Course Highlights
Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Sql Nosql Big Data and Hadoop All in One Course has been specially designed to help learners gain a good command of Sql Nosql Big Data and Hadoop All in One Course, providing them with a solid foundation of knowledge to become a qualified professional.
Through this Sql Nosql Big Data and Hadoop All in One Course, you will gain both practical and theoretical understanding of Sql Nosql Big Data and Hadoop All in One Course that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.
Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.
Learning outcome
- Familiar yourself with the recent development and updates of the relevant industry
- Know how to use your theoretical knowledge to adapt in any working environment
- Get help from our expert tutors anytime you need
- Access to course contents that are designed and prepared by industry professionals
- Study at your convenient time and from wherever you want
Course media
Why should I take this course?
- Affordable premium-quality E-learning content, you can learn at your own pace.
- You will receive a completion certificate upon completing the course.
- Internationally recognized Accredited Qualification will boost up your resume.
- You will learn the researched and proven approach adopted by successful people to transform their careers.
- You will be able to incorporate various techniques successfully and understand your customers better.
Requirements
- No formal qualifications required, anyone from any academic background can take this course.
- Access to a computer or digital device with internet connectivity.
Course Curriculum
Course Curriculum
| Section 01: Introduction | |||
| Introduction | 00:07:00 | ||
| Building a Data-driven Organization – Introduction | 00:00:00 | ||
| Data Engineering | 00:00:00 | ||
| 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 | 00:07:00 | ||
| HBase | 00:07:00 | ||
| HBase Architecture | 00:09:00 | ||
| HBase Installation | 00:09:00 | ||
| Apache Zookeeper | 00:07:00 | ||
| 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 | ||
| 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 | ||
| SQL-on-Hadoop: Wrap Up | 00:02:00 | ||
| 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 | 00:09:00 | ||
| Data Visualization | 00:11:00 | ||
| Building a Data-driven Organization – Conclusion | 00:07:00 | ||
| Conclusion | 00:03:00 | ||
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