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Benefits and Insights

Why use Spark?

Key differentiators & advantages of Spark

  • Free and Open Source: Users get free, unlimited access to Spark and all its offerings. They are free to modify and distribute it as they see fit, regardless of intent of use. An extensive community props it up to develop extensions and offer support. 
  • Advanced Processing: It enables insights to develop quickly through highly advanced processing techniques, including interactive data processing, distributed databases, in-memory processing to enable real-time data streaming and sophisticated analytics like machine learning. 
  • Functioning Versatility: A project does not need to completely change vendors to utilize Spark. It functions stand-alone or can be integrated into most mainstream big data systems. 
  • Ease Of Use: Users say Spark is comparatively easy to use in the landscape of big data processors. Prebuilt APIs make connections to datasets and third-party analytics components easier to execute.  
  • Fault Tolerance: Users need not worry about a crash that costs them all their data. Spark recovers operator state and lost work out-of-the-box, without the need for software extensions.  

Industry Expertise

Apache has been in the data analytics market since 1999 and has risen to be the premier provider of open-source solutions. Spark alone has more than 1000 contributors from at least 250 organizations and has become a near-essential tool for a big data project and a prerequisite integration option for end-to-end BDA solutions.

Key Features

  • Standalone Mode: Standalone mode is a web-based cluster manager for creating and distributing clusters on local machines, without using YARN or Apache Mesos. It can be used for local data processing or testing on a smaller scale. 
  • GraphX: A series of API that enable graph-parallel computation and graph generation within the system. It can accomplish ETL, iterative graphing and exploratory analysis. 
  • Machine Learning: The MLlib library enables machine learning at a big data level. It works with Python, R and Scala, and features machine learning pipeline construction and a community-supported set of algorithms. 
  • Distributed Datasets: Datasets are partitioned into smaller segments for distributed processing, called Resilient Distributed Datasets. RDDs are created by parallelizing a set or referencing an external one. 
  • Data Streaming: Spark Streaming is an extension that allows for a continuous data flow, enabling real-time analytics. It receives live data in a stream that it partitions into batches before sending it to the Spark Engine for processing through high-level abstraction called discretized stream.  
  • Integrations: Because it is open source, a vast community is constantly adding extensions and API to the core software. Spark can connect to virtually every mainstream data source, big data solution, warehouse/lake or visualization program. If the connector does not already exist, it could likely be developed. 

Limitations

Some of the product limitations include:
  •  Security is defaulted to off, potentially meaning deployments are vulnerable to attack 
  •  Backwards compatibility doesn’t appear to be supported in newer versions 
  •  Caching algorithm must be manually set up 
  •  In-memory processing occupies a large amount of memory  

Spark Suite Support

Apache does not offer traditional support for its products, rather relying on providing documentation and the open-source community to answer questions.
mail_outlineEmail:The vendor does not provide email support.
phonePhone: Phone support is not provided.
schoolTraining: The vendor provides documentation for all of its releases. Most training is accomplished through asking questions on Apache’s StackOverflow forum, where more than 58,000 posts have been created.
local_offerTickets: Ticket support is not offered.
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