Which data types are commonly stored in Data Lakes?
- Character, Date, Time, Array
- Integer, String, Float, Boolean
- Structured, Semi-structured, Unstructured, Binary
- Text, Numeric, Date, Boolean
Data Lakes commonly store structured, semi-structured, unstructured, and binary data types. This flexibility allows organizations to store and analyze various forms of data without the need for predefined schemas.
How does data profiling contribute to the effectiveness of the ETL process?
- Accelerating data processing, Simplifying data querying, Streamlining data transformation, Automating data extraction
- Enhancing data visualization, Improving data modeling, Facilitating data governance, Securing data access
- Identifying data anomalies, Ensuring data accuracy, Optimizing data storage, Validating data integrity
- Standardizing data formats, Enforcing data encryption, Auditing data access, Maintaining data backups
Data profiling in the ETL process involves analyzing data to identify anomalies, ensuring accuracy, optimizing storage, and validating integrity, which enhances the effectiveness and reliability of subsequent ETL operations.
What is the primary purpose of error handling in data pipelines?
- Enhancing data visualization techniques
- Identifying and resolving data inconsistencies
- Optimizing data storage efficiency
- Preventing data loss and ensuring data reliability
Error handling in data pipelines primarily focuses on preventing data loss and ensuring data reliability. It involves mechanisms to detect, capture, and address errors that occur during data processing, transformation, and movement. By handling errors effectively, data pipelines maintain data integrity and consistency, ensuring that accurate data is available for downstream analysis and decision-making.
Scenario: Your team is experiencing slow query performance in a production database. Upon investigation, you find that there are no indexes on the columns frequently used in the WHERE clause of queries. What would be your recommended solution to improve query performance?
- Create Indexes on the frequently used columns
- Increase server memory
- Optimize SQL queries
- Upgrade database hardware
To improve query performance, creating indexes on the columns frequently used in the WHERE clause can significantly reduce the time taken for query execution by allowing the database engine to quickly locate the relevant rows.
In which scenario would you consider using a non-clustered index over a clustered index?
- When you frequently query a large range of values
- When you need to enforce a primary key constraint
- When you need to physically reorder the table data
- When you want to ensure data integrity
A non-clustered index is considered when you frequently query a large range of values or when you want to avoid the overhead of reordering the physical data in the table, which is required by a clustered index.
________ is a NoSQL database that is optimized for high availability and partition tolerance, sacrificing consistency under certain circumstances.
- Cassandra
- MongoDB
- Neo4j
- Redis
Cassandra is a NoSQL database designed for high availability and partition tolerance in distributed environments. It follows the principles of the CAP theorem, prioritizing availability and partition tolerance over consistency in certain scenarios.
In an ERD, a ________ is a property or characteristic of an entity.
- Attribute
- Entity
- Key
- Relationship
An attribute in an ERD represents a property or characteristic of an entity. It describes the data that can be stored for each instance of the entity, contributing to the overall definition of the entity's structure.
What is a Slowly Changing Dimension (SCD) in Dimensional Modeling?
- A dimension that changes at a regular pace
- A dimension that changes frequently over time
- A dimension that changes unpredictably over time
- A dimension that rarely changes over time
A Slowly Changing Dimension (SCD) in Dimensional Modeling is a dimension that changes over time but not frequently. It typically records historical data, preserving the history of changes in the dimension.
In database systems, ________ is a technique used to replicate data across multiple nodes to enhance availability and fault tolerance.
- Clustering
- Partitioning
- Replication
- Sharding
Replication involves copying and maintaining identical copies of data across multiple nodes or servers in a database system. It improves availability by ensuring that data remains accessible even if one or more nodes fail. Additionally, replication enhances fault tolerance by providing redundancy, allowing the system to continue functioning even in the face of failures.
What are the advantages of using Dimensional Modeling over Normalized Modeling?
- Better query performance
- Easier data maintenance
- Enhanced scalability
- Reduced data redundancy
Dimensional Modeling offers better query performance compared to Normalized Modeling because it structures data in a way that aligns with how it is typically queried, resulting in faster and more efficient data retrieval. This is particularly advantageous for analytical and reporting purposes in data warehousing environments.