What users are saying about
133 Ratings
Top Rated
170 Ratings
133 Ratings
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Score 9 out of 100
Top Rated
170 Ratings
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Score 8.8 out of 100

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 | TrustRadius Reviewer

Elasticsearch

Elasticsearch is really well suited for searching text (Natural Language Processing) and you can fine tune the searches and scoring very well. I like the ability to find Significant Terms in the Index, where you can find aggregations that are really relevant to a specific search. It also allows for queries to lead to new queries via aggregations which is great for navigating your data. It is less suited to doing more complex aggregations where slices of data are required to be processing using guassian normalizations. And doing searches which join different documents is very very hard, and requires serious thought on how to denormalize data.
Keith Lubell | TrustRadius Reviewer

Pros

Apache Spark

  • Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
  • Faster in execution times compare to Hadoop and PIG Latin
  • Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
  • Interoperability between SQL and Scala / Python style of munging data
Nitin Pasumarthy | TrustRadius Reviewer

Elasticsearch

  • As I mentioned before, Elasticsearch's flexible data model is unparalleled. You can nest fields as deeply as you want, have as many fields as you want, but whatever you want in those fields (as long as it stays the same type), and all of it will be searchable and you don't need to even declare a schema beforehand!
  • Elastic, the company behind Elasticsearch, is super strong financially and they have a great team of devs and product managers working on Elasticsearch. When I first started using ES 3 years ago, I was 90% impressed and knew it would be a good fit. 3 years later, I am 200% impressed and blown away by how far it has come and gotten even better. If there are features that are missing or you don't think it's fast enough right now, I bet it'll be suitable next year because the team behind it is so dang fast!
  • Elasticsearch is really, really stable. It takes a lot to bring down a cluster. It's self-balancing algorithms, leader-election system, self-healing properties are state of the art. We've never seen network failures or hard-drive corruption or CPU bugs bring down an ES cluster.
Anonymous | TrustRadius Reviewer

Cons

Apache Spark

  • 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 | TrustRadius Reviewer

Elasticsearch

  • Elasticsearch is highly distributed, but it takes time to tune so you get the right performance out of your cluster.
  • The query language is not SQL, so it's not a straightforward conversion from an RDBMS to Elasticsearch for searching through data.
  • There are lots of ways to insert data into Elasticsearch, and some are better than others (batch vs. single insert). Need to experiment with your own data and environment.
Anatoly Geyfman | TrustRadius Reviewer

Likelihood to Renew

Apache Spark

No score
No answers yet
No answers on this topic

Elasticsearch

Elasticsearch 10.0
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 | TrustRadius Reviewer

Usability

Apache Spark

Apache Spark 8.7
Based on 3 answers
Apache integrates with multiple big data frameworks. It does not exert too much load on the disks. Moreover, it is easy to program and use. It reduces the headache of using different applications separately through its high-level APIs. Big data processing has never been as easy as it is with Apache Spark.
Partha Protim Pegu | TrustRadius Reviewer

Elasticsearch

Elasticsearch 10.0
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.
Anonymous | TrustRadius Reviewer

Support Rating

Apache Spark

Apache Spark 8.3
Based on 7 answers
1. It integrates very well with scala or python.2. It's very easy to understand SQL interoperability.3. Apache is way faster than the other competitive technologies.4. The support from the Apache community is very huge for Spark.5. Execution times are faster as compared to others.6. There are a large number of forums available for Apache Spark.7. The code availability for Apache Spark is simpler and easy to gain access to.8. Many organizations use Apache Spark, so many solutions are available for existing applications.
Yogesh Mhasde | TrustRadius Reviewer

Elasticsearch

Elasticsearch 7.7
Based on 12 answers
We've only used it as an opensource tooling. We did not purchase any additional support to roll out the elasticsearch software. When rolling out the application on our platform we've used the documentation which was available online. During our test phases we did not experience any bugs or issues so we did not rely on support at all.
Anonymous | TrustRadius Reviewer

Implementation Rating

Apache Spark

No score
No answers yet
No answers on this topic

Elasticsearch

Elasticsearch 9.0
Based on 1 answer
Do not mix data and master roles. Dedicate at least 3 nodes just for Master
Anonymous | TrustRadius Reviewer

Alternatives Considered

Apache Spark

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.
Anonymous | TrustRadius Reviewer

Elasticsearch

As far as we are concerned, Elasticsearch is the gold standard and we have barely evaluated any alternatives. You could consider it an alternative to a relational or NoSQL database, so in cases where those suffice, you don't need Elasticsearch. But if you want powerful text-based search capabilities across large data sets, Elasticsearch is the way to go.
Anonymous | TrustRadius Reviewer

Return on Investment

Apache Spark

  • 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
Surendranatha Reddy Chappidi | TrustRadius Reviewer

Elasticsearch

  • When we where initially exploring logging solutions, Splunk was the only vendor in town and they where extremely expensive ($60,000). We haven't revisited them since as ElasticSearch has accomplished all of our needs.
  • We haven't spent anything but Admin hours to maintain our ElasticSearch cluster. Right now we haven't incurred any cost of ownership as I have been maintaining the cluster myself.
  • We have a huge project to grow a new part of our business, but I am not sure if I can spend the time to really update cluster to support the new Logstash features & any syntax changes so I am reluctant to do so. Time is increasingly becoming scarce, so catering to the latest and greatest features that offer little to our organization isn't something we are interested in pursuing though we are going to need to update the ElasticStack eventually.
  • Since all of our metrics are in ElasticSearch, we have had nice trove of data to build our apps around, apps that require specific metrics. Prior to ElasticSearch, we had to build our own tools that handled that metric collection. The cost savings here is that we maintain a simple script that reports back information in our reporting interface vs rolling our own database metric solution that must be modified for every app we develop. That has equated to a huge saving in developer hours in our organization.
Colby Shores | TrustRadius Reviewer

Pricing Details

Apache Spark

General

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

Apache Spark Editions & Modules

Additional Pricing Details

Elasticsearch

General

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

Elasticsearch Editions & Modules

Edition
Standard$16.001
Gold$19.001
Platinum$22.001
EnterpriseContact Sales
  1. per month
  2. none
Additional Pricing Details

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