The DataServicesLabs folder provides comprehensive, real-world examples of how to integrate multiple Cloudera Data Services into unified workflows.
These labs are designed to simulate realistic enterprise scenarios, showcasing the end-to-end journey of data: - Ingestion - Transformation - Analysis - Visualization
The goal is to help practitioners understand not only individual service usage, but also how these services interoperate seamlessly in production-like pipelines.
Currently, this folder contains multiple workshops for the following Cloudera Data Services: * Cloudera Data Warehouse (CDW) * Cloudera Data Engineering (CDE) * Cloudera DataFlow (CDF) * Cloudera AI (CAI)
The Cloudera Data Warehouse (CDW) Workshops demonstrate how to run scalable, secure, and high-performance analytics on enterprise datasets.
Key learning outcomes include: - Creating and managing virtual warehouses for analytical workloads. - Running complex SQL queries on large-scale datasets. - Integrating CDW with BI/Visualization tools. - Leveraging auto-scaling and cost-optimization features.
The Cloudera Data Engineering (CDE) Workshops focus on building and orchestrating data pipelines at scale.
Key learning outcomes include: - Authoring and scheduling Spark jobs in a production environment. - Building repeatable data engineering pipelines. - Using Airflow for workflow orchestration with CDE. - Ensuring pipeline reliability and resource optimization.
The Cloudera DataFlow (CDF) Workshops provide hands-on experience with real-time data ingestion and processing.
Key learning outcomes include: - Building streaming pipelines with Apache NiFi. - Connecting to multiple sources such as Kafka, cloud storage, and databases. - Performing data transformation and routing in real time. - Managing scalability and monitoring data flows.
The Cloudera AI (CAI) Workshops provide hands-on exercises for building and deploying AI/ML solutions on the Cloudera platform.
Key learning outcomes include: - Developing ML models using Python and popular frameworks. - Managing ML lifecycle from training to deployment. - Integrating CAI with CDW, CDE, and CDF for end-to-end workflows. - Leveraging secure and governed environments for enterprise AI.
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