________ databases are specifically designed to handle semi-structured data efficiently.
- Columnar
- Document-oriented
- Graph
- Key-value
Document-oriented databases are specifically designed to handle semi-structured data efficiently by allowing flexibility in the schema and supporting nested structures within documents.
What are some key considerations when designing a data extraction process for real-time data sources?
- Batch processing, data partitioning, data encryption
- Data compression, data replication, data normalization
- Data quality, data profiling, metadata management
- Scalability, latency, data consistency
When designing a data extraction process for real-time data sources, key considerations include scalability to handle large volumes of data, minimizing latency, and ensuring data consistency across systems.
What is the role of Change Data Capture (CDC) in data extraction?
- Encrypting extracted data
- Generating reports based on extracted data
- Identifying changes in source data
- Optimizing data extraction
Change Data Capture (CDC) is responsible for identifying changes in source data since the last extraction, allowing for the extraction of only the modified data. This reduces processing time and resources.
Scenario: You are tasked with optimizing an ETL process that involves extracting data from multiple sources and transforming it before loading it into a data warehouse. What approach would you take to identify and prioritize optimization opportunities?
- Analyze query performance and database indexes.
- Implement parallel processing and distributed computing techniques.
- Profile and monitor system resource utilization to identify bottlenecks.
- Utilize caching mechanisms and in-memory computing.
When optimizing an ETL process, profiling and monitoring system resource utilization is crucial to identify performance bottlenecks. This involves analyzing CPU, memory, disk I/O, and network usage to prioritize optimization efforts.
What is shuffle in Apache Spark, and why is it an expensive operation?
- A data re-distribution process during transformations
- A process of joining two datasets
- A process of re-partitioning data for parallel processing
- A task scheduling mechanism in Spark
Shuffle in Apache Spark involves re-distributing data across partitions, often required after certain transformations like groupBy or sortByKey, making it an expensive operation due to data movement across the cluster.
Scenario: Your company is merging data from multiple sources into a single database. How would you approach data cleansing to ensure consistency and accuracy across all datasets?
- Identify and resolve duplicates
- Implement data validation checks
- Perform entity resolution to reconcile conflicting records
- Standardize data formats and units
Ensuring consistency and accuracy across datasets involves several steps, including standardizing data formats and units to facilitate integration. Identifying and resolving duplicates help eliminate redundancy and maintain data integrity. Entity resolution resolves conflicting records by identifying and merging duplicates or establishing relationships between them. Implementing data validation checks ensures that incoming data meets predefined standards and quality criteria.
While a logical data model focuses on what data is stored and how it relates to other data, a physical data model deals with ________.
- Business requirements
- Data modeling techniques
- Data normalization techniques
- How data is stored and accessed
A physical data model addresses the implementation details of how data is stored, accessed, and managed in a database system, whereas a logical data model concentrates on the logical structure and relationships of data.
How does data timeliness contribute to data quality?
- It ensures that data is up-to-date at all times
- It focuses on the consistency of data across different sources
- It prioritizes data availability over accuracy
- It validates the accuracy of data through statistical methods
Data timeliness is crucial for maintaining high data quality as it ensures that the information being used is current and relevant. Timely data allows organizations to make informed decisions based on the most recent information available, improving the effectiveness of business operations and strategic planning. It reduces the risk of using outdated data that may lead to errors or inaccuracies in analysis and decision-making processes.
How can data compression techniques be beneficial in ETL optimization?
- Enhanced data visualization, improved analytics
- Improved data quality, reduced processing time
- Increased storage requirements, slower data transfer
- Reduced storage requirements, faster data transfer
Data compression techniques in ETL optimization can benefit by reducing storage requirements and facilitating faster data transfer. Compressed data takes up less space and can be transmitted more quickly across the ETL pipeline.
What are the potential drawbacks of normalization in database design?
- Decreased redundancy
- Difficulty in maintaining data integrity
- Increased complexity
- Slower query performance
Normalization in database design can lead to increased complexity due to the need for multiple tables and relationships. This can make querying and understanding the database more difficult. Additionally, it can result in slower query performance as joins are required to retrieve related data.