Data Engineering

• Apache Airflow Programming and Automation (28 hours delivered over 4-5 days, online)

  Build Apache Airflow 3 workflows in Python: the airflow.sdk authoring interface, asset-based scheduling, DAG versioning, cloud integration, and plugins.

• Apache Airflow Administration and Orchestration (14 hours delivered over 2-4 days, online)

  Administer and scale Apache Airflow 3: configure executors and auth managers, deploy on Kubernetes, upgrade from Airflow 2, and monitor with OpenTelemetry.

• Apache Airflow 3 for Developers: Kubernetes-Native (21 hours delivered over 3 days, online or on-site)

  Run Apache Airflow 3.2 on Kubernetes: the API Server and Task SDK, the Kubernetes Executor and KubernetesPodOperator, dynamic task mapping, and observability.

• Data Engineering: Ansible, Airflow, and Snowflake (28 hours delivered over 5-10 days, online)

  Automate data engineering end to end: provision with Ansible, orchestrate workflows with Apache Airflow, and load into Snowflake cloud data warehousing.

• Task Automation with Python (14 hours delivered over 2-3 days, online)

  Automate workflows with Python: file ops, subprocesses, logging, CLI args, async, and API tasks, plus optional AI and distributed automation.

• Distributed Task Automation with Python Celery and RabbitMQ (16 hours delivered over 2-4 days, online)

  Hands-on distributed task automation with Python, Celery, and RabbitMQ: set up Docker environments, define tasks, and scale workflows with routing and Beat.

• Distributed Task Automation with Python, Kafka, and Celery (14 hours delivered over 2 days, online or on-site)

  Build batch data pipelines with Apache Kafka 4.x (KRaft mode) and Celery: topics, consumer groups, exactly-once semantics, Redis-backed workers, and DLQs.

• Distributed Task Automation with Python Faust and Kafka (16 hours delivered over 2-4 days, online)

  Distributed task automation with Python Faust and Kafka: containerize environments, process streaming data, manage state and fault tolerance, and scale.

• Practical Apache Spark for Data Pipelines (21 hours delivered over 3-5 days, online)

  Build scalable data pipelines with Apache Spark 4 and Python: Spark Connect, Spark SQL, DataFrames, and real-time stream processing.

• Jupyter Widgets Programming (21 hours delivered over 3 days, online)

  Build custom Jupyter widgets with ipywidgets 8.x and AnyWidget: Python widget classes, ESM TypeScript front-ends, and embedding in JupyterLab and Notebook 7.

• Generative AI and LLMs for Python Programmers (35 hours delivered over 5 days, online)

  End-to-end generative AI for Python developers: frontier models, prompt and context engineering, structured outputs, tool use, RAG, MCP agents, and evals.