# DataServicesLabs

**Repository Path**: mirrors_cloudera/DataServicesLabs

## Basic Information

- **Project Name**: DataServicesLabs
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-25
- **Last Updated**: 2026-04-18

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

= End-to-End Labs
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== Overview
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)**  

== CDW Workshops

=== Description
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.  

== CDE Workshops

=== Description
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.  

== CDF Workshops

=== Description
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.  

== CAI Workshops

=== Description
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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[NOTE]
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**Copyright Notice**  
All material is Copyright (c) 2020-2025 Cloudera, Inc. unless stated otherwise.
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