Big Data Analytics with Hadoop and Apache Spark
Learn how to leverage the power of Big Data analytics with Apache Hadoop and Apache Spark. This course provides an overview of the tools and techniques needed to build scalable and optimized data pipelines. ▼
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Course Feature
Cost:
Free Trial
Provider:
LinkedIn Learning
Certificate:
No Information
Language:
English
Start Date:
Self Paced
Course Overview
❗The content presented here is sourced directly from LinkedIn Learning platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
This course, Big Data Analytics with Hadoop and Apache Spark, provides an overview of how to leverage these two technologies to build scalable and optimized data analytics pipelines. Instructor Kumaran Ponnambalam will explore ways to optimize data modeling and storage on HDFS, discuss scalable data ingestion and extraction using Spark, and provide tips for optimizing data processing in Spark. Participants will gain an understanding of the fundamentals of big data analytics and how to use Hadoop and Apache Spark to build data pipelines.
[Applications]
The application of this course can be seen in various industries such as finance, healthcare, retail, and more. After completing this course, learners can use the knowledge gained to build scalable and optimized data analytics pipelines using Hadoop and Apache Spark. They can also use the techniques learned to optimize data modeling and storage on HDFS, as well as data ingestion and extraction using Spark. Additionally, learners can use the tips provided to optimize data processing in Spark.
[Career Paths]
1. Big Data Engineer: Big Data Engineers are responsible for designing, developing, and maintaining large-scale data processing systems. They use technologies such as Hadoop and Apache Spark to build data pipelines and optimize data storage and retrieval. As the demand for data-driven insights continues to grow, Big Data Engineers will be in high demand.
2. Data Scientist: Data Scientists use advanced analytics techniques to uncover insights from large datasets. They use technologies such as Hadoop and Apache Spark to process and analyze data, and then use their findings to inform business decisions. As the need for data-driven insights continues to grow, Data Scientists will be in high demand.
3. Data Analyst: Data Analysts use data to identify trends and patterns in order to inform business decisions. They use technologies such as Hadoop and Apache Spark to process and analyze data, and then use their findings to inform business decisions. As the need for data-driven insights continues to grow, Data Analysts will be in high demand.
4. Machine Learning Engineer: Machine Learning Engineers use machine learning algorithms to build predictive models from large datasets. They use technologies such as Hadoop and Apache Spark to process and analyze data, and then use their findings to inform business decisions. As the need for data-driven insights continues to grow, Machine Learning Engineers will be in high demand.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree program provides students with a comprehensive understanding of computer science fundamentals, including programming, data structures, algorithms, operating systems, and computer architecture. Students will also learn about the latest technologies and trends in the field, such as big data analytics, machine learning, and artificial intelligence.
2. Master of Science in Data Science: This degree program focuses on the application of data science principles and techniques to solve real-world problems. Students will learn about data mining, machine learning, predictive analytics, and other data-driven methods. They will also gain experience in working with big data technologies such as Hadoop and Apache Spark.
3. Master of Science in Business Analytics: This degree program focuses on the application of data analytics to business problems. Students will learn about data-driven decision making, predictive analytics, and other data-driven methods. They will also gain experience in working with big data technologies such as Hadoop and Apache Spark.
4. Doctor of Philosophy in Data Science: This degree program focuses on the development of advanced data science techniques and methods. Students will learn about data mining, machine learning, predictive analytics, and other data-driven methods. They will also gain experience in working with big data technologies such as Hadoop and Apache Spark. Additionally, they will develop the skills to design and implement data-driven solutions to complex problems.
Course Provider
Provider LinkedIn Learning's Stats at AZClass
Big Data Analytics with Hadoop and Apache Spark is a comprehensive course that teaches learners how to leverage these two technologies to build scalable and optimized data analytics pipelines. Learners will understand the fundamentals of big data, including data modeling and storage on HDFS, scalable data ingestion and extraction using Spark, and tips for optimizing data processing in Spark. The course also covers data science, data mining, machine learning and data visualization techniques. Learners will have the skills and knowledge to build and deploy their own big data analytics pipelines.
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Quiz
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1. What is HDFS?
2. What is Apache Spark?
3. What is the purpose of data ingestion?
4. Which of the following technologies is not covered in this course?
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