In this final part of the blog series, the data has been imported and transformed in Azure Synapse Analytics and now we can work with the data so it can be displayed in Power BI.
In this post, we focus on using SQL in Azure Synapse Analytics and conduct tasks such as creating tables and views, testing the tables, and formatting the data.
In the first post of this Azure Synapse Analytics & Power BI series, we cover data engineering with Power Query using Excel data files.
Building a minimum viable product is the next step after completion of the proof of concept. This post looks at how to build a successful MVP.
This article by Microsoft MVP Arun Sirpal discusses Azure Data Factory. With this tool, you have access to a fully managed serverless cloud data integration tool that scales on-demand. This is done by building pipelines – these data-driven workflows usually perform the following steps shown below.
This article by Microsoft MVP Arun Sirpal discusses Azure Logic Apps and gives a demonstration of its use with Twitter.
There are several factors that we consider the most important in successfully completing a software development project on time and in-budget. With this, we include the completed software doing what it’s supposed to do without error. And by the end of the project, everyone still remaining friends. So, we thought we would put our experiences into a blog series for you here.
As a startup, particularly a SaaS startup, this is what it is all about. Honing the key aspects to your idea and fine-tuning it into a truly workable, outstanding and viable product is the base of your whole enterprise. You need to see if it is practical, feasible, viable and truly useful and interesting for the end-user. This is where a Proof of Concept (POC) comes in.
The Query Store was introduced back in SQL Server 2016 and with the incoming release of SQL Server 2019, it remains a very important feature to know and understand to utilise in performance tuning exercises. Not only is this feature available with on-premises SQL Server but it is a key tool to use when troubleshooting performance issues in Azure SQL Database.
One of the real benefits of using Azure for Serverless work is not having to think about scaling for the most part, but there are times when you want to ensure that your costs do not become too high. For example, function calls may be running reports against a SQL Database...