Data Engineering & AI Interview Playbook

Decorators provide additional functionality to the functions without directly changing their definition of them. Basically, it takes the functions as an argument, adds functionality to them, and returns it.  Before diving deep into the concept of Decorators, let's first try to understand  What are functions in Python and what is an inner function? In Python everything is Objects, be it Class, Variables, and Functions . So functions are python first-class objects that can be used or passed as an argument. You can store the functions in variables, you can pass a function to another function as parameters, and you can also return the function from the function. Below is one simple example where we are treating functions as objects . def make_me_lowercase ( str ): return str .lower() print (make_me_lowercase( "HELLO World" )) copy_of_you = make_me_lowercase print (copy_of_you( "HELLO World" )) The output of the above calls for both the functio...

[ Updated for 2026] : [This article was originally written in 2022 and has been revised to reflect current Data Engineering trends.] Why SQL Still Matters for Data Engineers in 2026 Despite the rise of modern data stacks, cloud platforms, SQL remains one of the most critical skills for Data Engineers . In fact, SQL is often the first technical filter in Data Engineering interviews across startups, product companies, and large enterprises. Generally, there is a perception that SQL interviews are only for Data Analysts or Business Analysts but that's not the case. Writing SQL are essential part of Data Engineering specially for ELT pipelines that involves dbt. In 2026, SQL interviews are no longer about memorizing syntax. Interviewers focus on: How you think in SQL How you model and transform data How you handle scale, performance, and correctness How SQL supports analytics, reporting, and AI pipeline In this post, we are going to provide the frequently asked  SQL ...

Updated for 2026 This article was originally written in 2022 and has been revised to reflect modern Data Engineering interview expectations in 2026 , including cloud-native pipelines, SQL-heavy roles, and AI-ready data systems. In my previous post How to Prepare for Data Engineer Interviews (2026) , I discussed a structured approach to interview preparation. In this post, I cover frequently asked basic Data Engineering interview questions with brief answers . 👉 These questions are typically asked in early interview rounds , where interviewers want to assess: Conceptual clarity Practical understanding Ability to reason, not memorize You are not expected to go deep in these rounds — clarity matters more than depth. A. Programming (Python for Data Engineers) 1. What is a static method in Python? A static method is bound to a class rather than an instance. It does not access instance variables and can be called using the class name. Static methods are commonly used for utilit...

[ Updated for 2026] : [This article was originally written in 2022 and has been revised to reflect current Data Engineering and AI interview trends.] In recent years, the exponential growth of data—driven by cloud adoption, digital products, IoT, and AI systems has made data a core business asset for almost every organization. As a result, companies across industries are heavily investing in data platforms to enable analytics, real-time insights, and AI-driven decision-making. This shift has significantly increased demand for Data Engineering roles, and Data Engineers continue to be among the most in-demand and strategically critical profiles in the IT industry. By 2026, the role of a Data Engineer has evolved beyond traditional ETL development. Organizations now expect Data Engineers to design scalable, reliable, and AI-ready data platforms that can support analytics, machine learning, and Generative AI use cases. Modern data teams rely on Data Engineers to build robust ingestion p...

The name NoSQL itself tells us that it is "non-SQL" or "non-relational" database. Around 30 years back when the data used to be non-changing and smaller in size, traditional relational databases were more prominent like ORACLE, Postgres and so on which had fixed schemas. But during the last decade, the data has grown exponentially and it is also changing quickly. The traditional databases have failed to handle this BIG DATA effectively. So there was a need to introduce the database that can adapt itself with ever-changing data and that can handle the enormous size of data. And thus NoSQL databases came into the picture. Nowadays NoSQL databases have been referred to as "Not Only SQL" databases which means that these databases may support SQL like query languages and can be a part of polygot persistent architecture along with other relational databases. The data structures used in the NoSQL database are more efficient than the data structures used by ...

In the last post What is Apache Kafka , we discuss Kafka producer, Topics and Consumers. In this post, I am going to provide JAVA code for writing Kafka Producer and explain how it works. In this code, we are going to send data from CSV row by row to Kafka Topics for further consumption by Kafka Consumer . In this way, we can generate the continuous streams of Data. 1. First, you need to create a Kafka Topic. You can either do it from the console or do programmatically. Please go through the basic Quickstart from  Apache Kafka Website  and create Topics, send basic message from producer and consume it at consumer. Once, you have created Topic, its time to write Kafka Producer, which will send data to Topic. For creating Procuder we need to configure it some parameters. Lets, look at the configurations required for creating producer 1. List of Kafka Brokers 2. Serializer used for sending data to Kafka 3. Acknowledge from Kafka that messages are properly r...

Kafka is a fast, scalable, durable, and fault-tolerant publish-subscribe messaging system that sends messages between applications, processes, and servers. It is composed of three important components called producer (which acts as Sender), Topics (topic or category of messages), and Consumer (which acts as a Reciever) . Kafka aims to provide high-throughput, low latency for real-time data feeds. It is widely used for real-time streams analytics, ingestion data into Spark, Complex Event Processing, Log aggregation, etc. Before understanding Kafka Architecture, lets first understand what Kafka Broker is Kafka Broker: As Kafka is a distributed framework, Kafka's cluster consists of different servers called Brokers running Kafka. Producers publish the message to Kafka Topics within this Broker and consumer consumes this message from Topics. Kafka Architecture: Kafka Architecture is comprised of mainly 3 components Kafka Producer Kafka Topics Kafka Consumer ...