Master Big Data with Spark, Scala, PySpark, and Databricks

Explore Apache Spark with Databricks Community Edition and its Azure integration, create and manage clusters, access a data analytics platform, and spin up data objects for scalable pipelines.

Install Spark on Ubuntu and initialize the environment, following on-screen prompts to complete the setup, with Scala integration highlighted for seamless development.

Compare Spark and Hadoop as distributed processing frameworks, noting Spark's in-memory speed for real-time analytics and Hadoop's batch-oriented, disk-based MapReduce approach.

Spark's RDD is an immutable distributed dataset created by parallelizing collections or referencing external data, using lazy transformations and actions to build a dag.

Master Spark SQL enables fast in-memory analytics for structured data, using SQL and data frame APIs across Scala, Java, R, and Python, with a cost-based optimizer and scalable, fault-tolerant execution.

Explore how to set up Azure Databricks with a cluster and notebook, then connect to an Azure SQL Data Warehouse to read the cust table’s customer ID and name.

Explore Scala as a modern, multipartisan language that blends functional and object-oriented programming with concise, type-safe syntax, running on the JVM and interoperable with Java.

Learn basic Scala functionalities, from interoperable Java integration and core data types to building your first Scala program with IntelliJ IDEA, Maven, and a main object that builds and runs.

Explore the Scala if else construct with boolean expressions, use else if for multiple conditions, and build and run the project to see true or false outcomes.

Learn how the Scala do while loop executes at least once before evaluating its condition, and compare it with the while loop through a simple demo that prints a value.

Explore anonymous functions in Scala, where function literals become runtime function values. Declare a variable, assign a value, and call it to see it add one to an integer.

Explore PySpark, a Python API on the Spark engine, to write Python code that drives distributed processing with familiar Python syntax and libraries like NumPy and pandas.

Learn how to install and download Jupyter Notebook, then launch it in a browser to start working with Python.

Explore Python operators and functions, including arithmetic, comparison, assignment, logical, bitwise, membership, and identity operators, plus compound assignments and the def keyword used to define functions.

Explore Python’s collection types—lists, tuples, dictionaries, and sets—and learn how lists are mutable and ordered, while tuples are immutable, dictionaries store key-value pairs, and sets are unordered with no duplicates.

Learn how Python handles exceptions, including try-except-finally blocks, built-in exceptions, and common errors like syntax, zero division, and keyboard interrupt, to prevent crashes.

Learn how Python loops iterate over sequences with for and while constructs, including nested loops, break and continue, and conditional execution via if statements. Compare Python looping to other languages.

Learn to read a csv file in Python with pandas, create a data frame, and apply conditions to arrange data, leveraging pandas and NumPy for analysis.

Explore PySpark, a Python API for Apache Spark, enabling scalable, fault-tolerant big data processing with RDDs, DataFrames, Spark SQL, and MLlib for analytics and real-time streaming.

Discover how PySpark RDDs power large-scale data processing, from creating an RDD and filtering names starting with r to counting results and presenting a tabular view in Databricks.

Discover spark dataframes, a distributed row-and-column data structure optimized for large-scale processing, with type-safe columns, diverse data sources, and a rich API for filtering, joining, and statistical analysis.

Create student and marks data frames in PySpark, join them, filter where marks > 80 and subject is math, compute average marks, and read cost.txt into a data frame.