Updated: Aug 1
We live in a world that is continuously being evolved with new technologies. Everything that we know, has become or evolved into its digital form. In the context of Big data workloads, Spark and Dask have given businesses the option to scale and store new forms of data in a much more seamless form, also allowing businesses to undo what they are planning without suffering huge losses of revenue. Dask and Spark have played a major role in optimizing Big Data. For projects where the data needed to process, does not fit into the original memory & the existing libraries like Pandas and Numpy are also not of much use. Hence, for data scientists, this poses a problem when it comes to managing data that goes beyond the original memory capacity. Apache spark and Dask were created to make life much easier for data scientists in this regard by opting to work through a method called Parallel Computing.
While libraries like Numpy and pandas can be used to store huge amounts of data and are very helpful but their limitation of not going beyond the usage of one CPU makes them a little weaker. Now, with the creation of Spark and Dask data scientists have found dealing with this problem very easily. Moreover, With huge amounts of data storage, The processing speed of these datasets also had to be fast. It ultimately speeds up algorithms, parallelizes computing, parallelizes Pandas and NumPy and integrates them with libraries like sklearn and XGBoos
There are many other parallelizable solutions available in the market, but they are not transformable into a big DataFrame computation. Today these companies tend to solve their problems either by writing custom code with low-level systems like MPI, or complex queuing systems, or by heavy lifting with MapReduce.
With Apache Spark and Dask being the two most used in the industry and also widely appreciated it poses a dilemma for people on which one to use? which is best for them, for their businesses?
Dask and Apache are pretty similar in function but there are still certain things that make them different and give people based on their personal choice what they prefer more.
What is Dask?
Dask is a Python module and Big-Data tool that enables scaling pandas and NumPy. Like Spark, Dask supports parallel execution and handles out-of-memory data frames and arrays. But while Spark is written in Scala and offers a Python API (PySpark), Dask is a Pure Python solution that is part of the Python data science ecosystem.
While Spark is written in Scala language, This seems to be an issue for people who have not learned the Scala language or are simply not familiar with it. On the other hand, Dask shares a lot of characteristics with Spark with its shared ability of execution, where it does not take up the memory space that the operation requires but this action that we have programmed to perform is appended to the execution graph. Dask’s execution graph can be managed wwith the .compute(), .persist() method.
3 reasons to choose Dask over Spark
Integration with Numpy and Pandas
Pandas and Numpys are basic units of all Data Scientists and Dask also follows a very similar module. This fact results in a couple of positive outcomes for us. First is the common API; if you have good control over the pandas and NumPy API, you’ve already halfway migrated to Dask as Dask inherits most of these modules’ API.
The second is the ability of dask to work with these data frames, Dask automatically upgrades their functionality and makes them much better and faster.
2. Integration with SciKit-Learn and JobLib
Dask is closely integrated with SciKit-Learn and inherits its conventional API; this is a huge advantage over Spark, as pyspark.ml uses a whole new unconventional API that we need to learn. Dask offers some in-house preprocessing and machine learning algorithms, ranging from linear models and Naive Bayes to clustering, decomposition, and XG-Boost. Nonetheless, Dask supports the parallelization of most SKLearn models as a backend for the JobLib library.
3. A pure python solution
People choosing spark over dask have to be fluent in Scala whereas Dask only uses python language and also uses libraries that are available in the python language, If you are solely a python user then you will ultimately choose Dask because of your personal preference for data scientists, debugging and developing becomes much easier to handle with it.