Data Engineer Interview Questions & Answers
Hello All,
I have crafted this article to share Data Engineer interview tips and questions, drawing from my experience both as a candidate and from conducting over 50 interviews. If you encounter any new interview questions, feel free to share them in the comments, and I’ll do my best to update the article with the possibly most accurate answers.
Data engineers require a blend of technical, analytical, and problem-solving skills. Here are the some important skills for data engineers:
Data Engineer Skills
Programming Languages
- SQL — Must Have
- Python — Must Have , Python is a must-have skill. If you’re not fully proficient, be sure to learn the basics, such as file handling, working with JSON data, inserting and retrieving data, and using libraries like Pandas.
Data Warehousing
- This includes knowledge of data modeling (like star and snowflake schemas), ETL processes, and familiarity with data warehousing solutions such as Amazon Redshift, Google BigQuery, or Snowflake
ETL Tools
- From my experience, if you’re not specifically targeting a cloud-focused role like Google Cloud Data Engineer or AWS Data Engineer, it’s important to have experience with at least one ETL tool, such as Informatica, Datastage, or Ab Initio.
Big Data Technologies
- Good to have Knowledge of big data frameworks such as Apache Hadoop, Apache Spark, and Kafka for processing and managing large-scale data.
Cloud Platforms(GCP/AWS/Azure)
- Looking at current Job market I would flag it as Must have skill. Familiarity with cloud services like AWS, Google Cloud Platform (GCP), or Microsoft Azure for data storage, processing, and deployment.
Database Management
- This is again must have skills to Expertise in both relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra) for handling diverse data types
Data Pipeline Automation
- Ability to design and automate data pipelines for seamless data flow using tools like Airflow,
Understanding of Data Security
- This is must if you are at design side or mostly in senior role. Knowledge of data security practices and compliance standards to ensure data integrity and confidentiality
Generic Data Engineer Questions
Conceptual Differences Between a Database, Data Warehouse, Data Lake, and Data Mart
- Database: A database is an organized system for storing, managing, and retrieving structured data. It is optimized for handling daily transactional processes and supports real-time data operations.
- Data Warehouse: A data warehouse is a centralized repository designed to consolidate and store structured data from various sources. It is optimized for complex querying, reporting, and analysis, and typically handles historical data.
- Data Lake: A data lake is a vast storage repository that holds large volumes of raw and diverse data, including structured, semi-structured, and unstructured data. It provides flexibility for storing data in its native format and supports advanced analytics and machine learning.
- Data Mart: A data mart is a specialized subset of a data warehouse, focused on a specific business area or department. It provides tailored access to relevant data for particular business needs and analytical tasks.
What are Fact Tables and Dimension Tables?
- Fact Tables: Store quantitative data (measures) for analysis, such as sales amounts, and contain foreign keys that reference dimension tables.
- Dimension Tables: Store descriptive attributes (dimensions) related to the facts, such as time, product, and location.
https://www.geeksforgeeks.org/difference-between-fact-table-and-dimension-table/
https://www.javatpoint.com/difference-between-fact-tables-and-dimension-tables
What is a Slowly Changing Dimension (SCD)?
A Slowly Changing Dimension (SCD) is a dimension in a data warehouse that changes slowly over time, rather than changing on a regular schedule or in real-time. There are different types of SCDs:
- SCD Type 1: Overwrites existing data, no history tracking.
- SCD Type 2: Adds new records for changes, keeps full history with separate surrogate keys.
- SCD Type 3: Adds new columns to track limited history (typically one previous value).
Refer this amazing blog on SCD to get more detailed understanding
Medium Article
Explain Difference between OLTP vs. OLAP
OLTP (Online Transaction Processing):
OLTP systems are designed to manage transactional data. They handle a large number of short online transactions such as insert, update, and delete operations.
- Purpose: To support day-to-day operations and transactional applications.
- Examples: Banking systems, order entry systems, retail sales systems.
OLAP (Online Analytical Processing):
OLAP systems are designed to manage and analyze large volumes of data. They support complex queries, multidimensional analysis, and business intelligence activities.
- Purpose: To enable strategic analysis and decision-making through data warehousing and business intelligence tools.
- Examples: Data warehouses, financial reporting systems, market research analysis.
This AWS Blog explain OLTP vs OLAP very nicely
Explain the difference Between ETL and ELT
ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are two data integration processes that differ primarily in the sequence of their steps and the way data transformation is handled. Below, we’ll explore the key differences between these two approaches:
ETL (Extract, Transform, Load)
- Extract: Data is extracted from various sources. Transform: Data is transformed and cleaned before being loaded into the target system. Load: Transformed data is then loaded into the target data warehouse or data storage system.
ELT (Extract, Load, Transform)
- Extract: Data is extracted from various sources. Load: Raw data is immediately loaded into the target data storage system. Transform: Data transformation is performed within the target system after loading.
What are the key differences between SQL and NoSQL databases?
- SQL Databases: Use structured query language (SQL) for defining and manipulating data. They are relational databases that store data in tables with predefined schemas. Examples include MySQL, PostgreSQL, and Oracle.
- NoSQL Databases: Designed for flexible schema and can store unstructured or semi-structured data. They include document stores, key-value stores, wide-column stores, and graph databases. Examples include MongoDB, Cassandra, and Redis.
Detailed article to understand SQL vs NoSQL
Can you explain the concept of data partitioning and why it is used?
Data partitioning is the process of dividing a database or data warehouse into smaller, more manageable pieces, or partitions. It is used to improve performance, manageability, and scalability. By partitioning data, queries can be executed more efficiently, as they can target specific partitions rather than scanning the entire dataset.
Here is youtube Video explaining Partitioning
Explain Batch vs. Stream Processing
Batch processing and stream processing are two distinct methods for handling data workflows. Batch processing involves collecting data over a period and processing it in large chunks at scheduled intervals, which is efficient and simpler to manage but not suitable for real-time applications due to its higher latency. In contrast, stream processing handles data continuously in real-time as it arrives, making it ideal for applications like real-time monitoring and live analytics.
What are different Types of Data Models
When designing data warehouses, specific data modeling techniques are employed to optimize query performance and ensure data integrity. Among the most common are star schema, snowflake schema, and dimensional modeling. Each of these models is designed to support efficient querying and reporting.
1. Star Schema
A star schema is a type of database schema that is designed to optimize query performance in a data warehouse. It consists of a central fact table surrounded by dimension tables, forming a star-like structure.
2. Snowflake Schema
A snowflake schema is a more normalized form of the star schema, where dimension tables are further divided into related sub-dimension tables, resembling a snowflake shape.
3. Dimensional Modeling
Dimensional modeling is a design technique used for data warehouses that organizes data into dimensions and facts, facilitating easy querying and reporting. It encompasses both star and snowflake schemas.
Star Schema vs Snowflake Schema — Difference Between Them
What is Data mining?
Data mining is the process of analyzing large datasets to discover patterns, trends, correlations, and useful information that can help in decision-making. It involves using various techniques from statistics, machine learning, and database management to extract meaningful insights from raw data. The ultimate goal of data mining is to transform data into actionable knowledge
Consider a retail company that collects sales data from its stores. By applying data mining techniques, the company can identify patterns in customer purchasing behavior.
- Market Basket Analysis: The company discovers that customers who buy diapers are also likely to purchase baby wipes and baby formula. This insight can be used to organize store layouts or create bundled promotions.
The key components of data mining include data preprocessing (cleaning and transforming data), pattern discovery (using algorithms to identify trends and relationships), and knowledge representation (presenting the results in a meaningful way for decision-making).
Explain the concept of data lineage.
Data lineage refers to the tracking and visualization of data as it flows from its source to its destination. It helps in understanding the data’s origin, transformations, and journey through various processes, ensuring transparency, traceability, and data quality.
SQL related Interview Questions
Though SQL is must have skills for Data engineer , Interviewer prefer to ask SQL based interview question. You can prepare SQL related interview questions from this youtube channel. This is best what I found on Youtube for preparing SQL.
Google Cloud Data Engineer Questions
Explain how partitioning works in BigQuery.
Partitioning in BigQuery divides a table into segments, called partitions, based on the values of a specified column. The most common partitioning types are:
TimePartitioned Tables: These are partitioned based on a `DATE` or `TIMESTAMP` column.
IntegerRange Partitioned Tables: These are partitioned based on an integer column.
IngestionTime Partitioned Tables: These are automatically partitioned based on the ingestion time.
Benefits include improved query performance and reduced query costs by allowing BigQuery to scan only relevant partitions.
Here is youtube Video explaining Partitioning
What are clustered tables in BigQuery and why would you use them?
Clustering in BigQuery sorts the data based on the values of one or more columns, called clustering columns. This helps to:
Improve query performance by organizing related data together, making it faster to locate specific rows.
Reduce query costs by reducing the amount of data scanned.
For example, clustering a table on `user_id` and `timestamp` can speed up queries filtering on these columns.
How would you optimize BigQuery performance for large datasets?
Optimizing BigQuery performance involves:
- Partitioning Tables: Use date or range partitioning to limit the amount of data scanned.
- Clustering Tables: Organize data to improve query performance by reducing the amount of data scanned.
- Query Optimization: Use appropriate SQL functions and avoid using SELECT *.
- Materialized Views: Use materialized views to precompute and store query results.
- Storage Optimization: Use compressed and efficient data formats like Avro or Parquet.
- Use Proper Data Types: Choose the most appropriate and efficient data types for your columns.
- Use JOINs and Subqueries Wisely: Optimize JOINs and subqueries to avoid performance bottlenecks.
How do you secure data in GCP?
Securing data in GCP involves several practices:
- Encryption: Data is encrypted at rest and in transit.
- IAM: Use Identity and Access Management (IAM) to control access to resources.
- VPC: Set up Virtual Private Cloud (VPC) for network isolation and security.
- Auditing: Enable logging and auditing with Cloud Audit Logs.
- DLP: Use Cloud Data Loss Prevention (DLP) to detect and protect sensitive data.
How does BigQuery handle schema changes?
- BigQuery supports schema changes such as adding new columns and modifying column descriptions. You can add new columns without affecting existing data:
- Adding Columns: Use the `ALTER TABLE` statement.
- Deleting Columns: Not directly supported, but you can create a new table with the desired schema and copy the data over.
- Schema Auto-Detection: When loading new data, BigQuery can automatically detect and adjust the schema based on the incoming data.
How would you handle accidental data deletion in BigQuery?
Accidental data deletion can be a critical issue. In BigQuery, I would leverage the following features to mitigate this risk:
- Time Travel: This feature allows you to query and restore data to a specific point in time within a designated window.By enabling time travel, I can recover accidentally deleted data if the deletion occurred within the specified time frame.
- Fail-Safe: Although not directly accessible, BigQuery retains deleted data for an additional 7 days after the time travel window expires. In case of a catastrophic data loss, I can contact Google Cloud Customer Care to initiate a recovery process.
Apart from this we can setup data retenton policies, Backup strategies for regular backup/Snapshots.
What are UserDefined Functions (UDFs) in BigQuery?
UDFs allow you to define custom functions in SQL or JavaScript to perform complex calculations or transformations that are not possible with standard SQL functions.
SQL UDFs: Use SQL expressions.
JavaScript UDFs: Use JavaScript code to process input and produce output.
Example of a SQL UDF:
CREATE TEMP FUNCTION my_function(x INT64) AS (x * 3); SELECT my_function(10); -- Returns 30
Google Cloud Documentation
https://cloud.google.com/bigquery/docs/user-defined-functions
Explain the concept of federated queries in BigQuery
Federated queries allow you to query data stored outside of BigQuery, such as in Cloud SQL,Google Cloud Storage, Google Sheets, or Cloud Bigtable, without needing to load it into BigQuery first.
This is done by using external data sources and external tables.
To send a federated query use the EXTERNAL_QUERY function.
What are materialized views in BigQuery?
Materialized views store the results of a query physically, allowing for faster query performance on repeated queries.
They differ from standard views as they do not execute the underlying query every time they are accessed.
Materialized views are automatically refreshed by BigQuery to stay up-to-date with the base table.
CREATE MATERIALIZED VIEW
myproject.mydataset.my_mv_table AS (
SELECT
product_id,
SUM(clicks) AS sum_clicks
FROM
myproject.mydataset.my_base_table
GROUP BY product_id );https://cloud.google.com/bigquery/docs/materialized-views-intro
Can you explain the concept of a streaming buffer in BigQuery? How does it impact data ingestion and query performance?
The streaming buffer in BigQuery is a temporary storage area that allows for real-time data ingestion. When data is streamed into a BigQuery table using the streaming insert API, it first goes into the streaming buffer.The data remains in the streaming buffer for a short period (usually up to 90 minutes) before it is moved to the permanent table storage. You might not not perform DELETE/UPDATE operation on streaming buffer data. ( Now some additional feature to work on Streaming buffer data as well)
Life of a BigQuery streaming insert | Google Cloud Blog
What is the role of Google Cloud Data Catalog, and how does it integrate with other services?
Google Cloud Data Catalog is a fully managed metadata management service that helps you discover, manage, and understand data assets. It integrates with other services by:
- Metadata Management: Automatically cataloging metadata from BigQuery, Cloud Storage, and other data sources.
- Search and Discovery: Allowing users to search for data assets and understand their lineage.
- Policy Management: Providing data governance and access control features.
https://cloud.google.com/data-catalog/docs/concepts/overview
How to choose right data processing Tool among Dataflow vs Cloud Composer(Airflow) vs Dataproc vs data fusion in Google Cloud
Choosing correct tool depend on requirements as each of them have seperate pros & cons.
Google Cloud Dataflow is ideal for real-time and batch data processing, such as analyzing live user activity streams on an e-commerce site.
Cloud Composer (Airflow) is best for orchestrating workflows, like automating a daily ETL pipeline that extracts data from an API, transforms it, and loads it into BigQuery.
Dataproc excels at running large-scale data processing tasks with Hadoop or Spark, such as analyzing terabytes of log data.
Data Fusion is suited for building and managing ETL pipelines with a visual interface, for example, integrating customer data from various sources into a centralized data warehouse.
What is Google Cloud Platform (GCP), and what are its key services relevant to data engineering?
Google Cloud Platform (GCP) is a suite of cloud computing services offered by Google. Key services relevant to data engineering include:
- BigQuery: Fully managed, serverless data warehouse for analytics.
- Cloud Dataflow: Managed service for processing and analyzing streaming and batch data.
- Cloud Storage: Object storage service for storing and accessing data.
- Cloud Pub/Sub: Scalable messaging service for building event-driven systems.
- Cloud Dataproc: Managed Spark and Hadoop service for running clusters.
- Cloud Composer: Managed workflow orchestration service built on Apache Airflow.
- Cloud Spanner: Horizontally scalable, strongly consistent relational database service.
- Cloud Bigtable: NoSQL wide-column database service for real-time and batch analytics.
Can you explain the differences between Google BigQuery and Google Cloud SQL?
Google BigQuery is a fully managed, serverless data warehouse for analytics, while Google Cloud SQL is a fully managed relational database service for MySQL, PostgreSQL, and SQL Server. The key differences include:
- BigQuery is designed for analytics and supports large-scale data processing with SQL queries, while Cloud SQL is designed for transactional workloads and traditional relational database use cases.
- BigQuery is optimized for read-heavy analytics queries on large datasets, while Cloud SQL is optimized for transactional and operational workloads with ACID compliance.
- BigQuery is serverless, automatically scales to handle query loads, and charges based on usage, while Cloud SQL requires provisioning and managing database instances with fixed resources.
What is Cloud Dataflow, and how does it differ from Apache Beam?
Cloud Dataflow is a managed service for processing and analyzing streaming and batch data, while Apache Beam is an open-source unified programming model for defining both batch and streaming data processing pipelines. Cloud Dataflow is the fully managed service provided by Google Cloud Platform to execute Apache Beam pipelines. It abstracts the infrastructure management and offers autoscaling, monitoring, and fault tolerance features out of the box.
Describe the advantages of using Cloud Pub/Sub for building real-time data pipelines.
Cloud Pub/Sub is a scalable messaging service for building event-driven systems. Advantages of using Cloud Pub/Sub for building real-time data pipelines include:
- Scalability: Cloud Pub/Sub can handle millions of messages per second with low latency.
- Durability: Messages are persisted in the system even if subscribers are temporarily unavailable.
- Decoupling: Allows decoupling of message producers and consumers, enabling flexible and scalable architectures.
- Integration: Integrates seamlessly with other Google Cloud services like Dataflow, BigQuery, and Cloud Functions.
What is Cloud Dataprep, and how does it simplify the data preparation process?
Cloud Dataprep is a service that helps to visually explore, clean, and prepare data for analysis. It simplifies the data preparation process by providing a user-friendly interface with features such as data profiling, transformation suggestions, and visual data wrangling. Cloud Dataprep automatically detects data types, anomalies, and patterns, making it easier for users to clean and transform data without writing code.
Explain the role of Cloud Composer in orchestrating data pipelines on GCP.
Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. It allows users to author, schedule, and monitor workflows using Python and the Airflow API. Cloud Composer provides features such as DAG (Directed Acyclic Graph) scheduling, dependency management, task retries, and monitoring dashboards. It simplifies the orchestration of data pipelines by automating infrastructure management and providing a scalable and reliable workflow execution environment.
Describe the use cases for Cloud Spanner and how it differs from traditional relational databases.
Cloud Spanner is a horizontally scalable, strongly consistent relational database service designed for mission-critical applications. Use cases for Cloud Spanner include
Globally distributed databases requiring strong consistency and high availability.
Financial applications requiring transactional integrity and horizontal scalability.
Multi-regional analytics and reporting platforms requiring real-time data access.
Can you outline the benefits of using Data Loss Prevention (DLP) API in data security and compliance?
The Data Loss Prevention (DLP) API offers organizations several benefits, including sensitive data discovery, data protection, and privacy, risk mitigation, compliance assurance, data governance, customization, integration with cloud services, and continuous monitoring and remediation. It helps organizations identify, classify, and protect sensitive data to maintain data security, privacy, and regulatory compliance
Explain the difference between streaming inserts and batch inserts in Google BigQuery.
Streaming inserts allow you to stream data into BigQuery one record at a time in real-time, whereas batch inserts involve loading data into BigQuery in large batches using jobs or file uploads. Streaming inserts are suitable for scenarios where you need immediate analysis of real-time data, while batch inserts are more efficient for loading large volumes of data at once.
How does Dataflow handle data processing in both batch and streaming modes?
Dataflow is a unified stream and batch processing model based on Apache Beam. In batch mode, Dataflow processes bounded datasets by breaking them into manageable chunks and processing them in parallel across multiple workers. In streaming mode, Dataflow processes unbounded data streams by continuously ingesting and processing data in near-real-time, using windowing and triggering mechanisms to manage event time and processing time semantics.
Explain the concept of windowing in Apache Beam/Dataflow.
Windowing in Apache Beam/Dataflow allows you to divide the data stream into finite and logical time intervals called windows for processing. It enables you to perform computations over time-based or event-based windows, such as fixed windows, sliding windows, and session windows. Windowing helps manage the processing of streaming data by providing control over how data is grouped and aggregated within specific time boundaries.
What are some common use cases for using Apache Airflow in data engineering workflows?
Apache Airflow is a platform for programmatically authoring, scheduling, and monitoring workflows. Common use cases for Apache Airflow in data engineering workflows include:
- ETL (Extract, Transform, Load) processes for data ingestion and transformation.
- Orchestration of data pipelines across multiple systems and services.
- Workflow automation for data quality checks, model training, and deployment.
- Dependency management and scheduling of complex data processing tasks.
- Integration with external systems and services using custom operators and hooks.
How does Cloud Storage differ from Cloud Filestore in terms of file storage and access?
Cloud Storage is an object storage service designed for storing and accessing unstructured data, such as files, images, and backups, using a simple HTTP interface. Cloud Storage provides durable and scalable storage with global availability and strong consistency.
On the other hand, Cloud Filestore is a managed file storage service designed for storing and sharing files in a traditional file system format. Cloud Filestore provides fully managed NFS (Network File System) and SMB (Server Message Block) file shares with high performance and low latency, suitable for applications that require shared file access across multiple instances.
How does Cloud Composer simplify the deployment and management of Apache Airflow workflows on Google Cloud Platform?
Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. It simplifies the deployment and management of Apache Airflow workflows on Google Cloud Platform by providing the following features:
- Fully managed Apache Airflow environment with automatic scaling and upgrades.
- Integration with Google Cloud services and APIs for seamless workflow orchestration.
- Built-in monitoring, logging, and alerting capabilities for workflow execution.
- Support for custom Python packages and dependencies.
- Integration with Cloud Storage and Cloud Storage Bucket for storing DAGs and other artifacts.
Explain the benefits of using Cloud Storage for storing data in Google Cloud Platform.
Cloud Storage offers several benefits for storing data in Google Cloud Platform, including:
- Scalability: Cloud Storage can store petabytes of data and automatically scales to accommodate growing storage needs.
- Durability: Data stored in Cloud Storage is replicated across multiple geographic locations for high availability and durability.
- Security: Cloud Storage provides encryption at rest and in transit, access controls, and audit logging to protect data privacy and integrity.
- Cost-Effectiveness: Cloud Storage offers flexible pricing options, including pay-as-you-go and lifecycle management policies, to optimize storage costs.
- Integration: Cloud Storage integrates with other Google Cloud services and third-party tools for data processing, analytics, and archival.
How do you design a data pipeline in Google Cloud giving some use case?
Designing a data pipeline in Google Cloud typically involves:
- Data Ingestion: Using services like Cloud Pub/Sub or Dataflow for real-time or batch data ingestion. GCS also used for data ingestion or staging on cloud in form of files.
- Data Storage: Storing data in Cloud Storage (for raw data) or BigQuery (for structured data).
- Data Processing: Using Dataflow for ETL processes or Dataproc for big data processing.
- Data Analysis: Querying and analyzing data using BigQuery.
- Data Visualization: Creating dashboards and reports using Looker or Data Studio.
Describe a scenario where you had to troubleshoot a data pipeline issue. How did you approach the problem?
(Provide a specific example from your experience, explaining the issue, the troubleshooting steps you took, tools you used, and the resolution. Highlight your problem-solving skills and attention to detail.)
How would you design a data solution for a company with high-volume real-time data processing needs?
For high-volume real-time data processing:
- Data Ingestion: Use Cloud Pub/Sub for ingesting streaming data.
- Data Processing: Use Cloud Dataflow or Apache Beam to process and transform data in real-time.
- Data Storage: Store processed data in BigQuery for analytics or Cloud Storage for raw data.
- Data Visualization: Use Looker or Data Studio to create real-time dashboards and reports.
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About Me
As an experienced Fully certified (11x certified) Google Cloud Architect, Google Cloud champion Innovator, with over 7+ years of expertise in Google Cloud Networking,Data ,Devops, Security and ML, I am passionate about technology and innovation. Being a Champion Innovator and Google Cloud Architect, I am always exploring new ways to leverage cloud technologies to deliver innovative solutions that make a difference.
If you have any queries or would like to get in touch, you can reach me at Email address — vishal.bulbule@techtrapture.com or connect with me on LinkedIn at https://www.linkedin.com/in/vishal-bulbule/. For a more personal connection, you can also find me on Instagram at https://www.instagram.com/vishal_bulbule/?hl=en.
Instagram — https://www.instagram.com/vishal_bulbule/?hl=en.
Additionally, please check out my YouTube Channel at https://www.youtube.com/@techtrapture for tutorials and demos on Google Cloud.
