This module introduces the foundational components of Kubernetes clusters, including control plane and worker nodes, and explores essential resources such as pods, deployments, services, StatefulSets, ConfigMaps, and Secrets. Learners will gain practical knowledge of how these elements interact to manage and expose applications, as well as how to handle configuration and external traffic using the Gateway API.

This module guides learners through deploying and managing Kubernetes clusters both locally and in the cloud using AWS EKS and Google Cloud GKE. Participants will gain practical experience containerizing applications, orchestrating deployments, and running APIs on Kubernetes platforms.

This module introduces learners to contemporary data architectures, focusing on the Lambda and Kappa patterns for real-time and batch processing. You will explore the evolution of the data lakehouse, compare architectural approaches, and examine technologies for batch and real-time data serving. By the end, you'll understand how modern data stacks enable scalable, flexible analytics.

This module introduces learners to the essentials of processing large datasets using Apache Spark. You will set up a local PySpark environment, perform data transformations with DataFrames, and utilize Spark SQL for scalable analytics. Practical exercises include working with real-world datasets and understanding key concepts like narrow versus wide transformations and join strategies.

This module guides learners through installing Apache Airflow with Docker, building and managing data pipelines using Directed Acyclic Graphs (DAGs), and integrating Airflow with external tools like PostgreSQL and Amazon S3. Learners will also explore different Airflow executors and gain hands-on experience orchestrating real-world data workflows.

This module introduces learners to the fundamentals of Apache Kafka, including its distributed architecture, data delivery semantics, and integration with real-time processing frameworks like Spark. Participants will gain hands-on experience setting up Kafka, connecting it to databases, and building real-time data pipelines for ingesting and processing streaming data.

This module guides learners through deploying essential big data tools—Spark, Airflow, and Kafka—on Kubernetes using operators and Helm charts. Participants will gain hands-on experience configuring these technologies for scalable data pipelines and managing their deployment in a cloud-native environment.

This module guides learners through deploying and configuring Trino and Elasticsearch on Kubernetes to enable efficient querying and real-time data analysis. Participants will gain hands-on experience with distributed SQL engines, data visualization using Kibana, and connecting tools like DBeaver for interactive exploration. The module also covers best practices for managing both structured and unstructured data in modern cloud environments.

This module guides learners through deploying and orchestrating big data tools on Kubernetes, integrating batch and real-time processing systems, and automating data pipelines using Python, SQL, and APIs. Learners will gain hands-on experience configuring Spark jobs, setting up AWS Glue crawlers, and connecting Kafka with Elasticsearch for scalable data workflows.

This module guides learners through deploying and managing generative AI applications on Kubernetes, utilizing tools like Amazon Bedrock, Streamlit, and Retrieval-Augmented Generation (RAG) systems. Participants will address challenges such as bias and limitations in AI models, build and deploy applications, and integrate databases like DynamoDB for scalable solutions.

This module guides learners through advanced operational considerations for running big data workloads on Kubernetes, including monitoring, security, CI/CD, and automated scalability. It also highlights the essential team skills and cost control strategies needed for successful production deployments. By the end, you'll be equipped to optimize and manage big data environments effectively.