Cloud Platforms
Microsoft Azure
- Core services: Azure VMs, AKS, App Service, Azure Functions, Blob Storage, Cosmos DB
- Networking: VNet, NSG, Private Endpoints, Azure Front Door, Application Gateway
- Azure DevOps: Pipelines (YAML), Boards, Artifacts, repo management, environments
- IAM: Entra ID (AAD), Managed Identities, RBAC, Conditional Access
- Cost management, reserved instances, Azure Advisor
Amazon AWS
- Core services: EC2, ECS/EKS, Lambda, S3, RDS, DynamoDB, SQS/SNS
- AWS Lambda & Serverless: Event sources, cold start optimization, Lambda Layers, Step Functions
- Networking: VPC, subnets, security groups, Transit Gateway, Route 53
- IAM: Policies, roles, STS, permission boundaries, Organizations SCPs
- AWS CloudWatch: log insights, metric alarms, composite alarms, dashboards
Google Cloud Platform (GCP)
- Core services: GKE, Cloud Run, Cloud Functions, BigQuery, Cloud Storage, Pub/Sub
- GKE Autopilot vs Standard — operational trade-offs
- Cloud IAM: service accounts, Workload Identity Federation, org policies
- Cloud Build, Artifact Registry, Cloud Deploy for CI/CD pipelines
FinOps & Cloud Cost Management
- FinOps framework: Inform, Optimize, Operate phases
- Rightsizing compute, spot/preemptible instances, committed use discounts
- Cost allocation with tags and labels across teams/projects
- Tooling: AWS Cost Explorer, Azure Cost Management, GCP Billing, Infracost in CI
SAP Commerce Cloud
- SAP Commerce (Hybris) architecture: platform, extensions, backoffice, storefront
- OCC API, SmartEdit, CMS components
- SAP Commerce Cloud (CCv2): deployment pipeline, media storage, environment management
- Integration with Azure AD, S/4HANA, third-party payment/logistics systems
Observability & Monitoring
Prometheus, Grafana & Alertmanager
- Prometheus: Metrics model, PromQL, scrape configuration, recording rules, federation
- Grafana: Dashboard design, templating, alerting rules, annotations, unified alerting
- Alertmanager: Routing trees, silences, inhibitions, Slack/PagerDuty integration
- kube-prometheus-stack deployment and customization
OpenTelemetry & Distributed Tracing
- OTel SDK setup for Java, Node.js, Python — auto-instrumentation and manual spans
- Context propagation across HTTP, gRPC, and Kafka message headers
- OTel Collector: pipeline configuration, tail-based sampling, multi-backend export
- Backend integration: Grafana Tempo, Jaeger, Honeycomb, Datadog
Elasticsearch, Kibana & ELK / OpenSearch
- Elasticsearch: Index design, mapping, analyzers, query DSL, aggregations
- Logstash: Input/filter/output pipeline, Grok patterns, Elasticsearch bulk indexing
- Kibana: Discover, Lens dashboards, KQL, alerting, Canvas
- OpenSearch: Fork differences, OpenSearch Dashboards, security plugin
- Log pipeline design: structured JSON logging → Filebeat → Elasticsearch
Jaeger, Dynatrace / Datadog & AWS CloudWatch
- Jaeger: All-in-one vs distributed deployment, storage backends (Cassandra, Elasticsearch), sampling strategies
- Dynatrace: OneAgent, Davis AI, PurePath tracing, synthetic monitoring, DQL
- Datadog: Agent setup, APM, log management, dashboards, monitors, DDSQL
- CloudWatch: Log Insights queries, EMF metrics, Container Insights, Lambda monitoring
Keycloak (IAM / SSO)
- Realm configuration, client setup, user federation (LDAP/AD)
- OAuth2 flows: authorization code, client credentials, device flow
- Custom themes, event listeners, custom authenticators
- High-availability deployment on Kubernetes with Infinispan cache
Big Data & Business Intelligence
Apache Spark
- Spark fundamentals: RDD, DataFrame, Dataset — when to use which
- Spark SQL, query planning, catalyst optimizer, physical plans
- Streaming with Spark Structured Streaming: sources, sinks, watermarks
- Deployment: local, YARN, Kubernetes, Databricks
- Performance tuning: partitioning, caching, broadcast joins, shuffle optimization
Apache Iceberg & Apache Flink
- Iceberg: Table format internals, schema evolution, time travel, partition pruning
- Iceberg with Spark, Flink, and Trino — multi-engine data lake architecture
- Apache Flink: DataStream vs Table API, event time processing, checkpointing, exactly-once
- Flink SQL: CDC sources, Kafka integration, Iceberg sink
Big Data Engineering
- Data lake vs data warehouse vs lakehouse — architectural comparison
- Medallion architecture (bronze/silver/gold layers) with Spark + Iceberg
- Data quality: Great Expectations, Deequ, schema validation
- Orchestration: Apache Airflow, Dagster, Prefect — scheduling and lineage
JasperReports & Realtime BI
- JasperReports: Report design (Jaspersoft Studio), JRXML, subreports, charts, export formats (PDF, Excel, HTML)
- JasperReports Server: scheduling, permissions, REST API, embedded reports
- Realtime BI: Push-based dashboards with WebSocket/SSE, Grafana live streaming, Kibana real-time dashboards
- Combining Kafka streaming data with BI tools for live operational dashboards
Popular combination
Cloud + Observability is the most requested multi-topic training path: AWS/Azure fundamentals (Day 1–2) → Prometheus + Grafana (Day 3) → OpenTelemetry distributed tracing (Day 4). Gives teams full-stack visibility from infrastructure to application in four days.
Training Formats
Duration
Cloud platform modules: 2–3 days. Observability stack: 1–2 days. Big data path: 3–5 days.
Delivery
On-site or remote. Cloud labs run in your existing AWS/Azure/GCP account with dedicated sandboxed resources.
Certifications
Training can be structured to support AZ-900, AZ-104, AWS SAA, or GCP ACE preparation alongside practical work.
Language
German or English. Materials and lab guides available in both languages.
Book a Cloud or BI Training
From a one-day Grafana workshop to a week-long data engineering bootcamp — tell me what your team needs and I'll propose a focused curriculum that fits your timeline and stack.
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