MongoDB for Java Software Engineers

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MongoDB for Java Software Engineers

BD-345

MongoDB is a leading document database that changes the world developers known so far: no schema constraints, instant object persistency, rapid high availability, amazing performance and scale out support for the cloud generation.
This course provides a developer level introduction along with more advanced and useful features. The course will include hands-on practice with MongoDB and Java, creating a complete system from data design to implementation using Java and integration with BI and dashboard tools.

Note: This course can be adjusted to additional programming languages (except for Java) and other target audiences including DBA and DevOps based on customer request.

Audience

Target Audience: 
Software development managers, CTO, software architects, system architects, data architects and developers.

Previous knowledge and Requirements:
Software development and database design, experience with Linux and access to a Linux machine

** This course is designed for a team w/ up to 15 people in class.

Course Topics

Day 1

Module 1: Introduction to NoSQL

CAP Theorem
What are the main concerns with RDBMS (SQL Server, Oracle, MySQL)
Key-value stores
Column Family stores
Document DBs
Map Reduce

Module 2: Introduction to MongoDB

MongoDB product design and architecture
MongoDB installation
The Mongo Shell
Basic Operations
Lab: MongoDB installation and basic operations

Module 3: Data Model Design

Documents and collections core concepts
Data Model migration from rational DB to document store
Data model best practices
Lab: Data model design for the case

Day 2

Module 4: CRUD

Select
Update
Insert
Delete
Atomic Transactions
Bulk Operations
Lab: System implementation based on Java and MongoDB

Module 5: Tuning your code

Indexing
Query profiling
The query optimizer
Explain
Lab: Profile and tune queries

Day 3

Module 6: Backup, Security and Monitoring

Dump
Physical files backup
Authentication
Server status and system monitoring

Module 7: Scale and High Availability

Data replication with replica sets
Load distribution with sharding
Performance best practices
Aggregation Framework
Lab: Reporting implementation using MongoDB

Module 8: BI Integration

ELK stack (ElasticSearch and Kibana) integration w/ Mongo
Pentaho as a Reporting tools
Lab: Setup a Kibana dashboard based on the collected data

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