Expertise & Case Studies

We planned and executed major/minor Kubernetes upgrades without missing peak traffic windows. Using PDBs, surge upgrades, canary releases, and fully automated GitOps playbooks, the platform evolved in place while applications kept serving traffic.

• Planned maintenance downtime: 2h → 0m
• p95 error rate −60%
• Resource efficiency +18%

To handle growing data and long‑term retention, we deployed Ceph with cephadm, designed CRUSH maps by failure domains, and scaled S3 access with RGW. Rebalances and maintenance windows were automated to reduce operational toil.

• Rebalance time −40%
• S3 throughput +55%
• Data loss incidents: 0

Teams ship fast when security is built‑in. We integrated SAST/DAST, SBOM generation, and signed images behind CI quality gates, and enforced production consistency with OPA policy‑as‑code.

• Critical vuln MTTR: hours → seconds
• Image drift incidents −70%
• Secrets sprawl → 0

We generated realistic, geo‑distributed load using k6 and a managed proxy pool, surfaced bottlenecks across network/CPU/I/O layers, and tuned small details that unlocked big wins. Faster responses, higher RPS, same hardware.

• p95 latency: 900ms → 400ms
• Error rate: >3% → <1%
• RPS: +80% (no hardware change)

Redis clusters powering caches and counters needed both low latency and resilience. With Cluster + Sentinel, right persistence strategies, and client pooling, we achieved sub‑millisecond responses and predictable failovers.

• p99 latency: 2.3ms → 0.8ms
• Throughput +60%
• Failover < 3s

As event volume grew, consumer lag became critical. We redesigned partitioning, ISR, and rack awareness; added Schema Registry and MirrorMaker 2 for reliable replication. Streams calmed down once the flow was rebalanced.

• Ingest capacity: millions of msg/s
• Consumer‑lag incidents −75%
• Exactly‑once achieved

We built flexible routing across layers: HAProxy for advanced TCP distribution and NGINX for mTLS, SNI, and rate limiting. Canary and blue/green strategies reduced risk and made releases boring.

• Edge 5xx −50%
• Automatic rollback on canary failure
• Isolated multi‑tenancy with SNI/mTLS

Instead of scattered dashboards, we built one source of truth: Prometheus + Thanos for metrics, OpenTelemetry for traces, and Sentry for errors. SLOs became actionable, and alerts turned from noise into signals.

• MTTR: 45m → 12m
• Alert noise −60%
• Correlation time −45%

As read/write patterns diverged, a single cluster struggled. We introduced sharding, revisited indexing and schema design, and tuned app‑side connection pooling to hit throughput and latency goals.

• Bulk ingest +70%
• Query p95: 1.9s → 700ms
• Seamless upgrades (FCV plan)

Downtime was not an option. With Patroni coordination (Etcd/Consul), streaming replication, and pgBouncer, failovers became predictable and controlled. Planned switchovers and rollbacks turned into routine operations.

• Failover: < 10s
• Transaction errors −40%
• Risk‑free major upgrades (shadow plan)

We tackled latency and security at the edge with Argo smart routing, tiered caching, and precise WAF/bot rules. Release cutovers followed a clear runbook for zero‑downtime migrations.

• TTFB −35%
• Bot traffic −80%
• Zero‑downtime cutovers

With log volume rising, we balanced cost and speed using ILM tiers (hot/warm/cold), optimized ingest pipelines, and right‑sized shards. Searches got faster while storage costs dropped.

• Storage cost −40%+
• Query p95: 1.4s → 520ms
• Automated rolling upgrades