Exclusive Deal! 94% Off, Today Only!
Buy 1 or more contact sale
Learn how to make a genuine difference in your life by taking our popular [course_title]. Our commitment to online learning and our technical experience has been put to excellent use within the contents of these educational modules. By enrolling today, you can take your knowledge of SQL NoSQL Big Data and Hadoop to a whole new level and quickly reap the rewards of your study in the field you have chosen.
We are confident that you will find the skills and information that you will need to succeed in this area and excel in the eyes of others. Do not rely on substandard training or half-hearted education. Commit to the best, and we will help you reach your full potential whenever and wherever you need us.
Please Note that [course_title] provides valuable and significant theoretical training for all. However, it does not offer official qualifications for professional practices. Always check details with the appropriate authorities or management.
By completing the training in [course_title], you will be able to significantly demonstrate your acquired abilities and knowledge of SQL NoSQL Big Data and Hadoop. This can give you an advantage in career progression, job applications, and personal mastery in this area.
This course is designed to provide an introduction to SQL NoSQL Big Data and Hadoop and offers an excellent way to gain the vital skills and confidence to start a successful career. It also provides access to proven educational knowledge about the subject and will support those wanting to attain personal goals in this area. Full-time and part-time learners are equally supported and it can take just 20 to 30 hours to gain accreditation, with study periods being entirely customisable to your needs.
Once you have completed all the modules in the [course_title] course, you can assess your skills and knowledge with an optional assignment. Our expert trainers will assess your assignment and give you feedback afterwards.
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 | |||
Performance of Heat Exchanger (Example) | 00:15:00 | ||
Performance of Heat Exchanger (Effectiveness-NTU method) | 00:09:00 | ||
Performance of Heat Exchanger (LMTD method) | 00:12:00 | ||
HBase Installation | 00:09:00 | ||
Selection & Methods to Improve the Efficiency in Heat Exchanger | 00:16:00 | ||
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 | |||
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 | ||
The solution of Boiler efficiency by Indirect method | 00:17:00 | ||
Performance Evaluation of Boiler | 00:13:00 | ||
Boiler classification and Systems | 00:24:00 | ||
Section 12: Distributed Commit Log | |||
Data Architectures | 00:05:00 | ||
Introduction to Distributed Commit Logs | 00:07:00 | ||
Apache Kafka | 00:03:00 | ||
Data Modeling in Kafka I | 00:13:00 | ||
Data Modeling in Kafka II | 00:15:00 | ||
Energy saving in motors Part II | 00:09:00 | ||
Energy saving in motors part I | 00:10:00 | ||
Introduction | 00:04:00 | ||
Energy Efficient Motor | 00:17:00 | ||
Example: Kafka Streams | 00:15:00 | ||
Energy saving in motors Part III | 00:06:00 | ||
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 | ||
Data Visualization | 00:11:00 | ||
Building a Data-driven Organization – Conclusion | 00:07:00 | ||
Conclusion | 00:03:00 | ||
Assignment | |||
Assignment – SQL NoSQL Big Data and Hadoop | 00:00:00 |
1353
4.9
£799
848
4.9
£799
692
4.9
£799