting
- Alerts are routed to Slack, PagerDuty, or OpsGenie with linked runbooks
- Log aggregation via ELK or Grafana Loki for cross-job root cause analysis
- One-time diagnostic covering checkpoint audit, state backend fit, job graph review, and Kafka source configuration
- Delivered as a prioritized remediation report with effort estimates and expected impact per fix
Root-Cause Fixes, With Before-and-After Benchmarks
We fix Flink performance at the operator, state backend, network buffer, and Kafka source layers, not at the symptom layer.
- Operator-level backpressure analysis using Flink Web UI flame graphs and per-subtask metrics
- RocksDB tuning covering block cache, write buffer, and compaction thread pool
- Network buffer and channel capacity tuning for high-parallelism DataStream pipelines
- Key group rebalancing to eliminate data skew in keyed aggregation jobs
- Kafka partition alignment with Flink source parallelism for balanced consumer throughput
Architecture to Production Handover, Fully Documented
First Flink deployment or Spark migration, Ksolves delivers the full stack production-ready with runbooks included.
- Source-to-sink architecture design for Kafka, Kinesis, Pulsar, and JDBC sources
- Cluster installation on AWS (EMR, EKS), GCP (Dataproc, GKE), and Azure (AKS, HDInsight)
- Flink Kubernetes Operator deployment with autoscaling and lifecycle management
- DataStream API and Flink SQL development for CDC pipelines, aggregations, and CEP patterns
- Kafka-to-lakehouse pipelines connecting Flink to Apache Hudi, Iceberg, and Delta Lake
- CI/CD setup for JAR deployment and savepoint-based rolling upgrades
Zero Data Loss, Savepoint-First Every Time
Version upgrades, YARN to Kubernetes, or cross-cloud moves, all executed with full regression testing before cutover.
- Pre-upgrade audit covering API deprecations, operator UID validation, and state schema compatibility
- Savepoint-first workflow: savepoint, binary swap, restore, post-restore validation
- YARN or standalone to Flink Kubernetes Operator migration with autoscaling
- Cross-cloud migration with Kafka offset preservation and stateful job continuity
- Post-upgrade benchmarking, stability sign-off, and updated runbook documentation
Defense-in-Depth for GDPR, HIPAA, SOC 2
Authentication, encryption, access control, and audit logging for regulated Flink environments without impacting throughput.
- Kerberos authentication for Flink on YARN in Hadoop-secured environments
- SSL/TLS across all inter-process communication, REST API endpoints, and Web UI
- RBAC via reverse proxy with SSO and LDAP integration
- Secrets management with HashiCorp Vault and AWS Secrets Manager
- PII masking and tokenization within Flink operators before the downstream sink writes
- Audit logging for job submission, cancellation, savepoint triggers, and configuration changes
Through the Client's Lens
Why Ksolves Is a Trusted Choice of Global Teams for Apache Flink Support?
From troubleshooting checkpoint failures to redesigning entire pipeline architectures, Ksolves is your dedicated Apache Flink partner, combining SLA-backed support with hands-on production expertise.
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 Flink
Every industry runs Apache Flink differently, and Ksolves tailors support around its unique scale, latency, and operational demands.
Telecom
Ksolves manages real-time telecom stream processing environments, handling network telemetry ingestion, CDR event routing, and Flink cluster maintenance across distributed carrier infrastructure at scale.
Healthcare
With deep experience in HIPAA-compliant Flink deployments, we manage HL7 and FHIR event stream pipelines, patient data ingestion jobs, and audit-ready incremental processing across clinical data infrastructure.
E-Commerce
Having worked across e-commerce data ecosystems, we keep order state machines, inventory sync pipelines, and customer behaviour event streams in real-time operation across every fulfilment channel.
Fintech
Understanding what fintech stream processing demands, we manage Flink environments built for transaction event processing, fraud signal detection, and regulatory reporting pipelines where every record and every millisecond counts.
Entertainment
Working with entertainment platforms at scale, we support high-throughput Flink jobs for user engagement event aggregation, content metadata pipelines, and recommendation signal feeds that grow with audience demand.
Manufacturing
With hands-on manufacturing data experience, we connect shop floor IoT sensor streams and MES systems into time-windowed Flink pipelines with TTL-based state expiry via StateTtlConfig and efficient checkpoint management.
Retail
Understanding retail data complexity, we manage Flink pipelines connecting POS event streams, loyalty programme updates, and unified customer data feeds across physical and digital channels in real time.
Banking and Financial Services
As a compliance-aware Apache Flink support company, we support banking institutions with GDPR-capable pipelines, encrypted Flink deployments, and audit-ready stream processing for regulatory reporting across jurisdictions.
Logistics and Supply Chain
With proven logistics data experience, we manage Flink jobs covering shipment state events, warehouse telemetry streams, and carrier event ingestion with real-time windowed aggregation for operational dashboards.
Technology and SaaS
Working alongside technology companies, we support Flink pipelines routing multi-tenant product analytics, internal usage metrics, and billing event streams across cloud-native AWS and GCP infrastructure without disruption.
Ksolves: Insights from Enterprise Experts
Explore the latest real-time data processing trends, stream processing strategies, and expert insights for building scalable, reliable, and high-performance data environments.
Success Stories from Global Enterprises
Ksolves Big Data Experts have delivered excellence for multiple clients operating across industries. Explore the case studies and experience the Ksolves Impact.
Multi-Site CDR Pipeline for a Telecom Operator Across 4 Remote Locations
Challenge
CDR data from 4 remote sites had no unified ingestion- billing reconciliation was fully manual, causing revenue leakage as subscriber volumes grew.
Solution
NiFi agents at all 5 sites feed Kafka → Spark → Druid, with live Superset dashboards for billing and network teams.
Sub-second
Query Response on Live CDR Data
NiFi 1.27 → 2.7 Kubernetes Migration- Financial Services
Challenge
NiFi 1.27 is running on bare metal with no SSO, no scalability, and a growing compliance pipeline that the architecture couldn't support.
Solution
Migrated to NiFi 2.7 on Kubernetes with OneLogin SSO integration, zero downtime, completed in 6 weeks.
3X
Scalability Headroom - 6 Weeks, Zero Downtime
Eliminating ~900K Duplicate Oil Well Records via Azure Databricks
Challenge
The same wellbore appeared under 3–4 different IDs across 6,200 Excel files and 8 systems, causing royalty errors and a BLM audit risk.
Solution
Azure Databricks + PySpark deduplication with geospatial blocking and an ML model (F1=0.971), plus a human-in-the-loop MDM review portal.
~900K
Duplicate Records Eliminated
Petabyte CDR Migration from MapR to ClickHouse -Zero Data Loss
Challenge
Years of CDR data on an end-of-life MapR platform with no vendor support. Compliance queries took 4–6 hours, and regulators required signed proof of zero data loss.
Solution
Spark migrated data in resumable batches with 4 automated validation checks per batch. NiFi produced a signed migration certificate. ClickHouse was optimised for compliance queries from day one.
<8s
Compliance Query Time (from 4–6 hours)
AI-Ready Open Lakehouse on Red Hat OpenShift- Gulf Retailer
Challenge
SAP S/4HANA was too expensive. Cloud platforms unavailable across GCC. 16 TB of daily data needed sub-second processing, and Power BI reports couldn't be touched.
Solution
On-premises lakehouse on existing OpenShift: NiFi → Kafka → Flink → Iceberg on MinIO → Trino serving Power BI as a drop-in SAP BW replacement. Zero new hardware.
16 TB
Daily Data: Sub-Second SLA, Zero New Hardware
Multi-Site CDR Pipeline for a Telecom Operator Across 4 Remote Locations
Challenge
CDR data from 4 remote sites had no unified ingestion- billing reconciliation was fully manual, causing revenue leakage as subscriber volumes grew.
Solution
NiFi agents at all 5 sites feed Kafka → Spark → Druid, with live Superset dashboards for billing and network teams.
Sub-second
Query Response on Live CDR Data
NiFi 1.27 → 2.7 Kubernetes Migration- Financial Services
Challenge
NiFi 1.27 is running on bare metal with no SSO, no scalability, and a growing compliance pipeline that the architecture couldn't support.
Solution
Migrated to NiFi 2.7 on Kubernetes with OneLogin SSO integration, zero downtime, completed in 6 weeks.
3X
Scalability Headroom - 6 Weeks, Zero Downtime
Eliminating ~900K Duplicate Oil Well Records via Azure Databricks
Challenge
The same wellbore appeared under 3–4 different IDs across 6,200 Excel files and 8 systems, causing royalty errors and a BLM audit risk.
Solution
Azure Databricks + PySpark deduplication with geospatial blocking and an ML model (F1=0.971), plus a human-in-the-loop MDM review portal.
~900K
Duplicate Records Eliminated
Petabyte CDR Migration from MapR to ClickHouse -Zero Data Loss
Challenge
Years of CDR data on an end-of-life MapR platform with no vendor support. Compliance queries took 4–6 hours, and regulators required signed proof of zero data loss.
Solution
Spark migrated data in resumable batches with 4 automated validation checks per batch. NiFi produced a signed migration certificate. ClickHouse was optimised for compliance queries from day one.
<8s
Compliance Query Time (from 4–6 hours)
AI-Ready Open Lakehouse on Red Hat OpenShift- Gulf Retailer
Challenge
SAP S/4HANA was too expensive. Cloud platforms unavailable across GCC. 16 TB of daily data needed sub-second processing, and Power BI reports couldn't be touched.
Solution
On-premises lakehouse on existing OpenShift: NiFi → Kafka → Flink → Iceberg on MinIO → Trino serving Power BI as a drop-in SAP BW replacement. Zero new hardware.
16 TB
Daily Data: Sub-Second SLA, Zero New Hardware
Frequently Asked Questions
Everything you need to know before choosing an Apache Flink support partner.
Apache Flink managed services cover 24×7 job monitoring, cluster maintenance, checkpoint and savepoint management, state backend tuning, Kafka source lag alerting, version upgrades, and production incident response with full root cause analysis.
Checkpoint failures are most commonly caused by backpressured operators exceeding the checkpoint timeout, RocksDB write stalls from undersized block cache, network buffer exhaustion during barrier propagation, or state size exceeding available managed memory.
Ksolves identifies the bottleneck subtask using busyTimeMsPerSecond metrics and Flink Web UI flame graphs, then fixes the root cause — whether that is slow external I/O (Async I/O API), data skew (composite key repartitioning), or insufficient operator parallelism.
Yes. Flink upgrades are done by taking a savepoint from the running job, validating operator UIDs and state schema compatibility with the target version, swapping the cluster binaries, and restoring from the savepoint. Ksolves has managed Flink 1.x to Flink 2.0 upgrades with zero data loss.
Heap state backend stores state in JVM memory — fast but limited by available heap and prone to GC pressure at large state sizes. EmbeddedRocksDBStateBackend stores state on local disk with incremental checkpoint support — better for large state but adds serialization overhead per state access.
Flink handles late data using BoundedOutOfOrdernessWatermarks with a configured out-of-order tolerance, allowedLateness on window operators to hold the window open longer, and OutputTag-based side outputs to capture and separately process records that arrive after the window closes.
Apache Flink supports session mode and application mode on Kubernetes via the Apache Flink Kubernetes Operator. Per-job mode was deprecated in Flink 1.15 and removed in Flink 1.17 and is no longer available.
Flink is a true streaming engine processing one event at a time with millisecond latency. Spark Structured Streaming uses micro-batching with a latency in the seconds range. Flink offers more precise event time handling, richer state management via RocksDB, and native support for complex event processing via flink-cep.
High latency in Flink is typically caused by operator backpressure from a slow downstream sink, large RocksDB state access overhead, oversized checkpoint intervals delaying processing, data skew concentrating load on a single subtask, or insufficient network buffer allocation between operators.
Yes. Ksolves is an experienced Apache Flink consulting firm serving clients across North America with both US-business-hours-aligned coverage and global 24×7 follow-the-sun support with sub-15-minute critical incident SLAs.
