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Apache Hadoop vs. Apache Spark vs. Microsoft SQL Server

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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    Hadoop

    Score7.5 out of 10
    N/AHadoop is an open source software from Apache, supporting distributed processing and data storage. Hadoop is popular for its scalability, reliability, and functionality available across commoditized hardware.N/A

    Apache Spark

    Score8.8 out of 10
    N/AApache Spark is an open-source, distributed cluster-computing framework designed for large-scale data processing, batch transformations, real-time Streaming Analytics, and machine learning workloads. The platform executes distributed memory-centric computations across heterogeneous storage layers using unified APIs in Python, Scala, Java, SQL, and R.N/A

    Microsoft SQL Server

    Score8.7 out of 10
    N/AMicrosoft SQL Server is a relational database.

    $1,418

    Per License

    Pricing
    HadoopApache SparkMicrosoft SQL Server
    Editions & Modules
    No answers on this topic
    No answers on this topic
    Subscription
    $1,418.00
    Per License
    Enterprise
    $13,748.00
    Per License
    Offerings
    Pricing Offerings
    HadoopApache SparkMicrosoft SQL Server
    Free Trial
    NoNoNo
    Free/Freemium Version
    YesNoNo
    Premium Consulting/Integration Services
    NoNoNo
    Entry-level Setup FeeNo setup feeNo setup feeNo setup fee
    Additional Details———
    More Pricing Information
    Community Pulse
    HadoopApache SparkMicrosoft SQL Server
    Considered Multiple Products
    Apache
    Chose Hadoop
    Apache Spark can be considered as an alternative because of its similar capabilities around processing and storing big data. The reason we went with Hadoop was the literature available online and integration capability with platforms like R Studio. The popularity of Hadoop has …
    Incentivized
    Chose Hadoop
    Apache Spark has an in memory processing model, making it powerful for lightning fast data processing. Apache Spark also exposes Scala and Python in APIs which is one of the most commonly used programming languages in data analytic and data processing domains.
    Incentivized
    Chose Hadoop
    Spark is a good alternative to Hadoop that can have faster querying and processing performance and can offer more flexibility in terms of applications that it can support.

    Google BigQuery has also been a great alternative and is especially great in terms of ease of use. The …
    Incentivized
    Chose Hadoop
    Hands down, Hadoop is less expensive than the other platforms we considered. Cloudera was easier to set up but the expense ruled it out. MS-SQL didn't have the performance we saw with the Hadoop clusters and was more expensive. We considered MS-SQL mainly for its ability …
    Incentivized
    Chose Hadoop
    • For real-time streaming, use Spark; can provide a stark contrast to the way MR works
    • Hadoop offers a scalable, cost-effective and highly available solution for big data storage and processing.
    • Amazon Redshift is somewhat closer to Hadoop. But to analyze Petabytes of data Hadoop …
    Incentivized
    Chose Hadoop
    • For real-time streaming, use Spark; can provide a stark contrast to the way MR works
    • Use Hive for querying purposes
    Incentivized
    Chose Hadoop
    Hadoop provides storage for large data sets and a powerful processing model to crunch and transform huge amounts of data. It does not assume the underlying hardware or infrastructure and enables the users to build data processing infrastructure from commodity hardware. All the …
    Incentivized
    Apache
    Chose Apache Spark
    Apache Spark is a fast-processing in-memory computing framework. It is 10 times faster than Apache Hadoop. Earlier we were using Apache Hadoop for processing data on the disk but now we are shifted to Apache Spark because of its in-memory computation capability. Also in SAP …
    Incentivized
    Chose Apache Spark
    • Apache Spark works in distributed mode using cluster
    • Informatica and Datastage cannot scale horizontally
    • We can write custom code in spark, whereas in Datastage and Informatica we can only choose the different features proivided already.
    Incentivized
    Chose Apache Spark
    Spark is simply awesome to work on with any data sets and also has an in-memory database which makes it very flexible.
    Incentivized
    Chose Apache Spark
    1. Apache Spark is almost 100 % faster than Hadoop.
    2. Apache Spark is more stable than Amazon EMR.
    3. The end to end distributed machine library is more robust in Apache Spark.
    Incentivized
    Chose Apache Spark
    I prefer Apache Spark compared to Hadoop, since in my experience Spark has more usability and comes equipped with simple APIs for Scala, Python, Java and Spark SQL, as well as provides feedback in REPL format on the commands. At the same time, Apache Spark seems to have the …
    Incentivized
    Chose Apache Spark
    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 …
    Incentivized
    Chose 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 …
    Incentivized
    Chose Apache Spark
    Apache Pig and Apache Hive provide most of the things spark provide but apache spark has more features like actions and transformations which are easy to code. Spark uses optimization technique as we can select driver program and manipulate DAG (Directed Acyclic Graph)
    Python …
    Incentivized
    Chose Apache Spark
    Spark has primarily replaced my use of writing pure Hadoop MapReduce or Apache Pig jobs for processing data. I like the fact that I can alternate between the main programming languages that I know - Java and Python - and use those to learn the Scala API. Spark also can be …
    Incentivized
    Microsoft
    Chose Microsoft SQL Server
    Microsoft SQL Server is one of the fastest RDBMS systems available in the market. Pricing is a bit on the higher side but all the features it provides pretty much justifies it. It can be integrated with a large number of frameworks thus enabling to work on multiple frameworks …
    Incentivized
    Chose Microsoft SQL Server
    MS SQL server is the best database management tool available and it has been improving every year addressing the needs for ever growing data.
    Incentivized
    Chose Microsoft SQL Server
    I have been a SQL Server focused professional for over 20 years, so SQL Server is my first choice. I have experience and comfort, and the ability to get up to speed quickly. Oracle has been too expensive, though I think it has performed similar to SQL Server in the applications …
    Incentivized
    Key User Insights
    Would buy again
    100%
    Would buy again
    7 Answers
    100%
    Would buy again
    11 Answers
    100%
    Would buy again
    53 Answers
    Delivers good value for the price
    100%
    Delivers good value for the price
    7 Answers
    100%
    Delivers good value for the price
    11 Answers
    96%
    Delivers good value for the price
    45 Answers
    Happy with the feature set
    100%
    Happy with the feature set
    7 Answers
    100%
    Happy with the feature set
    11 Answers
    100%
    Happy with the feature set
    53 Answers
    Lived up to sales and marketing promises
    100%
    Lived up to sales and marketing promises
    6 Answers
    100%
    Lived up to sales and marketing promises
    8 Answers
    100%
    Lived up to sales and marketing promises
    39 Answers
    Implementation went as expected
    80%
    Implementation went as expected
    4 Answers
    100%
    Implementation went as expected
    11 Answers
    100%
    Implementation went as expected
    45 Answers
    Best Alternatives
    HadoopApache SparkMicrosoft SQL Server
    Small Businesses
    No answers on this topic
    No answers on this topic
    Amazon RDS
    Score8.1 out of 10
    Medium-sized Companies
    Cloudera Manager (no longer available standalone)
    Score9.9 out of 10
    No answers on this topic
    SingleStore
    Score8.2 out of 10
    Enterprises
    Amazon EMR
    Score9.1 out of 10
    No answers on this topic
    SAP IQ
    Score5.4 out of 10
    All AlternativesView all alternativesView all alternativesView all alternatives
    User Ratings
    HadoopApache SparkMicrosoft SQL Server
    Likelihood to Recommend
    8.0
    (37 ratings)
    9.0
    (24 ratings)
    7.9
    (107 ratings)
    Likelihood to Renew
    9.6
    (8 ratings)
    10.0
    (1 ratings)
    9.0
    (8 ratings)
    Usability
    8.0
    (6 ratings)
    8.0
    (4 ratings)
    7.6
    (17 ratings)
    Availability
    -
    (0 ratings)
    -
    (0 ratings)
    10.0
    (1 ratings)
    Performance
    8.0
    (1 ratings)
    -
    (0 ratings)
    9.0
    (1 ratings)
    Support Rating
    7.5
    (3 ratings)
    8.7
    (4 ratings)
    7.9
    (26 ratings)
    In-Person Training
    -
    (0 ratings)
    -
    (0 ratings)
    9.0
    (1 ratings)
    Online Training
    6.1
    (2 ratings)
    -
    (0 ratings)
    9.0
    (1 ratings)
    Implementation Rating
    -
    (0 ratings)
    -
    (0 ratings)
    9.0
    (6 ratings)
    Configurability
    -
    (0 ratings)
    -
    (0 ratings)
    10.0
    (1 ratings)
    Data Sharing and Collaboration
    7.7
    (10 ratings)
    -
    (0 ratings)
    9.0
    (5 ratings)
    Data Sources
    8.7
    (10 ratings)
    -
    (0 ratings)
    10.0
    (5 ratings)
    Ease of integration
    -
    (0 ratings)
    -
    (0 ratings)
    9.0
    (1 ratings)
    Product Scalability
    -
    (0 ratings)
    -
    (0 ratings)
    9.0
    (1 ratings)
    Vendor post-sale
    -
    (0 ratings)
    -
    (0 ratings)
    9.0
    (1 ratings)
    Vendor pre-sale
    -
    (0 ratings)
    -
    (0 ratings)
    9.0
    (1 ratings)
    User Testimonials
    HadoopApache SparkMicrosoft SQL Server
    Likelihood to Recommend
    Apache
    Altogether, I want to say that Apache Hadoop is well-suited to a larger and unstructured data flow like an aggregation of web traffic or even advertising. I think Apache Hadoop is great when you literally have petabytes of data that need to be stored and processed on an ongoing basis. Also, I would recommend that the software should be supplemented with a faster and interactive database for a better querying service. Lastly, it's very cost-effective so it is good to give it a shot before coming to any conclusion.
    Incentivized
    Read full review
    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.
    Incentivized
    Read full review
    Microsoft
    Microsoft SQL is ubiquitous, while MySQL runs under the hood all over the place. Microsoft SQL is the platform taught in colleges and certification courses and is the one most likely to be used by businesses because it is backed by Microsoft. Its interface is friendly (well, as pleasant as SQL can be) and has been used by so many for so long that resources are freely available if you encounter any issues.
    Incentivized
    Read full review
    Pros
    Apache
    • Handles large amounts of unstructured data well, for business level purposes
    • Is a good catchall because of this design, i.e. what does not fit into our vertical tables fits here.
    • Decent for large ETL pipelines and logging free-for-alls because of this, also.
    Incentivized
    Read full review
    Apache
    • 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
    Incentivized
    Read full review
    Microsoft
    • Easy to configure and use with Visual Studio and Dot Net
    • Easy integration with MSBI to perform data analysis
    • Data Security
    • Easy to understand and use
    • Very easy to export database and tables in the form of SQL query or a script
    Incentivized
    Read full review
    Cons
    Apache
    • Less organizational support system. Bugs need to be fixed and outside help take a long time to push updates
    • Not for small data sets
    • Data security needs to be ramped up
    • Failure in NameNode has no replication which takes a lot of time to recover
    Incentivized
    Read full review
    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
    Incentivized
    Read full review
    Microsoft
    • Microsoft SQL Server Enterprise edition has a high cost but is the only edition which supports SQL Always On Availability Groups. It would be nice to include this feature in the Standard version.
    • Licensing of Microsoft SQL Server is a quite complex matter, it would be good to simplify licensing in the future. For example, per core vs per user CAL licensing, as well as complex licensing scenarios in the Cloud and on Edge locations.
    • It would be good to include native tools for converting Oracle, DB2, Postgresql and MySQL/MariaDB databases (schema and data) for import into Microsoft SQL Server.
    Incentivized
    Read full review
    Likelihood to Renew
    Apache
    Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free
    Read full review
    Apache
    Capacity of computing data in cluster and fast speed.
    Read full review
    Microsoft
    We understand that the Microsoft SQL Server will continue to advance, offering the same robust and reliable platform while adding new features that enable us, as a software center, to create a superior product. That provides excellent performance while reducing the hardware requirements and the total cost of ownership of our solution.
    Incentivized
    Read full review
    Usability
    Apache
    As Hadoop enterprise licensed version is quite fine tuned and easy to use makes it good choice for Hadoop administrators. It’s scalability and integration with Kerberos is good option for authentication and authorisation. installation can be improved. logging can be improved so that it become easier for debugging purposes. parallel processing of data is achieved easily.
    Incentivized
    Read full review
    Apache
    If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
    Incentivized
    Read full review
    Microsoft
    SQL Server mostly 'just works' or generates error messages to help you sort out the trouble. You can usually count on the product to get the job done and keep an eye on your potential mistakes. Interaction with other Microsoft products makes operating as a Windows user pretty straight forward. Digging through the multitude of dialogs and wizards can be a pain, but the answer is usually there somewhere.
    Incentivized
    Read full review
    Reliability and Availability
    Apache
    No answers on this topic
    Apache
    No answers on this topic
    Microsoft
    Its does not have outages.
    Incentivized
    Read full review
    Performance
    Apache
    No answers on this topic
    Apache
    No answers on this topic
    Microsoft
    SSAS data cubes may some time slow down your Excel reports.
    Incentivized
    Read full review
    Support Rating
    Apache
    It's a great value for what you pay, and most Data Base Administrators (DBAs) can walk in and use it without substantial training. I tend to dabble on the analyst side, so querying the data I need feels like it can take forever, especially on higher traffic days like Monday.
    Incentivized
    Read full review
    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.
    Read full review
    Microsoft
    We managed to handle most of our problems by looking into Microsoft's official documentation that has everything explained and almost every function has an example that illustrates in detail how a particular functionality works. Just like PowerShell has the ability to show you an example of how some cmdlet works, that is the case also here, and in my opinion, it is a very good practice and I like it.
    Incentivized
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    In-Person Training
    Apache
    No answers on this topic
    Apache
    No answers on this topic
    Microsoft
    It was good
    Incentivized
    Read full review
    Online Training
    Apache
    Hadoop is a complex topic and best suited for classrom training. Online training are a waste of time and money.
    Read full review
    Apache
    No answers on this topic
    Microsoft
    very hands on and detailed training
    Incentivized
    Read full review
    Implementation Rating
    Apache
    No answers on this topic
    Apache
    No answers on this topic
    Microsoft
    Other than SQL taking quite a bit of time to actually install there are no problems with installation. Even on hardware that has good performance SQL can still take close to an hour to install a typical server with management and reporting services.
    Incentivized
    Read full review
    Alternatives Considered
    Apache
    Not used any other product than Hadoop and I don't think our company will switch to any other product, as Hadoop is providing excellent results. Our company is growing rapidly, Hadoop helps to keep up our performance and meet customer expectations. We also use HDFS which provides very high bandwidth to support MapReduce workloads.
    Incentivized
    Read full review
    Apache
    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.
    Incentivized
    Read full review
    Microsoft
    [Microsoft] SQL Server has a much better community and professional support and is overall just a more reliable system with Microsoft behind it. I've used MySQL in the past and SQL Server has just become more comfortable for me and is my go to RDBMS.
    Incentivized
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    Scalability
    Apache
    No answers on this topic
    Apache
    No answers on this topic
    Microsoft
    SQL server does handle growing demands of a mid sized company.
    Incentivized
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    Return on Investment
    Apache
    • There are many advantages of Hadoop as first it has made the management and processing of extremely colossal data very easy and has simplified the lives of so many people including me.
    • Hadoop is quite interesting due to its new and improved features plus innovative functions.
    Incentivized
    Read full review
    Apache
    • 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
    Incentivized
    Read full review
    Microsoft
    • Increased accuracy - We went from multiple users having different versions of an Excel spreadsheet to a single source of truth for our reporting.
    • Increased Efficiency - We can now generate reports at any time from a single source rather than multiple users spending their time collating data and generating reports.
    • Improved Security - Enterprise level security on a dedicated server rather than financial files on multiple laptop hard drives.
    Incentivized
    Read full review
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