Spark Scala Real-World Coding Frameworks and Testing

Set up a Windows environment for Spark, Scala, and Hive with IntelliJ. Learn to install JDK 11, IntelliJ, winutils, and configure Spark core and SQL dependencies for a Maven project.

Mac users install Java 11 and IntelliJ, then set up Spark with Scala and Hive, configure Maven dependencies, and run a Spark session with a sample data frame.

Master IntelliJ setups on Windows and Mac. Learn that older Java and Scala code runs on newer versions without issues, keeping concepts and code consistent.

Master the basics of Scala by creating a Maven project, configuring Scala, and learning var versus val, type inference, arrays, lists, maps, tuples, and loops.

Install PostgreSQL on Windows, use pgadmin and sql to create the Futurex schema and a course_catalog table with json course_section and creation_date, then insert and query records.

Connect a Spark application to PostgreSQL via JDBC, configure user credentials and URL, and fetch data from the future schema's Future X Course Catalog table into a DataFrame.

Import a spark scala project into IntelliJ from existing sources, configure maven with pom.xml, set JDK 1.8 and scala 2.11.8, and resolve dependencies.

Organize a Spark Scala project by creating a common package and objects to initialize the Spark session and centralize PostgreSQL properties, enabling methods to fetch data frames from PostgreSQL tables.

Configure log4j2 and SLF4J logging in a Maven-based Scala app with a Log4j2 properties file. Replace println with logger.info and experiment with info, warn, and error levels for structured logs.

Learn to implement exception handling in a Spark Scala app using try catch, Some and None, and option data frame, with logging and get in main.

Read data from a Hive table with Spark SQL, apply transformations to replace nulls with unknown or zero, and write the results to a PostgreSQL table.

Learn how to read json configuration with typesafe config, fetch the Postgres target table from a json file, and control data transformations via json-driven settings.

Learn how to pass and read command line arguments in a Spark program, debug with breakpoints in IntelliJ, and control program flow based on environment like dev or production.

Write data to a Hive table from a data frame by creating a temporary table, then using Spark SQL to persist to Hive, with dynamic table naming and error handling.

Learn to build and unit test Scala applications with JUnit and ScalaTest using Maven and IntelliJ, including creating Scala objects, implementing a division method, and writing test cases.

Use ScalaTest to unit test a Spark Scala app, building a dataframe, applying a replace null value transformation, and validating results with a flat spec.

Explore unit testing in scala using spark transformer tests, catching null pointer exceptions, asserting results, and leveraging fixtures and matchers to validate data frame transformations.

Throw and catch custom exceptions in Scala, intercept error messages, and validate data with tests that trigger a null course name in a Spark Scala workflow.

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Explore big data concepts by examining the five V's: volume, velocity, variety, veracity, and value, and how distributed storage and distributed computing enable scalable processing with Hadoop.

Explore how Hadoop enables distributed storage and computing through hdfs and mapreduce, with data nodes and a name node coordinating a scalable cluster.

Explore how HDFS distributes large data sets across a cluster using 128 MB blocks and a replication factor of three for fault tolerance, with trade-offs between storage and resilience.

Learn to set up a production-like big data Hadoop cluster with Google Cloud Dataproc, a fully managed service for Apache Hadoop and Spark, with per-second billing and $300 free credit.

Sign up for a Google Cloud free tier account and explore data engineering and machine learning with $300 in free credits, then monitor usage in the billing console.

Create a production-grade Hadoop and Spark cluster on Google Cloud Dataproc by configuring a three-node setup, enabling the Dataproc API, and SSH into the master node.

Learn to store and manage files in HDFS using Hadoop fs commands on a Dataproc cluster, including creating directories, putting files, listing, getting, and removing files.

Explore how Hadoop enables parallel processing with MapReduce on distributed data in HDFS. Learn map and reduce tasks, resource managers, and how Yarn enables scalable processing across clusters.

Discover Hive, a SQL-like tool that processes data in HDFS by translating queries to MapReduce. Learn how Hive stores metadata in metastore and uses tables and Hiveql to query data.

Query HDFS data with Hive on a Dataproc cluster. Map CSV data to Hive tables, both internal and external, and run MapReduce jobs via Yarn.

Analyze one billion records with Hive on HDFS, create and query the retail and country salary tables, and compute country averages using mappers and reducers for large-scale data processing.