A comprehensive course on Spark SQL as well as Data Frame APIs using Scala with complementary lab access
InstructorDurga Viswanatha Raju GadirajuThe curriculum covers both Spark SQL and Dataframe APIs, including essential operations like transformations, joins, and advanced windowing functions.
Instruction includes setting up single-node Hadoop clusters and cloud-based environments using AWS Cloud9 and GCP Ubuntu VMs.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
This webpage displays the curriculum and prerequisites for a Big Data certification course, highlighting technical requirements like Cloudera VM access.
This interface displays the structured course modules, including sections for Core Spark APIs, Data Analysis using SQL, and real-time data ingestion.
This interface displays the official registration form required to request hands-on lab access, allowing students to input their enrollment details and select their specific course platform.
The course dashboard displays the title CCA 175 - Spark and Hadoop Developer with a progress indicator showing that 4 out of 171 items are currently complete.
Initial cluster and environment configuration can be difficult, with signals suggesting a lack of effective support when technical hurdles arise during setup.
Learner feedback indicates a monotonous delivery style and potential confusion regarding access to practice environments.
Some advanced sections may show signs of aging, specifically regarding the direct use of SparkContext.
The curriculum aligns with the goal of transitioning into Data Engineering using Scala and Spark.