Apache Spark vs. Azure HDInsight

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
Azure HDInsight
Score 8.9 out of 10
N/A
HDInsight is an implementation of the Apache Hadoop technology stack on the Microsoft Azure cloud platform: It is based on the Hortonworks Hadoop distribution. Microsoft Azure HDInsight includes implementations of Apache Spark, HBase, Storm, Pig, Hive, Sqoop, Oozie, Ambari, etc. It also integrates with with business intelligence (BI) tools such as Power BI, Excel, SQL Server Analysis Services, and SQL Server Reporting Services.N/A
Pricing
Apache SparkAzure HDInsight
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkAzure HDInsight
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Best Alternatives
Apache SparkAzure HDInsight
Small Businesses

No answers on this topic

No answers on this topic

Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkAzure HDInsight
Likelihood to Recommend
9.8
(24 ratings)
9.1
(5 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
8.9
(4 ratings)
Support Rating
8.7
(4 ratings)
8.9
(4 ratings)
User Testimonials
Apache SparkAzure HDInsight
Likelihood to Recommend
Apache
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
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Microsoft
If you want to save costs and just pay for what you use, I highly recommend it. It will help you also to work with data for your reports and analytics. on the other hand I think it could be the subscription you have but high volume of data make it slow but not so much. anyway I think it's really good because it's from Microsoft which always is friendly to use it as all the suit they have.
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Pros
Apache
  • 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.
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Microsoft
  • Data is presented without interfering others (IT or other dept).
  • Data is managed properly and is available for retrievable any time.
  • Legacy use of CD/DVD and Pendrive are not required.
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Cons
Apache
  • 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
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Microsoft
  • The only problem I have come across is when loading large volumes of data I sometimes get an error message, I assume this means something is corrupt from within. I would love a way for this to be resolved without having to start over.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Microsoft
No answers on this topic
Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Microsoft
Azure HDInsight is usable on the top of Azure Data Lake and gives us the benefit of analyzing large scale data workload in Hadoop. Usability and support from Microsoft are outstanding.
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Support Rating
Apache
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.
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Microsoft
I highly recommend it.
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Alternatives Considered
Apache
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
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Microsoft
At this time I have not used any other similar products... I am open to it but Azure HDInsight and its components really work well for our organization.
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Return on Investment
Apache
  • 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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Microsoft
  • Less implementation cost.
  • No hosting services cost.
  • Faster data retrieval.
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