OPEN SOURCE LIBRARIES : APACHE SPARK OR DASK?

Updated: Sep 12



An open source library can be a great help in data management, scaling of huge

datas and allocation of workloads according to the demands of the programs. Two of

the most common source libraries to keep in mind while choosing a big data

processing capacity are ‘Apache Spark’ and ‘Dask’. Both have their pros and cons

and both have different sets of data acceptance.


So while Dask may work parallel with Python and focus on task scheduling, Spark

simultaneously focuses on Big Data Workloads. While none of them is bad over the

other, choosing a correct library is of great importance. Let’s now understand the key

differences between Apache Spark and Dask.


Apache Spark came out in the year 2010 and has been dominantly in use as an open

source library ever since. Dask however, came out in 2014, and has its dominance set

up in smaller programs it can directly link to.


But while Spark’s main target is the Traditional Intelligence Operations for

Businesses, Dask has a step in both, the Intelligence Operation in businesses, but

also Operations in Scientific situations.


When comparing according to size, the Dask is comparatively smaller and more

lightweight. But this also does mean that the Dask has less features than Spark. So

while Spark has its own ecosystem where it is developed to integrate itself with

different Apache projects, Dask relies on the Python Ecosystem to operate instead.

The major difference, and the one that impacts your choices the most is the

language that both the libraries work on. Apache Spark works on ‘Scala’, with

acceptance of a little bit of Python and R. Being based on Scala, it easily operates

with Java Virtual Machine too. Dask however, as said before, works on a Python

ecosystem and this understands ‘Python’ as a core. It does however, accept, C, C++,

LLVM or any other thing which can be linked to Python or its ecosystem.


When discussing the internal designs, Apache Spark definitely has a higher level of

in-built designing. The latter, Dask, lacks here in a sense that it has a lower level of

designing. But the algorithm acceptance is quite different in both. So while Spark

has a more intense design, it lacks the complex algorithm flows, and vice-versa, the

Dask can accept more sophisticated algorithms.


These are some of the major differences that make up Apache Spark and Dask.

While there are a lot of absolute differences, some similarities can bring to term the

features that both the libraries possess.


Both Spark and Dask have a capacity to scale to a thousand-node cluster from a

single node. Similarly, they can both read and write languages such as CSV, ORC,

JSON, etc. Similarly, they can both deploy on the same clusters too. So which one do