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.

Change Data Capture (CDC) is a technique used to identify and capture changes made to data in source systems, allowing only the modified data to be transferred, thus optimizing ETL processes.

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.

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.

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.

In Apache Airflow, an "Operator" is a unit of work or task that performs a specific action within a workflow. Operators can perform tasks such as transferring data, executing scripts, or triggering external systems. They are the building blocks of workflows in Airflow, allowing users to define the individual actions to be performed.

Data partitioning involves breaking down large datasets into smaller chunks to distribute the data loading process across multiple servers or nodes, enabling parallel processing and improving scalability and performance.

A Snowflake schema is a type of schema in Dimensional Modeling where dimension tables are normalized into multiple related tables, creating a more complex but potentially more efficient structure for querying data.

Exponential backoff involves increasing the delay between retry attempts exponentially after each failure. This strategy helps prevent overwhelming the system with retry attempts during periods of high load or when dealing with transient failures. By gradually increasing the delay, it allows the system to recover from temporary issues and reduces the likelihood of exacerbating the problem.

The primary goal of normalization in database design is to improve data integrity by minimizing redundancy, ensuring that each piece of data is stored in only one place. This helps prevent inconsistencies and anomalies.