What are the concepts in IBM DataStage?

With proper understanding of its core concepts, IBM DataStage becomes a familiar ETL tool for effective data integration. Here are its concepts: 

Core Concepts: 

 * ETL Process:

   * DataStage is built in principle on the ETL process and includes:

     * Extract: Pull data from any of source systems (databases,files,applications).

     * Transform: Cleanse, manipulate and reconvert the data into the desired format.

     * Load: Write the transformed data into target systems, data warehouses, and data marts.

 * Stages:

   * The "stages" are the building blocks of DataStage jobs. Each stage represents a single data processing function (for example, reading data, sorting data, and transforming data).

   * DataStage provides a very broad range of stages for many different data integration tasks.

 * Jobs:

   * DataStage "job" is the sequence of interconnected stages where the flow of data from source to target has been defined.

   * The graphical format is used for designing jobs. This allows the developer visualization of the data transformation process.

 * Parallel Processing:

   * DataStage is absolutely known for parallel processing; this allows it to work with very large amounts of data efficiently.

   * It allows processing data across several nodes, thereby improving performance.

 * Metadata:

   * DataStage is concerned about metadata management which deals with the storing and managing information about data sources, targets, and transformations.

   * This metadata is essential for data governance and data quality.

 * DataStage Designer:

   * This is the graphical user interface used for designing and developing a DataStage job. 

 * Sources and Targets: 

   * DataStage offers support for a wide variety of data sources and targets including:

     * Relational databases (like Oracle, IBM Db2, and SQL Server).

     * Sequential files (such as CSV, text files).

     * Enterprise applications. 

     * Big data platforms. 

 * Data Transformation: 

   * Rich transformation capabilities including: 

     * Data cleansing. 

     * Data validation. 

     * Data aggregation. 

     * Data lookup. 

     * Formatting data. 

Thus, IBM DataStage has built a robust platform to enable the strong foundations of data integration solutions, which are also scalable.