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Score 8.6 out of 101
88 Ratings
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Score 8.9 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.
Nitin Pasumarthy profile photo

Elasticsearch

As the name implies, when you need to search thousands, millions, or billions text-based documents for keywords, Elasticsearch is great. The way it indexes and internally analyzes the content of your documents is very powerful. Assuming you have enough servers in your cluster with fast enough storage, querying those documents becomes a breeze.
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Pros

  • Machine Learning.
  • Data Analysis
  • WorkFlow process (faster than MapReduce).
  • SQL connector to multiple data sources
Anson Abraham profile photo
  • It allows extremely fast search and filtering on large datasets
  • It has a very powerful aggregation engine that can allow for tons of customizable analytics and reports.
Josh Kramer profile photo

Cons

  • Memory management. Very weak on that.
  • PySpark not as robust as scala with spark.
  • spark master HA is needed. Not as HA as it should be.
  • Locality should not be a necessity, but does help improvement. But would prefer no locality
Anson Abraham profile photo
  • The documentation could be a bit more detailed and have more examples, especially for advanced functionality.
  • The ability to update/change existing live field mappings would be nice.
  • The ingest pipeline structure is a bit more complicated and confusing than previous implementations for using things like attachment plug-ins.
Josh Kramer profile photo

Likelihood to Renew

No score
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Elasticsearch10.0
Based on 1 answer
We're pretty heavily invested in ElasticSearch at this point, and there aren't any obvious negatives that would make us reconsider this decision.
Aaron Gussman profile photo

Usability

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Elasticsearch10.0
Based on 1 answer
To get started with Elasticsearch, you don't have to get very involved in configuring what really is an incredibly complex system under the hood. You simply install the package, run the service, and you're immediately able to begin using it. You don't need to learn any sort of query language to add data to Elasticsearch or perform some basic searching.If you're used to any sort of RESTful API, getting started with Elasticsearch is a breeze. If you've never interacted with a RESTful API directly, the journey may be a little more bumpy. Overall, though, it's incredibly simple to use for what it's doing under the covers.
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Implementation

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Elasticsearch9.0
Based on 1 answer
Do not mix data and master roles. Dedicate at least 3 nodes just for Master
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Alternatives Considered

There are a few newer frameworks for general processing like Flink, Beam, frameworks for streaming like Samza and Storm, and traditional Map-Reduce. I think Spark is at a sweet spot where its clearly better than Map-Reduce for many workflows yet has gotten a good amount of support in the community that there is little risk in deploying it. It also integrates batch and streaming workflows and APIs, allowing an all in package for multiple use-cases.
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Power and simplicity along with performance.
Josh Kramer profile photo

Return on Investment

  • Positive: we don't worry about scale.
  • Positive: large support community.
  • Negative: Takes time to set up, overkill for many simpler workflows.
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  • It has allowed fast searching on large datasets which allow our customers to conduct business in a timely and simple manner.
Josh Kramer profile photo

Pricing Details

Apache Spark

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

Elasticsearch

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