Choosing Spark in the Cloud: Why and how?

Spark is a distributed, general-purpose data processing engine with multiple applications. Programming languages supported by Spark include Python, Java, Scala, and R. Application developers and data scientists use Spark to analyze the query and transform data at scale rapidly. ETL, data ingestion, IoT, machine learning tasks, SQL batch jobs across large data sets, and processing streaming data from sensors are some of the frequently performed tasks in Spark.

Enterprises consider Spark to manage for its speed, unified design that enables users to combine streaming analytics, an easy-to-use programming model, interactive queries, graph computation, and machine learning within a single system. It simplifies user experience and offers a high-performance platform ideal for data exploration and end-to-end data pipelines.  Since it is available as an open-source platform, Spark can be downloaded and run by anyone on-premise. However, the chances of failure are higher in enterprises running Spark on-premise as against those that run in the cloud.

Some reasons why Spark is more successful in the cloud than on-premise are:

1)      Infrastructure management is a roadblock. Enterprises running Spark on-premise need six months to utilize their big data infrastructure into production. Spark, as a time-efficient technology, helps businesses leverage timely completion of tasks with several people contributing continuously.

2)      Operationalizing data lake architecture is the next consideration when Spark clusters are up and running. 

Although data scientists work with languages such as Python and R, they must know the trick to import the data and get the job up and running. Running the analytics and collaborating with colleagues at the same time is not an easy task while working with Spark. Also, the toolchain required for working with standalone Spark restrains users.

3)      Once all the queries and models are tested out, it’s time to move them into production. The production process entails turning the model over to engineering to re-implement everything you want in your all-new infrastructure.

Spark is highly preferred because its capabilities are accessible via a set of rich APIs that are designed to allow quick and easy interaction with data at scale. These APIs are well-documented and structured in a manner that will enable the smooth functioning of Spark. Overall, Spark is designed for speed and operates unrestricted in both memory and disk.

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Created on May 4th 2020 00:06. Viewed 237 times.

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