Apache Spark vs. Scala

Apache Spark has rich APIs for regular data transformations or for ML workloads or for graph workloads, whereas other systems may not such a wide range of support. Choose it when you need to perform data transformations for big data as offline jobs, whereas use MongoDB-like distributed database systems for more realtime queries.

Incentivized

If you are in the data science world, Scala is the best language to work with Spark, the defacto data science data store. I think that is really the main likely reason I would ever recommend Scala. Another reason is if you already have a team of programmers familiar with functional programming, e.g. they all have years of Haskell experience. In that case, I definitely think Scala is a superior and faster-growing language than Haskell and that picking up Scala after Haskell should be quick.

Incentivized

• It performs a conventional disk-based process when the data sets are too large to fit into memory, which is very useful because, regardless of the size of the data, it is always possible to store them.
• It has great speed and ability to join multiple types of databases and run different types of analysis applications. This functionality is super useful as it reduces work times
• Apache Spark uses the data storage model of Hadoop and can be integrated with other big data frameworks such as HBase, MongoDB, and Cassandra. This is very useful because it is compatible with multiple frameworks that the company has, and thus allows us to unify all the processes.

Incentivized

• Compatibility with Java: if you are switching off of Java onto a new language, one reason to pick Scala is that it is about 99% compatible with Java, so any Java libraries or code you were using before can be called from Scala (not vice-versa though).
• Great built-in features for managing concurrency (e.g. Futures, Actors, and Akka). Making the most of every single thread on the machines your Scala code is running on is much easier and safer than doing it with Java. Scala abstracts away thread pools and threads quite well with Futures. I wouldn't say Futures are easy to learn though....but they are definitely safer to use than pure threads.
• Null-pointer safety: In Scala, null pointers are rare because most libraries pass around a class called Option when whatever you are referencing could possibly be null. Options are first-class and the functional nature of Scala combined with Options means you can almost always avoid referencing a null directly using Option.map or Option.flatMap (see here for what they do https://www.scala-lang.org/api/current/scala/Option.html). That means you'll almost never encounter another null-pointer exception unless you do something quite stupid and avoidable. Java has Options for helping with this now, but it's not widely used and not nearly as powerful.

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

• The social media feed is not editable.
• The social media feed cuts off link previews in posts, which can hurt posts that rely on visual context.

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

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.

The Scala community is still pretty active and friendly. Martin Odersky, the creator Scala, and his team are sill quite passionate and gone above-and-beyond to fix bugs and address the need for more features. They also have a company called Lightbend that will help you integrate Scala into your engineering stack. I have heard mixed things about them but never worked with them myself so take what I say with a grain of salt.

Incentivized

We used Surprise Kit for one of the other research works. It is more fine-tuned to Recommendation systems and their algorithms. Apache Spark has MLlib for majority of ML problems. Where as software like Surprse Kit - it suitable for a specific task of Recommendations only

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

• Scala has helped market accounts in-branch in a more visually engaging way than your garden-variety collateral.
• Scala players have shut down on more than one occasion, and it can take time to order and receive a replacement.

Incentivized