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Apache Spark turning into API haven

Apache Spark is the hottest technology around in big data. It has the most generous contributions from the open source community. But with the latest release of Apache Spark 1.6, there is clearly a pattern evolving in where it is heading. And, currently that road seems to be an API haven. (Note, the term being used here is haven and not heaven).

Spark innovations in 2015

March 2015: Spark 1.3 released.

DataFrames introduced: SchemaRDD renamed and further innovated to give rise to DataFrames. DataFrames are not just RDDs with schema but have a huge army of useful operations that could be invoked with an exhaustive API.
For some strange reason, DataFrames were decided to be more of relational nature only and so were pitched directly along with Spark SQL. A developer could use either DataFrame API or could use SQL to query relational form data which could be residing in tables or any Spark supported data source (like Parquet, JSON etc.).

Nov 2015: Spark 1.6 released.

Datasets introduced: specialized DataFrames which can operate on specific JVM object type and not just rows. Essentially Dataset uses a logical plan created by Catalyst, the algorithmic engine behind Spark SQL/DataFrame and thus can do a lot of logical operations like sorting and shuffling. In a future release, you can expect DataFrames API to change and extend Dataset. 
Nutshell, Datasets are more intelligent objects compared to vanilla RDDs and if you might have guessed it, could be the future pinning of Spark API. 
Is it time to say "bye bye RDD, welcome Dataset"?

Happy or Upset with new Spark release?

Are you sounding relieved or almost upset with new Spark release– could depend on what stage are you in Spark journey.
For an indicative sample, answer could depend on what you doing.

You could be upset if:
- You have already been using Spark API and more or less re-settled yourself on Spark 1.3.x being distributed with CDH, HDP and MapR.
- You were wishing for a more powerful and SQL-rich Spark SQL but instead see focus shifting from SQL to programming API.
- You were wishing for a tighter integration between SQL and ML rather than DataFrames API and ML Pipelines API.

You could be delighted if:
- You are a devout Cloudera Impala or Hive fan and loved their performance! You always wanted to stick with these rather than Spark SQL offering more performant in-memory analytical power.
- You were a Storm and Mahout fan and were looking for ways to avoid shifting to Spark! You now know that coding in Spark may not be that cake-walk as it was promised to be since there are frequent API changes and over-reliance on experimental API for exposing functions in the promised unified platform.
- You were building your stealth next top big data product and were suddenly delighted to figure out that it’s difficult for users to remain settled in competing Spark! What was relevant in Spark around 6 months back may require re-factoring/re-engineering now.

Overall, the request to the brilliant and brave Spark core committers is to have a rethink and start loving the world outside programming API. Spark has been given the official throne of big data execution engine. So, it now needs to settle out on the surface while it keeps to continuously innovate under the covers. Spark is no longer ‘experimental’ and the term needs to move out of its documentation and strategy. It has to be hardened, enterprise grade and long serving to the end user. That's the only way for Spark to keep shining.


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