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...

My son told me yesterday that data analysis is boring. "Total money wired from one account type to another. The number of umbrellas sold over time to various age groups. Website clicks from different countries, broken down by time of day. Nothing exciting," he said. So I asked him: "What about analyzing the contents of garbage cans? Wouldn't that be fascinating? Or fridge contents—imagine predicting a family’s potential health issues based on what they eat (or forget to throw out). Dream logs could be just as intriguing. Imagine categorizing dreams by themes and emotions, analyzing trends based on sleep habits and cultural influences. How about haunted places and paranormal activity reports? Tracking ghost sightings by location, identifying patterns, and finding unexpected geographical correlations—now that’s a dataset with mystery! And then there’s one of my favourites: analyzing excuses for being late to work. Categorizing them by season, industry, or geography cou...

" You cannot create a Microsoft capacity using a personal account. Use your organizational account instead." What a disappointment and frustration! I've been trying to set up Microsoft Fabric for a while now and figure out how to work around this error. I use my personal Gmail for an MVP subscription to learn and experiment with Azure services. When I visit app.fabric.microsoft.com and try to use my private Gmail address, I keep getting a similar message: It's frustrating because I'm eager to dive into Fabric and explore its capabilities. I would get the same error trying to set up Azure Data Catalog, PowerBI embedded, and more. What are the different account types and how do they differ? A work or school account is created through Active Directory or other cloud directory services, such as Microsoft 365. On the other hand, a personal account is one that's manually created for individual use, consisting simply of a username and password. After digging into ...

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...

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 ...