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I was interviewed before Jan 2024.
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I applied via Recruitment Consulltant and was interviewed in Nov 2024. There were 2 interview rounds.
Various data warehousing techniques include dimensional modeling, star schema, snowflake schema, and data vault.
Dimensional modeling involves organizing data into facts and dimensions to facilitate easy querying and analysis.
Star schema is a type of dimensional modeling where a central fact table is connected to multiple dimension tables.
Snowflake schema is an extension of star schema where dimension tables are normali...
My analytics work has helped the organization make data-driven decisions, improve operational efficiency, and identify new opportunities for growth.
Developed data models and algorithms to optimize business processes
Generated insights from large datasets to drive strategic decision-making
Identified trends and patterns to improve customer experience and retention
Implemented data governance policies to ensure data quality
I would respond in various situations by remaining calm, assessing the situation, and providing a thoughtful and strategic solution.
Remain calm and composed
Assess the situation thoroughly
Provide a thoughtful and strategic solution
Communicate effectively with all parties involved
Both career and team are important, but ultimately career growth should be prioritized.
Career growth is essential for personal development and achieving professional goals.
A strong team can support career growth by providing mentorship, collaboration, and opportunities for learning.
Balancing career and team dynamics is key to long-term success in any role.
I applied via Naukri.com and was interviewed in Jun 2024. There were 3 interview rounds.
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
I applied via Approached by Company
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
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...
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 requ
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, Apach
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 is
I applied via Recruitment Consulltant and was interviewed in May 2024. There were 2 interview rounds.
SQL Scripts to write and also also asked to design an data model of my choice in Telecom Domain
I applied via Naukri.com and was interviewed in Feb 2024. There was 1 interview round.
I applied via Company Website and was interviewed before Aug 2021. There was 1 interview round.
I applied via Naukri.com and was interviewed in Feb 2021. There were 3 interview rounds.
Conceptual, logical and physical data models are different levels of abstraction in data modeling.
Conceptual model represents high-level business concepts and relationships.
Logical model represents the structure of data without considering physical implementation.
Physical model represents the actual implementation of data in a database.
Conceptual model is independent of technology and implementation details.
Logical mod...
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 sys...
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 o...
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
Kimball focuses on dimensional modelling while Inmon focuses on normalized modelling.
Kimball is bottom-up approach while Inmon is top-down approach
Kimball focuses on business processes while Inmon focuses on data architecture
Kimball uses star schema while Inmon uses third normal form
Kimball is easier to understand and implement while Inmon is more complex and requires more planning
Kimball is better suited for data ware...
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
I applied via Naukri.com and was interviewed in Apr 2021. There were 3 interview rounds.
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