Maria's journey in the data fields

Every data integration pipeline consists of 3 stages: Data Extraction (E), Data Transformation (T) and Data Load (L) During the  Data Extraction stage, the source data is read from its origins: transactional databases, CRM or ERP systems or through data scraping from web pages. During the  Data Transformation stage, the necessary modifications are applied to the source data. This includes data filtering, enrichment or merging with existing or other source datasets, data obfuscation, dataset structure alignment or validation, fields renaming and data structuring, according to the canonical data warehouse model.  During the Data Loading stage, the data is stored in the pipeline destination, which could be a staging area, data lake or data warehouse. There are two principal methods for the data integration process: transferring it from where it originated to the destination, where the data will be used for analysis, ETL and ELT. The difference between ETL and ELT pipelines lies in wheth

Many of us have hobbies. Some hobbies are quite common, such as travelling, painting, or playing computer games. I, however, have a rather unique hobby: organizing conferences. The more complicated the logistics and the more people who sign up, the more enjoyable it becomes. Yet, this hobby is quite time-consuming, time that could be dedicated to family or sleep. As the event day approaches, the tension mounts. There are too many details to manage, too many things to take care of. The feeling of being overwhelmed and terrified at the same time, creeps in, as mishaps can occur at any moment. This is especially true in our small, brave country where the sound of rocket alarms can disrupt seemingly peaceful moments, with potentially dire consequences. But eventually, the day arrives, and the energy is overwhelming. Rooms are filled with eager delegates ready to learn. There are excited speakers, delighted sponsors, and an abundance of delicious food, beer, and networking opportunities. I