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.
Scenario: Your company wants to implement a data warehousing solution using Hadoop technology. Which component of the Hadoop ecosystem would you recommend for ad-hoc querying and data analysis?
- Apache HBase
- Apache Hive
- Apache Spark
- Hadoop Distributed File System
Apache Spark is suitable for ad-hoc querying and data analysis due to its in-memory processing capabilities, which enable faster analytics on large datasets compared to other Hadoop components.
Which type of data model provides more detailed specifications compared to a conceptual model but is still independent of the underlying database system?
- Conceptual Data Model
- Logical Data Model
- Physical Data Model
- Relational Data Model
A Logical Data Model provides more detailed specifications than a conceptual model but is still independent of the underlying database system, focusing on the structure and relationships of the data.
What is the difference between a unique index and a non-unique index?
- A non-unique index allows duplicate values in the indexed column(s)
- A non-unique index does not allow NULL values in the indexed column(s)
- A unique index allows NULL values in the indexed column(s)
- A unique index allows only unique values in the indexed column(s)
A unique index enforces uniqueness, ensuring that each indexed value is unique, while a non-unique index allows duplicate values to be stored. Understanding this difference is crucial for data integrity and query optimization.
________ is a technique used in Dimensional Modeling to handle changes to dimension attributes over time.
- Fast Updating Dimension (FUD)
- Quick Altering Dimension (QAD)
- Rapidly Changing Dimension (RCD)
- Slowly Changing Dimension (SCD)
Slowly Changing Dimension (SCD) is a technique used in Dimensional Modeling to handle changes to dimension attributes over time. It involves maintaining historical data to accurately reflect changes in dimension attributes.
________ 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.