Apache Flink supports various deployment modes to run its distributed processing jobs. These include standalone mode, where Flink runs as a standalone cluster; YARN mode, where Flink integrates with Hadoop YARN for resource management; and Kubernetes mode, which leverages Kubernetes for container orchestration. Each mode offers different advantages and is suitable for different deployment scenarios, providing flexibility and scalability to Flink applications.

The transformation component of the ETL process is primarily targeted for optimization. This phase involves converting raw data into a format suitable for analysis, making it a critical area for performance improvement.

Data governance frameworks often address regulatory compliance such as the General Data Protection Regulation (GDPR). GDPR imposes strict requirements on the collection, storage, and processing of personal data, necessitating organizations to implement robust data governance practices to ensure compliance and mitigate risks associated with data privacy violations.

Streaming is a data loading strategy where data is continuously loaded into the target system in real-time as it becomes available, enabling organizations to process and analyze data as it flows, facilitating real-time decision-making and insights.

Eventual consistency in distributed databases is a consistency model where data may be inconsistent temporarily but will eventually converge to a consistent state across all nodes without intervention. It allows for updates to propagate asynchronously, accommodating network partitions, latency, and concurrent modifications while maintaining system availability and performance. While eventual consistency prioritizes system responsiveness and fault tolerance, applications must handle potential inconsistencies during the convergence period.

Workflow schedulers play a vital role in orchestrating batch processing workflows by coordinating the execution of individual tasks, managing task dependencies, and allocating computing resources efficiently. These schedulers help streamline the execution of complex data processing pipelines, ensure task sequencing, and optimize resource utilization for improved performance and scalability in batch processing environments.

Talend provides robust features for ensuring data quality and governance in ETL processes. This includes capabilities such as data profiling and cleansing to identify and correct inconsistencies, metadata management for organizing and tracking data assets, and role-based access control to enforce security policies.

Implementing a data governance framework can be challenging due to various factors. Common challenges include a lack of organizational buy-in, which may lead to resistance from different departments. Data silos hinder collaboration and data sharing across the organization. Compliance requirements impose additional constraints on data handling practices. Cultural resistance to change can slow down the adoption of governance policies and procedures. Addressing these challenges requires strategic planning, effective communication, and collaboration across different stakeholders.

The First Normal Form (1NF) is considered the most basic form of normalization, ensuring that each attribute in a table contains atomic values, without repeating groups or nested structures.

Incremental load is a technique used in ETL optimization where only the changes or new data are loaded into the target system, reducing the time and resources required for data loading processes.