In the last section we added a latest_videos table. Now we have two tables to store video data. This is a form of denormalization we use with Apache Cassandra™ in order to scale our application. But how do we keep denormalized tables in sync? Cassandra has a feature known as BATCH to help solve the denormalization issues.
Welcome to the Application Development training session using DataStax Apache Cassandratm as a Service (CaaS). In this course you will:
Spin up your own cloud Database
Connect to it using the DataStax Driver
Implement functionality in the video sharing website killrvideo.com
You will see that connecting your application to a Cassandra database is a snap. Pulling and mutating data with the driver is simple to do. Click on the Exercises tab above and we will introduce you to the features of this short course.
When data modeling for Apache Cassandratm, sometimes an organic data model may have some performance problems. In these cases, it is useful to understand how to use buckets to help improve your performance. This unit should make you a bucket expert!
In previous units we learned how to insert rows using simple strings and QueryBuilder. In this unit we advance to using PreparedStatement. PreparedStatements bring all kinds of good optimizations to your app, and they are easy to incorporate!
In previous units, when we wanted to use CQL from the driver, we used simple strings. In the next few units we will show you some more sophisticated approaches that have performance and style improvements. In this unit we will show you how to use QueryBuilder to improve your driver-based CQL queries.
In previous units we learned how to build the login functionality for KillrVideo. But, it's not very helpful to have users login if they can't register with the site! In this unit we'll build out the code to support user registration.
As we have learned, Apache Cassandratm does not do a read before a write, but sometimes we need Cassandra to do the read before we write - and we need it to be atomic. This is where Lightweight Transactions come in. In this unit we'll understand lightweight transactions and learn how to use them.
In this unit we will create the second of the two methods necessary to allow user login in your KillrVideo app!
In the last section we added a 
When data modeling for Apache Cassandratm, sometimes an organic data model may have some performance problems. In these cases, it is useful to understand how to use buckets to help improve your performance. This unit should make you a bucket expert!
In previous units we learned how to insert rows using simple strings and 
In previous units, when we wanted to use CQL from the driver, we used simple strings. In the next few units we will show you some more sophisticated approaches that have performance and style improvements. In this unit we will show you how to use 
In previous units we learned how to build the login functionality for KillrVideo. But, it's not very helpful to have users login if they can't register with the site! In this unit we'll build out the code to support user registration.
As we have learned, Apache Cassandratm does not do a read before a write, but sometimes we need Cassandra to do the read before we write - and we need it to be atomic. This is where Lightweight Transactions come in. In this unit we'll understand lightweight transactions and learn how to use them.
We created a couple of user-related tables and inserted some user rows. In this unit, we'll see how to access one of these tables using Java code.