Data Science & Data Engineering blogs
Thanks for reading. Here you will find a huge range of information in text, audio and video on topics such as Data Science, Data Engineering, Machine Learning Engineering, DataOps and much more. The show notes for “Data Science in Production” are also collated here.
If you keep up with the latest developments in the data space, you will probably know that Microsoft Fabric has now reached general availability. One of the trickiest things we’ve had to come to terms with is how concurrency works and how to run multiple notebooks at once without errors.
Read MoreWhen it comes to selecting data curation options in Fabric there are an entire haberdashery of options. Let’s explore some of those options, looking at which path might best suit which types of Analytics Engineering personas.
Read MoreIf you keep up with the latest developments in the data space, you will probably know that Microsoft Fabric has now reached general availability. One of the trickiest things we’ve had to come to terms with is how concurrency works and how to run multiple notebooks at once without errors.
Read MoreHow can you tell whether the Copilot preview is available in your Fabric tenant? This blog shows you how.
Read MoreDid you know you can use Tabular Editor with Fabric to build advanced Power BI semantic models? Find out some of the advantages that this approach can give you.
Read MoreLast week was Microsoft’s Ignite conference hosted in Seattle and we were on the edge of our seats for some highly anticipated announcements. Here’s mine and Johnny’s round up of the Fabric announcements.
Read MoreMeet your new favourite action hero, Data Activator. It’s the newest experience to be added to Microsoft’s unified Software-as-a-Service (SaaS) platform Fabric, having been made available in the public preview at the beginning of October. But what is it? What does it do? How can you use it?
Read MoreFabric wouldn’t be an end-to-end data analytics platform without data science, so in this blog we will explore that data science and machine learning capabilities of Microsoft Fabric and assess where the platform fits in the completive data science landscape.
Read MoreShould you care about what you call your artifacts when you create them in a Fabric Workspace? At Advancing Analytics, we say yes! Here we describe a naming convention we’ve adopted to make sure Fabric artifacts can be grouped and organised in a way that makes them quick and easy to identify.
Read MoreSince Microsoft Fabric has been announced there have been many questions and queries about licensing and in this blog post we will go through everything you need to know to get started.
Read MoreThis blog will walk you through the entire data lifecycle of ingesting data from SharePoint and Azure Blob Storage, through the lakehouse pattern, and finally to the reporting stage - all using Microsoft Fabric.
Read MoreIt’s really easy to get started with Microsoft Fabric. We can ingest data, transform it, and have it surfaced in Power BI in no time without creating a single Azure resource. That’s the easy part but how do we turn that into a robust, logical architecture that will give us a successful Analytics Platform?
Read MoreWith the advent of Fabric, many organisations with existing lakehouse implementations in Azure are wondering what changes Fabric will herald for them. Do they continue with their existing lakehouse implementation and design, or do they migrate entirely to Fabric?
Read MoreIn the data engineering field, we must ensure that large datasets are compressed efficiently to save storage space and reduce costs, but we also need to maintain strong query performance. This can be balanced by utilising the VertiParq engine and Delta in Microsoft Fabric.
Read MoreIf you have had a play around with Microsoft Fabric you will have seen that there are lots of different ways to get to your end goal. One of the most important decisions to make is whether to use a Lakehouse or Warehouse?
Read MoreAs data engineers, we face many challenges daily. Data is often distributed across many different sources, and frequently in a wide range of file types with varying levels of data quality. This is where OneLake comes in, described as the OneDrive for data.
Read More
