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Developing with Spark for Big Data (TTSK7505)

73 Lessons
40 hours
Intermediate

Course Description Learn advanced Big Data and Spark skills to …

What you'll learn
The essentials of Spark architecture and applications
How to execute Spark Programs
How to create and manipulate both RDDs (Resilient Distributed Datasets) and UDFs (Unified Data Frames)
How to persist and restore data frames
Essential NOSQL access
How to integrate machine learning into Spark applications
How to use Spark Streaming and Kafka to create streaming applications

Working with Apache Kafka

32 Lessons
16 hours
Intermediate

Course Description Learn to use Kafka as your real-time data …

What you'll learn
Overview of Streaming technologies
Kafka concepts and architecture
Programming using Kafka API
Kafka Streams
Monitoring Kafka
Tuning/Troubleshooting Kafka

Hadoop Developer Foundations

103 Lessons
32 hours
Intermediate

Course Description New – Learn about the Hadoop ecosystem and …

What you'll learn
Introduction to Hadoop
HDFS
YARN
Data Ingestion
HBase
Oozie
Working with Hive
Hive advanced
Hive in Cloudera/Hortonworks Distribution (or tools of choice)
Working with Spark
Spark Basics
Spark Shell
RDDs
Spark Dataframes and Datasets
Spark SQL
Spark API programming
Spark and Hadoop
Machine Learning (ML/MLlib)
GraphX
Spark Streaming

Introduction to Apache Spark Essentials (TTSK7502)

30 Lessons
16 hours
Intermediate

Course Description Learn the essentials of using Spark for your …

What you'll learn
The essentials of Spark architecture and applications
How to execute Spark Programs
How to create and manipulate both RDDs (Resilient Distributed Datasets) and UDFs (Unified Data Frames)
How Spark core components come together for complete applications

Introduction to Hadoop Administration (TTDS6503)

40 Lessons
24 hours
Intermediate

Course Description Learn how to install, maintain, monitor, troubleshoot, optimize, …

What you'll learn
Understand the benefits of distributed computing
Understand the Hadoop architecture (including HDFS and MapReduce)
Define administrator participation in Big Data projects
Plan, implement, and maintain Hadoop clusters
Deploy and maintain additional Big Data tools (Pig, Hive, Flume, etc.)
Plan, deploy and maintain HBase on a Hadoop cluster
Monitor and maintain hundreds of servers
Pinpoint performance bottlenecks and fix them

Big Data Fundamentals

57 Lessons
16 hours
Intermediate

Learn the benefits of big data and the underlying technologies, …

What you'll learn
Navigate the technology stacks and tools used to work with big data
Establish a common vocabulary on your teams for applying big data practices
Get an overview of how big data technologies work: Apache Hadoop, Spark, Pig, Hive, Sqoop, OOZIE, and FLUME
Design both functional and non-functional requirements for working with big data
Understand common business cases for big data
Differentiate between hype and what’s truly possible
Look at examples of real-world big data use cases
Select initiatives and projects that have high potential to benefit from big data applications
Understand what type of staffing, technical skills, and training is required for projects that incorporate or focus on big data

Implementing Big Data & AI for Business Professionals (TTML5501)

8 hours
Intermediate

AI in Business Seminar Series: Explore How AI & Machine …

What you'll learn
Learn which data is most useful to collect now and why it’s important to start collecting that data as soon as possible
Understand the intersection between big data, data science and AI (Machine Learning / Deep Learning) and how they can help you reach your business goals and gain a competitive advantage.
Understand the factors that go into choosing a Data Science system, including whether to go with a cloud-based solution
Explore common tools and technologies to aid in making informed decisions
Gain the skills required to build your DS/ AI team

Machine Learning Essentials with Python (TTML5506-P)

43 Lessons
24 hours
Intermediate

Learn how Artificial Intelligence is being applied in modern business. …

What you'll learn
Popular machine learning algorithms, their applicability and limitations
Practical application of these methods in a machine learning environment
Practical algorithm use cases and limitations
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