GitOps deployment review — ArgoCD Application/AppProject CRDs, app-of-apps pattern, sync policies (automated+self-heal+prune), drift detection and remediation, FluxCD HelmRelease/Kustomization, git repository structure for GitOps (app code vs config repo separation), image update automation, progressive delivery (Argo Rollouts canary/blue-green), GitOps for data platform components (Airflow/Kafka/Spark)
日本語の概要は準備中です。原文の説明を表示しています。
Grafana dashboard review — panel types (timeseries/stat/gauge/table/heatmap), variable templating (datasource/label_values/query), dashboard linking and drilldown, data platform dashboards (pipeline overview/DAG health/Kafka throughput/Spark performance), alerting from panels, annotation markers for deployments, dashboard-as-code (Grafonnet/Terraform grafana provider), performance optimization (query caching/recording rules), dark/light theme standards
日本語の概要は準備中です。原文の説明を表示しています。
Kafka production platform review — broker configuration (replication factor/min.insync.replicas/rack awareness/KRaft mode), topic design (partition count formula/compaction/retention), consumer group management (lag monitoring/rebalance tuning/cooperative sticky), producer tuning (acks/idempotence/compression/batching), security (SASL_SSL/ACLs/mTLS), JMX metrics to Prometheus (kafka-exporter/JMX exporter), alert rules (under-replicated partitions/ISR shrink/consumer lag/disk), capacity planning, Strimzi Kubernetes operator
日本語の概要は準備中です。原文の説明を表示しています。
Kubernetes cost optimization — idle workload detection (zero-replica deployments, always-pending jobs), overprovisioning analysis (request vs actual CPU/memory), namespace-level cost attribution, node bin-packing via requests tuning, spot instance strategy, LimitRange defaults, right-sizing with VPA recommendations, Goldilocks tool, resource efficiency metrics, namespace resource quotas for chargeback
日本語の概要は準備中です。原文の説明を表示しています。
Kubernetes security audit — RBAC review (ClusterRoleBindings/RoleBindings/ServiceAccount permissions), Pod Security Standards (privileged/hostPID/hostNetwork/runAsRoot), NetworkPolicy enforcement, Secret management (encryption at rest, external-secrets), image vulnerability scanning (Trivy/Grype), admission controllers (OPA Gatekeeper/Kyverno), audit logging, CIS Kubernetes Benchmark, supply chain security (SBOM/image signing)
日本語の概要は準備中です。原文の説明を表示しています。
Kubernetes storage review — PVC lifecycle (provisioning/binding/reclaim), StorageClass selection (SSD/HDD/NVMe), dynamic vs static provisioning, StatefulSet volume management, volume performance tuning (ReadWriteMany vs ReadWriteOnce), CSI drivers (AWS EBS/GCP PD/Ceph/Longhorn), PVC resize, snapshot and backup, storage class access modes, orphaned PVs detection, shared storage patterns (NFS/CephFS)
日本語の概要は準備中です。原文の説明を表示しています。
OpenTelemetry instrumentation for data platforms — Python auto-instrumentation (opentelemetry-bootstrap), manual span creation for ETL tasks, trace context propagation across Airflow→Spark→dbt, OTLP exporter configuration, W3C TraceContext headers in Kafka messages, resource attributes (service.name/environment/version), Baggage for partition metadata propagation, Collector pipeline (tail sampling), correlation between traces/logs/metrics, Grafana Tempo integration
日本語の概要は準備中です。原文の説明を表示しています。
Prometheus optimization — recording rules (level:metric:operations naming), cardinality explosion diagnosis (tsdb status API), scrape interval tuning, remote write configuration (batch size/queue capacity), Thanos/Mimir for long-term retention, federation vs remote write, AlertManager routing trees (inhibition/silencing), absent() alerts for missing series, relabeling to drop expensive labels, TSDB compaction, ServiceMonitor vs PodMonitor patterns
日本語の概要は準備中です。原文の説明を表示しています。
Secrets management review — HashiCorp Vault (KV v2/dynamic credentials/lease renewal/audit log), External Secrets Operator (Vault/AWS SSM/GCP Secret Manager → K8s secrets), secret rotation strategies (database dynamic credentials/TLS cert-manager), detecting secrets in code (gitleaks/truffleHog/Semgrep), Airflow connections from Vault backend, dbt profiles without hardcoded credentials, CI/CD secrets (GitHub OIDC/GitLab CI variables), no plaintext secrets in logs/configs
日本語の概要は準備中です。原文の説明を表示しています。
Streaming pipeline reliability — exactly-once semantics (idempotent producer/transactional API/read_committed/Kafka Streams EOS), at-least-once patterns with idempotent consumers, dead letter queue (DLQ) design with Kafka Connect SMT/custom error handler, poison message handling, reprocessing strategy, watermarks and late data handling, checkpoint/savepoint for Flink/Spark Streaming, consumer group rebalance storm prevention, end-to-end latency SLO monitoring, streaming reliability checklist
日本語の概要は準備中です。原文の説明を表示しています。
Terraform security scanning — tfsec static analysis (AWS/GCP/Azure misconfigurations), Checkov IaC policy checks (750+ rules, CIS benchmarks), tflint security rules, KICS scanner, S3 encryption/public access/versioning checks, IAM least-privilege patterns, security group rule review (0.0.0.0/0), KMS key rotation, VPC flow logs, CloudTrail enabled, pre-commit hooks, SARIF output for GitHub Security tab, policy-as-code with OPA/Sentinel
日本語の概要は準備中です。原文の説明を表示しています。
Agentic control plane for data platform — MCP (Model Context Protocol) server exposing platform tools to AI agents (FastMCP/Python SDK), natural language platform operations (trigger DAG/create topic/run dbt/query Trino via LLM), Claude-based platform assistant with tool use, multi-agent platform governance (audit agent/cost agent/reliability agent), MCP server deployment (Docker/Kubernetes), tool authorization and audit logging, agentic workflow patterns (plan-execute-verify), platform chat interface
日本語の概要は準備中です。原文の説明を表示しています。
AIOps autonomous query cost analyzer — Trino system.runtime.query_history cost SQL (CPU time/peak memory/bytes scanned), ClickHouse system.query_log top expensive queries, Spark UI cost attribution by user/job/DAG, BigQuery INFORMATION_SCHEMA.JOBS cost analysis, LLM-driven query rewrite recommendations (missing partition filters/broadcast hints/aggregation pushdown), cost anomaly detection (user over daily budget), automated query tagging, chargeback report by team/project
日本語の概要は準備中です。原文の説明を表示しています。
DataOps blameless postmortem generation — severity matrix (SEV1-4), postmortem Markdown template with timeline/impact/root cause/action items, 3 filled-in examples (Kafka rebalance/dbt incremental data loss/ClickHouse schema mutation), CAPA framework (Corrective/Action/Preventive/Action), impact quantification SQL, stakeholder communication templates, 60-min facilitation guide, postmortem review checklist, Git-based postmortem repository management
日本語の概要は準備中です。原文の説明を表示しています。
Workflow orchestration comparison and review — Airflow vs Prefect vs Dagster vs Temporal (architecture, execution model, dependency management, observability), when to choose each, migration patterns from Airflow to Prefect/Dagster, orchestrator evaluation scorecard, event-driven vs schedule-based triggering, Software-Defined Assets (Dagster) vs task-based (Airflow), hybrid orchestration patterns
日本語の概要は準備中です。原文の説明を表示しています。
Azure data platform review — ADLS Gen2 (hierarchical namespace/RBAC/lifecycle), Azure Synapse vs Databricks vs HDInsight trade-offs, Event Hubs (Kafka protocol compatible), ADF vs Azure Databricks for ETL, Azure Managed Airflow, AKS with Workload Identity (AAD Pod Identity), Key Vault for secrets, Azure Monitor/Log Analytics, Purview for data catalog/lineage, Private Endpoints for data services, cost management (reserved instances/spot VMs)
日本語の概要は準備中です。原文の説明を表示しています。
Kafka cost optimization — tiered storage (S3/GCS remote log offload, local.retention.ms vs retention.ms), topic retention right-sizing (per-topic audit SQL), log compaction economics, partition count right-sizing (over-partitioned cluster detection), consumer group idle cleanup, broker instance right-sizing (disk vs compute), Redpanda vs Confluent Cloud cost comparison, MirrorMaker2 cross-region cost, compression savings calculator
日本語の概要は準備中です。原文の説明を表示しています。
Kubernetes autoscaling review — HPA (CPU/memory/custom/external metrics), stabilizationWindowSeconds anti-flapping, VPA vs HPA decision, Cluster Autoscaler tuning (scale-down delay/utilization threshold), KEDA event-driven autoscaling (Kafka lag/queue depth), spot node optimization, pod disruption budgets, scaling behavior policies (Percent/Pods/Max/Min selectPolicy)
日本語の概要は準備中です。原文の説明を表示しています。
Kubernetes cluster health assessment — node status and pressure conditions (disk/memory/PID), pod failure diagnosis (CrashLoopBackOff/OOMKilled/Pending/Evicted), control plane health (API server/etcd/scheduler/controller-manager), resource quota utilization, scheduling failures (taints/affinity/insufficient resources), kubectl diagnostic commands, node eviction policies, kubelet troubleshooting
日本語の概要は準備中です。原文の説明を表示しています。
AIOps observability copilot — natural language to PromQL/LogQL translation, alert explanation in plain English (what fired/why/impact), Grafana dashboard auto-generation from service topology, anomaly narrative generation from metric patterns, on-call context enrichment (related alerts/recent deploys/runbook links), log pattern clustering for noise reduction, SLO status explanation, automated weekly observability health digest
日本語の概要は準備中です。原文の説明を表示しています。
Airflow cost optimization — KubernetesPodOperator right-sizing (request vs actual CPU/memory), spot node tolerations for batch tasks, task consolidation (reduce pod-per-task overhead), pool-based concurrency control, idle worker cleanup, log retention policy (S3 lifecycle), metadata DB right-sizing, CeleryExecutor worker autoscaling (KEDA Kafka/queue depth), DAG run frequency analysis (oversceduled DAGs), cloud cost attribution per DAG
日本語の概要は準備中です。原文の説明を表示しています。
DataOps CI/CD pipeline review — pipeline stages for data projects (lint/test/build/scan/deploy), trunk-based vs GitFlow branching, environment promotion (dev→staging→prod), artifact versioning (Docker image tags/dbt manifest), deployment gating (DQ checks/smoke tests/manual approval), rollback strategy, pipeline observability (DORA metrics), reusable workflow patterns, monorepo vs polyrepo CI strategy
日本語の概要は準備中です。原文の説明を表示しています。
GitHub Actions optimization for DataOps — path filters to skip unchanged components, concurrency groups (cancel-in-progress), job parallelization, caching strategies (pip/npm/Maven/Docker layers), self-hosted runners for heavy workloads, matrix strategy for multi-environment tests, reusable workflows to reduce duplication, OIDC for cloud credentials (no static keys), artifact retention, workflow timing analysis
日本語の概要は準備中です。原文の説明を表示しています。
Jenkins modernization for DataOps — migration from Freestyle to Declarative Pipeline, Jenkinsfile best practices, shared libraries (vars/src structure), parallel stages, Kubernetes agent pods (docker-in-docker vs kaniko), credential management (withCredentials/Jenkins Credential Store), pipeline-as-code with Multibranch Pipeline, Blue Ocean UI, migration path to GitHub Actions, JCasC (Jenkins Configuration as Code)
日本語の概要は準備中です。原文の説明を表示しています。