This course provides application developers with technical overview of Big Data and NoSQL (Not Only SQL) database systems. Effective use of NoSQL systems and understanding the appropriate ways of handling Big Data leads to the creation of the next-generation of high-performance and robust solutions. This intensive training course aims at making students cognisant of NoSQL systems capabilities and how they can be leveraged.
Cost: Price on application
Duration: 3 Days
This course is not available as part of our public schedule but can be provided on a customised client specific basis.
This course is aimed at technical leads and application developers.
Delegates should be familiar with Java programming using the Eclipse development environment.
Delegates will understand:
Defining Big Data.
Big Data Stores Overview.
Big Data Business Intelligence and Analytics.
Google App Engine Interfaces.
Working with MongoDB.
Defining Big Data:
Transforming Data into Business Information.
Definition of Big Data.
Challenges Posed by Big Data.
The Cloud and Big Data.
The Business Value of Big Data.
Big Data: Hype or Reality?
Big Data Systems Overview:
Limitations of Relational Databases.
NoSQL Database Systems.
The CAP theorem.
Limitations of NoSQL Databases.
Big Data Sharding.
Amazon S3 Security.
Data Lifecycle Management with Amazon S3.
Amazon S3 Cost Monitoring.
Google App Engine.
App Engine Billing.
Hadoop Core Components.
Hadoop Distributed File System.
MongoDB Operational Intelligence.
MongoDB Use Cases.
Big Data Business Intelligence and Analytics:
Comparison with Other Systems.
NoSQL Data Querying and Processing.
Analyzing Big Data with Hadoop.
Making things simpler with Pig Latin.
Example of a Pig Script in Batch Mode.
Amazon Elastic MapReduce.
Business Analytics with Hive.
The UnQL Specification.
Working with Google App Engine:
Big Data in Google App Engine.
App Engine Datastore.
Google Cloud SQL.
Google Cloud Storage.
Java Data Store API.
App Engine Services.
Working with MongoDB:
Drivers and Client Libraries.
MongoDB Data Model.
Security and Authentication.
Data import and Export.
Managing MongoDB Lifecycles.
Querying, Limiting, Sorting and Aggregating data.
Optimizing Queries with Indexes.