Big Data Engineering Bootcamp with GCP, and Azure Cloud

Learn to write MySQL queries to create databases and tables (including without constraints), insert data, and drop tables and databases, with step-by-step guidance and hands-on exercises.

Practice MySQL basics by creating a database and tables, inserting and querying records, and dropping objects through hands-on exercises, covering students and courses tables.

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

Learn how to use MySQL alter table to add, modify, rename, and drop columns in an existing table, with practical examples on the employees table.

Practice MySQL data manipulation by creating a customer_info table with auto increment primary key, inserting records, handling null salaries, and updating, deleting, and altering 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.

Learn how default constraints assign a value when none is provided, and explore index creation and composite keys to uniquely identify records in MySQL.

Begin your big data journey by mastering Python fundamentals for PySpark and Spark, covering variables, lists, tuples, file handling, logging, NumPy, and pandas.

Install and configure Anaconda and VS Code to create a Python data science workspace, manage conda environments, set path, and run notebooks with ipynb support.

Set up a Python project in VS Code by creating and activating a conda environment with Python 3.12, and run Python files and Jupyter notebooks with ipykernel.

Explore Python syntax and semantics, including single-line and multi-line comments, case sensitivity, indentation, and common errors, with practical examples of variable assignment, type inference, and line continuation.

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 data types in Python, including integers, floating point numbers, strings, and booleans, with type conversion and casting. Understand memory usage and common errors during operations.

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.

Learn to master conditional statements in Python, including if, else, and elif, with nested logic and practical examples like leap years, even/odd checks, and basic real-world use cases.

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 practical real-world uses of Python lists, including to-do lists, student grades, inventory, and user feedback, with append, remove, and in-list checks.

Explore sets in Python, unordered collections of unique items. Learn creation, add and remove operations, membership tests, and set operations like union, intersection, difference, symmetric difference, plus counting unique words.

Explore Python tuples, their immutable, ordered nature, and practical operations. Pack and unpack tuples, access elements, perform concatenation and repetition, explore indexing, counting, slicing, and nested tuples with hands-on examples.

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

Define and call Python functions, and learn syntax, default parameters, and the return statement. Explore positional and keyword arguments, and variable-length arguments.

Explore Python function examples, including temperature conversion, password strength checks, shopping cart total cost, and palindrome validation. Learn recursion with factorials, file word frequency, and email validation using regex.

Explore lambda functions in Python, learning anonymous functions defined with the lambda keyword, their single-expression logic, and practical uses with map, filter, and examples like squaring lists.

Explore the Python map function to apply a function over iterables, convert results to lists, and use lambdas, multiple iterables, and built-ins like int and upper for data transformation.

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 modules and packages in the big data engineering bootcamp, learn import techniques, aliasing, and installing packages with pip; build custom packages with __init__.py and use NumPy for arrays.

Explore the Python standard library by examining core modules like array, math, random, os, json, csv, datetime, time, and re, and learn practical usage for real projects.

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.

Create and manage directories with os, list files with os.listdir, and build paths with os.path.join. Check existence and type with os.path.exists, os.path.isfile, and os.path.isdir, and distinguish absolute and relative paths.

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 inheritance in Python, including single and multiple inheritance, with practical examples like car and Tesla classes. Learn how to use super and init to share attributes and behavior.

Explore polymorphism in Python by seeing how different classes share a common interface through method overriding and abstract base classes, with practical dog, cat, rectangle, and circle examples.

Explore encapsulation in Python, contrast with abstraction, and learn to design maintainable code by using public, private, and protected variables with getters and setters.

Explore how abstraction in Python hides complex implementation, exposing only necessary features, through abstract base classes and abstract methods, with vehicle and car examples.

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.

Design and raise custom exceptions in Python to validate input, such as checking age for an exam form, and handle them with try-except blocks to provide clear user messages.

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 Python iterators enable efficient looping and memory management through lazy loading, using iter and next to traverse lists and strings while handling StopIteration.

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.

Explore Python decorators, learn function copy and closures, and understand how decorators modify function behavior without changing the original code, with practical examples.

Master numpy, the fundamental Python library for scientific computing, to create, reshape, and perform vectorized operations on arrays and matrices using built-in functions like ones, zeros, and identity.

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 manipulate and analyze data with pandas, perform exploratory data analysis, handle missing values with fillna and mean, and apply grouping and merging to derive insights.

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.

Learn how to use Python with SQLite to connect to a database, create tables, and perform CRUD operations via cursor and execute statements, including bulk inserts.

Learn to implement Python logging with the built-in logging module, configure basicConfig, and use levels like debug, info, warning, error, and critical, with timestamp formats and app.log output.

Learn to create and configure multiple loggers in Python, assigning module-specific loggers with getLogger, setting levels, and emitting debug, warning, and error messages.

Implement a multi-logger Python application that logs to app1.log using basic configuration, level, format, and handlers, and demonstrate debugging, errors, and division by zero handling.

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.

Master SQL foundational skills to interact with big data using spark sql and hive, essential for data scientists, analysts, and engineers; practice writing queries to build hands-on competence.

Explore the fundamentals of big data, including the five v's, ELT vs ETL, cloud vs on premise, and the data engineer's role across warehouses and lakes.

Discover how big data extends beyond traditional RDBMS to handle large, fast, and complex datasets, illustrated by hotel bookings and user behavior, with Google BigQuery and NoSQL databases.

Explore the five v's of big data—volume, velocity, variety, veracity, and value—within the big data engineering bootcamp with GCP and Azure Cloud, covering real-time, near real-time, and batch processing.

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 trade-offs between on premise infrastructure and cloud solutions for big data systems, including cost, scalability, maintenance, security, and disaster recovery across public, private, hybrid, and community cloud models.

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.

Explains the differences between ETL and ELT, detailing extract, transform, load versus extract, load, transform, and shows when to use data lakes or data warehouses for different data.

Explore the roles of data engineers and big data engineers, compare ETL and ELT pipelines, and examine Hadoop and Spark for distributed storage and processing.

Explore Hadoop architecture and distributed storage concepts, and learn through practicals on Google Cloud with a free $300 credit and three months trial, preparing for AWS and Azure object storage.

Explore Hadoop, an open source framework for distributed, scalable storage and parallel processing across multi-node clusters handling massive data, inspired by Google's GFS and MapReduce.

Hadoop provides horizontal scalability, fault tolerance via data replication, and distributed processing on commodity hardware, highlighting open-source Apache foundation efficiency for real-world big data like Amazon-scale analytics.

Discover the Hadoop ecosystem of open source tools for storing, processing, and analyzing large data. Learn core components—HDFS, MapReduce, and YARN—and how Hadoop 2.0 decouples resource management from execution.

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.