Data consistency measures the extent to which data is uniform and coherent across different sources, systems, and time periods. It ensures that data values are standardized, follow predefined formats, and remain unchanged over time. Consistent data facilitates accurate comparisons, analysis, and decision-making processes within an organization.

The streaming feature in ETL tools like Apache NiFi enables real-time data processing and streaming analytics, allowing for the continuous processing of data as it flows through the system.

Data serialization involves converting data from one format to another without altering its content. It's commonly used in scenarios such as converting data to JSON or XML formats for transmission or storage.

In a batch processing pipeline, data processing occurs at scheduled intervals. Data is collected over a period of time and processed in batches, typically during off-peak hours or at predetermined times when system resources are available. Batch processing is advantageous for handling large volumes of data efficiently and can be useful for tasks like daily reports generation, data warehousing, and historical analysis.

Apache Airflow provides robust mechanisms for handling retries and errors in workflows. It offers automatic retries for failed tasks with customizable settings such as retry delay and maximum retry attempts. Error handling policies are configurable at both the task and workflow levels, allowing users to define actions to take on different types of errors, such as retrying, skipping, or failing tasks. Task-level retries enable granular control over retry behavior, enhancing workflow resilience and reliability.

Data modeling tools often incorporate data validation features to ensure consistency and enforce rules during the design process. This helps maintain the integrity and quality of the database schema.

End-to-end latency is a commonly monitored metric in data pipeline monitoring to assess the time it takes for data to traverse the pipeline from its source to its destination. It measures the overall delay or latency experienced by data as it moves through various stages of processing within the pipeline. Monitoring end-to-end latency helps ensure timely data delivery and identifies potential performance bottlenecks or delays in the pipeline.

The process of ensuring data consistency and correctness in real-time data processing systems is known as data integrity. Data integrity mechanisms help maintain the accuracy, reliability, and validity of data throughout its lifecycle, from ingestion to analysis and storage. This involves enforcing constraints, validations, and error handling to prevent data corruption or inaccuracies.

Metadata is crucial in Data Lakes for tracking lineage, understanding data origins, and ensuring data quality by providing information about the structure, meaning, and context of the stored data, facilitating its discovery, understanding, and usability.

Data quality is an essential factor in determining the success of the ETL (Extract, Transform, Load) process. High-quality data ensures accurate analytics and decision-making, leading to better outcomes.

Database normalization primarily aims to minimize redundancy and dependency in a database schema, leading to improved data integrity and reducing anomalies such as update, insertion, and deletion anomalies.

Normalization involves decomposing a large table into smaller, related tables to eliminate redundancy and improve data integrity by reducing the chances of anomalies.