________ is a strategy where the delay between retry attempts increases exponentially after each failed attempt.
- Exponential backoff
- Fixed interval
- Incremental delay
- Linear regression
Exponential backoff is a retry strategy commonly used in data processing systems, where the delay between retry attempts increases exponentially after each failed attempt. This approach helps in managing congestion, reducing contention, and improving the efficiency of retry mechanisms in distributed environments. By increasing the delay exponentially, the system reduces the likelihood of retry storms and mitigates the impact of transient failures or overload situations on system performance.
A ________ is a unique identifier for each row in a table and is often used to establish relationships between tables in a relational database.
- Composite Key
- Foreign Key
- Primary Key
- Unique Key
A primary key is a unique identifier for each row in a table and is often used to establish relationships between tables in a relational database. It ensures that each row is uniquely identifiable within the table.
What is the role of data mapping in the data transformation process?
- Ensuring data integrity
- Establishing relationships between source and target data
- Identifying data sources
- Normalizing data
Data mapping involves establishing relationships between source and target data elements, enabling the transformation process to accurately transfer data from the source to the destination according to predefined mappings.
What is the purpose of monitoring in data pipelines?
- Designing data models
- Detecting and resolving issues in real-time
- Generating sample data
- Optimizing SQL queries
Monitoring in data pipelines serves the purpose of detecting and resolving issues in real-time. It involves tracking various metrics such as data throughput, latency, error rates, and resource utilization to ensure the smooth functioning of the pipeline. By continuously monitoring these metrics, data engineers can identify bottlenecks, errors, and performance degradation promptly, enabling them to take corrective actions and maintain data pipeline reliability and efficiency.
Scenario: Your team is tasked with designing a big data storage solution for a financial company that needs to process and analyze massive volumes of transaction data in real-time. Which technology stack would you propose for this use case and what are the key considerations?
- Apache Hive, Apache HBase, Apache Flink
- Apache Kafka, Apache Hadoop, Apache Spark
- Elasticsearch, Redis, RabbitMQ
- MongoDB, Apache Cassandra, Apache Storm
For this use case, I would propose a technology stack comprising Apache Kafka for real-time data ingestion, Apache Hadoop for distributed storage and batch processing, and Apache Spark for real-time analytics. Key considerations include the ability to handle high volumes of transaction data efficiently, support for real-time processing, fault tolerance, and scalability to accommodate future growth. Apache Kafka provides scalable and durable messaging, Hadoop offers distributed storage and batch processing capabilities, while Spark enables real-time analytics with its in-memory processing engine. This stack ensures the processing and analysis of massive transaction data in real-time, meeting the requirements of the financial company.
Which data quality assessment technique focuses on identifying incorrect or inconsistent data values?
- Data auditing
- Data cleansing
- Data profiling
- Data validation
Data cleansing is a data quality assessment technique that focuses on identifying and correcting incorrect or inconsistent data values. It involves various processes such as parsing, standardization, and enrichment to ensure that data is accurate and reliable for analysis and decision-making. By detecting and rectifying errors, data cleansing enhances the overall quality and usability of the dataset.
What are data quality metrics used for?
- Assessing the quality of data
- Data visualization
- Generating random data
- Storing large volumes of data
Data quality metrics are used to assess the quality and reliability of data. These metrics help in evaluating various aspects of data such as accuracy, completeness, consistency, timeliness, and validity. By measuring these metrics, organizations can identify data issues and take corrective actions to improve data quality, which is crucial for making informed decisions and ensuring the effectiveness of data-driven initiatives.
How can parallel processing be utilized in ETL optimization?
- Distributing tasks across multiple nodes
- Performing tasks sequentially on a single node
- Serializing data processing
- Splitting data into smaller chunks for simultaneous processing
Parallel processing in ETL optimization involves distributing tasks across multiple nodes or cores, enabling simultaneous processing and faster execution of ETL jobs, thus improving overall performance.
Data ________ involves identifying and mitigating risks associated with data assets.
- Governance
- Quality
- Risk Management
- Security
Data risk management involves identifying and mitigating risks associated with data assets within an organization. It encompasses assessing potential threats to data integrity, confidentiality, and availability, as well as evaluating vulnerabilities in data management processes and infrastructure. By identifying and addressing risks proactively, organizations can safeguard their data assets against potential breaches, unauthorized access, data loss, and other adverse events.
How does data validity differ from data accuracy in data quality assessment?
- Data validity assesses the reliability of data sources, while accuracy evaluates the timeliness of data
- Data validity ensures that data is up-to-date, while accuracy focuses on the consistency of data
- Data validity focuses on the completeness of data, whereas accuracy measures the precision of data
- Data validity refers to whether data conforms to predefined rules or standards, while accuracy measures how closely data reflects the true value or reality
Data validity and accuracy are two distinct dimensions of data quality assessment. Data validity refers to the extent to which data conforms to predefined rules, standards, or constraints, ensuring that it is fit for its intended purpose. On the other hand, data accuracy measures how closely data reflects the true value or reality it represents. While validity ensures data adherence to rules, accuracy evaluates the correctness and precision of the data itself, regardless of its conformity to predefined criteria. Both aspects are essential for ensuring high-quality data that can be trusted for decision-making and analysis purposes.