Name: SQL NoSQL Big Data and Hadoop
Price: 25 GBP

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Curriculum

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 and hard copy dispatch by next working day
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. Also, you can have your printed certificate delivered by post (shipping cost £3.99). 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 Curriculum

Section 01: Introduction
	Introduction	00:07:00
	Building a Data-driven Organization – Introduction	00:04:00
	Data Engineering	00:06:00
	Learning Environment & Course Material	00:04:00
	Movielens Dataset	00:03:00
Section 02: Relational Database Systems
	Introduction to Relational Databases	00:09:00
	SQL	00:05:00
	Movielens Relational Model	00:15:00
	Movielens Relational Model: Normalization vs Denormalization	00:16:00
	MySQL	00:05:00
	Movielens in MySQL: Database import	00:06:00
	OLTP in RDBMS: CRUD Applications	00:17:00
	Indexes	00:16:00
	Data Warehousing	00:15:00
	Analytical Processing	00:17:00
	Transaction Logs	00:06:00
	Relational Databases – Wrap Up	00:03:00
Section 03: Database Classification
	Distributed Databases	00:07:00
	CAP Theorem	00:10:00
	BASE	00:07:00
	Other Classifications	00:07:00
Section 04: Key-Value Store
	Introduction to KV Stores	00:02:00
	Redis	00:04:00
	Install Redis	00:07:00
	Time Complexity of Algorithm	00:05:00
	Data Structures in Redis : Key & String	00:20:00
	Data Structures in Redis II : Hash & List	00:18:00
	Data structures in Redis III : Set & Sorted Set	00:21:00
	Data structures in Redis IV : Geo & HyperLogLog	00:11:00
	Data structures in Redis V : Pubsub & Transaction	00:08:00
	Modelling Movielens in Redis	00:11:00
	Redis Example in Application	00:29:00
	KV Stores: Wrap Up	00:02:00
Section 05: Document-Oriented Databases
	Introduction to Document-Oriented Databases	00:05:00
	MongoDB	00:04:00
	MongoDB Installation	00:02:00
	Movielens in MongoDB	00:13:00
	Movielens in MongoDB: Normalization vs Denormalization	00:11:00
	Movielens in MongoDB: Implementation	00:10:00
	CRUD Operations in MongoDB	00:13:00
	Indexes	00:16:00
	MongoDB Aggregation Query – MapReduce function	00:09:00
	MongoDB Aggregation Query – Aggregation Framework	00:16:00
	Demo: MySQL vs MongoDB. Modeling with Spark	00:02:00
	Document Stores: Wrap Up	00:03:00
Section 06: Search Engines
	Introduction to Search Engine Stores	00:05:00
	Elasticsearch	00:09:00
	Basic Terms Concepts and Description	00:13:00
	Movielens in Elastisearch	00:12:00
	CRUD in Elasticsearch	00:15:00
	Search Queries in Elasticsearch	00:23:00
	Aggregation Queries in Elasticsearch	00:23:00
	The Elastic Stack (ELK)	00:12:00
	Use case: UFO Sighting in ElasticSearch	00:29:00
	Search Engines: Wrap Up	00:04:00
Section 07: Wide Column Store
	Introduction to Columnar databases	00:06:00
	HBase	00:07:00
	HBase Architecture	00:09:00
	HBase Installation	00:09:00
	Apache Zookeeper	00:06:00
	Movielens Data in HBase	00:17:00
	Performing CRUD in HBase	00:24:00
	SQL on HBase – Apache Phoenix	00:14:00
	SQL on HBase – Apache Phoenix – Movielens	00:10:00
	Demo : GeoLife GPS Trajectories	00:02:00
	Wide Column Store: Wrap Up	00:04:00
Section 08: Time Series Databases
	Introduction to Time Series	00:09:00
	InfluxDB	00:03:00
	InfluxDB Installation	00:07:00
	InfluxDB Data Model	00:07:00
	Data manipulation in InfluxDB	00:17:00
	TICK Stack I	00:12:00
	TICK Stack II	00:23:00
	Time Series Databases: Wrap Up	00:04:00
Section 09: Graph Databases
	Introduction to Graph Databases	00:05:00
	Modelling in Graph	00:14:00
	Modelling Movielens as a Graph	00:10:00
	Neo4J	00:04:00
	Neo4J installation	00:08:00
	Cypher	00:12:00
	Cypher II	00:19:00
	Movielens in Neo4J: Data Import	00:17:00
	Movielens in Neo4J: Spring Application	00:12:00
	Data Analysis in Graph Databases	00:05:00
	Examples of Graph Algorithms in Neo4J	00:18:00
	Graph Databases: Wrap Up	00:07:00
Section 10: Hadoop Platform
	Introduction to Big Data With Apache Hadoop	00:06:00
	Big Data Storage in Hadoop (HDFS)	00:16:00
	Big Data Processing : YARN	00:11:00
	Installation	00:13:00
	Data Processing in Hadoop (MapReduce)	00:14:00
	Examples in MapReduce	00:25:00
	Data Processing in Hadoop (Pig)	00:12:00
	Examples in Pig	00:21:00
	Data Processing in Hadoop (Spark)	00:23:00
	Examples in Spark	00:23:00
	Data Analytics with Apache Spark	00:09:00
	Data Compression	00:06:00
	Data serialization and storage formats	00:20:00
	Hadoop: Wrap Up	00:07:00
Section 11: Big Data SQL Engines
	Introduction Big Data SQL Engines	00:03:00
	Apache Hive	00:10:00
	Apache Hive : Demonstration	00:20:00
	MPP SQL-on-Hadoop: Introduction	00:03:00
	Impala	00:06:00
	Impala : Demonstration	00:18:00
	PrestoDB	00:13:00
	PrestoDB : Demonstration	00:14:00
	SQL-on-Hadoop: Wrap Up	00:02:00
Section 12: Distributed Commit Log
	Data Architectures	00:05:00
	Introduction to Distributed Commit Logs	00:07:00
	Apache Kafka	00:03:00
	Confluent Platform Installation	00:10:00
	Data Modeling in Kafka I	00:13:00
	Data Modeling in Kafka II	00:15:00
	Data Generation for Testing	00:09:00
	Use case: Toll fee Collection	00:04:00
	Stream processing	00:11:00
	Stream Processing II with Stream + Connect APIs	00:19:00
	Example: Kafka Streams	00:15:00
	KSQL : Streaming Processing in SQL	00:04:00
	KSQL: Example	00:14:00
	Demonstration: NYC Taxi and Fares	00:01:00
	Streaming: Wrap Up	00:02:00
Section 13: Summary
	Database Polyglot	00:04:00
	Extending your knowledge	00:08:00
	Data Visualization	00:11:00
	Building a Data-driven Organization – Conclusion	00:07:00
	Conclusion	00:03:00
Resources
	Resources – SQL NoSQL Big Data and Hadoop	00:00:00