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102 Ratings
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Score 8.1 out of 101
102 Ratings
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Score 8.5 out of 101

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Likelihood to Recommend

Apache Flume

Apache Flume is well suited in small batch and near real time processing projects, taking data from one point to another with local processing (I mean not external enrichment).
Filtering, transforming and multiple push destinations are common grounds for Flume.
It is not so nice to use if your data needs external enrichment (taking data from external databases or web services), as transactions and (micro)batches may lead to reprocessing and it relies upon the application to avoid duplicates.
Juan Francisco Tavira profile photo

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.
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Pros

  • Multiple sources of data (sources) and destinations (sinks) that allows you to move data form and to any relevant data storage
  • It is very easy to setup and run
  • Very open to personalization, you can create filters, enrichment, new sources and destinations
Juan Francisco Tavira profile photo
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
Thomas Young profile photo

Cons

  • Apache Flume develops new functionality at a slower pace than other OpenSource projects, it is well behing Kafka and has some compatibiliy issues with latest releases
  • It lack HA or FT, it relies on third party management software like Hortonworks or Cloudera
Juan Francisco Tavira profile photo
  • Apache Spark requires some advanced ability to understand and structure the modeling of big data. The software is not user-friendly.
  • The graphics produced by Apache Spark are by no means world-class. They sometimes appear high-schoolish.
  • Apache Spark takes an enormous amount of time to crunch through multiple nodes across very large data sets. Apache Spark could improve this by offering the software in a more interactive programming environment.
Thomas Young profile photo

Alternatives Considered

Apache Flume is a very good solution when your project is not very complex at transformation and enrichment, and good if you have an external management suite like Cloudera, Hortonworks, etc. But it is not a real EAI or ETL like AB Initio or Attunity so
you need to know exactly what you want.On the other hand being an opensource project give Apache a lot of room to personalize thanks to its plug-able architecture and has a very nice performance having a very low CPU and Memory footprint, a single server can do the job on many occasions, as opposed to the multi-server architecture of paid products.
Juan Francisco Tavira profile photo
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
Nitin Pasumarthy profile photo

Return on Investment

  • Flume has simplified a lot many of our ingest procedures, easier to deploy and integrate than a classical EAI, reducing the time to market
  • But opposed to EAIs if the project starts to grow in complexity Apache Flume project may not be as suitable
Juan Francisco Tavira profile photo
  • overall positive impact to the business for analysis of big data using hadoop file system
  • very well received by data scientists in the business despite its shortcoming on analytical dashboarding
Shiv Shivakumar profile photo

Pricing Details

Apache Flume

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

Apache Spark

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