Press "Enter" to skip to content

Category: Data

Detecting Data Changes in Power BI Incremental Refresh

Published 2022-08-01 by Kevin Feasel

Chris Webb writes some M:

One feature of Power BI incremental refresh I’ve always been meaning to test out is the ability to create your own M queries to work with the “detect data changes” feature, and last week I finally had the chance to do it. The documentation is reasonably detailed but I thought it would be a good idea to show a worked example of how to use it to get direct control over what data is refreshed during an incremental refresh.

Read on to see how it works, including a couple gotchas around things like the shape of query results.

Comments closed

Finding Sample Data Online

Published 2022-07-13 by Kevin Feasel

Mara Pereira goes searching for data:

Have you ever struggled to find sample data to play with in Power BI?

Did you spend hours (sounds crazy, but it happened to me too!) just looking for a dataset with insurance data? Healthcare data? Housing prices data?

Did you ever wonder “where are people finding the data to create those Netflix and Amazon reports that seem to be everywhere these days? Seriously, tell me your secret!”?

Click through for three good sites. Another one I’ve taken to is the US Bureau of Labor Statistics, which is the federal department responsible for tracking things like employment data, consumer prices, and compensation data. The plus side to these datasets is that you get the whole range of data cleanup, warehousing, querying, and analytics over data which is both real and fairly interesting. For a broader take, data.gov has open source data hosted by the US federal government, though I haven’t spent much time working with it.

Comments closed

Visualizing Kafka Stream Lineage

Published 2022-07-07 by Kevin Feasel

David Araujo and Julia Peng show off stream lineage in Confluent Cloud:

Stream Lineage is a tool Confluent built to address the lack of data visibility in Kafka and event-driven architectures. Confluent’s Stream Lineage provides an interactive map of all your data flows that enable users to:

1. Understand what data flows are running both now or at any point in the past

2. Trace where each data flow originated from

3. Track how data is transformed along its journey

4. Observe where each data flow ends up

Read on to see how it works.

Comments closed

Data Lakehouse Cleanrooms in Databricks

Published 2022-06-30 by Kevin Feasel

Matei Zaharia, et al, announce an interesting idea:

We are excited to announce data cleanrooms for the Lakehouse, allowing businesses to easily collaborate with their customers and partners on any cloud in a privacy-safe way. Participants in the data cleanrooms can share and join their existing data, and run complex workloads in any language – Python, R, SQL, Java, and Scala – on the data while maintaining data privacy.

With the demand for external data greater than ever, organizations are looking for ways to securely exchange their data and consume external data to foster data-driven innovations. Historically, organizations have leveraged data sharing solutions to share data with their partners and relied on mutual trust to preserve data privacy. But the organizations relinquish control over the data once it is shared and have little to no visibility into how data is consumed by their partners across various platforms. This exposes potential data misuse and data privacy breaches. With stringent data privacy regulations, it is imperative for organizations to have control and visibility into how their sensitive data is consumed. As a result, organizations need a secure, controlled and private way to collaborate on data, and this is where data cleanrooms come into the picture.

Read on to learn more about how this all works. It’s definitely a lot better than sending off a bunch of CSVs…

Comments closed

Unicode Character Generation in Power Query

Published 2022-06-30 by Kevin Feasel

Meagan Longoria needs more Unicode:

You may have used the UNICHAR() function in DAX to return Unicode characters in DAX measures. If you haven’t yet read Chris Webb’s blog post on the topic, I recommend you do. But did you know there is a Power Query function that can return Unicode characters? This can be useful in cases when you want to assign a Unicode character to a categorical value.

Click through to see how this works.

Comments closed

Data Governance in Databricks with Unity Catalog

Published 2022-06-29 by Kevin Feasel

Paul Roome, et al, announce the upcoming GA for Databricks Unity Catalog:

Today we are excited to announce that Unity Catalog, a unified governance solution for all data assets on the Lakehouse, will be generally available on AWS and Azure in the upcoming weeks. Currently, you can apply for a public preview or reach out to a member of your Databricks account team.

In a previous blog, we set out our vision for a governed lakehouse and how Unity Catalog can help customers simplify governance at scale. This blog will explore the most recent updates to Unity Catalog and our growing partner ecosystem.

Click through for those updates and to sign up for the public preview if so inclined.

Comments closed

PHI De-Identification in Databricks with NLP

Published 2022-06-24 by Kevin Feasel

Amir Kermany, et al, share a set of notebooks:

John Snow Labs, the leader in Healthcare natural language processing (NLP), and Databricks are working together to help organizations process and analyze their text data at scale with a series of Solution Accelerator notebook templates for common NLP use cases. You can learn more about our partnership in our previous blog, Applying Natural Language Processing to Health Text at Scale.

To help organizations automate the removal of sensitive patient information, we built a joint Solution Accelerator for PHI removal that builds on top of the Databricks Lakehouse for Healthcare and Life Sciences. John Snow Labs provides two commercial extensions on top of the open-source Spark NLP library — both of which are useful for de-identification and anonymization tasks — that are used in this Accelerator:

This is a really interesting scenario.

Comments closed

Example Data Pre-Processing Activities

Published 2022-06-22 by Kevin Feasel

Aayush Srivastava takes us through some pre-processing activities in machine learning:

After selecting the raw data for ML training, the most important task is data pre-processing. In broad sense, data preprocessing will convert the selected data into a form we can work with or can feed to ML algorithms. We always need to preprocess our data so that it can be as per the expectation of machine learning algorithm

Read on for examples of pre-processing steps and how pre-processing differs from data cleaning.

Comments closed

Unity Catalog in Azure Databricks

Published 2022-06-10 by Kevin Feasel

Paul Roome, et al, announce Unity Catalog:

We are excited to announce that data lineage for Unity Catalog, the unified governance solution for all data and AI assets on lakehouse, is now available in preview.

This blog will discuss the importance of data lineage, some of the common use cases, our vision for better data transparency and data understanding with data lineage, and a sneak peek into some of the data provenance and governance features we’re building.

Click through to see what it currently supports. My curious question is around whether this and Microsoft Purview will play nice in an Azure Databricks setup.

Comments closed

Knowledge Graphs, Data Fabrics, and Data Meshes

Published 2022-06-06 by Kevin Feasel

Alan Morrison describes the history of three concepts:

By 2014, SAP was using “in-memory data fabric” to describe a virtual data warehouse, a key element of its HANA “360-degree customer view” product line. Gartner for its part uses the term “data fabric” to this day as an all-encompassing means of heterogeneous data integration. From a 2021 post on data fabric architecture: 

Read on for a high-level discussion of what each is and how it fits into the context of data warehouses and data lakes.

Comments closed