Gradually, as storage gets faster and local SSD storage becomes more popular, etc. disk access times are significantly decreasing. In these regards, perhaps the best example are the SSDs Optane systems, notable for their much lower read/ write latencies than with traditional SSD’s, in addition to being directly connected through the PCIe bus: (more…)
In the last few years, we are increasingly finding more hybrid environments where some SQL Servers are being migrated to the Cloud. In these cases, other applications, services, ERPs or even SQL Server instances continue to be based OnPremise in the initial data center. This means that in the event of any connections between both environments, these will be restricted by bandwidth and higher latencies, as opposed to other connections that do not go across both environments.
One of the issues that many of our customers face when attempting to migrate OnPremise instances to the Cloud is the lack of a simple “shared storage”. Although there are some alternatives supported by third-party software or SDS solutions that allow us to configure a Failover Cluster instance in Azure, these are highly complex, therefore adding significant further costs to the solution’s TCO.
I’m sure that the most “senior” readers will remember the possibilities available in old SQL Server versions to do backups using named pipes. And by older versions, I mean “really old”, since this functionality was marked as obsolete in SQL Server 7 and, although it remained in SQL 2000, it was completely removed from SQL Server 2005 and later versions.
Regardless of the tools used for data analysis, normally the way to display the results is a Word document or a PowerPoint presentation.
In this post, we will create a PowerPoint presentation and insert a series of graphics and text programmatically, using the OfficeR and rvg packages together. We will also take advantage of the occasion to present (for those who do not know) the ‘Pipe’ operator, very useful when nesting functions.
In an on-premises environment when we propose solutions to geographical disasters, the most common option is log shipping. The use of asynchronous database mirroring or availability groups with asynchronous replicas is also common but includes an additional risk that is not usually contemplated. We refer to the “speed” with which the changes are transferred, as quickly as the network and the target system allow us. This means that when the disaster has a human origin, an important error when we become aware of it, we will have this error replicated and applied. Obviously, a better solution would be to combine both options, which are not exclusive, with which we would cover more disaster scenarios increasing the cost of the solution. (more…)
In this post, we will code a script in python (with Visual Studio 2017) to create a program which we can execute as a windows service in order to extract (in almost real time) the tweets related to certain words or hashtags, store them in a SQL server database, and then consume them with Power BI. (more…)
After learning about Python fundamentals and basics about working with data, it is time to start with more exciting parts of this Python for SQL Server Specialists series.
In this article you will learn about the most important libraries for advanced graphing, namely matplotlib and seaborn, and about the most popular data science library, the scikit-learn library. (more…)
Today we will show you how we can refresh a dataset published in Power BI from a Power Shell Script that we would invoke at the end of our ETL process.
We will use the Power BI libraries for power shell to connect to our power Bi portal and send an instruction to refresh a data set. This could be useful to improve our ETL processes, refreshing our on-line datasets used in Power Bi portal before loading data into our data-warehouse and/or our OLAP/Tabular database send an instruction to. (more…)
In my previous article, you learned Python fundamentals. I also introduced the basic data structures. You can imagine you need more advanced data structures for analyzing SQL Server data, which comes in tabular format. In Python, there is also the data frame object, like in R. It is defined in the pandas library. You communicate with SQL Server through the pandas data frames. But before getting there, you need first to learn about arrays and other objects from the numpy library.
In this article, you will learn about the objects from the two of the most important Python libraries, namely, as mentioned, numpy and pandas. (more…)