Hadoop Intermediate
Self-Paced
Learning Style
Course
Learning Style
Intermediate
Difficulty
5 Hours
Course Duration
Course Info
Certificate
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9:00 AM - 5:00 PM CST
Duration - 0 Days -
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The course covers the concepts to process and analyze large sets of data stored in HDFS. It teaches Sqoop and Flume for data ingestion.
Course Information
About this course:
Hadoop Intermediate training course is designed to give you in-depth knowledge about the Hadoop framework discussed in our Hadoop and MapReduce Fundamentals course. The course covers the concepts to process and analyze large sets of data stored in HDFS. It teaches Sqoop and Flume for data ingestion.
Course Objective:
- Get a basic understanding of the different components of Hadoop ecosystem
- Understand and work with Hadoop Distributed File System (HDFS)
- Ingest data using Sqoop and Flume
- Use HBase, understand its architecture and data storage
- Get essential knowledge of Pig and its components
- Master resilient distribution datasets (RDD) in detail
- Understand the common use cases of Spark and various interactive algorithms
Audience:
- Hadoop is becoming an essential tool in the ever-growing Big-Data architecture. This training is designed to benefit:
- Software developers and architects working in Big-Data organizations
- Business and technical analytics professionals
- Senior IT professionals
- Data management professionals
- Project managers
- Data scientists
Prerequisite:
- There are no formal prerequisites for learning this course.
- However, the candidates are strongly advised to opt for Hadoop: Fundamentals course before undertaking this course.
- In addition to this, functional knowledge of Core Java and SQL will be beneficial.
Outline
More Information
| Subjects | Big Data |
|---|---|
| Lab Access | No |
| Technology | Hadoop |
| Learning Style | Self-Paced Learning |
| Learning Type | Course |
| Difficulty | Intermediate |
| Course Duration | 5 Hours |
| Language | English |
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Course Expert:
Tom Robertson
(Data Science Enthusiast)
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.
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