How Open Source is Pushing the Future of Data Science
Isabella Ferreira
Published at 08/06/2021
Views 2658

According to Stastista, 74 zettabytes of data will be available in 2021. With the development of new technologies such as 5G networks and AI, it is expected that the data production will grow more and more over time. The question is how to make this data more accessible?

In fact, accessibility to this amount of data has allowed the development of technologies that are focused on data-driven business decisions and outcomes [2]. Big data analytics helps organizations to have new insights, make faster and informed decisions, and reduce their costs [2]. A data scientist comes into play when it is necessary to analyze all this data. That is, a data scientist is the person responsible for helping organizations to achieve their goals by parsing and analyzing data and creating routines to run on the data in order to identify patterns and trends and then visualize them [3].

So, how is open source pushing the future of data science?

There are three main reasons in which open source is helping the field of data science.

  1. Open source allows companies to try different tools at a very low cost as well as to find professionals working with specific data science tools. For example, with open source, it is possible to find Python developers, and consequently find talented developers working on data science and machine learning frameworks such as PyTorch, Tensorflow, and Scikit-learn, that are also built directly with Python.

  2. Open source allows companies and different stakeholders to** have access to large amounts of data and different models**. Without open source, this would be a challenge for both small companies and individuals that do not have this amount of data and resources available.

  3. Open source allows people to learn about data science. With the large amount of open source data science frameworks available, people interested in data science are able to practice what they learn in textbooks.

But… Dealing with large amounts of data can be challenging!

When dealing with large amounts of data storage and computing needs can be a problem! Companies and individuals might struggle to accommodate the storage and computing needs because big data is getting more and more complex! To address this problem, data science with cloud computing has become popular, and the field of Data as a Service (DaaS) has been created. DaaS uses cloud computing to provide data storage, data processing, data integration, and data analytics to companies or individuals. The cool thing about DaaS is that it allows different companies and departments inside a company to share data easily with each other and obtain actionable insights.

Despite analyzing and processing data, most of the time it is necessary to run machine learning models to get insights from the data. For that, cloud-native Machine Learning (ML) and Artificial Intelligence (AI) come into play. Cloud native ML allows companies and individuals to deploy AI and deep learning models to a scalable environment in the cloud. With cloud native ML, it is easier to access the data, and deploy programs without having the experience with coding [4]. Additionally, the user can debug, evaluate and replicate results directly from the cloud [4]. Finally, the cloud environment is elastic, which means that you can customize how much data and where it will be stored. The environment will grow or shrink depending on your needs. Cloud native ML has some advantages:

  • It allows reliable scalability. That is, you can expand your computational needs or storage without having to change the software.
  • You can use microservices for target development. That will allow you to do faster deployment and increase your team capabilities.
  • It allows you to have data lakes, i.e., you can store the data in its raw format. That might allow you to do better training and deploying newer models.

Where should you start?

A data scientist may use tools to help with their job. Although many tools are available to help handling and analyzing big data, open source software has become a very desirable option for allowing different stakeholders to try different tools with easy access to up-to-date solutions with low cost. For example, the Apache open source family (such as Spark, Kafka, Hadoop, Tomcat, and Cassandra) has an entire ecosystem to help with big data.

Whether you are a software developer that wants to contribute to open source projects focused on data science or a user, there are some projects that are good starting points. Most popular open source data science projects manage their source code on GitHub, and here is a short list if you would like to start learning them:

  • R and Python are the most popular programming languages for data science, and they are themselves open source.
  • scikit-Learn is a machine learning library (ML) for Python that allows you to do many ML tasks such as clustering and classification.
  • Numpy offers numerical computation tools that help with data science.
  • Pandas is mostly used for data manipulation and analysis.
  • PyTorch is a Python machine learning framework with many features from prototyping to deployment.
  • Tensorflow is used for building and training neutral networks.

Concerning cloud computing services for data science, the most popular ones are Amazon Web Services, Google Cloud Platform, and Microsoft Azure.

Want to increase your team capabilities and do faster deployment? Take a look at the TARS Framework. TARS is a microservices framework that helps to speed up the development and the deployment of software that deals with big data.

Open source is making data science easier and available to all. As a consequence, cloud computing is helping data science and machine learning to deal with the challenges of data storage and computing needs.

About the author:

Isabella Ferreira is an Ambassador at TARS Foundation, a cloud-native open-source microservice foundation under the Linux Foundation.