20+ Senior Data Engineer Interview Questions and Answers for Freshers

Dataflow and Dataproc are both processing services in GCP, but with different approaches and use cases.

• Dataflow is a fully managed service for executing batch and streaming data processing pipelines.

• Dataproc is a managed Spark and Hadoop service for running big data processing and analytics workloads.

• Dataflow provides a serverless and auto-scaling environment, while Dataproc offers more control and flexibility.

• Dataflow is suitable for real-time streaming and complex data tran...read more

Types of dimensions and slowly changing dimensions (SCDs) with use cases

• Types of dimensions include conformed, junk, degenerate, and role-playing dimensions

• SCD Type 1: Overwrite existing data, useful for correcting errors

• SCD Type 2: Create new records for changes, useful for tracking historical data

• SCD Type 3: Add new columns for changes, useful for limited historical tracking

• SCD Type 4: Create separate tables for historical data, useful for large dimensions

To delete duplicates from a table in Spark and SQL, you can use the DISTINCT keyword or the dropDuplicates() function.

• In SQL, you can use the DISTINCT keyword in a SELECT statement to retrieve unique rows from a table.

• In Spark, you can use the dropDuplicates() function on a DataFrame to remove duplicate rows.

• Both methods compare all columns by default, but you can specify specific columns to consider for duplicates.

• You can also use the partitionBy() function in Spark to remov...read more

Application Master in Spark is responsible for negotiating resources with the ResourceManager and executing tasks on the cluster.

• Responsible for negotiating resources with the ResourceManager

• Manages the execution of tasks on the cluster

• Monitors the progress of tasks and reports back to the driver program

Optimization techniques in Spark, SQL, BigQuery, and Airflow.

• Use partitioning and bucketing in Spark to optimize data processing.

• Optimize SQL queries by using indexes, query rewriting, and query optimization techniques.

• In BigQuery, use partitioning and clustering to improve query performance.

• Leverage Airflow's task parallelism and resource allocation to optimize workflow execution.

Extract connection imports data into Tableau while live connection directly connects to the data source.

• Extract connection creates a static snapshot of data while live connection accesses real-time data from the source.

• Extract connection is useful for large datasets or when offline access is needed.

• Live connection is beneficial for real-time analysis and when data needs to be updated frequently.

• Examples: Extract connection - importing a CSV file into Tableau. Live connection ...read more

Spark is a distributed processing engine, Airflow is a workflow management system, and BigQuery is a fully managed data warehouse.

• Spark is designed for big data processing and provides in-memory computation capabilities.

• Airflow is used for orchestrating and scheduling data pipelines.

• BigQuery is a serverless data warehouse that allows for fast and scalable analytics.

• Spark can be integrated with Airflow to schedule and monitor Spark jobs.

• BigQuery can be used as a data source or...read more

Tableau is a data visualization tool that offers various functionalities to create interactive dashboards and reports.

• Data blending and joining

• Data aggregation and filtering

• Creating calculated fields and parameters

• Mapping and geospatial analysis

• Dashboard and report creation

• Collaboration and sharing

• Integration with other tools and platforms

Performance enhancements in PySpark involve optimizing code, tuning configurations, and utilizing efficient data structures.

• Use partitioning to distribute data evenly across nodes

• Cache intermediate results to avoid recomputation

• Optimize joins by broadcasting smaller tables

• Use efficient data formats like Parquet or ORC

• Tune Spark configurations for memory and parallelism

Designing an end-to-end architecture involves creating a framework that connects all components of a system.

• Identify the requirements and goals of the system

• Choose appropriate technologies and tools

• Design data flow and storage mechanisms

• Create a scalable and fault-tolerant system

• Ensure security and privacy of data

• Test and validate the system

• Implement monitoring and logging mechanisms

Parameterized procedures in BigQuery allow for dynamic SQL queries with user-defined parameters.

• Parameterized procedures in BigQuery use variables to pass values into SQL queries.

• They help prevent SQL injection attacks by separating SQL code from user input.

• Parameters can be used in WHERE clauses, JOIN conditions, and other parts of the query.

• Example: CREATE PROCEDURE myProcedure(IN param1 INT64, IN param2 STRING) BEGIN SELECT * FROM myTable WHERE column1 = param1 AND column2...read more

SQL is a programming language used for managing and manipulating relational databases.

• SQL stands for Structured Query Language

• It is used to create, modify, and query databases

• Common SQL commands include SELECT, INSERT, UPDATE, and DELETE

• SQL can be used with various database management systems such as MySQL, Oracle, and SQL Server

Transformations in Databricks involve manipulating data using functions like map, filter, reduce, etc.

• Transformations are operations that are applied to RDDs in Databricks

• Common transformations include map, filter, reduce, flatMap, etc.

• Transformations are lazy evaluated and create a new RDD

• Example: map transformation to convert each element in an RDD to uppercase

Default join in Tableau is inner join

• Default join in Tableau is inner join, which only includes rows that have matching values in both tables

• Other types of joins in Tableau include left join, right join, and full outer join

• To change the default join type in Tableau, you can drag the field from one table to another and select the desired join type

Spark is a distributed computing framework that provides in-memory processing capabilities for big data analytics.

• Spark has a master-slave architecture with a central coordinator called the Spark Master and distributed workers called Spark Workers.

• It uses Resilient Distributed Datasets (RDDs) for fault-tolerant distributed data processing.

• Spark supports various data sources like HDFS, Cassandra, and S3 for input/output operations.

• It includes components like Spark SQL for stru...read more

One pyspark optimization technique is using broadcast variables to efficiently distribute read-only data across all nodes.

• Use broadcast variables to efficiently distribute read-only data across all nodes

• Avoid shuffling data unnecessarily by using partitioning and caching

• Optimize data processing by using appropriate transformations and actions

I have extensive experience working with various Azure services such as Azure Data Factory, Azure Databricks, Azure SQL Database, and Azure Blob Storage.

• Experience with Azure Data Factory for ETL processes

• Proficiency in using Azure Databricks for big data processing

• Knowledge of Azure SQL Database for data storage and querying

• Familiarity with Azure Blob Storage for storing unstructured data

Optimizing Spark involves tuning configurations, partitioning data, using efficient transformations, and caching intermediate results.

• Tune Spark configurations for optimal performance

• Partition data to distribute workload evenly

• Use efficient transformations like map, filter, and reduce

• Cache intermediate results to avoid recomputation

Spark is a distributed computing framework for processing big data.

• Spark is built around the concept of Resilient Distributed Datasets (RDDs)

• It supports various programming languages like Scala, Java, Python, and R

• Spark provides high-level APIs like DataFrames and Datasets for structured data processing

• It includes libraries for SQL, streaming, machine learning, and graph processing

• Spark can run on various cluster managers like YARN, Mesos, and Kubernetes

Blending is the process of combining multiple data sources or models to create a single, unified dataset or prediction.

• Blending involves taking the outputs of multiple models and combining them to improve overall performance.

• It is commonly used in machine learning competitions to create an ensemble model that outperforms individual models.

• Blending can also refer to combining different data sources, such as blending demographic data with sales data for analysis.

Left join in SQL combines rows from two tables based on a related column, including all rows from the left table.

• Left join keyword: LEFT JOIN

• Syntax: SELECT columns FROM table1 LEFT JOIN table2 ON table1.column = table2.column

• Retrieves all rows from table1 and the matching rows from table2, if any

• Non-matching rows from table2 will have NULL values for columns from table2