Maria's journey in the data fields

Imagine, you are baking a cake. You have all the ingredients except eggs. Of course, you could improvise but most probably instead of the moist chocolate cake, you end up with a dry science experiment. Incomplete data leaves everyone unsatisfied. During the decision-making party, every data quality dimension is an important guest with a unique vibe: Good Data should be C omplete , when all data attributes, that describe data in its fullness, are present as a part of your data. This guest keeps the party snacks stock full and makes sure no one gets hungry. Incomplete data leads to half-baked insights. Good Data should be Accurate . This means that the data correctly describes its objects and accurately reflects the reality. This party guest is a perfectionist, checking that the playlist is perfectly chosen. Good Data should be Timely . This means that the data is fresh. No one wants to eat last week's sushi. It's not only unappetizing; it is downright risky. This guest makes sur...

Kafka isn’t just a buzzword—it’s the backbone your data pipelines have been waiting for. Who new that messaging system, like Apache Kafka should hold a central place in a Data Engineer toolbelt.  Apache Kafka is a low-latency distributed data streaming platform for real-time data processing. Kafka can handle large volumes of data and is very helpful for distributed data integration projects. Top 2 reasons why you might need Kafka in your Data Integration architecture 1. Support multiple destinations by decoupling data producers and data consumers. Data in source will be processed only once, which lowers an overall cost in consumption-based data producer databases and we can add new/change existing destinations without changing the extraction components. 2. Ability to deal with massive amounts of data , supports high throughput and scalability. Decoupling pipeline extract and load stages is an important Data Integration principle and can improve pipeline flexibility, extract and loa...