The sqlcmd is a very powerful tool to run SQL Server scripts, T-SQL commands. It is also very useful in disaster recovery situations like restoring the master database.
The sqlcmd can also be used in the Cloud, specifically with SQL Server Azure. In this new article, we will describe how to connect from a local machine to an Azure Virtual Machine (VM) with SQL Server Installed using sqlcmd.
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Previously I have introduced string concatenation by demonstrating how not to use Transact-SQL to concatenate strings. In this part I will focus on XML composition, and will demonstrate how to correctly implement it in order to concatenate strings.
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This article is a part of three articles series to explore SQL Concatenation techniques.
Having to represent sets of data as strings is a very common requirement in information management, even in modern times where a variety of more or less elaborate standards for storing, and moving, data are at our disposal. For instance, XML, JSON, or similar techniques, allow the data to be extracted from one data source, using a well-known standard, and be stored temporarily until being loaded into a destination data store, or until being consumed in some other way. Actually, both XML as well as JSON might even be used as a standard way of storing data permanently; especially, if the consumers expect the data to use one or the other format.
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A few days back I was looking at ways to access raw data from within Microsoft Dynamics CRM in an effort to extract the raw data and to place it in our data warehouse. I started to explore utilizing OData and SSIS to pull the necessary data from the cloud to our local warehouse.
Whilst there are known authentication issues between Dynamics CRM and the Microsoft OData SSIS data source (and thus we could not utilize this access method), I thought it to be so very powerful, that I began looking for other constructive manners in which to utilize the OData Source.
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In my last chapter, How to migrate your database to an Azure Virtual Machine I showed you the steps to connect to a SQL Server in an Azure VM by using SQL Server Management Studio (SSMS). In this new chapter, we will show you how to work with SQL Server Integration Services (SSIS) to export a local table and its data to an Azure Virtual Machine (VM) with SQL Server installed.
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In one of my articles about Microsoft Azure, I show how to create credentials and how to connect to an Azure Database using SQL Server Management Studio (SSMS). In this new article, we will talk about the Azure Virtual Machines and we will learn how to connect to them using our local SSMS. You will require unblocking ports, add endpoints and other tasks that I will explain later. We will also learn how to export a local database to an Azure Virtual Machine with SQL Server installed.
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A few days back I encountered an interesting challenge. The client wanted to have copies of the nightly backups of the transactional databases restored on a warehouse server, to be utilized to update the warehouse.
The over all process
Prior to the pushing the daily backup to the warehouse server, the previous days restore is deleted. The important point being that the “SQLShackFinancial” database is no longer present on the warehouse server. Having been deleted, downloading of the backup file begins and the restore of the current backup version begins. Normal warehouse processing then ensues and so the cycle continues.
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In my last article entitled “Which fields do my reports actually use”, we had a quick look at a practical implementation of an XPATH query to obtain a list of database table fields being utilized by our reports. Reporting Services reports are really no more than XML documents. The fields utilized by our reports reside in Reporting Services datasets.
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Modeling for the xVelocity/Vertipaq engine is a completely different beast than modeling for your trusty multi-dimensional SSAS cubes.
In-memory = blazingly fast; At least that’s what you would think.
As Tabular models gain popularity with business users and developers alike, we’re starting to see that this isn’t always the case.
We’re going to take a look at some of the common errors and mistakes and how to avoid them.
And since the PowerPivot engine is the same – you will learn how to tune your PowerPivot-based Excel workbooks as well.
Have you ever felt like pulling your hair out, trying to ascertain exactly which fields in your existing Reporting Services datasets are being utilized by your reports. This happened to me recently during a corporate conversion and cleanup exercise for a database migration to the cloud.
The “aha moment” came after having presented a paper at the PASS SQL Server Nordic Rally (March 2015), when one attendee came up to me and asked if I knew of a method to do this. As they say ‘necessity is the mother of invention’ and spiking my interest, I played around until I came up with the solution that we are going to chat about today. The end solution may be seen below
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Oft times we are forced into situations where we must clearly think outside of the box. In today’s “get together”, we are going to discuss a challenge that I encountered during the last week of February of this year. The client had been charting weekly business calls placed by his sales reps. Our client had been tracking these results within an Excel spreadsheet (see the screen dump below) and he would be using this spreadsheet to report the sales reps progress going forward. My task was to source this data for the corporate reports in Reporting Services, from this spreadsheet and do so on a weekly basis. The client, being resistant to change, was not willing to change the format of the spreadsheet to something more conducive to be utilized by the chart that he wished to produce (see immediately below).
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In sequence of the first article of the In-Memory OLTP Series that explained the main pillars, database creation and a briefly overview of the checkpoint files, now we will take a look into the table creation and data types allowed in the In-Memory OLTP feature in SQL Server 2014.
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This article is divided in three parts, if you started here, check the first and second part to find the previous steps.
Now we need to define if the instance will work in “Windows Authentication Mode” or “Mixed Mode”. I usually choose “Mixed Mode” because there’s always an application that don’t work with domain accounts. By choosing this option you need to specify the password for the famous “sa” login. Notice that you can change the authentication mode anytime! You also need to add accounts to receive the Sysadmin privilege, this is very important… Even more if you choose the “Windows Authentication Mode”.
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This article is divided in three parts, if you started here, check the first part to find the previous steps.
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In my last article, I show how to create Database Mirroring for high availability. This time, I will work on Log Shipping.
The Log Shipping as the name says ships the Transactional Log from the Primary Servers to the Secondary Servers.
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Do you want to install a SQL Server 2014 clustered instance? Not sure how to proceed? What are the requisites? This series of articles will show this, step-by-step.
In this article I’m going to explain, step-by-step, how to install clustered SQL Server instance. The SQL Server version that we will be using is the 2014, or SQL Server 12, the latest SQL Server version, so far…
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Sometimes our Database fails and we need to reestablish the Database services immediately. Is there a SQL Server solution for high availability?
With Database Mirroring, we can have a replica of the database ready to be used if the main database fails. In this article, we will give a step-by-step tutorial about Database Mirroring. There are other solutions for high availability like high AlwaysOn, Clusters and Log Shipping, but in this chapter, we will start with Mirroring.
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Introduction
As you will remember from our last “get together” we created an application that permitted us to report upon financial data based upon an unorthodox financial year. In fact, our fiscal year started in July and ended in June. We created a chart to display the data.
In today’s “get together” we are going to push our application a bit further and build in a subreport which will bring up the underlying data when the end user clicks upon the chart for any particular month. Thus should the user click on February 2015, then all of February’s data (for the selected funds) is shown in a matrix. If the user chooses March, then March’s data is shown.
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In order to connect on- premise and Azure we have two options, a point-to-site and a site-to-site VPN. On this article I will be showing how to configure a site-to-site VPN using a routing and remote access, step-by-step.
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I recently heard from a lady from overseas who wanted to find a quick and dirty mechanism of extracting data for a given date range (based upon a fiscal year that started July 1st and ended June 30th). The idea interested me and as always, I had to try it out.
In today’s “get together”, we are going to have a look at how this may be achieved.
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When creating a hash index in a memory optimized table we need to define a value for the bucket count. Think twice before set a random value, you may pay the price later… On this article we will explain the effects of a bad defined bucket count and how to monitor it.
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In our last two chats, we discussed enterprises that have had financial years that began in July and ended at the end of June. One of our clients works with this fiscal calendar and their financial folks are Excel “Fundi’s” (Fundisa is a Nguni word for “expert”). Many of their reports contain the current month’s sales, in addition, carrying running totals from the beginning of the fiscal year to date. Read more »
Oft times we are forced into situations where we clearly need to think outside of the box. A case at hand arose early in 2014 where one of our client’s required a “quick and dirty” front end to modify data within a table that reflected the outstanding balances (of their clients) and the attempts that they had made to recover these funds. Master Data Services seemed to be the way to go!
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In our last get together I mentioned that oft times SQL Server reports are created due to a dire business need to be used once and never again. Further, some reports that we believe are not often used could be “top of the pops” unbeknown to us. A guess as to a number of times a report is used per month, in addition to the statistics behind each report should not be guesswork, but rather monitored actively to ensure that frequently used reports are both efficient and effective. Further, those reports that are either not used or have not been run in quite some time, should perhaps be removed in order to keep the server clean and not cluttered.
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A few months back, I presented a paper at SQL Saturday 327 in Johannesburg, South Africa. Late last month I received an email from one of the attendees. His issue was quite interesting and I decided to share it with you. The gentleman wanted a SSIS script that would permit him to extract data from a SQL Server database table and place it in a CSV file with a dynamically allocated name. Being a strong advocate of using the SSIS toolbox, I experimented with an alternative solution. We are going to construct THIS SOLUTION in today’s get together.
Let’s get started.
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