Apache Spark

Apache Spark

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Score 8.7 out of 100
Apache Spark

Overview

Recent Reviews

Apache Spark in Telco

10 out of 10
July 22, 2021
Apache Spark is being widely used within the company. In Advanced Analytics department data engineers and data scientists work closely in …
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A powerhouse processing engine.

9 out of 10
September 19, 2020
We use Apache Spark for cluster computing in large-scale data processing, ETL functions, machine learning, as well as for analytics. Its …
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Apache Spark Review

7 out of 10
March 16, 2019
We used Apache Spark within our department as a Solution Architecture team. It helped make big data processing more efficient since the …
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What is Databricks Lakehouse Platform?

Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data…

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What is Apache Spark?

Apache Spark Technical Details

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Reviews and Ratings

 (147)

Ratings

Reviews

(1-22 of 22)
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Thomas Young | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • In one sense, Apache Spark has been a positive ROI because it helps us figure out details of the vast amounts of data. Sometimes the software leads to answers to questions that are surprising. Small data software tools probably would have failed in discovering some of the insights Spark makes possible.
  • Spark has been a negative ROI in the sense that it takes lots and lots of time to produce simple answers to simple questions, and often the answers are what was expected. Because of the confirmatory rather than insightful nature of the software, it seems like a lot of effort for the results garnered.
  • Apache Spark represents a positive ROI on the instances when it gives a well-producing machine learning model, a model that produces predictions that actually get used.
Surendranatha Reddy Chappidi | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Business leaders are able to take data driven decisions
  • Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
  • Business is able come up with new product ideas
Carla Borges | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
  • It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
  • Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
  • It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.
Nitin Pasumarthy | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Review Source
  • Switching from PIG Latin to Apache Spark sped up the overall development time and also the resource utilization has gone up.
  • Our offline jobs also run faster than traditional map-reduce like systems.
  • Integrating with Jupyter like notebook environments, the development experience becomes more pleasant and we can iterate much faster.
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
Jordan Moore | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • By learning Spark, we can become certified and/or provide proper recommendations or implementations on Spark solutions.
  • With a background in Hadoop distributed processes, it has been easy to understand and diagnose how Spark handles the transfer of data within a cluster. Especially when using YARN as the resource manager and HDFS as the data source.
  • Staying up to date with the latest changes to Spark has become a repetitive task. While most Hadoop distributions only support Spark 1.6 at the moment, Spark 2.0 has introduced some useful features, but those require a re-write of existing applications.