StreamSets Data Collector Training
StreamSets Data Collector Course Overview
High-performance deployments deserve high-caliber training. The fastest, most reliable way to build proven skills in StreamSets is via expert instructor-led hands-on classroom training in a structured learning environment.
This two-day hands-on training course provides a comprehensive introduction to StreamSets Data Collector. Participants will learn how to create complex pipelines that ingest data from a variety of sources, manipulate that data, and then export it to destinations including Apache Kafka, relational database management systems, and Apache Hadoop. Throughout the course, hands-on exercises reinforce the concepts being discussed.
Requirements
Students preferably should have a general knowledge of operating systems, networking, programming concepts, and databases.
Audience
The course is designed for those who will be designing, building, and running data flow pipelines, including data engineers, data developers, data analysts, data scientists, ETL developers, and data architects. No prior knowledge of StreamSets Data Collector is required.
Objectives
Introduction
Lab environment
Course Resources
Overview of the StreamSets
DataOps Platform
DataOps Platform Overview
StreamSets DataOps Architecture and Use Cases
Custom Examples
An Introduction to StreamSets Data Collector
Getting Started with Data Collector
SDC Overview
Building Pipelines
Previewing Data
Running the Pipeline
Pipeline Development
Connectors
Processors & Evaluators
Executors
Expression Language
Pipeline Events, Rules, and Alerts
Generating and Handling Events
Metric Rules
Data Rules
Reading, Writing and Transforming Data
Flat Files
Relational Databases: MySQL, Oracle, and Change Data Capture
Messaging Broker Systems: Kafka
Event Based: APIs
Distributed Storage: HDFS
Lookups: Relational Databases
Administration and Monitoring
Monitoring your SDC instances
Troubleshooting and Tuning
Troubleshooting errors and broken pipelines
Tuning configurations to speed up pipelines
Handling Data Drift
Data Drift Rules with MySQL