Starting with SQL Server 2017, Microsoft announced you can run SQL in Docker containers. This is great news for DBA’s because we tend to run multiple installations of SQL Server locally and with Docker we can spin up (or remove) a SQL instance fairly quickly.
I’m not going to go into what Docker is because there’s
an entire website
that goes into much more detail than I ever could. This post is going to focus
on a simple SQL installation on Windows 10 using Docker. Let ‘s gooooooo!
–name – this is the name of the container
you are creating
-d – Runs the container in the background
otherwise the container will take over your cmd when it starts
– p port:port – Maps a TCP port on the host
environment (first value) with a TCP port in the container (second value). In
this example, SQL Server is listening on TCP 14331 in the container and this is
exposed to the port, 1433, on the host. I’m using 14331 because I’ll create
multiple containers and each container needs a different port number.
-e – Creates environment variables under the
container execution runtime, for example, sa password and End User License
sa_password – Create sa password for SQL
instance (Hint, don’t use a $)
ACCEPT_EULA – Yes
It will take a few minutes to finish the install, but it’s
much shorter than a regular SQL install. Once finished, run the following to
get the IP address of the container (make sure to use your container name after
Support for SQL Server 2008 and 2008 R2 ended on July 9, 2019. That
means the end of regular security updates. However, if you move those
SQL Server instances to Azure or Azure Stack, Microsoft will give you
three years of extended security updates at no additional cost. If
you’re currently running SQL Server 2008 or 2008 R2 and you are unable
to update to a later version of SQL Server, you will want to take
advantage of this offer rather than running the risk of facing a future
security vulnerability. An unpatched instance of SQL Server could lead
to data loss, downtime, or a devastating data breach.
If you have a SQL Server failover cluster instance (FCI) or use hypervisor-based high availability, or other clustering technology on-premises for high availability, you’ll probably need the same in Azure. If you need to migrate to Azure/Azure Stack at end of support, and you require high availability for SQL Server 2008/2008 R2, there’s only one solution recommended by Microsoft: SIOS DataKeeper. SIOS DataKeeper enables clustering in the cloud, including the creation of a SQL Server 2008/2008 R2 failover cluster instance, allowing you to achieve your high availability goals.
Planning and building SQL Server in RDS doesn’t have to
scare you. It’s actually pretty easy and in this post will go over planning a
SQL Server deployment in RDS, creating SQL Server in RDS, and last but not
least configuring the new instance of SQL Server.
Once you can verify that your environment will run
properly in RDS you’ll need to look at the pricing model. When you setup RDS
for SQL Server, the software license is included. AWS used to have a program
called “Bring your own license” or “BYOL”, which allowed you to use a license
that was already bought from Microsoft via an agreement or other. This has been
rumored to expire on June 30, 2019. The software license that is included means
that you don’t need to purchase SQL Server licenses separately. AWS holds the
license for the SQL Server database software. Amazon RDS pricing includes the
software license, underlying hardware resources, and Amazon RDS management
capabilities. The pricing will depend on the selections such as size, edition,
The following editions are supported in RDS:
Notice, Developer Edition is not included with RDS and
Web Edition supports only public and internet-accessible webpages, websites,
web applications, and web services.
You can also choose from On-demand or reserved
instances. On-Demand DB Instances let you pay for compute capacity by the hour
your DB Instance runs with no long-term commitments. This frees you from the
costs and complexities of planning, purchasing, and maintaining hardware and
transforms what are commonly large fixed costs into much smaller variable
costs. This is good for development environments where you can power on and off
the server as it’s being used.
Reserved Instances give you the option to reserve a DB
instance for a one or three year term and in turn receive a significant
discount compared to the On-Demand Instance pricing for the DB instance. Amazon
RDS provides three RI payment options — No Upfront, Partial Upfront, All
Upfront — that enable you to balance the amount you pay upfront with your
effective hourly price.
RDS provides a selection of instance types optimized to
fit different relational database use cases. Instance types comprise varying
combinations of CPU, memory, storage, and networking capacity and give you the
flexibility to choose the appropriate mix of resources for your database. Each
instance type includes several instance sizes, allowing you to scale your
database to the requirements of your target workload. View more details here: https://aws.amazon.com/rds/instance-types/
Another item to look at when planning your deployment is
storage. RDS uses Amazon Elastic Block Store (Amazon EBS) volumes for database
and log storage. Depending on the amount of storage requested, Amazon RDS
automatically stripes across multiple Amazon EBS volumes to enhance
RDS offers three different storage types:
General Purpose SSD – also called gp2, this storage type
offers cost-effective storage that can be used for a broad range of different
workloads. These volumes deliver single-digit millisecond latencies and the
ability to burst to 3,000 IOPS for extended periods of time. I would recommend
putting small to medium sized databases on this type.
Provisioned IOPS – This storage type is designed for I/O
intensive workloads, particularly database workloads that require low I/O
latency and consistent throughput. This is also built on SSD and targeted for
IO intensive, high performance databases. Cost wise, this is the highest of the
three storage types.
Magnetic – This storage type is mostly used for backward
compatibility. Amazon recommends using gp2 or Provisioned IOPS for any new
builds. This is ideal for test and dev environments when performance isn’t a
concern. This is the cheapest of the three storage types.
One more item to consider when planning the deployment
is network connectivity. Applications will more than likely need to connect to
your RDS environment so there are a few import concepts to look at it.
Availability Zones – this is simply a data center in an AWS region. The following AWS regions exist.
Virtual Private Cloud – also called VPC, this is an
isolated virtual network that can span multiple Availability Zones. It’s used
to group different types of resources to the network that need to talk to each
Virtual Private Cloud – also called VPC, this is an isolated virtual network that can span multiple Availability Zones. It’s used to group different types of resources to the network that need to talk to each other.
Now that we’ve outlined some of the deployment planning
tasks, let’s build an instance through the AWS console.
Once inside the console, we’ll click on RDS under the Database heading:
Once we are on the home page for RDS, we can click Create Database under Get Started. There’s also info for Pricing and costs and some documentation on getting started:
Notice in the top right corner is the Availability Zone in which you are logged into. In my case, I’m logged into US East (Ohio) since I’m on the Central Time Zone and Ohio is located closer to me than any other zone:
Back to the Select Engine page. For this post, I’m going to install Microsoft SQL Server Express, but you can see the other database engine platforms that are available and the associated editions:
Next page we can see some of our database details. There are all items we discussed in the planning deployment section above. License model, DB engine version, DB instance class, Time Zone, Storage Type and allocated storage are all configurable on this page. Below are my selections:
Scroll down to Settings header and configure the DB instance identifier, Master username and password. The DB instance identifier is a unique name for your DB instances across the current region. For this RDS instance I’ll name it SQLFreelancer:
On the Advanced Settings page we’ll configure Network and Security, Windows Authentication, Database Options such as port number, Encryption (where available), Backup retention, Monitoring, Performance Insights, Maintenance options, and Deletion protection. I’m going to choose all the defaults for this post, but this is a page where you want to make sure you choose what is best for your environment.
Once you are finished on the Advanced Settings page, click Create Database.
Now how easy was that? Creating a new DB instance took about 2 minutes. Once your instance is created let’s click on View DB Instance details:
The details page gives you all sorts of info about your instance:
As defined by Amazon, Amazon Relational Database Service
(Amazon RDS) makes it easy to set up, operate, and scale a relational database
in the cloud. It provides cost-efficient and resizable capacity while
automating time-consuming administration tasks such as hardware provisioning,
database setup, patching and backups. It frees you to focus on your
applications so you can give them the fast performance, high availability,
security and compatibility they need.
RDS is also referred to as a Database as a Service
(DbaaS) or Platform as a Service (PaaS) not to be confused with Infrastructure
as a Service (IaaS) which we’ll discuss in the next paragraph.
You can choose any DB platform such as
Oracle, MySQL, SQL Server, Amazon Aurora, PostgreSQL, and MariaDB
You create a Virtual Machine and install
OS and DB platform such as SQL Server
DbaaS takes care of backups, High
Availability, Patching, OS, underlying hardware
Iaas will only take care of the VM host
layer and it’s hardware. You will need to manage patching, HA, security, etc.
This is essentially like an on premise server.
Being an Operational DBA, there are a few tasks that RDS
will take over freeing up time for the DBA to focus on other things. Some of
those tasks include the following:
Backups: RDS will continuously take backups
and allow point in time restore capabilities. We no longer have to worry about
disk space or archiving backups to another location.
HA: RDS can automatically setup mirroring to
another data center which allows for redundancy of databases.
Patching: RDS will automatically patch your
SQL Server based on a maintenance window defined by you.
Add Resources such as CPU/Memory: RDS can
increase CPU or Memory on demand as opposed to managing an on premise where the
server might need downtime and you would have to orchestrate the change with
Upgrade: With a push of a button you can
automatically upgrade SQL Server and easily roll back if necessary.
Monitoring: Instead of buying a third party
monitoring tool and running through the setup RDS provides a service called
CloudWatch that can easily tap into SQL Server and alert when things go wrong.
Wow! All of these items make managing a SQL Server much
easier for a DBA right? Do you even need a DBA if you’re running RDS? Of course
you do! While it does make some tasks easier for a DBA, RDS will not do the
Write queries, tune queries, test queries:
RDS has no knowledge about the data in each DB. Only a DBA knows the
application and business processes to write and tune queries.
Manage DB security, change control,
configuration settings: Only a DBA familiar with all of the procedures of his/her
company can really make sure the environment is secure, that all changes are
being documented, and that a specific configuration applies to what the
databases are supposed to do.
Tune indexes or maintenance: Again, only the
DBA knows what databases might need indexes or aren’t using specific indexes.
You also know when to run maintenance procedures.
Now that we’ve discussed some of the pros and cons, why
do businesses use DbaaS?
The speed of provisioning increases business value
because instead of waiting weeks to bring a server online including purchasing
software, servers, licenses, managing resources, etc. you can click a few
buttons in the Amazon console and have a fresh SQL Server online in a matter of
The automation of regular tasks means there’s less
possibility of human mistake and less hours spent by the admins patching and
managing certain parts of the servers, which means no more late nights.
Employees can now spend more time query tuning, deploying new functionality,
and making sure the performance is the best. All of this leads to increased
revenue for the business.
Using bookmarks in Power BI help you capture the currently configured
view of a report page, including filtering and the state of visuals, and later
let you go back to that state by simply selecting the saved bookmark.
You can also create a collection of
bookmarks, arrange them in the order you want, and subsequently step through
each bookmark in a presentation to highlight a series of insights, or the story
you want to tell with your visuals and reports.
In this post we’ll quickly go over
how to create a few bookmarks and view them as a slideshow if you will.
I’m going to use my March Madness Report I created in an earlier post. Once my report is opened in Power BI Desktop, I’m going to click on the View tab in the ribbon and select “Bookmarks Pane”
This should bring up a new Bookmarks pane inside PBI Desktop:
Remember, bookmarks are used to capture the current view of the report so I’m going to use the default view where I’m showing all data and I’m going to name the bookmark “Home”. Make sure all filters are selected to show all data and click Add under the bookmark pane. This will create a new Bookmark, named Bookmark 1. Click the ellipsis and select rename to rename the bookmark appropriately.
Next, I like North Carolina, so I’m going to go to my Team Filter and choose North Carolina which will show me data for only this team.
In my bookmark pane, I’m going to click Add again and rename to North Carolina.
Next, I want to view data on North Carolina from 2000 to present so I’ll change the Year Filter.
In my bookmark pane, I’m going to click Add again and rename to North Carolina 2000-present.
Now, if I click on any of bookmarks,
it will take me to the data that was saved for each. This is a great way to
present data in a meeting/conference so you don’t have to manually change the
filters during the engagement.
We can also click the View button in the Bookmark pane to view a slideshow using the arrows at the bottom to navigate:
I wrote a post a few weeks about creating an Azure Windows VM so wanted to follow up with a post about creating an AWS Windows VM to compare both platforms. I like Azure and AWS so I’m not going to throw either one under the bus. Both are great and easy to use.
Let’s create an AWS (EC2) Windows VM.
Log into the AWS portal and click on EC2 under All Services, Compute:
Next, click Launch Instance:
Step 1 allows you to choose an Amazon Machine Image or AMI. There are tons of options here, but for this post, I’m going to use Microsoft Windows Server 2019 Base
Once I click Select, I’m brought to Step 2: Choose an Instance Type. Instance Types comprise varying combinations of CPU, memory, storage, and networking capacity and give you the flexibility to choose the appropriate mix of resources for your applications. Each instance type includes one or more instance sizes, allowing you to scale your resources to the requirements of your target workload. More info here: https://aws.amazon.com/ec2/instance-types/
For this post, and for cost sake, I’m going to use the free tier t2.micro type which is 1 CPU, 1GB RAM
Once I’ve selected my instance type I’ll click
Next:Configure Instance Details.
Step 3: Configure Instance Details is where we’ll
configure our new server. Let’s go down the list.
Number of Instances – This is the number of servers you want to create. If you need 5 of the same servers, this makes it easy.
Spot Instances – A Spot Instance is an unused EC2 instance that is available for less than the On-Demand price. Because Spot Instances enable you to request unused EC2 instances at steep discounts, you can lower your Amazon EC2 costs significantly. The hourly price for a Spot Instance is called a Spot price. The Spot price of each instance type in each Availability Zone is set by Amazon EC2, and adjusted gradually based on the long-term supply of and demand for Spot Instances. Your Spot Instance runs whenever capacity is available and the maximum price per hour for your request exceeds the Spot price.
Subnet: the range of IP addresses in your VPC that can be used to isolate different EC2 resources from each other or the internet.
Auto-assign Public IP – requests a public IP address from Amazon’s public IP address pool, to make the server reachable from the internet.
Placement Group: You can launch or start instances in a placement group, which determines how instances are placed on underlying hardware. When you create a placement group, you specify one of the following strategies for the group:
Cluster – clusters instances into a low-latency group in a single Availability Zone
Partition – spreads instances across logical partitions, ensuring that instances in one partition do not share underlying hardware with instances in other partitions
Spread – spreads instances across underlying hardware
There is no charge for creating a placement group. Learn more: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/placement-groups.html
Capacity Reservations – enables you to
reserve capacity for your Amazon EC2 instances in a specific Availability Zone
for any duration. This gives you the ability to create and manage capacity
reservations independently from the billing discounts offered by Reserved
Instances (RI). By creating Capacity Reservations, you ensure that you always
have access to EC2 capacity when you need it, for as long as you need it.
Domain join directory – enables you to join
a domain that you’ve already created.
IAM role – automatically deploys AWS credentials
to resources that assume it.
Shutdown behavior – specifies what happens
when an OS level shutdown is performed.
Enabled termination protection – You can
protect instances from being accidentally terminated. Once enabled, you won’t
be able to terminate the instance until this option has been disabled.
Monitoring – Monitor the instance with
Tenancy – You can select to run your server
on a shared server or a dedicated server.
Elastic Graphics – Enables graphic
For this post I’ll use defaults and click Next.
Step 4 is Add Storage.
I’m not going to go over each Storage option, but you can get more info here: https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/Storage.html?icmpid=docs_ec2_console
Selecting default and clicking next.
Step 5: Add Tags.
Like Azure, A tag consists of a case-sensitive key-value
pair. For example, you could define a tag with key = Name and value =
Webserver. A copy of a tag can be applied to volumes, instances or both. Tags
will be applied to all instances and volumes
Step 6 is Configure Security Group
A security group is a set of firewall rules that control the traffic for your instance. On this page, you can add rules to allow specific traffic to reach your instance. For example, if you want to set up a web server and allow Internet traffic to reach your instance, add rules that allow unrestricted access to the HTTP and HTTPS ports. By default, the RDP port is added, but it allows all IP addresses to connect. Changing the Source column will allow you to filter what IP’s are able to RDP into the server. For this post, I’m going to change the Source column to allow “My IP”
Next…and last page is a summary of the options selected. To finish configuring the instance, click Launch.
After clicking launch, you will see a popup where you can create or use an existing key pair. A key pair consists of a public key that AWS stores, and a private key file that you store. Together, they allow you to connect to your instance securely. For Windows AMIs, the private key file is required to obtain the password used to log into your instance. For Linux AMIs, the private key file allows you to securely SSH into your instance.
Once the new VM is created, you can go back to the EC2 dashboard and click on Instances to see the new VM:
Creating a dynamic title in Power BI helps present the data and let’s the viewers know what the data is filtered on. In this post I’ll go over how to do this…
I have a sales report that I’d like to add a title that is based on the Order Date Slicer. Currently, the title is static text “Sales Report”
my dynamic title, I’ll first need to create a measure table that has my Order
Date data. In this case, that table is FactInternetSales and the column is
a measure, click New Measure in the Power BI Desktop ribbon
Next, you’ll see a window where you can type code. In this example, I’ll use the following DAX
see a window where you can type code. In this example, I’ll use the following
Order Date Title = “Sales For ” & MIN ( FactInternetSales[OrderDate] ) & ” to “ & MAX ( FactInternetSales[OrderDate] )
walk through this real quick.
The first line (Order Date Title = “Sales For “ &) is basically naming the measure and adding the beginning text for the title. The second line (MIN ( FactInternetSales[OrderDate] ) & “ to “) is finding the minimum order date from FactInternetSales.OrderDate and then adding the “to” text. The last line (MAX ( FactInternetSales[OrderDate] ) is finding the maximum order date from FactInternetSales.OrderDate.
This one was pretty easy. Once I’ve typed my DAX, hit the checkmark to make sure there are no errors and the click off screen.
Our measure has been created! Let’s go back and find it under the FactInternetSales fields pane.
Next, let’s click on the Card Visualization and move and size it appropriately to fit in our title space.
While the card is highlighted, click on the new measure from the Fields pane and it will populate the card with the measure we created.
The only thing left to do is format the title and we’re all set! If we change the Order Date Slicer, you’ll notice the title changes with the date. See live example at the beginning of this post.
At the beginning of the year I set a goal to learn something
new. I’ve always loved business intelligence and bringing data to life in the
form of dashboards and charts so for the 1st half of the year I wanted
to focus on Microsoft’s Power BI. I’m not going to explain what Power BI is,
but if you want to read up on it go here: https://powerbi.microsoft.com/en-us/
This post is just going to show off my dashboard. 😊 See live example above.
I’m a huge sports fan and the best time of the year happens to fall in March. Besides my birthday being in March, it’s also March Madness. Hours and hours of basketball. I could of used AdventureWorks for my dataset, but I wanted to use something I’m interested in. I found some data containing every NCAA tournament game result since 1985 (when the tournament was expanded to the 64 team bracket). The dataset contains the year, round (1-6), seed of the teams (1-16), region (1-4) and the scores. Perfect. Let’s use this to create a dashboard.
There’s not a ton of data, but I used what I could and tried
to answer some questions around wins and upsets. Here’s a screenshot of the
You can see Wins By Team (Duke with 93, North Carolina with
78, etc), Wins by Seed, National Championships, and Upsets vs Wins by Year. You
can also see that a total of 2142 games have been played with 199 different
teams in the tournament.
This was really fun and answers a lot of the questions I was thinking in my head while designing. The top left corner also has slicers which help filter the data. For example, if I wanted to see only the data for 2015 I could change the Year slicer to 2015 and it would update all my visualizations:
You can see that Duke won the National Championship from the National Championships visualization. If you hover over the Wins and Upsets visualization, you’ll see there were 30 upsets out of 63 games.
Let’s say I want to view data for a certain Team. Let’s choose Alabama Crimson Tide. If I change the Team slicer to Alabama I can see some data based around this team.
Alabama has won 19 NCAA tournament games, 0 national
championships, has been a 5 or 7 seed 21% of the time and they’ve had a few
upsets along the way. Not bad for a football school.
What about data for the National Championship game? I can change the Round slicer to 6, which is the National Championship round and view the data this way.
I can see out of 34 games, there has only been 16 different
teams make the National Championship. Duke leads the way with 6, followed by
North Carolina and Connecticut with 4. The 1 seed has played in this game 59%
of the time, and there were upsets in 1988, 1990, 1997, 2003, 2006, and 2016.
We can also click on the visualizations themselves to view data. For example, if we reset our slicers to show all data and click on the #1 seed in the Wins By Seed Donut Chart we see the following:
We can see that the #1 seed has played in 419 games with a
total of 41 different teams. Duke has won 51 games as the #1 seed while North
Carolina has won 46. Duke has also won the National Championship 4 times as the
#1 seed and in 1999 the #1 seed won 17 games which is the highest.
Really cool stuff. I loved working on this project and
working with this data.