Common challenges during the implementation of a data warehouse include data integration from disparate sources, performance tuning to optimize query processing, and scalability to handle increasing data volumes efficiently.

In a streaming processing pipeline, a watermark is a mechanism for handling late data and ensuring correctness in event time processing. It represents a threshold that defines how far behind the event time can be considered before processing is considered complete. Watermarks are used to track the progress of event time and allow the system to determine when all relevant events for a given window have been processed, enabling accurate window-based computations in stream processing applications.

Data consistency measures the extent to which data is uniform and coherent across different sources, systems, and time periods. It ensures that data values are standardized, follow predefined formats, and remain unchanged over time. Consistent data facilitates accurate comparisons, analysis, and decision-making processes within an organization.

To successfully implement a data governance framework, the company should start by developing comprehensive data governance policies and procedures. These policies should be aligned with organizational goals, regulatory requirements (such as GDPR, CCPA), and industry best practices. Additionally, conducting a data inventory and assessment to identify critical data assets and their usage is crucial for understanding data governance needs and priorities. Establishing a data governance council comprising stakeholders from different business units can provide governance oversight and ensure cross-functional collaboration.

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.