4 OLT courses in London

Business Intelligence: Virtual In-House Training Business Intelligence (BI) refers to a set of technology-based techniques, applications, and practices used to aggregate, analyze, and present business data. BI practices provide historical and current views of vast amounts of data and generate predictions for business operations. The purpose of Business Intelligence is the support of better business decision making. This course provides an overview of the technology and application of BI and how it can be used to improve corporate performance. What you will Learn You will learn how to: Specify a data warehouse schema Identify the data and visualization to be used for data mining and Business Intelligence Design a Business Intelligence user interface Getting Started Introductions Agenda Expectations Foundation Concepts The challenge of decision making What is Business Intelligence? The Business Intelligence value proposition Business Intelligence taxonomy Business Intelligence management issues Sources of Business Intelligence Data warehousing Data and information Information architecture Defining the data warehouse and its relationships Facts and dimensions Modeling, meta-modeling, and schemas Alternate architectures Building the data warehouse Extracting Transforming Loading Setting up the data and relationships Dimensions and the Fact Table Implementing many-to-many relationships in data warehouse Data marts Online Analytical Processing (OLAP) What is OLAP? OLAP and OLTP OLAP functionality Multi-dimensions Thinking in more than two dimensions What are the possibilities? OLAP architecture Cubism Tools OLAP variations - MOLAP, ROLAP, HOLAP BI using SOA Applications of Business Intelligence Applying BI through OLAP Enterprise Resource Planning and CRM Business Intelligence and financial information Business Intelligence User Interfaces and Presentations Data access Push-pull data access Types of decision support systems Designing the front end Presentation formats Dashboards Types of dashboards Common dashboard features Briefing books and scorecards Querying and Reporting Reporting emphasis Retrofitting Talking back Key Performance Indicators Report Definition and Visualization Typical reporting environment Forms of visualization Unconstrained views Data mining What is in the mine? Applications for data mining Data mining architecture Cross Industry Standard Process for Data Mining (CISP-DM) Data mining techniques Validation The Business Intelligence User Experience The business analyst role Business analysis and data analysis Five-step approach Cultural impact Identifying questions Gathering information Understand the goals The strategic Business Intelligence cycle Focus of Business Intelligence Design for the user Iterate the access Iterative solution development process Review and validation questions Basic approaches Building ad-hoc queries Building on-demand self-service reports Closed loop Business Intelligence Coming attractions - future of Business Intelligence Best practices in Business Intelligence

This course focuses on the beginner-level concepts of cloud computing in two different arenas. The first part is to explore the world of database technologies or DBaaS (Database as a Service) and the second part revolves around IaaS (Infrastructure as a Service) model.

Prepare for the AWS Certified Solutions Architect - Associate (SAA-C03) exam. Learn about the AWS Management Console, S3 buckets, instances, database services, cloud security, costs associated with AWS, Amazon Elastic Compute Cloud (EC2), Amazon Virtual Private Cloud (VPC), Amazon Simple Storage Service (S3), and Amazon Elastic Block Store (EBS).

With this 2-in-1 course, you will get access to AWS Technical Essentials and AWS Certified Solutions Architect - Associate certification exam content.

Register on the SQL NoSQL Big Data and Hadoop today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion. The SQL NoSQL Big Data and Hadoop is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The SQL NoSQL Big Data and Hadoop Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) 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 Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the SQL NoSQL Big Data and Hadoop, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content 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 Resources Resources - SQL NoSQL Big Data And Hadoop 00:00:00

Overview This comprehensive course on SQL NoSQL Big Data and Hadoop will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This SQL NoSQL Big Data and Hadoop comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? At the end of the course there will be an online written test, which you can take either during or after the course. After successfully completing the test you will be able to order your certificate, these are included in the price. Who is This course for? There is no experience or previous qualifications required for enrolment on this SQL NoSQL Big Data and Hadoop. It is available to all students, of all academic backgrounds. Requirements Our SQL NoSQL Big Data and Hadoop is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Learning this new skill will help you to advance in your career. It will diversify your job options and help you develop new techniques to keep up with the fast-changing world. This skillset will help you to- Open doors of opportunities Increase your adaptability Keep you relevant Boost confidence And much more! Course Curriculum 14 sections • 130 lectures • 22:34:00 total length •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 •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 •Distributed Databases: 00:07:00 •CAP Theorem: 00:10:00 •BASE: 00:07:00 •Other Classifications: 00:07:00 •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 •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 •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 •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: 00:05:00 •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 •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 •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 •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 •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 •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 •Assignment -SQL NoSQL Big Data and Hadoop: 00:00:00