Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
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Presto
Score 10.0 out of 10
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Presto is an open source SQL query engine designed to run queries on data stored in Hadoop or in traditional databases.
Teradata supported development of Presto followed the acquisition of Hadapt and Revelytix.
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Salesforce CRM Analytics
Score 8.5 out of 10
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Salesforce CRM Analytics (formerly Tableau CRM) is a cloud-based business intelligence solutions and analytics software. It provides users with automated data discovery, CRM-connected analytics, top-down views of data, augmented analytics, predictive insights, and customizable data visualization tools.
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 …
There are a few newer frameworks for general processing like Flink, Beam, frameworks for streaming like Samza and Storm, and traditional Map-Reduce. I think Spark is at a sweet spot where its clearly better than Map-Reduce for many workflows yet has gotten a good amount of …
I think Presto is one of the best solutions out there today at the cutting edge for interactive query analysis. One of the challenges is presto is a niche tool for the interactive query use case and doesn't have the knobs and whistles as much as Spark. In the foreseeable future …
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.
Presto is for interactive simple queries, where Hive is for reliable processing. If you have a fact-dim join, presto is great..however for fact-fact joins presto is not the solution.. Presto is a great replacement for proprietary technology like Vertica
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.
Linking, embedding links and adding images is easy enough.
Once you have become familiar with the interface, Presto becomes very quick & easy to use (but, you have to practice & repeat to know what you are doing - it is not as intuitive as one would hope).
Organizing & design is fairly simple with click & drag parameters.
Presto was not designed for large fact fact joins. This is by design as presto does not leverage disk and used memory for processing which in turn makes it fast.. However, this is a tradeoff..in an ideal world, people would like to use one system for all their use cases, and presto should get exhaustive by solving this problem.
Resource allocation is not similar to YARN and presto has a priority queue based query resource allocation..so a query that takes long takes longer...this might be alleviated by giving some more control back to the user to define priority/override.
UDF Support is not available in presto. You will have to write your own functions..while this is good for performance, it comes at a huge overhead of building exclusively for presto and not being interoperable with other systems like Hive, SparkSQL etc.
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.
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
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.
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.
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
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.
Presto is good for a templated design appeal. You cannot be too creative via this interface - but, the layout and options make the finalized visual product appealing to customers. The other design products I use are for different purposes and not really comparable to Presto.
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.
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.
