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| 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 | |||