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.

Data lineage and metadata management play a vital role in ensuring the traceability, transparency, and reliability of data in the ETL process, which is essential for data governance and maintaining data quality.

Data pipelines play a vital role in real-time data processing by capturing and storing streams of data from various sources, such as sensors, applications, or IoT devices, for further analysis. These pipelines facilitate the continuous flow of data from source to destination, ensuring data reliability, scalability, and efficiency in real-time analytics and decision-making processes.

Data pipeline monitoring contributes to cost optimization in cloud environments by identifying and mitigating resource inefficiencies. Monitoring tools provide insights into resource utilization, helping optimize compute, storage, and network resources based on actual demand and usage patterns. By identifying underutilized or over-provisioned resources, organizations can right-size their infrastructure, reducing unnecessary costs while ensuring performance and scalability. This proactive approach to resource management helps optimize cloud spending and maximize ROI.

In real-time data processing, data is processed immediately as it is generated, without significant delay. This ensures that insights and actions can be derived from the data in near real-time, allowing for timely decision-making and response to events or trends. Real-time processing systems often employ technologies like stream processing to handle data as it flows in.

Natural Language Processing (NLP) is the recommended method for transforming unstructured text data into a structured format. NLP techniques such as tokenization, part-of-speech tagging, and named entity recognition can extract valuable insights from text 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.

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.

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.