Egen Data Engineer Interview Questions and Answers

I am a Senior Data Engineer with experience in developing data pipelines and optimizing data storage for various projects.

• Developed data pipelines using Apache Spark for real-time data processing

• Optimized data storage using technologies like Hadoop and AWS S3

• Worked on a project to analyze customer behavior and improve marketing strategies

My day-to-day job in the project involved designing and implementing data pipelines, optimizing data workflows, and collaborating with cross-functional teams.

• Designing and implementing data pipelines to extract, transform, and load data from various sources

• Optimizing data workflows to improve efficiency and performance

• Collaborating with cross-functional teams including data scientists, analysts, and business stakeholde...read more

DAGs handle fault tolerance by rerunning failed tasks and maintaining task dependencies.

• DAGs rerun failed tasks automatically to ensure completion.

• DAGs maintain task dependencies to ensure proper sequencing.

• DAGs can be configured to retry failed tasks a certain number of times before marking them as failed.

Shuffling is the process of redistributing data across partitions in a distributed computing environment.

• Shuffling is necessary when data needs to be grouped or aggregated across different partitions.

• It can be handled efficiently by minimizing the amount of data being shuffled and optimizing the partitioning strategy.

• Techniques like partitioning, combiners, and reducers can help reduce the amount of shuffling in MapRed

Repartition increases or decreases the number of partitions in a DataFrame, while Coalesce only decreases the number of partitions.

• Repartition can increase or decrease the number of partitions in a DataFrame, leading to a shuffle of data across the cluster.

• Coalesce only decreases the number of partitions in a DataFrame without performing a full shuffle, making it more efficient than repartition.

• Repartition is typically...read more

Incremental data is handled by identifying new data since the last update and merging it with existing data.

• Identify new data since last update

• Merge new data with existing data

• Update data warehouse or database with incremental changes

SCD stands for Slowly Changing Dimension, a concept in data warehousing to track changes in data over time.

• SCD is used to maintain historical data in a data warehouse.

• There are three types of SCD - Type 1, Type 2, and Type 3.

• Type 1 SCD overwrites old data with new data.

• Type 2 SCD creates a new record for each change, preserving history.

• Type 3 SCD maintains both old and new values in the same record.

• SCD is important for...read more

Reverse a string using SQL and Python codes.

• In SQL, use the REVERSE function to reverse a string.

• In Python, use slicing with a step of -1 to reverse a string.

Use Spark and SQL to find the top 5 countries with the highest population.

• Use Spark to load the data and perform data processing.

• Use SQL queries to group by country and sum the population.

• Order the results in descending order and limit to top 5.

• Example: SELECT country, SUM(population) AS total_population FROM table_name GROUP BY country ORDER BY total_population DESC LIMIT 5

To find different records for different joins using two tables

• Use the SQL query to perform different joins like INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN

• Identify the key columns in both tables to join on

• Select the columns from both tables and use WHERE clause to filter out the different records

A catalyst optimizer is a query optimization tool used in Apache Spark to improve performance by generating an optimal query plan.

• Catalyst optimizer is a rule-based query optimization framework in Apache Spark.

• It leverages rules to transform the logical query plan into a more optimized physical plan.

• The optimizer applies various optimization techniques like predicate pushdown, constant folding, and join reordering.

• By o...read more

Used query optimization techniques to improve performance in database queries.

• Utilized indexing to speed up search queries.

• Implemented query caching to reduce redundant database calls.

• Optimized SQL queries by restructuring joins and subqueries.

• Utilized database partitioning to improve query performance.

• Used query profiling tools to identify and optimize slow queries.

Use the len() function to check the length of the data frame.

• Use len() function to get the number of rows in the data frame.

• If the length is 0, then the data frame is empty.

• Example: if len(df) == 0: print('Data frame is empty')

Cores and worker nodes are decided based on the workload requirements and scalability needs of the data processing system.

• Consider the size and complexity of the data being processed

• Evaluate the processing speed and memory requirements of the tasks

• Take into account the parallelism and concurrency needed for efficient data processing

• Monitor the system performance and adjust cores and worker nodes as needed

Enforcing schema ensures that data conforms to a predefined structure and rules.

• Ensures data integrity by validating incoming data against predefined schema

• Helps in maintaining consistency and accuracy of data

• Prevents data corruption and errors in data processing

• Can lead to rejection of data that does not adhere to the schema