Apache Spark vs. Salesforce CRM Analytics

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

For us it really comes down to that book management and next best contact for our advisors. When we're thinking about a book of business that may range, depending on the advisor, from 400 clients to a thousand clients, how do they really optimize their time? Who do they call next? Who do they work with to make sure not only they're keeping those clients engaged, they're not leaving the firm going to other advisors who they haven't talked to in a while who might need their attention. That's really where that CRM analytics is really proven pretty powerful for us.

Incentivized

• 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

• Interactive Dashboards, it [consists] of wide variety of charts
• Data from different sources can be easily integrated with it
• Security, it provides easy way to secure and share the information with the users
• Support actions like opening hyperlink etc
• Almost everything can be done from configuration
• Data can easily be managed from dataflow.

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

• Implementation takes time and resources. It is a heavy lift to implement and at first, it can take a little bit of time to understand what you are looking at. But once it's implemented it's easy to get started.
• Without any BI expertise or resources available to your organization, the implementation of this is difficult. If you aren't used to BI tools and don't have an expert in house, the terminology can be difficult to understand at first.
• Their support is not on hand to help you if you encounter any issues, at least not on all the plans or the basic plans. Real-time support service is an add-on, so you'll need to be patient if you require help or pay extra money.
• More functionality for the tool is needed to compete with other heavyweights in the arena like Tableau, Qlik, and Microstrategy. Still lacks the robustness, functionality, and flexibility other competing products possess.

Incentivized

Capacity of computing data in cluster and fast speed.

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

For someone who don't have coding background, this could be a useful tool and fairly easy to learn and use given the good support. However, if you know other open source tools, it would be much easier to use the other tools and the knowledge is more transferable in the future.

Incentivized

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.

I was not able to be in interaction much with Salesforce support team since every feature works the way it should be working. So far I have not experienced any bug or major glitches that would delay the result of my work and performance. There is also a hotline in our company for Salesforce issue but so far I have not used it.

Incentivized

An implementation partner would certainly result in greater output in a more efficient amount of time. However, I have found implementation partners to be extremely expensive for the output received (at least working for a non-profit company they are frequently unaffordable). Internal implementation does help with usable output though since internal knowledge would better know the data architecture and business processes

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

Tableau is the absolute top of the class when it comes to business intelligence, but it doesn't make sense for every business case. In our case, we needed a simple data visualization platform for our CRM platform and sales pipeline. Salesforce Analytics, while nowhere near as robust, did the job we needed it to do perfectly in a significantly more cost-effective manner.

Incentivized

• 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

• I would say it's been positive just because as a company, anyone that has access to it can go in there and pull any company information and we're very up to date then on all of our client base. So I would say it's been a very positive impact.

Incentivized