20+ Data Architect Interview Questions and Answers

The 7 layers in Azure Data Factory for pipelining data from on-premises to Azure Cloud

• 1. Ingestion Layer: Collects data from various sources such as on-premises databases, cloud storage, or IoT devices.

• 2. Storage Layer: Stores the ingested data in a data lake or data warehouse for processing.

• 3. Batch Layer: Processes data in batches using technologies like Azure Databricks or HDInsight.

• 4. Stream Layer: Processes real-time data streams using technologies like Azure Stream Anal...read more

Working for Adobe is exciting due to their innovative culture, cutting-edge technology, and global impact.

• Innovative culture fosters creativity and encourages experimentation

• Cutting-edge technology provides opportunities to work with the latest tools and techniques

• Global impact means that work has a wide-reaching influence and can make a difference in the world

• Opportunities for growth and development through training and mentorship programs

• Collaborative and inclusive work env...read more

To convert a normal file to a flat file in Python, you can read the file line by line and write the data to a new file with a delimiter.

• Open the normal file in read mode

• Read the file line by line

• Split the data based on the delimiter (if applicable)

• Write the data to a new file with a delimiter

Use USERELATIONSHIP function in Power BI to activate different dates for different analysis.

• Create multiple relationships between tables using USERELATIONSHIP function

• Specify which relationship to use in DAX calculations

• Example: USERELATIONSHIP('Date'[Date], 'Sales'[OrderDate])

To improve database performance, query fine tuning is necessary.

• Identify slow queries and optimize them

• Use indexing and partitioning

• Reduce data retrieval by filtering unnecessary data

• Use caching and query optimization tools

• Regularly monitor and analyze performance metrics

OLTP is for transactional processing while OLAP is for analytical processing.

• OLTP databases are designed for real-time transactional processing.

• OLAP databases are designed for complex analytical queries and data mining.

• OLTP databases are normalized while OLAP databases are denormalized.

• OLTP databases have a smaller data volume while OLAP databases have a larger data volume.

• Examples of OLTP databases include banking systems and e-commerce websites while examples of OLAP databa...read more

Exception handling in Python for classes with subclasses involves using try-except blocks to catch and handle errors.

• Use try-except blocks to catch exceptions in both parent and subclass methods

• Handle specific exceptions using multiple except blocks

• Use super() to call parent class methods within subclass methods

• Reraise exceptions if necessary using 'raise'

Dimensional model includes various types of dimensions such as conformed, junk, degenerate, and role-playing.

• Conformed dimensions are shared across multiple fact tables.

• Junk dimensions are used to store low-cardinality flags or indicators.

• Degenerate dimensions are attributes that do not have a separate dimension table.

• Role-playing dimensions are used to represent the same dimension with different meanings.

• Other types of dimensions include slowly changing dimensions and rapidl...read more

Implemented a data architecture using a combination of relational databases and data lakes for efficient data storage and processing.

• Utilized a combination of relational databases (e.g. MySQL, PostgreSQL) and data lakes (e.g. Amazon S3) for storing structured and unstructured data.

• Implemented ETL processes to extract, transform, and load data from various sources into the data architecture.

• Designed data models to ensure data integrity and optimize query performance.

• Used tools...read more

I have used HUDI and Iceberg in my previous project for managing large-scale data lakes efficiently.

• Implemented HUDI for incremental data ingestion and managing large datasets in real-time

• Utilized Iceberg for efficient table management and data versioning

• Integrated HUDI and Iceberg with Apache Spark for processing and querying data

The current project architecture is a microservices-based architecture with a combination of cloud and on-premise components.

• Utilizes Docker containers for microservices deployment

• Uses Kubernetes for container orchestration

• Includes a mix of AWS and on-premise servers for scalability and cost-efficiency

• Employs Apache Kafka for real-time data streaming

• Utilizes MongoDB for data storage and retrieval

A data pipeline architecture is a framework for processing and moving data from source to destination efficiently.

• Identify data sources and destinations

• Choose appropriate tools for data extraction, transformation, and loading (ETL)

• Implement data quality checks and monitoring

• Consider scalability and performance requirements

• Utilize cloud services for storage and processing

• Design fault-tolerant and resilient architecture

Lambda architecture is a data processing architecture designed to handle massive quantities of data by using both batch and stream processing methods.

• Combines batch processing layer, speed layer, and serving layer

• Batch layer processes historical data in large batches

• Speed layer processes real-time data

• Serving layer merges results from batch and speed layers for querying

• Example: Apache Hadoop for batch processing, Apache Storm for real-time processing

Data governance capabilities refer to the ability to manage and control data assets effectively.

• Establishing policies and procedures for data management

• Ensuring compliance with regulations and standards

• Implementing data quality controls

• Managing data access and security

• Monitoring data usage and performance

• Providing training and support for data users

SCD type 2 using merge statement involves updating existing records and inserting new records in a dimension table.

• Use MERGE statement to compare source and target tables based on primary key

• Update existing records in target table with new values from source table

• Insert new records from source table into target table with new surrogate key and end date as null

Datalake is a centralized repository that allows storage of large amounts of structured and unstructured data. Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads.

• Datalake is a storage repository that holds vast amounts of raw data in its native format until needed.

• Delta Lake is an open-source storage layer that brings ACID transactions to big data workloads.

• Delta Lake provides data reliability and performance improv...read more

Data Vault is a modeling methodology for designing highly scalable and flexible data warehouses.

• Data Vault focuses on long-term historical data storage

• It consists of three main components: Hubs, Links, and Satellites

• Hubs represent business entities, Links represent relationships between entities, and Satellites store attributes of entities

• Data Vault allows for easy scalability and adaptability to changing business requirements

ETL stands for Extract, Transform, Load. It is a process used to extract data from various sources, transform it into a consistent format, and load it into a target database or data warehouse.

• Extract: Data is extracted from multiple sources such as databases, files, APIs, etc.

• Transform: Data is cleaned, standardized, and transformed into a consistent format to meet the requirements of the target system.

• Load: The transformed data is loaded into the target database or data ware...read more

Yes, I am open to relocation for the right opportunity.

• I am willing to relocate for a position that aligns with my career goals and offers growth opportunities.

• I have previous experience relocating for work and have found it to be a positive experience.

• I am open to exploring new locations and cultures as part of my career development.

A data architect can contribute to the organization by designing and implementing efficient data systems.

• Designing and implementing data models

• Ensuring data security and privacy

• Optimizing data storage and retrieval

• Collaborating with stakeholders to understand data needs

• Providing guidance on data governance and compliance

Azure Data Factory is a cloud-based data integration service that allows you to create, schedule, and manage data pipelines.

• Azure Data Factory is used to move and transform data from various sources to destinations.

• It supports data integration processes like ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform).

• You can create data pipelines using a visual interface in Azure Data Factory.

• It can connect to on-premises and cloud data sources such as SQL Server, Azure...read more

Yes, I have onsite exposure in previous roles.

• I have worked onsite at various client locations to gather requirements and implement solutions.

• I have experience collaborating with cross-functional teams in person.

• I have conducted onsite training sessions for end users on data architecture best practices.

• I have participated in onsite data migration projects.

• I have worked onsite to troubleshoot and resolve data-related issues.

Window function coding test involves using window functions in SQL to perform calculations within a specified window of rows.

• Understand the syntax and usage of window functions in SQL

• Use window functions like ROW_NUMBER(), RANK(), DENSE_RANK(), etc. to perform calculations

• Specify the window frame using PARTITION BY and ORDER BY clauses

• Practice writing queries with window functions to get comfortable with their usage

A data model for book lending

• Create entities for books, borrowers, and loans

• Include attributes such as book title, author, borrower name, loan date, and due date

• Establish relationships between books and borrowers through loan transactions

• Consider additional attributes like book genre, borrower contact information, and loan status