Apache Spark vs. Presto vs. SAP HANA Cloud

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

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

Incentivized

I think if you have a large organization, it's probably the product and the marketplace to go to. We're a large management consulting firm operating in four to seven countries. And generally speaking, I think that's the size and the scope where it scales best. I can't speak to smaller companies, but I can't see smaller companies leveraging the benefits as much as a larger organization can.

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

• 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.

Incentivized

• Real-time reporting and analytics on data: because of its in-memory architecture, it is perfect for businesses that need to make quick decisions based on current information.
• Managing workload with complex data: it can handle a vast range of data types, including relational, documental, geospatial, graph, vector, and time series data.
• Developing and deploying intelligent data applications: it provides various tools for such applications and can be used for machine learning and artificial intelligence to automate tasks, gain insights from data, and make predictions.

• 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

• 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.

Incentivized

• Requires higher processing power, otherwise it won't fly. How ever computing costs are lower. Incase you are migrating to cloud please do not select the highest config available in that series . Upgrading it later against a reserved instance can cost you dearly with a series change
• Lack of clarity on licensing is one major challenge
• Unless S/4 with additional features are enabled mere migration HANA DB is not a rewarding journey. Power is in S/4

Incentivized

Capacity of computing data in cluster and fast speed.

We would rate our likelihood of renewing at 9/10. SAP HANA Cloud has proven to be a highly reliable and scalable data platform that consistently delivers strong performance. Its seamless integration with our overall SAP landscape, combined with improved analytics and real-time data capabilities, makes it a core part of our long-term technology strategy.

Incentivized

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 is useful solution which helps you improve SAP applications performance. It offers you faster data processing, robust disaster management, higher availability, scalability, advanced analytical capabilities, etc. It provides you simple, clean, organized user interface designed to facilitate smooth navigation. Its user interface is simple and intuitive which allow you to complete task efficiently.

Incentivized

so far, we didn't get any outage

Incentivized

so far good

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.

However, I am not the right person to answer this as we have another department to handle support and contact the service provider for any support required. Although i will say that they are the quick respondent and knows how to handle querry of the customers and provide quick and better support.

Incentivized

Professional GIS people are some of the most risk-averse there are, and it's difficult to get them to move to HANA in one step. Start with small projects building to 80% use of HANA spatial over time.

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

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.

Incentivized

I have deep knowledge of other disk based DBMSs. They are venerable technology, but the attempts to extend them to current architectures belie the fact they are built on 40 year old technology. There are some good columnar in-memory databases but they lack the completeness of capability present in the HANA platform.

Incentivized

I don't have visibility in licensing

Incentivized

Limitation of training deliverable by organization

Incentivized

We are still in process for the first applciaiton

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

• Presto has helped scale Uber's interactive data needs. We have migrated a lot out of proprietary tech like Vertica.
• Presto has helped build data driven applications on its stack than maintain a separate online/offline stack.
• Presto has helped us build data exploration tools by leveraging it's power of interactive and is immensely valuable for data scientists.

Incentivized

• ROI has always been high in terms of the functionality that it offers and the security features it comes with.
• Managing large volumes of data in real-time is not an easy task, but it does it pretty well with faster data processing.

Incentivized