Data modeling tools like ERWin or Visio support reverse engineering by enabling tasks such as importing existing database schemas, generating entity-relationship diagrams, extracting metadata, and synchronizing the schema with changes made in the tool.

Web Scraping is a data extraction technique that involves querying data from web pages and web APIs. It allows for automated retrieval of data from various online sources for further processing and analysis.

Anomaly detection is a prevalent technique used in monitoring data pipelines to identify unusual patterns or deviations from expected behavior. By analyzing metrics such as throughput, latency, error rates, and data quality, anomaly detection algorithms can flag potential issues such as system failures, data corruption, or performance degradation, allowing data engineers to take proactive measures to mitigate them.

Consistency in data quality metrics refers to the uniformity and coherence of data across various sources, systems, and applications. It ensures that data elements have the same meaning and format wherever they are used, reducing the risk of discrepancies and errors in data analysis and reporting. Consistent data facilitates interoperability, data integration, and reliable decision-making processes within organizations.

Data profiling in the data extraction phase involves analyzing the structure and quality of the data to identify patterns, anomalies, and issues, which helps in making informed decisions during the data pipeline process.

Apache MapReduce divides tasks into Map and Reduce phases for processing large datasets. The Map phase handles input data and generates key-value pairs, while the Reduce phase aggregates and processes these pairs.

To address performance issues in an ETL pipeline, reviewing and optimizing data transformation logic and SQL queries is essential. This involves identifying inefficient queries or transformations and optimizing them for better performance.

Apache Cassandra serves as a NoSQL distributed database management system in big data storage solutions. It is designed for high scalability and fault tolerance, allowing for the storage and retrieval of large volumes of structured and semi-structured data across multiple nodes in a distributed manner. Cassandra's decentralized architecture and support for eventual consistency make it well-suited for use cases requiring high availability, low latency, and linear scalability, such as real-time analytics, IoT data management, and messaging applications.

In an ERD, a Many-to-Many relationship indicates that each instance of one entity can be associated with multiple instances of another entity, and vice versa, allowing for complex associations between entities.

Data reconciliation involves comparing data from different sources or systems to ensure consistency and accuracy. It helps identify discrepancies, such as missing or mismatched data, between datasets. This process is crucial in data integration projects to ensure that data from various sources align properly and can be combined effectively.