Data accuracy is an example of a data quality metric. It measures the extent to which data values correctly represent the real-world objects or events they are intended to describe. High data accuracy indicates that the information in the dataset is reliable and free from errors, while low accuracy suggests inaccuracies or discrepancies that may impact decision-making and analysis. Assessing and maintaining data accuracy is essential for ensuring the credibility and trustworthiness of organizational data assets.

A stored procedure in the context of RDBMS is a precompiled set of SQL statements that can be executed as a single unit. It allows for modularizing and reusing code, enhancing performance, and improving security by controlling access to database operations.

Indexing plays a crucial role in enhancing database performance by improving data retrieval speed. It involves creating data structures (indexes) that enable faster lookup of records based on specific columns or expressions commonly used in queries. By efficiently locating relevant data without scanning the entire dataset, indexing reduces query processing time and enhances overall system responsiveness, especially for frequently accessed data.

Indexing can slow down write operations because every INSERT or UPDATE operation requires the index to be updated, which adds overhead. This trade-off between read and write performance should be carefully considered when designing databases.

Encoding is the process of converting categorical data into numerical values, allowing for easier analysis and processing. Common techniques include one-hot encoding and label encoding.

Validity is a data quality metric that evaluates whether data adheres to predefined rules or constraints. It assesses the correctness and appropriateness of data based on established criteria, ensuring that data meets specified standards and requirements. Valid data contributes to the overall reliability and usefulness of information within a dataset.

The primary purpose of an ETL tool like Apache NiFi or Talend is to extract data from disparate sources, transform it as required, and load it into a target destination, such as a data warehouse or database.

NoSQL databases are often used in scenarios where the volume of data is variable, and the data structure is subject to frequent changes, as they provide schema flexibility and horizontal scalability to accommodate changing needs.

Kudu is a storage solution in the Hadoop ecosystem specifically designed for handling small files efficiently. It serves as a complementary storage layer alongside Hadoop Distributed File System (HDFS) and is optimized for workloads involving random access to data, such as time-series data or small analytical queries.

Data Lake architecture facilitates data exploration and analysis through centralized storage, a schema-on-read approach, scalability, and flexibility. This allows users to analyze diverse data sets without predefined schemas, promoting agility and innovation.