What is after Spark?

After Spark 2.0, RDDs are replaced by Dataset, which is strongly-typed like an RDD, but with richer optimizations under the hood. However, we highly recommend you to switch to use Dataset, which has better performance than RDD. See the SQL programming guide to get more information about Dataset.

What is faster than Apache Spark?

The data processing is faster than Apache Spark due to pipelined execution. By using native closed-loop operators, machine learning and graph processing is faster in Flink.

Is Spark faster than BigQuery?

Developers describe Google BigQuery as “Analyze terabytes of data in seconds”. Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google’s infrastructure Load data with ease. Spark is a fast and general processing engine compatible with Hadoop data.

Is Spark still popular?

According to Eric, the answer is yes: “Of course Spark is still relevant, because it’s everywhere. Most data scientists clearly prefer Pythonic frameworks over Java-based Spark.

Is Spark map reduce?

Spark is a Hadoop enhancement to MapReduce. The primary difference between Spark and MapReduce is that Spark processes and retains data in memory for subsequent steps, whereas MapReduce processes data on disk. As a result, for smaller workloads, Spark’s data processing speeds are up to 100x faster than MapReduce.

What is the next big thing after spark?

Cloud, on-premise, and embedded versions are available. Next Think after Spark would be Flink which would deal with streaming data and paralellism can be achieved much more optimized than batch processing hadoop or spark.

What should I do after spark?

Next Think after Spark would be Flink which would deal with streaming data and paralellism can be achieved much more optimized than batch processing hadoop or spark. That’s the beauty of Quora where you can ask questions that don’t give many options to answer reasonably (yet I could not have resisted to answer it :))

Why do people still use spark for machine learning?

When Spark first hit the scene, it solved a lot of problems that people using Hadoop faced, especially when it came to iteration-heavy workloads. Machine learning began a hype cycle at a somewhat similar time and ML workloads are very well suited for Spark, thereby fueling the Spark fire.