Which of the following is a characteristic of Data Lakes?
- Schema enforcement
- Schema normalization
- Schema-on-read
- Schema-on-write
A characteristic of Data Lakes is schema-on-read, meaning that the structure of the data is applied when it's read rather than when it's written, allowing for greater flexibility and agility in data analysis.
What role does data profiling play in data modeling best practices?
- Defining data schema
- Generating sample data
- Identifying data quality issues
- Optimizing database performance
Data profiling in data modeling involves analyzing and understanding the quality and characteristics of data, including identifying anomalies and inconsistencies, which is crucial for ensuring data quality.
The concept of ________ allows real-time data processing systems to respond to events or changes immediately.
- Batch processing
- Event-driven architecture
- Microservices architecture
- Stream processing
Event-driven architecture is a design approach that enables real-time data processing systems to respond to events or changes immediately, without waiting for batch processing cycles. This architecture allows systems to react dynamically to incoming events or triggers, enabling timely actions, notifications, or updates based on real-time data streams. It is well-suited for applications requiring low latency, high scalability, and responsiveness to dynamic environments.
In Kafka, the ________ is responsible for storing the committed offsets of the consumers.
- Broker
- Consumer
- Producer
- Zookeeper
In Kafka, Zookeeper is responsible for storing the committed offsets of the consumers. Zookeeper manages various aspects of Kafka's distributed system, including coordination and metadata management.
________ analysis assesses the consistency and correctness of data values within a dataset.
- Data cleansing
- Data integration
- Data profiling
- Data validation
Data profiling analysis involves examining the quality and characteristics of data within a dataset. It assesses various aspects such as consistency, correctness, completeness, and uniqueness of data values. Through data profiling, data engineers can identify anomalies, errors, or inconsistencies in the dataset, which is crucial for ensuring data quality and reliability in subsequent processes like cleansing and integration.
Scenario: Your team is designing a complex data pipeline that involves multiple tasks with dependencies. Which workflow orchestration tool would you recommend, and why?
- AWS Glue - for its serverless ETL capabilities
- Apache Airflow - for its DAG (Directed Acyclic Graph) based architecture allowing complex task dependencies and scheduling
- Apache Spark - for its powerful in-memory processing capabilities
- Microsoft Azure Data Factory - for its integration with other Azure services
Apache Airflow would be recommended due to its DAG-based architecture, which enables the definition of complex workflows with dependencies between tasks. It provides a flexible and scalable solution for orchestrating data pipelines, allowing for easy scheduling, monitoring, and management of workflows. Additionally, Airflow offers a rich set of features such as task retries, logging, and extensibility through custom operators and hooks.
Metadata management tools often use ________ to track changes in data lineage over time.
- Auditing
- Compression
- Encryption
- Versioning
Metadata management tools often use auditing mechanisms to track changes in data lineage over time. Auditing helps monitor modifications, access, and usage of metadata, ensuring accountability, compliance, and data governance. It provides a historical record of metadata changes, facilitating troubleshooting and maintaining data lineage accuracy.
Which consistency model is typically associated with NoSQL databases?
- Causal consistency
- Eventual consistency
- Linearizability
- Strong consistency
NoSQL databases typically adopt the eventual consistency model, where updates to the data propagate asynchronously, providing a higher level of availability and partition tolerance at the expense of consistency.
________ is a data extraction technique that involves extracting data from a source system's log files, typically in real-time.
- API Integration
- Change Data Capture (CDC)
- ELT (Extract, Load, Transform)
- ETL (Extract, Transform, Load)
Change Data Capture (CDC) is a data extraction technique that involves extracting data from a source system's log files in real-time, enabling near real-time analysis and processing of the captured data.
Scenario: Your team is tasked with integrating data from multiple sources into a centralized database. What steps would you take to ensure data consistency and accuracy in the modeling phase?
- Design a robust data integration architecture to handle diverse data sources
- Establish data lineage and documentation processes
- Implement data validation rules and checks to ensure accuracy
- Perform data profiling and cleansing to identify inconsistencies and errors
Ensuring data consistency and accuracy during data integration involves steps such as data profiling and cleansing to identify and rectify inconsistencies, implementing validation rules, and establishing documentation processes to maintain data lineage and traceability.