Project Name
Cut Big Data Support Wait Times by 90% by Migrating a Bank Off Cloudera
Our client is a leading North American financial services institution managing large-scale data processing workloads across multiple business lines. With operations spanning global capital markets, asset management, and securities servicing, the organisation processes vast volumes of transactional and analytical data daily.
Their Big Data ecosystem underpinned risk calculations, compliance reporting, and client analytics, making pipeline availability a regulatory requirement, not just an operational preference.
A strategic decision was made to eliminate dependency on proprietary distributions and move toward a fully self-managed open-source data platform, driven by escalating vendor support gaps and the need to resolve critical issues in hours rather than weeks.
A financial institution whose data infrastructure depended on a vendor that took three weeks to answer a critical support ticket.
- Unacceptable Vendor Support SLAs: Cloudera support response times ranged from one to three weeks for critical issues, leaving production Hadoop clusters in degraded states with no actionable resolution path and no interim workaround available to the internal team.
- Proprietary Lock-In on Core Components: Cloudera Manager, proprietary security configurations, and bundled services created deep coupling, making it impossible to patch, upgrade, or troubleshoot individual components independently without vendor involvement.
- No In-House Diagnostic Capability: The engineering team lacked direct access to low-level cluster configuration and logs abstracted away by Cloudera's management layer, delaying root-cause analysis and extending every incident far beyond its technical complexity.
- Regulatory Pressure on Pipeline Uptime: As a regulated financial institution, extended pipeline downtime directly impacted compliance reporting windows and risked regulatory scrutiny, making vendor-dependent degradation an institutional liability, not just an operational inconvenience.
- Complex Hive Workload Dependencies: Hundreds of Hive queries, scheduled jobs, and downstream ETL pipelines were tightly coupled to Cloudera-specific configurations, making migration a high-risk exercise with zero tolerance for output divergence across compliance-critical workloads.
- Skill Gap in Open-Source Hadoop Administration: The team had operated exclusively within Cloudera's managed environment and lacked hands-on experience administering standalone Hadoop, HDFS, YARN, and Hive clusters, making knowledge transfer an essential deliverable alongside the technical migration.
Ksolves, an AI-first DevOps consulting company, designed and executed a phased migration strategy that decoupled every Cloudera-proprietary dependency and re-established the client's Big Data ecosystem on a fully open-source, self-managed Hadoop and Hive stack. The governing principle was clear: zero data loss, minimal workload disruption, and full operational ownership transferred to the client's engineering.
- Standalone Hadoop Cluster Provisioning: Deployed and configured a production-grade Hadoop cluster with HDFS, YARN, and MapReduce, replacing Cloudera's bundled distribution with community-supported Apache components tuned specifically for the client's financial data workload profiles and throughput requirements.
- Hive Migration and Workload Portability: Migrated all Hive metastore configurations, table definitions, UDFs, and scheduled queries from Cloudera Hive to standalone Apache Hive, validating output parity across every critical pipeline before any production traffic was cut over.
- Operational Tooling and Monitoring: Built custom monitoring dashboards and alerting pipelines to replace Cloudera Manager's oversight functions, giving the team direct visibility into HDFS health, YARN resource utilisation, and job execution metrics without proprietary tooling dependency.
- Security and Access Control Reconfiguration: Re-implemented authentication, authorisation, and audit controls using Kerberos and Apache Ranger to replace Cloudera's proprietary security layer, maintaining the institution's full regulatory security posture throughout and after migration.
- Knowledge Transfer and Runbook Development: Delivered comprehensive operational runbooks and conducted hands-on training sessions with the client's data engineering team, enabling fully autonomous cluster management, incident response, and capacity decisions post-migration with no ongoing external dependency.
Technology Stack
| Category | Technology |
|---|---|
| Platform | Apache Hadoop (HDFS + YARN) |
| Processing | Apache Hive |
| Processing | MapReduce |
| Infrastructure | Apache Ranger + Kerberos |
| Migration | Custom Migration Tooling |
From three-week vendor support queues and proprietary lock-in to full in-house resolution capability on open-source infrastructure, the team now owns completely.
- Vendor Support Resolution Time Reduced by 90%: In-house team now resolves issues within 1–2 business days instead of waiting 1–3 weeks for vendor responses.
- Zero Proprietary Dependencies Remaining: Entire ecosystem now runs on open-source Apache components with complete ownership and no vendor lock-in.
- Full Workload Migration With Zero Data Loss: All Hive queries, ETL jobs, and pipelines migrated successfully with validated output parity across critical workloads.
- Compliance Reporting Stability Restored: Self-managed clusters and direct monitoring eliminated vendor-related delays impacting regulatory reporting.
- Full Team Self-Sufficiency Achieved: Engineering team now independently manages the Hadoop and Hive stack using Ksolves-delivered runbooks and training.
Vendor lock-in at the data infrastructure layer is not a commercial inconvenience at a regulated financial institution; it is a compliance risk, an operational liability, and a direct constraint on the engineering team’s ability to do its job. This institution had all three, compounding with every support ticket that went unanswered for weeks. Ksolves eliminated the dependency entirely. The institution now runs a fully self-managed, open-source Hadoop and Hive ecosystem where critical issues that once sat in a vendor queue for three weeks are resolved internally in hours. Every workload migrated with zero data loss, every compliance pipeline runs on infrastructure the team fully owns, and the engineering capability to sustain and evolve the platform now sits inside the organisation, not with a vendor.
Is Your Big Data Platform Held Back by Vendor Lock-in and Unacceptable Support SLAs?
