Python and Kafka: Why Do Data Scientists Require Them

Python is a versatile programming language that is mainly used to build software and websites, conduct data analysis, as well as automate tasks. Kafka, on the other hand, is an open source software processing platform used to build data pipelines and applications that can adapt data streams. Interestingly, of all the Fortune 100 companies, 60% use Kafka for applications!

The World Is One Big Data Problem

Andrew McAfee said that “the world is one big data problem”, and to solve that problem we need Data Scientists. While the amount of data is growing exponentially (estimated to be beyond 180 zettabytes by the year 2025), the need for data scientist skills is also changing.
 
Contemporary businesses depend highly on data analytics to make data-driven decisions. They also lean on machine learning and automation to develop their IT strategies. Data scientists help these companies meet business objectives by putting their heads into discovering insights from Big data.

Transforming and Manipulating Data in Retail Banking- A case

An American department store chain that has an in-store banking facility depends on customer data to thrive. The data is collected in a central Teradata warehouse as it is shared with many applications that enable the store’s retail banking, reporting needs, and supply chain. To streamline the process, the company has mandated Python for the purpose of data manipulation, however, the flexibility to create unique versions of Python is given to the team. This flexibility helps teams to maintain their Python distributions. But, maintaining distributions on AIX is complex and hence requires good technical support.
 
To address the issue, the company built a single ActiveState Python that delivers data manipulation and processing requirements of all the teams. ActivePython offers its users the ability to perform Extract, Transform, and Load (ETL) across business units, multiple use cases, and numerous targets of data migration. This resulted in less technical support thus enhancing the engineering velocity.
 
The above mentioned is one of the many use cases of Python in one of the multiple business domains.

The Netflix Way

Did you know that Apache Kafka is the reason behind Netflix’s smooth stream processing? Netflix uses Kafka exhaustively for messaging, eventing, as well as streaming. Kafka is a bridge for all of Netflix’s communications- studio-wide as well as point-to-point. It is Apache Kafka that gives Netflix its linear scalability, high durability, and multi-tenant architecture. If you intend to work as a data scientist on Netflix, being skilled in Apache Kafka is a must.

Do Data Scientists Need to be Skilled in Python and Kafka?

Python and Apache Kafka are two of the top 10 data scientist skills. If you are planning to dive deep into the field and work with FAANG companies, get yourself comfortable with both of these skills. To start the learning process, first, evaluate your current understanding of Python and Kafka. Once you have thoroughly assessed your knowledge and figured out the gaps in your skills, go for data science certifications that will bridge the gap. While there are numerous platforms that offer certification courses, how do you choose the most suitable one?

Remember the acronym GRIP while selecting a certification course. Here’s some quick information on GRIP.

Global Recognition: The requirement of data scientists is available across countries. So be very careful while researching that a particular course is globally recognized by different international bodies. This will open a plethora of opportunities for you, thus giving access to work on exciting projects available across borders.
Real-Business Solutions: Theoretical knowledge is not enough and companies look for people who can apply theoretical knowledge to real-world applications. Hence the search for the courses that put emphasis on real-business case studies Higher the number of case studies, the better will be the quality of learning.
Industry-relevant Topics: Learning a new technology adds value only when you can use it in problem-solving. So, you need to choose the data science certification course that covers industry-relevant topics extensively.
Pricing: Pricing is an important factor to consider while selecting the certifications courses. The price range of the courses varies from $495 to $1000. However, if you already have a fair understanding of the subject and need to work on specific sub-parts, then enroll yourself accordingly. This will be a price-effective way of choosing the course.

Conclusion

Since 2019, there has been a 46% increase in the hiring of the data science industry. What’s more interesting is that approximately 11 million data science jobs will be available by the year 2026. This shows the importance of upskilling in the field of data science. If you intend to make a career as a data scientist, ensure to get excellent hands-on experience on Python and Kafka. The more comfortable you are with the application of Python and Kafka in data manipulation as well as data processing, the more chances you joining the sexiest job of the 1st century.

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