• 5+ hours of video lectures and digital resources are available on demand.
• Learn at your own speed with affordable, high-quality E-learning content. 
• When you finish the course, you will be given a certificate of completion.
• Enhance your resume from a globally recognised Accredited Qualification.
• You’ll discover how effective salespeople use a researched and proven technique to boost their careers.
• You’ll be able to successfully implement several practical sales strategies and have a deeper understanding of your consumers.

The following learners find this “SQL NoSQL Big Data and Hadoop” course most relevant to their jobs and studies:

• Database Administrators: Enhance skills for managing relational and non-relational databases efficiently
• Data Analysts: Learn to navigate diverse data ecosystems and leverage Big Data technologies
• Software Developers: Expand expertise in database systems and Big Data for robust application development
• IT Professionals: Stay current with evolving technologies crucial for effective information management
• Business Intelligence Specialists: Acquire tools to analyse and extract insights from complex datasets
• Data Scientists: Strengthen foundation in handling diverse data structures for advanced analytics
• System Architects: Deepen understanding of database and Big Data systems for optimal architecture design
• IT Managers: Gain insights into technologies shaping data landscapes for strategic decision-making
• Data Engineers: Develop proficiency in managing and processing large-scale data with Hadoop and SQL.
• Technology Enthusiasts: Satisfy curiosity by exploring cutting-edge technologies transforming the data management landscape

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

Learners must submit a comprehensive summerised assignment on all the units that will be assessed by our expert tutor if all the learning outcomes are met as per the standard set by QLS. After the quality check, learner will be provided with the “SQL NoSQL Big Data and Hadoop” certificate.

An endorsed certificate will be issued to learners at the end of the course as recognition of course completion. Provided that learner completes all the assessment of a course can claim for certificate. 

QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions including your progression routes into further higher education. It is a non-accredited course.

There are no  formal requirements needed for this “SQL NoSQL Big Data and Hadoop” course. So learners do not require any prior qualifications to enrol in this course. 

The “SQL NoSQL Big Data and Hadoop” course provides highly demanded skills in the industry, especially:

• Database Administrator: £30,000 – £60,000 per annum
• Data Analyst: £25,000 – £50,000 per annum
• Software Developer (Database Focus):  £35,000 – £70,000 per annum
• IT Manager (Data and Systems): £45,000 – £90,000 per annum
• Business Intelligence Specialist: £35,000 – £65,000 per annum
• Data Scientist: £40,000 – £80,000 per annum
• System Architect (Data): £50,000 – £90,000 per annum
• Big Data Engineer: £40,000 – £75,000 per annum
• Technology Consultant (Data Management): £40,000 – £80,000 per annum
• IT Project Manager (Data Projects): £50,000 – £100,000 per annum