Likelihood to Recommend
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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Azure Data Lake is an absolutely essential piece of a modern data and analytics platform. Over the past 2 years, our usage of Azure Data Lake as a reporting source has continued to grow and far exceeds more traditional sources like MS SQL, Oracle, etc.
Read full review Pros 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 Read full review Setting up Azure Data Lake Storage account, container is quite easy Access from anywhere and easy maintenance Integration with Azure Data Factory service for end to end pipeline is pretty easy Can store Any form of data (Structured, Unstructured, Semi) in faster manner Read full review 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 Read full review study for the certifications also to have them as a reference for work when you have any questions about applying a configuration to the equipment. The Internet interface is simple and easy to use. Capacity is good and it's good that HP continues to innovate with this technology Read full review Likelihood to Renew
Capacity of computing data in cluster and fast speed.
Senior Software Developer (Consultant)
Read full review Usability
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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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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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
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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Azure Data Lake Storage from a functionality perspective is a much easier solution to work with. It's implementation from
went smooth, and continued usage is definitely better. However,
was significantly cheaper overall between the high transaction fees and cost of storage due to growth. The two both have their advantages and disadvantages, but the functionality of Azure Data Lake Storage outweighed it's cost
Read full review Return on Investment 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 Read full review Instead of having separate pools of storage for data we are now operating on a single layer platform which has cut down on time spent on maintaining those separate pools. We have had more of an ROI with the scalability as we are able to control costs of storage when need be. We are able to operate in a more streamlined approach as we are able to stay within the Azure suite of products and integrate seamlessly with the rest of the applications in our cloud-based infrastructure Read full review ScreenShots