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What you will learn

• Describe parallel programming using Resilient Distributed Datasets (RDDs), DataFrames and SparkSQL. Understand how Catalyst and Tungsten benefit Spark programmer and see how ETL work using DataFrames.
• Learn about scaling out using the IBM Spark Environment in Watson Studio, running Spark on Kubernetes, setting Spark configurations, and performing monitoring and performance tuning.
• Apply your knowledge of the characteristics, features, benefits, limitations, and applications of the more popular Big Data processing tools, including Hadoop, HDFS, Hive and HBase.
• Acquire real-world data engineering and machine learning skills using Spark Structured Streaming, DataFrames, GraphFrames, Spark ML, Regression, Classification, and clustering, including the k-means algorithm and ETL using Spark.
• Gain hands-on experience using SparkSQL, Apache Spark on IBM Cloud.
• Differentiate between the four main categories of NoSQL repositories and work hands-on with MongoDB, Cassandra and IBM Cloudant.

Courses in this program