IBM Senior Data Engineer Interview Questions, Process, and Tips

I am a Senior Data Engineer with experience in building scalable data pipelines and optimizing data processing workflows.

• Experience in designing and implementing ETL processes using tools like Apache Spark and Airflow

• Proficient in working with large datasets and optimizing query performance

• Strong background in data modeling and database design

• Worked on projects involving real-time data processing and streaming analytic

Decorators in Python are functions that modify the behavior of other functions or methods.

• Decorators are defined using the @decorator_name syntax before a function definition.

• They can be used to add functionality to existing functions without modifying their code.

• Decorators can be used for logging, timing, authentication, and more.

• Example: @staticmethod decorator in Python is used to define a static method in a class.

SQL query to group by employee ID and combine first name and last name with a space

• Use the GROUP BY clause to group by employee ID

• Use the CONCAT function to combine first name and last name with a space

• Select employee ID, CONCAT(first_name, ' ', last_name) AS full_name

Constructors in Python are special methods used for initializing objects. They are called automatically when a new instance of a class is created.

• Constructors are defined using the __init__() method in a class.

• They are used to initialize instance variables of a class.

• Example: class Person: def __init__(self, name, age): self.name = name self.age = age person1 = Person('Alice', 30)

Indexing in SQL is a technique used to improve the performance of queries by creating a data structure that allows for faster retrieval of data.

• Indexes are created on columns in a database table to speed up the retrieval of rows that match a certain condition in a WHERE clause.

• Indexes can be created using CREATE INDEX statement in SQL.

• Types of indexes include clustered indexes, non-clustered indexes, unique indexes, an...read more

Spark works well with Parquet files due to its columnar storage format, efficient compression, and ability to push down filters.

• Parquet files are columnar storage format, which aligns well with Spark's processing model of working on columns rather than rows.

• Parquet files support efficient compression, reducing storage space and improving read performance in Spark.

• Spark can push down filters to Parquet files, allowing f...read more