Big Data: SQL NoSQL Big Data and Hadoop
336 Students
Last updated: November 9, 2023
Language: English
Flexible Schedule
Overview
Uplift Your Career & Skill Up to Your Dream Job – Learning Simplified From Home!
Kickstart your career & boost your employability by helping you discover your skills, talents and interests with our special Big Data: SQL NoSQL Big Data and Hadoop Course. You’ll create a pathway to your ideal job as this course is designed to uplift your career in the relevant industry. It provides professional training that employers are looking for in today’s workplaces.
The Big Data: SQL NoSQL Big Data and Hadoop Course is one of the most prestigious training offered at StudyHub and is highly valued by employers for good reason. This Big Data: SQL NoSQL Big Data and Hadoop Course has been designed by industry experts to provide our learners with the best learning experience possible to increase their understanding of their chosen field.
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Certification
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Prerequisites
This Big Data: SQL NoSQL Big Data and Hadoop does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Big Data: SQL NoSQL Big Data and Hadoop was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.
Career path
As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Big Data: SQL NoSQL Big Data and Hadoop is a great way for you to gain multiple skills from the comfort of your home.
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 – Big Data: SQL NoSQL Big Data and Hadoop | 00:00:00 | ||
| Assignment | |||
| Assignment – Big Data: SQL NoSQL Big Data and Hadoop | 00:00:00 | ||
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This course includes:
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22 hours, 33 minutes
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336 Students
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