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A 3 Step Guide to Getting Started with NoSQL I have been looking in to NoSQL databases for few months and would like to share my experience with it. This is a post might help you if you indent to start learning about the NoSQL Databases. I would try to link the resources which I found useful here.
NoSQL is not just about BigData There is so much debate on the SQL vs NoSQL subject, and probably this is our natural way of understanding and learning what’s the best way of storing data. After publishing the small experiment on MongoDB aggregating framework, I was challenged by the JOOQ team to match my results against Oracle. Matching MongoDB and Oracle is simply honoring Mongo, as Oracle is probably the best SQL DB engine.
Simple CRUD using Servlet 3.0, Redis/Jedis and CDI In this post we will build a simple user crud. The data will be stored in Redis. To interact with Redis we will use Jedis library. CDI for Depedency Injection and Servlet 3.0 for the view. Let’s start with the Redis/Jedis part. You can find some overview on Redis and Jedis in these posts.
MongoDB and Web applications Current scenarios call for a need of NoSQL Databases like Apache Cassandra, Mongo DB to deal with such ever increasing unstructured data. NoSQL database provides looser consistency models than traditional RDBMS for storage and retrieval of data. NoSQL databases store the data as highly optimized key–value pairs, and this leads to simple retrieval and appending operations, hence increasing the performance in terms of low latency and high throughput.
Practical NoSQL experiences with Apache Cassandra Most of the backend systems I’ve worked with over the years have employed relational database storage in some role. Despite many application developers complaining about RDBMS performance, I’ve found that with good design and implementation a relational database can actually scale a lot further than developers think. Often software developers who don’t really understand relational databases tend to blame the database for being a performance bottleneck, even if the root cause could actually be traced to bad design and implementation.