Apache Spark vs. ProofHub

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

It suits well for those businesses who want to operate on a global level without investing too much over creating infrastructure. A business can easily reach out to various clients, customers, partners and other stakeholders and communicate and collaborate with them in a fast and transparent manner and can effectively tap any opportunities coming its way. Such opportunities if
properly implemented can lead to gains for the parties involved.

• 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

• The obvious, proofing! We need something in-house that can do this instead of hiring one person for this job. Each person on our marketing team knows how to use it.
• Organizes team projects in a friendly way.

Incentivized

• 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

• Proofhub can improve a bit in areas of helping people customize their accounts a bit more. That makes managing work even more easier, when you have things just the way you want them to be.

Capacity of computing data in cluster and fast speed.

With an array of useful features available that solve all our work issues, each department has an access to it. Each one knows what’s going on with different teams. This makes collaboration easier, as different teams just need one tool to come together and get things done. I think this is a great product! it has really helped my company get MUCH better organized.

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

It's convenient, simple, and clear.

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.

Great support website and had reps follow up multiple times in our trial process. Getting started was very straightforward and adding people is easy too.

Incentivized

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

We used Basecamp in the past, but we did encounter some problems while working with it. But these concerns have been better addressed in ProofHub. It has a simple interface which is so easy to operate and this was not the case in Basecamp. ProofHub lets us quickly chat with our team members over various work matters through its inbuilt chat feature and resolve them fast while Basecamp provides this feature through a third-party tool which added to our expenses and it was not a very convenient affair. ProofHub has got an inbuilt proofing tool which allows us to get quality feedback over designs instantly saving our time and costs, whereas Basecamp enables proofing through a third party tool which again was not convenient for us. ProofHub’s casper mode feature helps us to protect privacy and secrecy over confidential issues but Basecamp lacks such an important feature. So ProofHub has more to offer and better too in comparison to Basecamp. (answer to Describe how ProofHub stacks up against them and why you selected ProofHub.

• 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

• Proofhub has a great impact on our ROI. Due to a systematic approach of handling each clients’ project, we have been able to double the number of clients we had prior to signing up for Proofhub.