Python and Kafka: A Must-Have Skill-Set of a Data Scientist

Kafka is a very useful instrument to bring you data architecture that can be streamed in real-time so that you also get access to real-time analytics. Not only is it a fast and scalable messaging system, but Kafka is also durable, tolerant to faults, and allows publish-subscribe. Providing a high throughput, replication features, and reliability, Kafka is a messaging system that can be applied for a multitude of things starting from tracking calls (including service calls), to tracking things that the traditional MOM cannot track such as IoT sensor data.

Coming to Python is an object-oriented and interpreted high-level programming language that comes along with dynamic semantics. Python is easy to learn since it consists of syntaxes, and unlike most of the other computer languages, Python actually resembles English quite a lot. And it is flexible as well since you find more than 125,000 third-party libraries that will provide you with all the necessary information that you might need in order to learn how to use Python for web processing, machine learning, and so on. And the best part? Python can be used by numerous industries thanks to its flexibility, including data science, finance trading, and web development.

1.Role of a Data Scientist –

Every organization, regardless of their industry, has started to utilize more and more data to make their everyday operations a lot more smooth flowing. It falls upon the data scientist to make an interpretation out of the raw data that has been provided by the organization, and then extract meanings that are valuable out of it. This information is basically used to find patterns that may tend to recur in the industry, and hence find solutions to the complex business problems, and hence help the organization grow smoothly. The role of a data scientist include –

  1. Extracting valuable data or data mining from data sources.
  2. Making use of machine learning tools in order to select suitable features that will further create as well as optimize classifiers.
  3. Preprocessing both unstructured as well as structured data.
  4. Enhancing the procedures of data collection of an organization so that all relevant information and data is included, will further help in the development of analytic systems.

2. Use-cases With Python and Kafka –

There are multiple use cases when it comes to Kafka and Python. Let us share a few use cases with you –

1. Monitoring Your Activity –

A great benefit of both Python and Kafka is that they can be actively used for monitoring your activities. These activities can belong to multiple sources such as websites, physical sensors as well as devices. When a producer has published some amount of raw data from any source of data, they can later be utilized through data science to look up patterns and trends in the industry.

2. Messaging –

Kafka as well as Python, if used properly can both act as great message brokers amongst numerous service platforms. In the case that you are implementing an architecture microservice, you have the scope to make use of this microservice both as a consumer as well as another producer. Let us take the example of a microservice where you have the responsibility to create new accounts along with a different one where you send emails to users asking them about creating accounts.

3. Log Aggregation –

Kafka, as well as Python, can both be utilized to collect logs from a multitude of different systems, and then to store them up in a system that has been centralized for further processing.

4. Database –

It is worth mentioning that Kafka also works as a database. By that we do not mean a typical database where you can query the data according to your needs, rather it acts as a database where you can store your precious data in Kafka for as long as you wish to, without any worries of consuming it!

5. ETL –

It is thanks to the special feature that Kafka has that lets you almost stream real-time so that you can easily have an ETL that has been based solely upon your needs.

3. Why a Data Scientist Must-Have Python and Kafka Skills –

Programming Language –

Programming is a way through which human beings can communicate with computers. As a data scientist, you do not need to become the best programmer, but it does definitely help to become comfortable with the languages. This will come in handy when you shall be required to code the processes of ETL and create data pipelines. The easiest to learn with the richest library, we have already mentioned the bid pros when it comes to Python. With Python, you will observe that machine learning tasks become rather easy to learn, not to mention so does web scraping and preprocessing of big data once you learn how to make use of spark – the default language of airflow.

Apache Kafka –

For most businesses, tracking and analyzing real-time data before moving on to process it has become a necessity. Needless to say, data scientist skills must include managing streaming data. It is indeed true that most events tend to lose their value with time. For instance, if there is a sporting event happening, you will want to be in touch with an instant update, analysis, and insight right?

4. Data Science Certifications Can Help In Acquiring These Skills –

Hence, you must become aware that Python and Kafka are many sought-after skills in the industry, which is bound to level you up to the best in the industry role of data scientist and engineer. Getting a data science certification will only provide you with plenty of knowledge as well as experience in this prospect, but it shall also provide you with all other skills necessary to become a successful data scientist.

Final Word

In today’s world, you need to keep yourself updated with technology and be fluent in all necessary skills to achieve prosperity, and a data science certification is bound to make you fluent in all!



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