What users are saying about

Apache Spark

99 Ratings

Amazon Redshift

88 Ratings

Apache Spark

99 Ratings
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Score 8.6 out of 101

Amazon Redshift

88 Ratings
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Score 8.4 out of 101

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Likelihood to Recommend

Apache Spark

Apache Spark has rich APIs for regular data transformations or for ML workloads or for graph workloads, whereas other systems may not such a wide range of support. Choose it when you need to perform data transformations for big data as offline jobs, whereas use MongoDB-like distributed database systems for more realtime queries.
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Amazon Redshift

Redshift is ideal for small teams. It is fully managed. CloudWatch metrics are provided out-of-the-box, and it integrates well with other AWS products, such as DMS. The Redshift console is among the better AWS consoles. Redshift offers adequate performance. Spectrum offers a convenient way to access our data lake, but we have encountered issues with recent versions.
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Pros

  • Machine Learning.
  • Data Analysis
  • WorkFlow process (faster than MapReduce).
  • SQL connector to multiple data sources
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  • Redshift is fully managed. Small teams do not have the resources to maintain a cluster. CloudWatch metrics are provided out-of-the-box, and it is easy to configure alarms.
  • Redshift's console allows you to easily inspect and manage queries, and manage the performance of the cluster.
  • Redshift is ubiquitous; many products (e.g., ETL services) integrate with it out-of-the-box.
  • Writing .csvs to S3 and querying them through Redshift Spectrum is convenient.
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Cons

  • Resource heavy, jobs, in general, can be very memory intensive and you will want the nodes in your cluster to reflect that.
  • Debugging, it has gotten better with every release but sometimes it can be difficult to debug an error due to ambiguous or misleading exceptions and stack traces.
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  • VACUUM is a pain, its unclear exactly how often it needs to be done.
  • Redshift has a limit on how many concurrent writes and reads you can do that won't scale to 100s of people using it.
  • Redshift lacks some Postgres queries that make some standard SQL operations hard.
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Alternatives Considered

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
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Redshift may have the lowest barrier to entry for adopting any columnar database. This is mainly due to (1) the ease of use (signup/setup/etc) common to many Amazon Web Services and (2) since Redshift started as a fork of PostgreSQL (8.4) this eases the transition from RDBMS (principles/semantics/etc) to columnar databases. Once you gain an understanding of columnar databases and the use-cases for which they are a good fit, you'll be in a much better position to evaluate the many emerging (and compelling) alternatives available (especially those that are free and open source)
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Return on Investment

  • We were able to make batch job faster by 20 times as compared to MapReduce
  • With the language support like Scala, Java, and Python, easily manageable
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  • Amazon Redshift is our main data source. We use it for almost every analysis we made. These analyses are driving our decisions and strategy.
  • Prezi is a data-driven company. Every decision we make is based on an analyses or a dashboard using datasets on Redshift.
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Pricing Details

Apache Spark

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Amazon Redshift

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details