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What you would learn in Big Data Foundation for Developers course?
Apache Hadoop, Yarn, Hive, and Spark are popular big data tools that various organizations use to create Big Data Analytics solutions. Students can build big data applications with these tools to analyze data and extract essential insights from the data. At the end of the course, the students should be able to create a unique big data development environment, understand the basic concepts that comprise Hadoop, Yarn, Hive, and Spark to transfer data between and within the big data cluster, and process dataset using Map/Reduce model running Map/Reduce as well as Spark jobs using Yarn, Learn to process big data with the Scala programming language, which is available in Spark Utilize RDDs and data frames for processing big data, and use Parquet format to store data and finally, use the Machine Learning Libraries of Spark to create Machine Learning solutions like decision trees, recommendation engines, Linear Regression and Anomaly detection.
This is a hands-on development course, and you'll take part in more than 50 different activities in this class. While Java is required, the basics required for Scala are taught to ensure you can develop Scala software to manage data using Spark. The course provides the foundation for developers who want to join teams working on big data within their company.
Apache Hadoop, Hive, and Spark are viral tools for big data utilized by numerous organizations. Don't let your skills become obsolete.
Learn to master the most sought-after machine learning and extensive data abilities
Learn from 20 demonstrations plus more than fifty exercises that push you to the limits of what you learned in the class and become a prominent data expert.
You will use machine learning techniques with Spark to address business challenges such as forecasting, recommendation engines, and detection of anomalies.
After this course, you'll be able to set up the big data cluster, transfer data to it, and process it using big data tools.
Big data queries can be accessed by using Hive and process extensive data by using data frames Spark.
Data is stored in Parquet format to make use of predicate pulldowns. chain multiple chains multiple transformations of data like pivoting and windowing
Includes an introduction to Scala to use in conjunction with Spark
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