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Spark SQL and Spark 3 using Scala Hands-On with Labs

A comprehensive course on Spark SQL as well as Data Frame APIs using Scala with complementary lab access

  1. Topics
  2. Development
  3. Scala Programming

Spark SQL and Spark 3 using Scala Hands-On with Labs

InstructorDurga Viswanatha Raju Gadiraju
Duration24h 12m
Students22.7K
Rating4.6 (3,121)
Sponsored
Price
$14.99
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Comidoc Analysis

Spark SQL and Scala-based Data Engineering

Strengths

Comprehensive API Coverage

The curriculum covers both Spark SQL and Dataframe APIs, including essential operations like transformations, joins, and advanced windowing functions.

Infrastructure Provisioning

Instruction includes setting up single-node Hadoop clusters and cloud-based environments using AWS Cloud9 and GCP Ubuntu VMs.

Limitations

Environment Setup Friction

Initial cluster and environment configuration can be difficult, with signals suggesting a lack of effective support when technical hurdles arise during setup.

Instructional Consistency

Learner feedback indicates a monotonous delivery style and potential confusion regarding access to practice environments.

Content Aging

Some advanced sections may show signs of aging, specifically regarding the direct use of SparkContext.

Best suited to

  • Aspiring Data Engineers transitioning from traditional SQL or Java roles
  • Learners seeking hands-on practice with Spark 2 and s3 environments

Less suited to

  • Those requiring high-engagement, energetic instructional delivery
  • Learners who prefer guided, interactive support for environment setup

Comidoc Score

6.2/10

Worth considering
Intermediate level

Comidoc verdict

The learning path begins with infrastructure setup, covering AWS Cloud9, GCP, and single-node Hadoop clusters before progressing through Scala fundamentals into Spark 2 and 3. This progression focuses on both SQL syntax and Dataframe APIs.

Detailed coverage of transformations and aggregations provides a strong technical foundation. However, the experience is hampered by a difficult setup process that lacks sufficient interactive guidance, alongside an instructional style described as monotonous by some learners.

Editorial course preview

What the public course preview actually shows

These 4 complementary views highlight concrete, legible examples from the course presentation.

Preview 1 of 4

This webpage displays the curriculum and prerequisites for a Big Data certification course, highlighting technical requirements like Cloudera VM access.

Preview 2 of 4

This interface displays the structured course modules, including sections for Core Spark APIs, Data Analysis using SQL, and real-time data ingestion.

Preview 3 of 4

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.

Preview 4 of 4

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.

Selected from the course's public promotional preview. These images document visible presentation material only; they do not represent the complete paid curriculum.

This course is best suited for self-directed learners who have the patience to troubleshoot their own environments and are looking for a technical deep dive into Spark's dual API capabilities.

Score breakdown

Curriculum depth
7.3

The curriculum covers essential Scala, HDFS, and Spark-specific functions, though some signals suggest certain advanced sections may be aging.

Applied learning
7.3

The course includes numerous hands-on tasks and exercises across various Spark versions.

Clarity & experience
5.0

Instructional signals are mixed, ranging from high praise for thoroughness to criticism regarding a monotonous delivery style.

Currency & reliability
5.0

While it covers Spark 3, some learner signals suggest certain technical approaches may be aging.

Audience fit
5.8

The curriculum aligns with the goal of transitioning into Data Engineering using Scala and Spark.

More Related Topics

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  • Spark SQL29
  • RDDs & DataFrames11
  • Functional Programming130
  • Functional Programming28
  • Big Data Processing with Spark57
  • Scala Type System4
  • Akka Actor Model5
  • Gatling Performance Testing11
  • Data Streaming50