Spark

What is Spark?

Apache Spark is an open-source tool. This framework can run in a standalone mode or on a cloud or cluster manager such as Apache Mesos, and other platforms. It is designed for fast performance and uses RAM for caching and processing data.

Spark was introduced by Apache Software Foundation for speeding up the Hadoop computational computing software process.

Who is Founder of Spark?

Matei Zaharia is a Romanian-Canadian computer scientist, educator and the creator of Apache Spark

What are Spark big data workloads?

. This includes MapReduce-like batch processing, as well as real-time stream processing, machine learning, graph computation, and interactive queries. With easy to use high-level APIs, Spark can integrate with many different libraries, including PyTorch and TensorFlow. To learn the difference between these two libraries, check out our article on PyTorch vs. TensorFlow.

What is Spark used for?

Apache Spark is an open-source, distributed processing system used for big data workloads. 
Based on  in-memory caching, and optimized query execution for fast analytic queries against data of any size.It very popular now a days

What are language Spark support?

Apache Spark has built-in support for Scala, Java, R, and Python with 3rd party support for the .NET CLR and more

There are five main components of Apache Spark:

Apache Spark Core. The basis of the whole project. Spark Core is responsible for necessary functions such as scheduling, task dispatching, input and output operations, fault recovery, etc. Other functionalities are built on top of it.

Spark Streaming. This component enables the processing of live data streams. Data can originate from many different sources, including Kafka, Kinesis, Flume, etc.

Spark SQL. Spark uses this component to gather information about the structured data and how the data is processed.

Machine Learning Library (MLlib). This library consists of many machine learning algorithms. MLlib’s goal is scalability and making machine learning more accessible.

Are Spark and Hadoop the same?

Apache Hadoop and Apache Spark are both open-source frameworks for big data processing with some key differences. Hadoop uses the MapReduce to process data, while Spark uses resilient distributed datasets (RDDs).
As against a common belief, Spark is not a modified version of Hadoop and is not, really, dependent on Hadoop because it has its own cluster management. Hadoop is just one of the ways to implement Spark.
Spark uses Hadoop in two ways – one is storage and second is processing. Since Spark has its own cluster management computation, it uses Hadoop for storage purpose only.

Can we run Spark without Hadoop?

Yes, Apache Spark can run without Hadoop, standalone, or in the cloud. Spark doesn't need a Hadoop cluster to work. Spark can read and then process data from other file systems as well

Is Spark easier than Hadoop?

Spark has been found to run 100 times faster in-memory, and 10 times faster on disk. It's also been used to sort 100 TB of data 3 times faster than Hadoop MapReduce on one-tenth of the machines.

What is RDD?

RDD stands for Resilient Distribution Datasets. It is a fault-tolerant collection of parallel running operational elements. The partitioned data of RDD is distributed and immutable. There are two types of datasets:

Parallelized collections: Meant for running parallelly.

Hadoop datasets: These perform operations on file record systems on HDFS or other storage systems.

What is YARN in Spark?

YARN is one of the key features provided by Spark that provides a central resource management platform for delivering scalable operations throughout the cluster.

YARN is a cluster management technology and a Spark is a tool for data processing.