Novitas India Interview Questions and Answers

1:M and M:M relationships in data modeling are handled using foreign keys and junction tables.

• For 1:M relationships, a foreign key is added in the 'many' side table referencing the primary key in the 'one' side table.

• For M:M relationships, a junction table is created with foreign keys referencing the primary keys of both related tables.

• Example: In a bookstore database, a book can have multiple authors (M:M), so a junction table 'Book_Author' will have book_id and author_id co...read more

Database table versioning is the practice of maintaining historical data in a table by creating new versions of records instead of updating existing ones.

• Database table versioning involves creating new records for each change instead of updating existing records.

• It allows for tracking changes over time and maintaining a history of data.

• Common techniques for database table versioning include using effective dating or timestamp columns.

• Example: Instead of updating a customer's ...read more

Improving data warehouse/DB performance involves optimizing queries, indexing, hardware, and data modeling.

• Optimize queries by using appropriate indexes, avoiding unnecessary joins, and limiting the amount of data retrieved.

• Implement proper indexing strategies to speed up data retrieval, such as creating indexes on frequently queried columns.

• Upgrade hardware components like CPU, memory, and storage to handle larger workloads efficiently.

• Optimize data modeling by denormalizing...read more

A federated data warehouse is a system that combines data from multiple sources into a single, virtual database.

• It allows for querying and analyzing data from different sources without physically moving the data.

• Data remains in its original location but can be accessed and queried as if it were in a single database.

• Federated data warehouses are useful for organizations with diverse data sources and distributed data.

• Examples include using federated queries to combine sales dat...read more

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 for historical data with real-time stream processing for current data

• Allows for both speed and accuracy in data processing

• Enables fault tolerance and scalability

• Example: Using Apache Hadoop for batch processing and Apache Storm for stream processing