Establishing data quality metrics tailored to a financial institution's needs involves considering factors such as Completeness (all necessary data present), Validity (data conforms to defined rules and standards), Accuracy (data reflecting true values), and Timeliness (data being up-to-date). Additionally, ensuring the relevance of data to business objectives, precision in measurement, reliability in data sources, and scalability for future growth are essential for effective data quality management in financial institutions. These metrics enable informed decision-making, regulatory compliance, and risk management.

A Snowflake Schema in Dimensional Modeling is a schema design where dimension tables are normalized by splitting them into multiple related tables, resembling a snowflake shape when visualized.

Data freshness assesses the currency and relevance of data by evaluating how recently it was collected or updated. It reflects the timeliness of data in relation to the context or requirements of a particular use case or decision-making process. Fresh and relevant data enables organizations to make informed decisions based on current information, improving agility and competitiveness.

Data Virtualization is a data warehousing architecture that enables the integration of data from various sources in real-time, without physically moving or transforming the data.

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.

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.

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.

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.

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.

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

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.

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.