Data warehouses are non-volatile data repositories that play a vital role in making business decisions
by analyzing the operational data. Real-time data warehouses focus as increasing data freshness levels.
The tools and techniques for increasing this freshness level are therefore rapidly evolving.
Extract-Transform-Load (ETL) tools feed data from operational databases into data warehouses.
Traditionally, these ETL tools use batch processing and operate offline at regular time intervals,
for example on a nightly or weekly basis. Naturally, users prefer to have up-to-date data to make their
decisions, therefore there is a demand for real-time ETL tools. The Data Transformation is an important
phase in ETL where source data is transformed into warehouse format and necessary enrichment of Master Data
is performed using join operator. In my PhD research I investigated a family of stream-based joins and
proposed a robust stream-based join for Real-time Data Warehousing.