StreamSets Data Collector Training

HANDS ON TRAINING

BUILD PROVEN SKILLS

2 DAY COURSE

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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
The SDC User Interface
Building Pipelines
Previewing Data
Running the Pipeline

 

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

Data Collector Security
Securing your Data Collector
Kerberos

Troubleshooting and Tuning your extremists environment
Identifying issues
Troubleshooting issues
Working with Support

Handling Data Drift
Data Drift Rules
Hive

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