Scenario: A regulatory audit requires your organization to provide a comprehensive overview of data flow and transformations. How would you leverage metadata management and data lineage to address the audit requirements effectively?
- Depend solely on manual documentation for audit, neglect data lineage analysis, limit stakeholder communication
- Document metadata and data lineage, analyze data flow and transformations, generate comprehensive reports for audit, involve relevant stakeholders in the process
- Ignore metadata management and data lineage, provide limited data flow information, focus on compliance with regulatory requirements only
- Use generic templates for audit reports, overlook data lineage and metadata, minimize stakeholder involvement
Leveraging metadata management and data lineage involves documenting metadata and data lineage, analyzing data flow and transformations, and generating comprehensive reports for the audit. Involving relevant stakeholders ensures that the audit requirements are effectively addressed, providing transparency and compliance with regulatory standards.
Scenario: Your company is planning to implement a new data warehouse solution. As the data engineer, you are tasked with selecting an appropriate data loading strategy. Given the company's requirements for near real-time analytics, which data loading strategy would you recommend and why?
- Bulk Loading
- Change Data Capture (CDC)
- Incremental Loading
- Parallel Loading
Change Data Capture (CDC) captures only the changes made to the source data since the last extraction. This approach ensures near real-time analytics by transferring only the modified data, reducing the processing time and allowing for quicker insights.
The ________ approach involves defining a maximum number of retry attempts to prevent infinite retries.
- Constant Backoff
- Exponential Backoff
- Incremental Backoff
- Linear Backoff
The Exponential Backoff approach involves increasing the waiting time between each retry attempt exponentially. It helps prevent overwhelming a service with repeated requests and reduces the load during transient failures. By defining a maximum number of retry attempts, it also prevents infinite retries, ensuring system stability and graceful degradation under high loads or failure scenarios.
One potential disadvantage of denormalization is increased ________ due to redundant data.
- Complexity, Data
- Complexity, Storage
- Data, Complexity
- Storage, Data
One potential disadvantage of denormalization is increased complexity due to redundant data. Denormalizing tables can introduce redundancy, making data maintenance more complex and increasing the risk of inconsistencies. This complexity can lead to challenges in data management and maintenance.
Which pipeline architecture is suitable for processing large volumes of data with low latency requirements?
- Batch architecture
- Lambda architecture
- Microservices architecture
- Streaming architecture
A streaming architecture is suitable for processing large volumes of data with low latency requirements. In a streaming architecture, data is processed in real-time as it arrives, allowing for immediate insights and actions on fresh data. This architecture is well-suited for use cases such as real-time analytics, fraud detection, and IoT data processing, where timely processing of data is crucial.
Scenario: You are tasked with processing a large batch of log data stored in HDFS and generating summary reports. Which Hadoop component would you use for this task, and why?
- Apache Hadoop MapReduce
- Apache Kafka
- Apache Pig
- Apache Sqoop
Apache Hadoop MapReduce is ideal for processing large batch data stored in HDFS and generating summary reports. It provides a scalable and fault-tolerant framework for parallel processing of distributed data.
Which of the following is a key characteristic of distributed systems?
- Centralized control
- Fault tolerance
- Low network latency
- Monolithic architecture
Fault tolerance is a key characteristic of distributed systems, referring to their ability to continue operating despite individual component failures. Distributed systems are designed to handle failures gracefully by replicating data, employing redundancy, and implementing algorithms to detect and recover from faults without disrupting overall system functionality. This resilience ensures system availability and reliability in the face of failures, a crucial aspect of distributed computing.
When is the use of regular expressions (regex) commonly applied in data transformation?
- Encrypting data
- Extracting patterns from unstructured data
- Filtering data
- Sorting data
Regular expressions (regex) are often used in data transformation to extract specific patterns or structures from unstructured data sources, facilitating the process of data parsing and extraction for further processing.
What strategies can be employed to optimize index usage in a database?
- All of the above
- Regularly analyze and update statistics on indexed columns
- Remove indexes on frequently updated columns
- Use covering indexes to include all required columns in the index
To optimize index usage, it's essential to regularly analyze and update statistics on indexed columns, remove unnecessary indexes, and use covering indexes to avoid lookups to the main table, thereby improving query performance.
Can you identify any specific scenarios where denormalization can lead to performance improvements over normalization?
- Complex data relationships
- OLAP (Online Analytical Processing) scenarios
- OLTP (Online Transaction Processing) scenarios
- Reporting and analytical queries
Denormalization can improve performance in scenarios such as reporting and analytical queries where data retrieval from multiple tables is common, as it reduces the need for complex joins and improves query performance.