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...

Last week I have attended an Enterprise Data World summit. This is one of my favourite summits as they always have a broad choice of sessions on any level for data professionals. Unfortunately, due to work arrangements, I was only present on the second day of the summit. The first session's original title was “Accelerating Data Management with DataOps” but the speaker didn’t show up. Summit organizers have asked another speaker, Doug Needham from DataOps.live, to fill up and he made up a replacement session just in a few minutes before the session was supposed to start. That takes a lot of knowledge of the topic and courage to stand up for the disappointed audience that was expecting someone else. Doug is a senior data solutions architect and was doing Data Ops long before the term was invented. This amazing hour was full of exciting aspects of data operations, examples from Doug’s own rich experience and an overview of the Data Ops product his company is working on. I have very m...