Hadoop is a widely used technology for implementing Data Lakes due to its ability to store and process large volumes of diverse data in a distributed and fault-tolerant manner.

Batch processing is a common optimization approach for transforming large datasets in ETL pipelines, where data is processed in discrete batches, optimizing resource utilization and throughput.

Implementing a circuit-breaking mechanism involves monitoring performance metrics such as database latency and error rates. By setting thresholds for these metrics, the system can detect when the database is overloaded and temporarily halt requests to prevent further degradation of downstream systems. This allows time for the database to recover and prevents cascading failures throughout the pipeline.

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.

Apache Hadoop MapReduce is responsible for processing large datasets in parallel across a distributed cluster by breaking down tasks into smaller subtasks that can be executed on different nodes.