Section 01: Introduction |
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Introduction |
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00:07:00 |
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Building a Data-driven Organization – Introduction |
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00:04:00 |
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Data Engineering |
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00:06:00 |
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Learning Environment & Course Material |
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00:04:00 |
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Movielens Dataset |
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00:03:00 |
Section 02: Relational Database Systems |
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Introduction to Relational Databases |
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00:09:00 |
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SQL |
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00:05:00 |
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Movielens Relational Model |
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00:15:00 |
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Movielens Relational Model: Normalization vs Denormalization |
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00:16:00 |
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MySQL |
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00:05:00 |
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Movielens in MySQL: Database import |
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00:06:00 |
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OLTP in RDBMS: CRUD Applications |
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00:17:00 |
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Indexes |
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00:16:00 |
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Data Warehousing |
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00:15:00 |
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Analytical Processing |
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00:17:00 |
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Transaction Logs |
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00:06:00 |
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Relational Databases – Wrap Up |
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00:03:00 |
Section 03: Database Classification |
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Distributed Databases |
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00:07:00 |
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CAP Theorem |
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00:10:00 |
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BASE |
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00:07:00 |
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Other Classifications |
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00:07:00 |
Section 04: Key-Value Store |
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Introduction to KV Stores |
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00:02:00 |
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Redis |
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00:04:00 |
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Install Redis |
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00:07:00 |
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Time Complexity of Algorithm |
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00:05:00 |
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Data Structures in Redis : Key & String |
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00:20:00 |
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Data Structures in Redis II : Hash & List |
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00:18:00 |
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Data structures in Redis III : Set & Sorted Set |
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00:21:00 |
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Data structures in Redis IV : Geo & HyperLogLog |
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00:11:00 |
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Data structures in Redis V : Pubsub & Transaction |
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00:08:00 |
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Modelling Movielens in Redis |
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00:11:00 |
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Redis Example in Application |
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00:29:00 |
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KV Stores: Wrap Up |
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00:02:00 |
Section 05: Document-Oriented Databases |
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Introduction to Document-Oriented Databases |
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00:05:00 |
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MongoDB |
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00:04:00 |
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MongoDB Installation |
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00:02:00 |
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Movielens in MongoDB |
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00:13:00 |
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Movielens in MongoDB: Normalization vs Denormalization |
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00:11:00 |
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Movielens in MongoDB: Implementation |
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00:10:00 |
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CRUD Operations in MongoDB |
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00:13:00 |
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Indexes |
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00:16:00 |
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MongoDB Aggregation Query – MapReduce function |
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00:09:00 |
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MongoDB Aggregation Query – Aggregation Framework |
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00:16:00 |
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Demo: MySQL vs MongoDB. Modeling with Spark |
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00:02:00 |
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Document Stores: Wrap Up |
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00:03:00 |
Section 06: Search Engines |
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Introduction to Search Engine Stores |
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00:05:00 |
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Elasticsearch |
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00:09:00 |
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Basic Terms Concepts and Description |
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00:13:00 |
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Movielens in Elastisearch |
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00:12:00 |
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CRUD in Elasticsearch |
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00:15:00 |
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Search Queries in Elasticsearch |
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00:23:00 |
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Aggregation Queries in Elasticsearch |
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00:23:00 |
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The Elastic Stack (ELK) |
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00:12:00 |
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Use case: UFO Sighting in ElasticSearch |
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00:29:00 |
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Search Engines: Wrap Up |
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00:04:00 |
Section 07: Wide Column Store |
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Introduction to Columnar databases |
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00:06:00 |
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HBase |
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00:07:00 |
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HBase Architecture |
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00:09:00 |
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HBase Installation |
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00:09:00 |
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Apache Zookeeper |
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00:06:00 |
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Movielens Data in HBase |
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00:17:00 |
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Performing CRUD in HBase |
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00:24:00 |
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SQL on HBase – Apache Phoenix |
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00:14:00 |
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SQL on HBase – Apache Phoenix – Movielens |
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00:10:00 |
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Demo : GeoLife GPS Trajectories |
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00:02:00 |
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Wide Column Store: Wrap Up |
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00:04:00 |
Section 08: Time Series Databases |
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Introduction to Time Series |
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00:09:00 |
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InfluxDB |
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00:03:00 |
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InfluxDB Installation |
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00:07:00 |
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InfluxDB Data Model |
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00:07:00 |
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Data manipulation in InfluxDB |
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00:17:00 |
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TICK Stack I |
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00:12:00 |
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TICK Stack II |
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00:23:00 |
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Time Series Databases: Wrap Up |
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00:04:00 |
Section 09: Graph Databases |
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Introduction to Graph Databases |
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00:05:00 |
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Modelling in Graph |
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00:14:00 |
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Modelling Movielens as a Graph |
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00:10:00 |
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Neo4J |
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00:04:00 |
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Neo4J installation |
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00:08:00 |
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Cypher |
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00:12:00 |
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Cypher II |
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00:19:00 |
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Movielens in Neo4J: Data Import |
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00:17:00 |
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Movielens in Neo4J: Spring Application |
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00:12:00 |
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Data Analysis in Graph Databases |
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00:05:00 |
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Examples of Graph Algorithms in Neo4J |
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00:18:00 |
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Graph Databases: Wrap Up |
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00:07:00 |
Section 10: Hadoop Platform |
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Introduction to Big Data With Apache Hadoop |
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00:06:00 |
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Big Data Storage in Hadoop (HDFS) |
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00:16:00 |
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Big Data Processing : YARN |
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00:11:00 |
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Installation |
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00:13:00 |
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Data Processing in Hadoop (MapReduce) |
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00:14:00 |
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Examples in MapReduce |
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00:25:00 |
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Data Processing in Hadoop (Pig) |
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00:12:00 |
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Examples in Pig |
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00:21:00 |
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Data Processing in Hadoop (Spark) |
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00:23:00 |
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Examples in Spark |
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00:23:00 |
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Data Analytics with Apache Spark |
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00:09:00 |
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Data Compression |
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00:06:00 |
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Data serialization and storage formats |
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00:20:00 |
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Hadoop: Wrap Up |
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00:07:00 |
Section 11: Big Data SQL Engines |
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Introduction Big Data SQL Engines |
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00:03:00 |
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Apache Hive |
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00:10:00 |
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Apache Hive : Demonstration |
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00:20:00 |
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MPP SQL-on-Hadoop: Introduction |
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00:03:00 |
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Impala |
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00:06:00 |
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Impala : Demonstration |
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00:18:00 |
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PrestoDB |
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00:13:00 |
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PrestoDB : Demonstration |
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00:14:00 |
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SQL-on-Hadoop: Wrap Up |
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00:02:00 |
Section 12: Distributed Commit Log |
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Data Architectures |
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00:05:00 |
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Introduction to Distributed Commit Logs |
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00:07:00 |
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Apache Kafka |
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00:03:00 |
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Confluent Platform Installation |
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00:10:00 |
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Data Modeling in Kafka I |
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00:13:00 |
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Data Modeling in Kafka II |
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00:15:00 |
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Data Generation for Testing |
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00:09:00 |
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Use case: Toll fee Collection |
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00:04:00 |
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Stream processing |
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00:11:00 |
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Stream Processing II with Stream + Connect APIs |
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00:19:00 |
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Example: Kafka Streams |
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00:15:00 |
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KSQL : Streaming Processing in SQL |
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00:04:00 |
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KSQL: Example |
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00:14:00 |
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Demonstration: NYC Taxi and Fares |
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00:01:00 |
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Streaming: Wrap Up |
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00:02:00 |
Section 13: Summary |
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Database Polyglot |
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00:04:00 |
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Extending your knowledge |
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00:08:00 |
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Data Visualization |
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00:11:00 |
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Building a Data-driven Organization – Conclusion |
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00:07:00 |
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Conclusion |
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00:03:00 |
Resources |
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Resources – SQL NoSQL Big Data and Hadoop |
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00:00:00 |
Assignment |
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Assignment -SQL NoSQL Big Data and Hadoop |
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3 weeks, 3 days |
Order Your Certificate |
|
Order Your Certificate QLS |
|
00:00:00 |