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Configuring model properties in Power BI allows you to create a model which is far more discoverable and is able to better support the visualisations you need. There are several different model properties which can be configured, some of these focus on discoverability whilst others allow you to alter the ways in which data is sorted/displayed/summarised in the reports.


Learning DAX and Power BI – CALCULATE

by Carmel Eve

This is the final blog in a series about DAX and Power BI. This post focuses on the CALCULATE function, which is a unique function in DAX. The CALCULATE function has the ability to alter filter contexts, and therefore can be used to enable extremely powerful and complex processing. This post covers some of the most common scenarios for using CALCULATE, and some of the gotchas in the way in which these different features interact!


In order to effectively work with our data in Power BI we need to structure the model as best to support the representations we need. This process is called data modelling. Data modelling includes loading, shaping, cleansing and enhancing the data.

This post runs through some of the important steps used in data modelling, and gives an example of loading and shaping data using Power BI.


This is the sixth blog in a series about DAX and Power BI. This post focuses on relationships and related tables. These relationships allow us to build up intricate and powerful models using a combination of sources and tables. The use of relationships in DAX powers many of the features around slicing and page filtering of reports.


Jess and Carmel recently gave a talk at Azure Oxford on “Combatting illegal fishing with Machine Learning and Azure – for less than £10 / month). The recording of that talk is now available for viewing!

The talk focuses on the recent work we completed with OceanMind. They run through how to construct a cloud-first architecture based on serverless and data analytics technologies and explore the important principles and challenges in designing this kind of solution. Finally, we see how the architecture we designed through this process not only provides all the benefits of the cloud (reliability, scalability, security), but because of the pay-as-you-go compute model, has a compute cost that we could barely believe!


Learning DAX and Power BI – Table Functions

by Carmel Eve

This is the fifth blog in a series on DAX and Power BI. This post focuses on table functions. In DAX, table functions return a table which can then be used for future processing. This can be useful if, for example, you want to perform an operation over a filtered dataset. Table functions, like most functions in DAX, operate under the filter context in which they are applied.


Learning DAX and Power BI – Aggregators

by Carmel Eve

This is the fourth blog in a series about DAX and Power BI. We have so far covered filter and row contexts, and the difference between calculated columns and measures. This post focuses on aggregators. We cover the limitations of the classic aggregators, and demontrate the power of the iterative versions. We also highlight some of the less intuitive features around how these functions interact with both filter and row contexts.


This is the third blog in a series about learning DAX and Power BI. The first two blogs focused on filter and row contexts. We are now moving on to talk about calculated columns and measures. These are the main features used to support the display of complex visuals. They allow you to combine columns, aggregate values, reformat data, and much more. The difference between these features can get a bit confusing so we’ve attempted to make that clearer using some concrete examples!


Learning DAX and Power BI – Row Contexts

by Carmel Eve

Here is the second blog in a series around learning DAX and Power BI. This post focuses on row contexts, which are used when iterating over the rows of a table when, for example, evaluating a calculated column. Row contexts along with filter contexts underpin the basis of the DAX language. Once you understand this underlying theory it is purely a case of learning the syntax for the different operations which are built on top of it.


Learning DAX and Power BI – Filter Contexts

by Carmel Eve

Here is the first in a series of blog posts around understanding DAX and Power BI. This post focuses on filter contexts. which are a central concept which is vital for being able to write effective and powerful DAX!

In this series Carmel walks through the main ideas and syntax surrounding the DAX language, and provides examples of using these over a dataset. DAX is an extremely powerful language. Using these techniques it is possible to build up complex reports which provide the insight you really need!


Power BI Data Type Mappings

by Ed Freeman

If you’ve worked with Power BI at all, you’ll have probably realised that there are numerous mediums through which you work with (potentially the “same”) data. Data types across these mediums can be called different things, but actually refer to the same thing. They can also (unsurprisingly) be called different things and actually mean different things. It’s useful to know what the corresponding data types are across these mediums, as you may need to, for example, convert queries from one format to another. This blog and containing report intend to clarify what the corresponding data types are across each of the separate mediums within Power BI.


In this blog from the Azure Advent Calendar 2019 we discuss building a secure data solution using Azure Data Lake. Data Lake has many features which enable fine grained security and data separation. It is also built on Azure Storage which enables us to take advantage of all of those features and means that ADLS is still a cost effective storage option!

This post runs through some of the great features of ADLS and runs through an example of how we build our solutions using this technology!


Here at endjin we’ve done a lot of work around data analysis and ETL. As part of this we have done some work with Databricks Notebooks on Microsoft Azure. Notebooks can be used for complex and powerful data analysis using Spark. Spark is a “unified analytics engine for big data and machine learning”. It allows you to run data analysis workloads, and can be accessed via many APIs. This means that you can build up data processes and models using a language you feel comfortable with. They can also be run as an activity in a ADF pipeline, and combined with Mapping Data Flows to build up a complex ETL process which can be run via ADF.


Overflowing with dataflow part 2: TPL Dataflow

by Carmel Eve

This is the second blog in a series about data flow. This post delves into TPL dataflow.

The task parallel library is a .NET library which aims to make parallel processing and concurrency simpler to work with. The TPL dataflow library is specifically aimed at making parallel data processing more understandable via a pipeline-based model.


Overflowing with dataflow part 1: An overview

by Carmel Eve

This is the first blog in a series about dataflow. The series focuses on TPL dataflow, but this post gives an overview of dataflow as a whole.

The crucial thing to understand when using dataflow is that the data is in control. In most conventional programming languages, the programmer determines how and when the code will run. In dataflow, it is the data that drives how the program executes. The movement of data controls the flow of the program.