Amazon Athena vs. Apache Spark

If you are looking to take a lot of the traditional "database administration" work off someone's plate, going with Amazon Athena certainly has "no code" options to optimize lots of database tasks. I would say this option is less appropriate if you have other Microsoft things at play, such as Power BI.

Incentivized

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

• Nested Schemas like JSON data structure
• Ability to adapt the data model to fit your queries better
• Performance Improvement

Incentivized

• Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
• Apache Spark does a fairly good job implementing machine learning models for larger data sets.
• Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.

Incentivized

• Query manager can incoperate GUI based query designer
• Auto-completion engine sometimes overwrite the query
• Time range selection should be implicit

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

Capacity of computing data in cluster and fast speed.

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.

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.

Amazon Athena, a product from Amazon, competes with offerings from Google and Microsoft. Overall, I think your database choice depends on some of the other applications you are running at your company. For example, if you are using Microsoft Power BI for reporting needs, you might want to consider going the Azure route.

Incentivized

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 type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python

Incentivized

• Easy to query terabytes of data with faster response
• Pricing model is also cheap
• No indexing and partitioning

Incentivized

• Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
• Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
• Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.

Incentivized