Apache Spark is now a household name for Machine Learning and distributed Data Science. This has made Spark the go-to choice for big data machine learning applications even if it requires work with terabytes or petabytes of data. It’s efficient and very convenient usage model and interface has led to many adopting this as their prime platform for distributed machine learning. Also, the Apache Spark works as a dominating force for data analysts.
The twenty-first century has been the story of resources getting smaller and cheaper while accessibility to these devices has reached higher numbers each quarter. Hence, there is no doubt that this has led to an enormous boom of data flowing on all platforms the internet has facilitated. So how do you process, analyze and make meaningful experience changing applications? The answer is machine learning and artificial intelligence.
Apache Spark has been the dictator of this evolution from simple internet usage to personalized, user-based internet experience through its powerful enablement of large scale machine learning applications. It is not that people couldn’t work on big data before; it is merely that ever so in a long time, a technological paradigm arrives where everything else becomes outdated overnight.
Spark brought about such an effect. Not only was it a hundred times faster than Hadoop, but it also supported many use cases that previously big data applications could not, such as artificial intelligence itself. At that point, it made its way to the next revolution in the making.
In today’s world where there is a dire need of user diverse and personalized products and services, machine learning has never been the need of the hour as much as it is now. This is because today company revenues and their acceptance in the market depends on this personalization, recommendation systems, their automatic demographic classification, and demand predictions. Even a few years ago, statisticians and data scientists were able to crunch these numbers and data using simple statistics and everyday math, of course through R or python.
But now, it is almost outside the purview of a human or a group’s capability to work with such huge volumes and varieties of data that these organizations and companies amass. So now, their role in the organization has been more to build and deploy the infrastructure rather than the actual mathematical model.
MLib
To facilitate this, Apache Spark came up with its own, very powerful machine learning library, ‘MLib’ which embodies all that a machine learning researcher needs, a simple way of usage, capable of scaling to huge applications and one that is compatible with all the other mainstream tools used in the process. Therefore this level of scalability, compatibility to multiple programming languages and the speed it provides over alternatives help Apache Spark rise as the choice of many Data Scientists as they can work through the machine learning faster and more accurately. The largely growing number of applications and use cases are using Spark and the adoption of MLib continues to see growing numbers.
Simplicity, Scalability, and Compatibility
The best shot on providing simplicity to the data scientists was achieved by enabling them to use familiar APIs, tools, and languages like R and Python. In fact, the process became so simple that beginners now could just load some library and run a distributed model out of the box while people with higher levels of expertise were given the freedom to tune and hyper tune the models even more as per their requirements and use case by adjusting parameters and learning models – all of this on huge sums of data possible only through Spark.
Scalability too often is enumerated by machine learning engineers and researchers as one of the most important features of Spark. This is done to an extent where the same algorithm over the same data can be run on diverse platforms – from someone’s portable personal computer to a huge maintained powerful cluster of machines without any issues. This is important because as organizations and businesses grow, this enables machine learning engineers to use the same workflow and systems for even larger sums of data and users. It is often that these tools and models that make and tweak are written on familiar tools and languages like R, Python, Tensorflow API, sci-kit learn, etc.
Spark provides the power to integrate these important tools to the distributed framework it provides. This is achieved by the two APIs/libraries Spark Data Frames and MLlib. Some examples include SparkR (made specifically for R). Implementing Apache Spark Services gives faster real-time solutions and streaming.
Future Steps
It is common to use that the support for Python 2 will be deprecated as soon as we see 2020’s first sunrise. And since its debut in 2015, Spark has provided support for both Python2 and its more advanced and fresher version Python3. But in recent years, a unanimous notion has been permeating the technology space that supports for Python2 has limited the extensive use of the powerful language Python3. This has resulted in burdened Spark with facilities that are becoming redundant with time. And hence in a sensational website offering, they declared that their support for Python2 will be eventually dropped considering Python 2’s EOL (End of Life) is also nearing.
Despite these controversial decisions, Spark with its APIs and libraries is not only a powerful tool to run faster and more efficient models but also at its core it has revolutionized how large sums of data and machine learning can be handled in today’s world with increased scalability and compatibility – in a simple manner.
