In the 2nd part of the Power BI 101 series, check what is Data Shaping and why learning this concept can bring your Power BI data model to new heights
This is in contrast to REST APIs which expose a suite of URLs each of which ... could go down, an asynchronous action could fail, an exception could be thrown. ... When designing a GraphQL schema, it's important to keep in mind all the ...
IOT Data Analytics: Data Model. Best Practices of DW Modelling applied on IoT data for most flexible and efficient analytics
Life Cycle of Data Science. An overview on the various stages of a Data Science project
Stocker is a Python class-based tool used for stock prediction and analysis. (for complete code refer GitHub) Stocker is designed to be very easy to handle.
In this article, I want to go more in-depth on the DirectQuery option, as I have a feeling that this option is still underused (for good or for bad, we’ll try to examine in this article).
Override date filter in Power BI. Overriding standard date filtering in Power BI can be trickier than you can imagine.
Go, get’em, Sherlock! Doing Data Profiling in Power BI like a PRO. Being a “data-detective” doesn’t sound appealing to you? Check why you should reconsider your stance
Check important differences between implicit and explicit measures in the Power BI data model, and why you should consider avoiding the easier path. I will focus solely on Measures, and try to explain in-depth the difference between explicit and implicit measures.
Role-playing dimensions in Power BI. Check this simple modeling technique to avoid data model redundancy
The Definitive Intro To DataVault. And how it compares to other traditional data warehouse modelling techniques like dimensional modelling
Choosing the optimal indexing strategy for your SQL Server workloads is one of the most challenging tasks. Learn in which scenarios you can benefit from using Columnstore indexes over traditional B-tree structures.
This dataset originally consists of 14,993 observations and 24 variables. All the data are based in the Malaysia area so our analysis is country-specific and culture-oriented.
How to leveThese days most companies are moving towards the concept of “Citizen Data Scientists” by giving tools to subject matter experts (SME) to create their own machine learning models. The advantage of this approach is that it provides a meaningful interpretation of the results and nothing gets lost in translation between the data scientist and the SME.rage the power of Alteryx to analyze your data without the need for programming
Using aggregate functions on columns is fine, but doing the same on rows is — pure beauty! And all this with a single line of DAX code!
Additional access patterns in DynamoDB data modeling. In my previous article about DynamoDB data modeling, I mentioned three methods that we can use to determine the secondary indexes.
The final part of “Brain & Muscles” behind Power BI series, shows a real-life showcase of data model optimization and emphasizes general rules for reducing data model
In the 2nd part of “Brain & Muscles” behind Power BI series, learn about data compression and how VertiPaq chooses the right algorithm to make your data model optimal.
Have you ever wondered what makes Power BI so fast and powerful when it comes to performance? In the 1st part of the series, meet VertiPaq — “brain & muscles” behind Power BI!
Data Preparation Techniques and Its Importance in Machine Learning. “Data are just summaries of thousands of stories, tell a few of those stories to help make the data meaningful.”