Data cleansing is the process of identifying and resolving inconsistencies, errors, and discrepancies in data to ensure its quality before it is used for analysis or other purposes.

Structured Streaming is a high-level API in Apache Spark that enables scalable, fault-tolerant processing of real-time data streams. It provides a DataFrame-based API, allowing developers to apply the same processing logic to both batch and streaming data, simplifying the development of real-time analytics applications and ensuring efficient handling of streaming data.

Data profiling involves analyzing the structure, content, and relationships within the dataset to identify anomalies, inconsistencies, and inaccuracies. By prioritizing data profiling, you can gain insights into the overall quality of the dataset, including missing values, duplicates, outliers, and inconsistencies, which is crucial for ensuring data accuracy and reliability.

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.

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.

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.

Grouping is a common data transformation method used to aggregate data in ETL processes. It involves combining rows with similar characteristics and summarizing their values to create consolidated insights or reports.

Forward Engineering in data modeling tools like ERWin or Visio enables users to generate SQL scripts for creating database objects, such as tables, views, and indexes, based on the designed schema.

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.

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.

The primary goal of data security is to protect data from unauthorized access, disclosure, alteration, or destruction. It encompasses various measures such as encryption, access controls, authentication mechanisms, and regular security audits to safeguard sensitive information from malicious actors and ensure confidentiality, integrity, and availability.

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.