When Cassandra Makes Sense

Anmol Sarna explains the pros and cons of using Apache Cassandra:

But as we know nothing is perfect. So is the Cassandra Database. What I mean by this is that you cannot have a perfect package. If you wish for one brilliant feature then you might have to compromise on the other features. In today’s blog, we will be going through some of the benefits of selecting Cassandra as your database as well as the problems/drawbacks that one might face if he/she chooses Cassandra for his/her application.
I have also written some blogs earlier which you can go through for reference if you want to know What Cassandra isHow to set it up and how it performs its Reads and Writes.

The only question we have is that should we or should we not pick Cassandra over the other databases that are available. So let’s start by having a quick look at when to use the Cassandra Database. This will give a clear picture to all those who are confused in decided whether to give Cassandra a try or not.

This is a level-headed analysis of Cassandra, so check it out.

Related Posts

Working with Columns in Spark

Achilleus has a two-parter on working with columns in Spark. Part 1 covers some of the basic syntax and several functions: Also, we can have typed columns which is basically a column with an expression encoder specified for the expected input and return type. scala> val name = $"name".as[String]name: org.apache.spark.sql.TypedColumn[Any,String] = namescala> val name = […]

Read More

Selecting a List of Columns from Spark

Unmesha SreeVeni shows us how we can create a list of column names in Scala to pass into a Spark DataFrame’s select function: Now our example dataframe is ready.Create a List[String] with column names.scala> var selectExpr : List[String] = List("Type","Item","Price") selectExpr: List[String] = List(Type, Item, Price) Now our list of column names is also created.Lets […]

Read More

Categories

August 2018
MTWTFSS
« Jul Sep »
 12345
6789101112
13141516171819
20212223242526
2728293031