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SQL NoSQL Big Data and Hadoop



    Are you struggling with maintaining all your data? Or feeling like you are falling apart? The database management system can help you to organize and maintain all your data without any worries. Learn SQL and big data from this SQL NoSQL Big Data and Hadoop course and manage all data of your business efficiently.

    In this SQL NoSQL Big Data and Hadoop course, you will learn what is data-driven organisation and data engineering and how you can use it. You will learn how you can use a relational database, document-oriented database and other functions. You will be able to understand the function of the search engine, big data and graphs in your database. This course will teach you different advanced techniques of database management to maintain and organize your data in an efficient way.

    This course will teach you how you can use simple database functions for your daily data management. You will also be able to understand and related different data tables for calculating and evaluating large data sets from this course.

    Course design

    The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

    You are taught through a combination of

    • Video lessons
    • Online study materials

    Will I receive a certificate of completion?

    Upon successful completion, you will qualify for the UK and internationally-recognised CPD accredited certification. You can choose to make your achievement formal by obtaining your PDF Certificate at the cost of £9 and Hard Copy Certificate for £15.

    Why study this course

    It doesn’t matter if you are an aspiring professional or absolute beginner; this course will enhance your expertise and boost your CV with critical skills and an accredited certification attesting to your knowledge.

    The SQL NoSQL Big Data and Hadoop is fully available to anyone, and no previous qualifications are needed to enrol. All One Education needs to know is that you are eager to learn and are over 16.

    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

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