Incremental loading is a technique in ETL processes where only the changed data since the last extraction is loaded, reducing the amount of data transferred and improving performance.

Diagnosing a sudden increase in latency requires analyzing system logs, examining network traffic, tracing transaction execution, and utilizing profiling tools. These actions can help identify bottlenecks, resource contention issues, or inefficient code paths contributing to latency spikes. Historical trend analysis, capacity planning, and configuration reviews are essential for proactive performance management but may not directly address an ongoing latency issue. Similarly, options related to data integrity, data sources, and data transformation logic are more relevant for ensuring data quality than diagnosing latency issues.

Auto-scaling is a crucial feature in cloud-based data pipelines that enables automatic adjustment of computing resources based on workload demand. By dynamically provisioning or deallocating resources such as compute instances or storage capacity, auto-scaling ensures optimal performance and cost-efficiency, allowing data pipelines to handle fluctuating workloads effectively without manual intervention.

The purpose of data cleansing in data transformation is to identify and remove redundant, inaccurate, or inconsistent data from the dataset. This ensures that the data is accurate, reliable, and suitable for analysis or other downstream processes.

Text normalization is a data cleansing method that involves correcting misspellings, typos, and grammatical errors in textual data to ensure consistency and accuracy. It may include tasks like converting text to lowercase, removing punctuation, and expanding abbreviations to their full forms, making the data more suitable for analysis and processing.

A robust data lineage solution in metadata management comprises several key components. Data capture mechanisms are essential for capturing metadata at various stages of the data lifecycle, including data ingestion, transformation, and consumption. A metadata repository serves as a centralized storage system for storing lineage information, metadata attributes, and relationships between data assets. Lineage visualization tools enable stakeholders to visualize and understand complex data flows, dependencies, and transformations effectively. Impact analysis capabilities allow organizations to assess the downstream effects of changes to data sources, schemas, or business rules, helping mitigate risks and ensure data integrity. Together, these components form the foundation of an effective data lineage solution that supports data governance, compliance, and decision-making processes.

A cardinality constraint in an ERD specifies the number of instances of one entity that can be associated with the number of instances of another entity, indicating the relationship's multiplicity.

Encryption is the process of converting plaintext (ordinary, readable data) into ciphertext (encoded, unreadable data) using cryptographic algorithms. It ensures that unauthorized users cannot access or understand the information without the appropriate decryption key, thereby maintaining data confidentiality and security. Encryption is crucial for safeguarding sensitive information during transmission and storage.

Outlier detection is a method used in data quality assessment to identify data values that deviate significantly from the expected range or distribution of values within a dataset. Outliers can indicate errors, anomalies, or valuable insights in the data and are important to identify and address for accurate analysis and decision-making.

Data governance plays a crucial role in data modeling by establishing policies, procedures, and standards to ensure data quality, consistency, and compliance with regulations.