Big Data Engineering Bootcamp with GCP, and Azure Cloud

Explore data manipulation and table alterations in MySQL, covering null values, update and delete statements, and altering tables to add, modify, or drop columns.

Explore how MySQL constraints shape data integrity by defining primary keys, foreign keys, not null, unique, and check constraints; learn to create and modify tables with alter and drop commands.

Explore variables in Python by declaring and assigning values, learn naming conventions, understand dynamic typing, and work with int, float, string, and boolean types through practical examples.

Explore Python operators, including arithmetic, comparison, and logical operators, with practical examples like a and b calculations, float division, modulus, and exponential, culminating in a simple calculator.

Master lists in Python by learning creation, access, and slicing, using powerful methods like append, insert, remove, pop, and sort, plus list comprehension and enumeration.

Explore Python dictionaries from creation to manipulation, including accessing, modifying, and iterating; master nested structures, comprehension, and common methods like keys, values, and items.

Use the Python filter function to create iterators that select items from iterables based on a condition, applying lambdas to filter even numbers and ages greater than 25 from dictionaries.

Master Python file operations, including reading and writing text and binary files with the with open approach. Learn line-by-line reads, appending, overwriting, and writing multiple lines with write lines.

Explore how to handle errors gracefully in Python using try, except, else, and finally blocks, and implement robust exception handling for input validation, zero division error, and file operations.

Explore object-oriented programming in Python by modeling classes and objects, including constructors, attributes, and methods. Illustrate with a bank account example to show object creation and the use of self.

Explore Python's magic methods (dunder), including __init__, __str__, and __repr__, and how they define object behavior for arithmetic and comparison, plus overriding them for custom printing.

Operator overloading in Python is explored by overriding magic methods like __add__, __sub__, and __gt__ to customize arithmetic and comparisons, illustrated with a vector class.

Discover how generators in Python use yield to create memory-efficient iterators that generate values lazily. See squaring examples and reading large files line by line, with a quick iterators comparison.

Learn pandas essentials for data analysis in Python, including series and data frames, reading CSVs, and manipulating and indexing data with iloc, at, and iat.

Learn to read data from json, csv, html, and excel using pandas' read_json, read_csv, read_html, and read_excel, and convert frames to json with to_json.

Develop your python foundation for big data by focusing on map and lambda functions, mastering advanced python concepts, and practicing with pyspark via google colab for hands-on learning.

Discover how big data challenges drive resource planning, comparing monolithic and distributed systems; learn how clusters of nodes enable true scaling through storage, memory, and CPU resources.

Design a good big data system by prioritizing scalability, reliability, and fault tolerance, leveraging a distributed design with scalable storage and processing, cost effectiveness, and data security.

Explore the differences between databases, data warehouses, and data lakes, and learn when to use ETL versus ELT for structured, semi-structured, and unstructured data.

Explore the Hadoop ecosystem components: HDFS, MapReduce, Yarn, Hive, Pig, Sqoop, Oozie, HBase, Mahout, Flume, and Zookeeper to understand storage, processing, ingestion, and workflow orchestration in big data.