Name: SQL NoSQL, Big Data and Hadoop
Price: 85 USD
Rating: 5 (61 reviews)

This course offers a deep dive into the world of data management, focusing on SQL, NoSQL databases, Big Data concepts, and Hadoop. Participants will explore the differences between relational (SQL) and non-relational (NoSQL) databases, learning when and how to use each. The course also covers the fundamentals of Big Data, its challenges, and the role of Hadoop in processing massive datasets. Designed for data professionals and enthusiasts, this course provides hands-on experience in database management, query optimization, and distributed computing. By the end, learners will have a solid understanding of managing, storing, and analyzing data at scale.

Who is this course for?

By taking this SQL, NoSQL, Big Data, and Hadoop Course, you’re able to open yourself to new opportunities within the data engineering and analytics career path. Such careers may include:

Data Engineer (£40,000 to £70,000)
Big Data Analyst (£45,000 to £80,000)
Database Administrator (£35,000 to £65,000)

Module 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
Module 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
Module 03: Database Classification
	Distributed Databases	00:07:00
	CAP Theorem	00:10:00
	BASE	00:07:00
	Other Classifications	00:07:00
Module 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
Module 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
Module 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
Module 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	00:05:00
Module 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
Module 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
Module 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
Module 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
Module 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
Module 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

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SQL NoSQL, Big Data and Hadoop

Course Curriculum