Overview:

BDAW is a learning event that addresses advanced big data architecture topics. BDAW brings together technical contributors into a group setting to design and architect solutions to a challenging business problem. The workshop addresses big data architecture problems in general, and then applies them to the design of a challenging system.

Throughout the highly interactive workshop, participants apply concepts to real-world examples resulting in detailed synergistic discussions.The workshop is conducive for participants to learn techniques for architecting big data systems, not only from Cloudera’s experience but also from the experiences of fellow participants.

Delivery Method and Course Duration:

Classroom: 3 days

Intended Audience & Prerequisites:

To gain the most from the workshop, participants should have working knowledge of technologies such as HDFS, Spark, MapReduce, Hive/Impala, Data Formats and relational database management systems. Detailed API level knowledge is not needed, as there will not be any programming activities.

The workshop will be divided into small groups to discuss the problems and develop solutions. Each group will select a spokesperson who will present the group’s findings to the workshop. There will not be any programming labs, but we will have solutions implemented and deployed in the cloud for demos during the workshop

Course outlines:

1.      Introduction

2.      Workshop Application Use Cases

• Oz Metropolitan
• Architectural questions
• Team activity: Analyze Metroz Application Use Cases

3.      Application Vertical Slice

• Definition
• Minimizing risk of an unsound architecture
• Selecting a vertical slice
• Team activity: Identify an initial vertical slice for Metroz

4.      Application Processing

• Real time, near real time processing
• Batch processing
• Data access patterns
• Delivery and processing guarantees
• Machine Learning pipelines
• Team activity: identify delivery and processing patterns in Metroz, characterize response time requirements, identify Machine Learning pipelines

5.      Application Data

• Three V’s of Big Data
• Data Lifecycle
• Data Formats
• Transforming Data
• Team activity: Metroz Data Requirements

6.      Scalable Applications

• Scale up, scale out, scale to X
• Determining if an application will scale
• Poll: scalable airport terminal designs
• Hadoop and Spark Scalability
• Team activity: Scaling Metroz

7.      Fault Tolerant Distributed Systems

• Principles
• Transparency
• Hardware vs. Software redundancy
• Tolerating disasters
• Stateless functional fault tolerance
• Stateful fault tolerance
• Replication and group consistency
• Fault tolerance in Spark and Map Reduce
• Application tolerance for failures
• Team activity: Identify Metroz component failures and requirements

8.      Security and Privacy

• Principles
• Privacy
• Threats
• Technologies
• Team activity: identify threats and security mechanisms in Metroz

9.      Deployment

• Cluster sizing and evolution
• On-premise vs. Cloud
• Edge computing
• Team activity: select deployment for Metroz

10.     Technology Selection

• HDFS
• HBase
• Kudu
• Relational Database Management Systems
• Map Reduce
• Spark, including streaming, SparkSQL and SparkML
• Hive
• Impala
• Cloudera Search
• Data Sets and Formats
• Team activity: technologies relevant to Metroz

11.     Software Architecture

• Architecture artifacts
• One platform or multiple, lambda architecture
• Team activity: produce high level architecture, selected technologies, revisit vertical slice
• Vertical Slice demonstration

12.     Wrap Up