Advantages of PySpark

The language helps data scientists to avoid always having to downsample large sets of data. For tasks such as building a recommendation system or training a machine learning system, using PySpark is something to consider. It is important for you to take advantage of distributed processing can also make it easier to argument existing data sets with other types of data and the example it includes like combining share-price data with weather data.

Data scientists and other Data Analyst professionals will benefit from the distributed processing power of PySpark. The workflow for accomplishing this becomes incredibly simple like never before and data scientists can build an analytical application in Python, also it can aggregate and transform the data, then bring the consolidated data back with PySpark. There is no arguing with the fact that PySpark would be used for the creation and evaluation stages.

  • In-Memory Computation in Spark: With in-memory processing, it helps you increase the speed of processing. And the best part is that the data is being cached, allowing you not to fetch data from the disk every time thus the time is saved. For those who don’t know, PySpark has DAG execution engine that helps facilitate in-memory computation and acyclic data flow that would ultimately result in high speed.
  • Swift Processing: When you use PySpark, you will likely get high data processing speed of about 10x faster on the disk and 100x faster in memory. By reducing the number of read-write to disk, this would be possible.
  • Dynamic in Nature: Being dynamic in nature, it helps you to develop a parallel application, as Spark provides 80 high-level operators.
  • Fault Tolerance in Spark : Through Spark abstraction-RDD, PySpark provides fault tolerance. The programming language is specifically designed to handle the malfunction of any worker node in the cluster, ensuring that the loss of data is reduced to zero.
  • Real-Time Stream Processing : PySpark is renowned and much better than other languages when it comes to real-time stream processing. Earlier the problem with Hadoop MapReduce was that it can manage the data which is already present, but not the real-time data. However, with PySpark Streaming, this problem is reduced significantly.
  • Simple to write : We can say it is very simple to write parallelized code, for simple problems.
  • Framework handles errors : While it comes to Synchronization points as well as errors, framework easily handles them.
  • Algorithms :Many of the useful algorithms are already implemented in Spark.
  • Libraries :In comparison to Scala, Python is far better in the available libraries. Since the huge number of libraries are available so most of the data science related parts from R are ported to Python. Well, this does not happen in the case with Scala.
  • Good Local Tools : For Scala, there are no good visualization tools but there are some good local tools available in Python.
  • Learning Curve : As compared to Scala, the learning curve is less in Python.
  • Ease of use :Again as compared to Scala, Python is easy to use.