Loading...

Dotnet core features top 10

The action can be an operation that returns a value to the calling application or export data to the storage system Examples of actions are count, collect, save, etc

The action can be an operation that returns a value to the calling application or export data to the storage system Examples of actions are count, collect, save, etc These actions are different from transformations and are close to the reduce functionality of Map-Reduce RDD can be persisted for future computation, that is, RDD can be kept in memory or saved on disk This means RDDS can be reloaded and kept in memory. You can even treat the RDD as an SQL-store, using a concept called Data-Frames.

Dotnet core features

Example, if you have 10 records, you can update or delete any record. So, DSM is treated as a in-memory database in which you can look at or update/delete any record. Abstraction is always at the level of a single-record Deals with access to arbitrary memory locations In RDD, a new RDD can be created as result of a transformation of an existing RDD. Here, you cannot point to individual records or you are not talking about pointed-updates. Here you are only talking about transformations. Abstraction is always at the level of a set of records. Deals with transformations.

There is more than one language possibility for writing a Spark application. In this module, we will use Scala. There are other language avenues for writing a Spark application. The most obvious one is Java. Spark also supports Python. Spark even supports another big data language, R.

Step By Step process on new technologies