Skip to main content

What is Apache Cassandra?


Apache Cassandra is a NoSQL database technology system. To understand Cassandra deeply, one first must go through the concept of database, especially non-relational or NoSQL. Cassandra is important for business, and is also becoming popular for its features which we will see later in the post. Well for now, lets’ first its history as in where it came from and how.


It was first build by Facebook for carrying out search enquiries. In 2008, it was declared as an open source technology as people can use it for free and contribute towards its growth. Next, in 2009 it was accepted by Apache and now it is an Apache-based project. Since it is an open-source, it is free. Well, there is Datastrax version, it is paid one and it works on top of Apache Cassandra. The paid version one can help you in processing and monitoring data quite fast. However, most of the companies prefer using Apache Cassandra, which is also used by learners and developers.

Apache Cassandra Overview:
Apache Cassandra lies somewhere between Google’s Bigtable and Amazon’s Dynamo. It is one of the popular NoSQL databases, like Mongo DB and Apache HBase. Cassandra is open source, works on distributed technology, and in CAP theorem, it fits for AP, means availability and partition (fault tolerance). Cassandra’s architecture consists of nodes only, where collective nodes are called clusters. It doesn’t work on Master and Slave node method like Hadoop, well here every node is equal and one can retrieve or process data from any node, even if one node goes down. And for data replication, users can create as many as replicas as they wish. Because of distributed node system, Cassandra processes data very fast and there is no risk of losing data. Another great aspect is that user can add as many nodes as possible for data storage and it scales horizontally. Adding data nodes in Cassandra is extremely helpful and easy.

Features:

Writes Fast: Cassandra is designed in such a way that it can store massive amount of data along with the ability to write data really fast, without affecting the schema on-read factor.

Transaction Support: Cassandra supports ACID: Atomicity, Consistency, Isolation, Durability. For online transactions Cassandra is unbeatable.

Amazing Distribution: Because of node system methodology, it is easy to replica data and, data can be accessed from any data center.

Flexible Data Storage: Since Cassandra is NoSQL database, it can accommodate structured, semi-structured and unstructured database without the hassles of schema on-read and -write.

Scalable: If the need arises to increase data, well you can add nodes to it in a horizontal way. In RDBMS, horizontal scaling is not possible, thus Cassandra is gaining momentum. Or we can say that it offers linear performance, which assures quick response time while performing a query in data.

Almost Failure Free: Since according to CAP theorem, Cassandra provides A&P, it is considered that Cassandra records no failure, because upon failure of one node, users can access another node. Here all nodes are equal.

Cassandra is not a substitute for RDBMS, well it is an alternate to it. Many big companies across the world use it for its phenomenal performance record. In the next post, we will see about its architecture.  

Comments

  1. Je pense justement à faire une liste d'idées de blogs.

    ReplyDelete
  2. hi!,I love your writing very much! proportion we
    keep up a correspondence more about your article on AOL?
    I need a specialist in this space to unravel my problem.

    May be that is you! Taking a look forward to look you.

    ReplyDelete
  3. You really make it seem so easy with your presentation but I
    find this matter to be actually something which I think I would never understand.
    It seems too complicated and very broad for
    me. I'm looking forward for your next post, I'll try to get the hang of it!

    ReplyDelete
  4. I would like to thank you for the efforts you've put in writing
    this website. I'm hoping to check out the same high-grade blog posts from you later on as well.
    In truth, your creative writing abilities has inspired me to
    get my own website now ;)

    ReplyDelete
  5. For newest information you have to go to see internet and on web I found this website as a best website for hottest
    updates.

    ReplyDelete
  6. Hi, i think that i saw you visited my website thus i came to “return the favor”.I am
    attempting to find things to improve my web site!I suppose its ok
    to use some of your ideas!!

    ReplyDelete
  7. Since the admin of this website is working, no
    uncertainty very rapidly it will be renowned, due to its quality contents.

    ReplyDelete

Post a Comment

Popular posts from this blog

Why is Python becoming a Trend among Data Scientists?

Internet technology has set the world on fire. New revolutions are always around the corner. But did you ever notice that nowadays new revolutions are mostly based on technology and driven by data. It is data that is being generated everywhere via the internet. So what’s big deal about it? Well, the data we get from Internet is big data. Websites, social media, servers and so on...all contribute for data. It is data that is driving the demand-supply chain that serves the human race. Since we have been generating humongous amount of data every day, we have data scientists who drive value from it, so that humans can lead life of meaning and purpose and of convenient.


We now got hunch that Python has something to do with big data and work profile of data scientists. Now let’s get back to the point and seek answers as why data scientists are loving languages like Python and R over the traditional programming languages.
Let the pictures below speak for them, as a picture speaks a thousan…

What Topics in Python Should You learn for Data Analysis?

First off, understand there is difference between developing full-fledged software and doing data analysis using Python as a programming language. Clearly, here your aim is to do data analysis using Python, so learning Python becomes imperative for you. Right? Well, most of the people new to ‘big data’ and ‘data science’ go pell-mell, as they do not know where the correct essence of learning lies. They think that learning Python from A to Z will make them smarter, may be it can, but that's too much time consuming. As a new aspirant, you should be able to make out as what you should exactly learn for doing data analysis using Python.

In this post, we will go through the most-likely path which will make you self-confident in Python and subsequently in data analysis.

Step 1 - Basics:
Your learning process starts with rudimentary knowledge. Learning resources for general are different than selected learning. So, be it anything, you must learn the basics involved in Python. To learn…