Change Data Capture (CDC) refers to a set of techniques used to identify and capture changes made to data in a database. This skill involves monitoring and recording insertions, updates, and deletions to ensure that data remains consistent across different systems. Knowledge of Change Data Capture (CDC) is utilized to facilitate data synchronization, enable real-time analytics, and support data warehousing by providing a reliable mechanism for tracking changes without requiring full data refreshes.

IBM InfoSphere DataStage is an ETL (Extract, Transform, Load) tool used for various data integration projects. It allows users to extract data from various sources, transform it for various purposes and load it into a target system. IBM InfoSphere DataStage is known for its powerful functionality, ability to handle large amounts of data, and its ease of use. It is widely used in industries such as finance, healthcare, and retail to improve data management and business intelligence.

Online Transaction Processing (OLTP) is the process of managing and executing transactions in real-time over the internet. It requires a specialized skill set that includes knowledge of database management systems, programming languages, and web technologies. The development of OLTP systems involves designing, testing, and implementing complex algorithms and data structures that can handle high volumes of transactions with high levels of accuracy and reliability. OLTP is critical for businesses that rely on online transactions, such as e-commerce platforms, banking systems, and air ticket booking systems. Due to the sensitive nature of the data involved in OLTP, security is also a crucial aspect of this skill.

Query Performance refers to the efficiency and speed at which database queries are executed and results are returned. This skill encompasses the analysis and optimization of query structures, indexing strategies, and execution plans to enhance data retrieval processes. Knowledge of Query Performance is used to identify bottlenecks, reduce response times, and improve overall system responsiveness in database management.

Very Large Databases (VLDB) refer to databases that are characterized by their immense size, typically exceeding terabytes of data, and the complexity of managing such vast amounts of information. This skill involves understanding the architecture, design, and management techniques necessary to store, retrieve, and manipulate large datasets efficiently. Knowledge of Very Large Databases (VLDB) is utilized in various applications, including data warehousing, big data analytics, and enterprise resource planning, where effective data handling and performance optimization are critical for decision-making and operational efficiency.