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106 Ratings
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Score 8.5 out of 101
99 Ratings
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Score 8.7 out of 101

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Likelihood to Recommend

Apache Spark

The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
Thomas Young profile photo

Elasticsearch

It works well for what we need. Short sharp logs of data from ongoing consistent processes.
Ben Williams profile photo

Pros

  • Machine Learning.
  • Data Analysis
  • WorkFlow process (faster than MapReduce).
  • SQL connector to multiple data sources
Anson Abraham profile photo
  • Free of SQL: ES does not have the overhead of relying on SQL. In fact, you can use most (if not all) DBMs out there.
  • Java: Normally, this is not a strength: Java is slow and cumbersome. I believe in this case, it's truly a feature: by utilizing a language with universal support, it makes ES VERY DevOps friendly, simply by being able to focus on Problem-oriented vs Solutions-based thinking.
  • Although ES has been known to consume RAM, it's very flexible, and I have implemented on a number of distinct hardware configuration with success.
  • Linux: It's not locked down to an OS (which is the way of the future), and as a result-running it on Linux means you get the power of Linux, in a data science package.
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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
  • Query syntax can be hard for developers to pick up, especially if they are used to SQL.
  • Tooling leaves a lot to be desired, especially compared to the RDMS tooling that is out there.
  • Updates to Elastic search data aren't the fastest, especially compared to some other nosql solutions like MongoDB
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Likelihood to Renew

No score
No answers yet
No answers on this topic
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

No score
No answers yet
No answers on this topic
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

No score
No answers yet
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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

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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Elasticsearch is widely popular and it's mostly free. Its ecosystem, ability to scale, ease to set up, integration with other systems, highly usable API make it really great compared to its competition.
Manish Rajkarnikar profile photo

Return on Investment

  • 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.
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  • Most of elasticsearch is free except few things which most of the organizations can live without or have a workaround. Not having to pay splunk whole bunch of money is a huge ROI right there.
  • Indexing the logs and making it searchable has a huge impact on the way we operate. Developers no longer have to log in to the system to know what's happening. Especially when we have hundreds of servers, having a central place for all the logs is essential to operate the system.
  • It's really easy to set up and maintain even in a scale. Its hot and warm cluster notion is awesome. The self-maintenance makes a huge impact on the need for system admins.
Manish Rajkarnikar 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