In data lineage, metadata management primarily focuses on managing descriptive information about data. This includes capturing, storing, organizing, and maintaining metadata related to data lineage, such as data definitions, data lineage relationships, data quality metrics, and data usage policies. Effective metadata management ensures that accurate and comprehensive lineage information is available to support various data-related initiatives, including data governance, compliance, analytics, and decision-making.

Normalization is a critical aspect of data modeling best practices that focuses on organizing data to minimize redundancy, improve efficiency, and ensure data integrity.

Talend provides support for real-time data integration, allowing users to integrate data in real-time, which is essential for scenarios requiring timely data processing and analytics.

The Consensus problem in distributed computing refers to the challenge of achieving agreement among a group of nodes or processes despite the possibility of failures and delays in communication. It's essential for ensuring the consistency and correctness of distributed systems, as nodes must agree on decisions even in the face of network partitions or faulty nodes.

Kafka Streams provides a Java API for building real-time stream processing applications. This API allows developers to process data in real-time and perform various operations on Kafka topics.

In batch processing, data is collected and processed in discrete groups or batches. These batches are processed together at a scheduled interval, rather than immediately upon arrival. Batch processing is often used for tasks that can tolerate latency and don't require real-time processing, such as generating reports, data analysis, and ETL (Extract, Transform, Load) operations.

A Relational Database Management System (RDBMS) is designed primarily to manage structured data stored in tables, allowing for efficient storage, retrieval, and manipulation of data through relational operations like select, insert, update, and delete.

The streaming feature in ETL tools like Apache NiFi enables real-time data processing and streaming analytics, allowing for the continuous processing of data as it flows through the system.

In a batch processing pipeline, data processing occurs at scheduled intervals. Data is collected over a period of time and processed in batches, typically during off-peak hours or at predetermined times when system resources are available. Batch processing is advantageous for handling large volumes of data efficiently and can be useful for tasks like daily reports generation, data warehousing, and historical analysis.

Data serialization involves converting data from one format to another without altering its content. It's commonly used in scenarios such as converting data to JSON or XML formats for transmission or storage.