Overview:
Welcome to "SQL, NoSQL, Big Data, and Hadoop!" This comprehensive course is designed to provide you with a thorough understanding of various data storage and processing technologies, including SQL, NoSQL, Big Data, and Hadoop. In today's data-driven world, it's essential to be familiar with a range of data technologies to handle diverse data types and volumes effectively. In this course, you'll learn how to work with relational and non-relational databases, manage big data, and utilize Hadoop for distributed data processing.- Interactive video lectures by industry experts
- Instant e-certificate
- Fully online, interactive course with Professional voice-over
- Developed by qualified first aid professionals
- Self paced learning and laptop, tablet, smartphone friendly
- 24/7 Learning Assistance
- Discounts on bulk purchases
Main Course Features:
- Comprehensive coverage of SQL fundamentals for relational database management
- Exploration of NoSQL databases such as MongoDB and Cassandra for handling unstructured data
- Introduction to Big Data concepts and technologies, including Hadoop and MapReduce
- Hands-on projects and exercises for practical application of SQL, NoSQL, and Big Data concepts
- Implementation of data processing workflows using Hadoop ecosystem tools like Hive and Pig
- Real-world case studies and examples demonstrating the application of SQL, NoSQL, and Hadoop
- Access to datasets and resources for practicing SQL and Big Data processing
- Supportive online community for collaboration and assistance throughout the course
Who Should Take This Course:
- Data engineers and analysts seeking to expand their knowledge of data storage and processing technologies
- Software developers interested in understanding how different data technologies work together in modern applications
- Business intelligence professionals aiming to leverage Big Data and Hadoop for data analysis and insights
- Students and professionals looking to enhance their skills in SQL, NoSQL, and Big Data technologies
Learning Outcomes:
- Master SQL fundamentals for relational database management and querying
- Understand the principles and use cases of NoSQL databases for handling diverse data types
- Gain insights into Big Data concepts and technologies, including Hadoop and MapReduce
- Learn how to manage and process large-scale data using Hadoop ecosystem tools
- Develop practical skills through hands-on projects and exercises in SQL, NoSQL, and Hadoop
- Build a portfolio of projects showcasing proficiency in SQL, NoSQL, and Big Data processing
- Apply data storage and processing techniques to real-world scenarios effectively
- Stay updated with the latest advancements and best practices in SQL, NoSQL, Big Data, and Hadoop technologies.
Certification
Once you’ve successfully completed your course, you will immediately be sent a digital certificate. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.Assessment
At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.Course Content
129 Lectures 22hr 33min
Section 01: Introduction - 5 lessons
Introduction- Building a Data-driven Organization – Introduction
- Data Engineering
- Learning Environment & Course Material
- Movielens Dataset
Section 02: Relational Database Systems - 12 lessons
- Introduction to Relational Databases
- SQL
- Movielens Relational Model
- Movielens Relational Model: Normalization vs Denormalization
- MySQL
- Movielens in MySQL: Database import
- OLTP in RDBMS: CRUD Applications
- Indexes
- Data Warehousing
- Analytical Processing
- Transaction Logs
- Relational Databases – Wrap Up
Section 03: Database Classification - 4 lessons
- Distributed Databases
- CAP Theorem
- BASE
- Other Classifications
Section 04: Key-Value Store - 12 lessons
- Introduction to KV Stores
- Redis
- Install Redis
- Time Complexity of Algorithm
- Data Structures in Redis : Key & String
- Data Structures in Redis II : Hash & List
- Data structures in Redis III : Set & Sorted Set
- Data structures in Redis IV : Geo & HyperLogLog
- Data structures in Redis V : Pubsub & Transaction
- Modelling Movielens in Redis
- Redis Example in Application
- KV Stores: Wrap Up
Section 05: Document-Oriented Databases - 12 lessons
- Introduction to Document-Oriented Databases
- MongoDB
- MongoDB Installation
- Movielens in MongoDB
- Movielens in MongoDB: Normalization vs Denormalization
- Movielens in MongoDB: Implementation
- CRUD Operations in MongoDB
- Indexes
- MongoDB Aggregation Query – MapReduce function
- MongoDB Aggregation Query – Aggregation Framework
- Demo: MySQL vs MongoDB. Modeling with Spark
- Document Stores: Wrap Up
Section 06: Search Engines - 10 lessons
- Introduction to Search Engine Stores
- Elasticsearch
- Basic Terms Concepts and Description
- Movielens in Elastisearch
- CRUD in Elasticsearch
- Search Queries in Elasticsearch
- Aggregation Queries in Elasticsearch
- The Elastic Stack (ELK)
- Use case: UFO Sighting in ElasticSearch
- Search Engines: Wrap Up
Section 07: Wide Column Store - 11 lessons
- Introduction to Columnar databases
- HBase
- HBase Architecture
- HBase Installation
- Apache Zookeeper
- Movielens Data in HBase
- Performing CRUD in HBase
- SQL on HBase – Apache Phoenix
- SQL on HBase – Apache Phoenix – Movielens
- Demo : GeoLife GPS Trajectories
- Wide Column Store: Wrap Up
Section 08: Time Series Databases - 8 lessons
- Introduction to Time Series
- InfluxDB
- InfluxDB Installation
- InfluxDB Data Model
- Data manipulation in InfluxDB
- TICK Stack I
- TICK Stack II
- Time Series Databases: Wrap Up
Section 09: Graph Databases - 12 lessons
- Introduction to Graph Databases
- Modelling in Graph
- Modelling Movielens as a Graph
- Neo4J
- Neo4J installation
- Cypher
- Cypher II
- Movielens in Neo4J: Data Import
- Movielens in Neo4J: Spring Application
- Data Analysis in Graph Databases
- Examples of Graph Algorithms in Neo4J
- Graph Databases: Wrap Up
Section 10: Hadoop Platform - 14 lessons
- Introduction to Big Data With Apache Hadoop
- Big Data Storage in Hadoop (HDFS)
- Big Data Processing : YARN
- Installation
- Data Processing in Hadoop (MapReduce)
- Examples in MapReduce
- Data Processing in Hadoop (Pig)
- Examples in Pig
- Data Processing in Hadoop (Spark)
- Examples in Spark
- Data Analytics with Apache Spark
- Data Compression
- Data serialization and storage formats
- Hadoop: Wrap Up
Section 11: Big Data SQL Engines - 9 lessons
- Introduction Big Data SQL Engines
- Apache Hive
- Apache Hive : Demonstration
- MPP SQL-on-Hadoop: Introduction
- Impala
- Impala : Demonstration
- PrestoDB
- PrestoDB : Demonstration
- SQL-on-Hadoop: Wrap Up
Section 12: Distributed Commit Log - 15 lessons
- Data Architectures
- Introduction to Distributed Commit Logs
- Apache Kafka
- Confluent Platform Installation
- Data Modeling in Kafka I
- Data Modeling in Kafka II
- Data Generation for Testing
- Use case: Toll fee Collection
- Stream processing
- Stream Processing II with Stream + Connect APIs
- Example: Kafka Streams
- KSQL : Streaming Processing in SQL
- KSQL: Example
- Demonstration: NYC Taxi and Fares
- Streaming: Wrap Up
Section 13: Summary - 5 lessons
- Database Polyglot
- Extending your knowledge
- Data Visualization
- Building a Data-driven Organization – Conclusion
- Conclusion
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