#apachetraining

There can be a great career opportunity for the Apache Spark Certification trainee which is lightning faster.

Apache Spark is mainly developed for the data science purpose to make abstraction easier. Spark can provide you APIs of very high level for Java, Scala, Python and R Programming.

You may get surprised to know that in data processing, Apache Spark is the largest open source project.

Let me Know you some Sparkling Features of Apache Spark :-

1) Dynamic in Nature

We can quickly produce a parallel application, as Spark grants 80 high-level operators.

2)  Reusability

The Apache Spark code can be reused for batch-processing, join stream toward historical data or run ad-hoc queries.

3) Cost Efficient

Apache Spark is a much better cost-effective solution for handling Big data problem because in Hadoop large capacity for storage and the large data center is need during replication.

4) Support for Complicated Analysis

Spark introduced with dedicated tools for a high level of data management which includes streaming data, declarative/interactive queries, machine learning map reduce.

5) Swift Processing

Working with Apache Spark, you can produce a high level of data processing speed which is about 100 times faster in memory and 10 times faster on the disk. This is made feasible by decreasing the number of read-write to disk.

6) Integrated with Hadoop

You will get surprised to know that spark can run independently also and with Hadoop YARN Cluster Manager also and thus it can read existing Hadoop data. which makes Spark flexible.

7) Support Multiple Languages

It is very unique and additional advantage of Apache Spark that it Support for multiple languages like Java, R, Scala, Python. Thus, it provides dynamicity and overcomes the limitation of Hadoop that it can build applications only in Java.

8) Memory Computation in Spark

With in-memory processing, we can improve the data processing speed. Here the data is being stored so there is no need to fetch data from the disk every time which saves your precious time. The reason behind it is Spark has DAG execution engine which helpful in-memory computation and acyclic data flow which results in high speed.

9) Lazy Evaluation in Apache Spark

All the changes which we make in Apache Spark RDD are Idle in nature, that is it does not give the decision at the correct away rather a new RDD is formed from the current one. Consequently, this improves the performance of the system.

Now here comes the most important feature of Apache Spark i.e,.

10) Fault Tolerance in Spark

Apache Spark gives you fault tolerance by Spark abstraction-RDD. Spark RDDs are developed for handling the failure of any working node of the cluster which guarantees the loss of data reduced to zero.