It is not uncommon to find a wide range of situations among our customers in terms of virtual machine performance with SQL Server. In many cases, we find situations where performance levels are far from ideal but, in general terms, virtual machines themselves are not to blame. What usually happens is that when we move SQL Server to a virtual machine, we become constrained by a maximum or limited amount of resources (CPU/ memory/ IO) that is significantly different to that of the physical machine. (more…)
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…)
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…)
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…)
Python is one of the most popular programming languages. It is a general purpose high level language. It was created by Guido van Rossum, publicly released in 1991. SQL Server 2016 started to support R, and SQL Server 2017 adds support for Python. Now you can select your preferred language for the data science and even other tasks. R has even more statistical, data mining and machine learning libraries, because it is more widely used in the data science community; however, Python has broader purpose than just data science, and is more readable and might thus be simpler to learn. This is the first of the four articles that introduce Python to SQL Server developers and business intelligence (BI) specialists. This means that the articles are more focused on Python basics and data science, and less on general programming with Python.