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
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Introduction
00:07:00 -
Building a Data-driven Organization – Introduction
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Data Engineering
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Learning Environment & Course Material
00:04:00
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Movielens Dataset
00:03:00
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Introduction to Relational Databases
00:09:00
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SQL
00:05:00
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Movielens Relational Model
00:15:00
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Movielens Relational Model: Normalization vs Denormalization
00:16:00
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MySQL
00:05:00
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Movielens in MySQL: Database import
00:06:00
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OLTP in RDBMS: CRUD Applications
00:17:00
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Indexes
00:16:00
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Data Warehousing
00:15:00
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Analytical Processing
00:17:00
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Transaction Logs
00:06:00
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Relational Databases – Wrap Up
00:03:00
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Distributed Databases
00:07:00
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CAP Theorem
00:10:00
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BASE
00:07:00
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Other Classifications
00:07:00
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Introduction to KV Stores
00:02:00
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Redis
00:04:00
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Install Redis
00:07:00
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Time Complexity of Algorithm
00:05:00
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Data Structures in Redis : Key & String
00:20:00
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Data Structures in Redis II : Hash & List
00:18:00
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Data structures in Redis III : Set & Sorted Set
00:21:00
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Data structures in Redis IV : Geo & HyperLogLog
00:11:00
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Data structures in Redis V : Pubsub & Transaction
00:08:00
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Modelling Movielens in Redis
00:11:00
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Redis Example in Application
00:29:00
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KV Stores: Wrap Up
00:02:00
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Introduction to Document-Oriented Databases
00:05:00
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MongoDB
00:04:00
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MongoDB Installation
00:02:00
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Movielens in MongoDB
00:13:00
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Movielens in MongoDB: Normalization vs Denormalization
00:11:00
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Movielens in MongoDB: Implementation
00:10:00
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CRUD Operations in MongoDB
00:13:00
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Indexes
00:16:00
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MongoDB Aggregation Query – MapReduce function
00:09:00
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MongoDB Aggregation Query – Aggregation Framework
00:16:00
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Demo: MySQL vs MongoDB. Modeling with Spark
00:02:00
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Document Stores: Wrap Up
00:03:00
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Introduction to Search Engine Stores
00:05:00
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Elasticsearch
00:09:00
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Basic Terms Concepts and Description
00:13:00
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Movielens in Elastisearch
00:12:00
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CRUD in Elasticsearch
00:15:00
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Search Queries in Elasticsearch
00:23:00
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Aggregation Queries in Elasticsearch
00:23:00
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The Elastic Stack (ELK)
00:12:00
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Use case: UFO Sighting in ElasticSearch
00:29:00
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Search Engines: Wrap Up
00:04:00
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Introduction to Columnar databases
00:07:00
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HBase
00:07:00
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HBase Architecture
00:09:00
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HBase Installation
00:09:00
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Apache Zookeeper
00:07:00
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Movielens Data in HBase
00:17:00
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Performing CRUD in HBase
00:24:00
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SQL on HBase – Apache Phoenix
00:14:00
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SQL on HBase – Apache Phoenix – Movielens
00:10:00
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Demo : GeoLife GPS Trajectories
00:02:00
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Wide Column Store: Wrap Up
00:05:00
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Introduction to Time Series
00:09:00
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InfluxDB
00:03:00
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InfluxDB Installation
00:07:00
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InfluxDB Data Model
00:07:00
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Data manipulation in InfluxDB
00:17:00
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TICK Stack I
00:12:00
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TICK Stack II
00:23:00
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Time Series Databases: Wrap Up
00:04:00
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Introduction to Graph Databases
00:05:00
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Modelling in Graph
00:14:00
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Modelling Movielens as a Graph
00:10:00
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Neo4J
00:04:00
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Neo4J installation
00:08:00
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Cypher
00:12:00
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Cypher II
00:19:00
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Movielens in Neo4J: Data Import
00:17:00
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Movielens in Neo4J: Spring Application
00:12:00
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Data Analysis in Graph Databases
00:05:00
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Examples of Graph Algorithms in Neo4J
00:18:00
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Graph Databases: Wrap Up
00:07:00
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Introduction to Big Data With Apache Hadoop
00:06:00
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Big Data Storage in Hadoop (HDFS)
00:16:00
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Big Data Processing : YARN
00:11:00
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Installation
00:13:00
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Data Processing in Hadoop (MapReduce)
00:14:00
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Examples in MapReduce
00:25:00
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Data Processing in Hadoop (Pig)
00:12:00
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Examples in Pig
00:21:00
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Data Processing in Hadoop (Spark)
00:23:00
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Examples in Spark
00:23:00
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Data Analytics with Apache Spark
00:09:00
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Data Compression
00:06:00
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Data serialization and storage formats
00:20:00
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SQL-on-Hadoop: Wrap Up
00:02:00
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Introduction Big Data SQL Engines
00:03:00
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Apache Hive
00:10:00
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Apache Hive : Demonstration
00:20:00
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MPP SQL-on-Hadoop: Introduction
00:03:00
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Impala
00:06:00
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Impala : Demonstration
00:18:00
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PrestoDB
00:13:00
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PrestoDB : Demonstration
00:14:00
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SQL-on-Hadoop: Wrap Up
00:02:00
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Data Architectures
00:05:00
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Introduction to Distributed Commit Logs
00:07:00
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Apache Kafka
00:03:00
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Confluent Platform Installation
00:10:00
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Data Modeling in Kafka I
00:13:00
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Data Modeling in Kafka II
00:15:00
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Data Generation for Testing
00:09:00
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Use case: Toll fee Collection
00:04:00
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Stream processing
00:11:00
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Stream Processing II with Stream + Connect APIs
00:19:00
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Example: Kafka Streams
00:15:00
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KSQL : Streaming Processing in SQL
00:04:00
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KSQL: Example
00:14:00
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Demonstration: NYC Taxi and Fares
00:01:00
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Streaming: Wrap Up
00:02:00
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Database Polyglot
00:04:00
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Extending your knowledge
00:09:00
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Data Visualization
00:11:00
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Building a Data-driven Organization – Conclusion
00:07:00
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Conclusion
00:03:00
£25
This course includes:
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Duration:22 hours, 22 minutes
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Access:1 Year
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Units:129
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