Here you have the concept of compute units. No such thing as DTUs here but just as confusing.
Compute Units are a measure of CPU processing throughput guaranteed to be available to a single Azure Database for MySQL server. A Compute Unit is a blended measure of CPU and memory resources. In general, 50 Compute Units equate to half-core, 100 Compute Units equate to one core, and 2000 Compute Units equate to twenty cores of guaranteed processing throughput available to your server. I am not going to rehash official documentation on these concepts so I recommend reading https://docs.microsoft.com/en-gb/azure/mysql/concepts-compute-unit-and-storage
Different database product, different metric, it seems. Check out Arun’s post as he walks you through the process step by step.
Why Is MySQL Platform as a Service Important?
I am going to answer this question. There are a lot of advantages to creating, using and developing against data storage within a PaaS offering. One of the biggest for me is backups. Microsoft is automatically taking backups of the MySQL databases you create within Azure. These are real, full backups. Microsoft validates the backups. As I write this, you’ll have the ability to restore your entire database, to any point in time, at intervals of five minutes, over a 35 day preceding period. By programming against a MySQL database within Azure, you are gaining protection of the information you’re storing within your database, and you don’t have to do anything to benefit from this. It’s all part of the service.
Read the whole thing.
One of the promises of Azure SQL Data Warehouse is the ability to have petabyte scale. The ability to quickly scale data, and have that data scale independently of compute resources. So when one I my clients emailed me yesterday with this screenshot, needless to say I was concerned.
As you can see, when the properties screen shows a max size of 5 GB.
Click through for the reason why.
For the rest of this post, I assume that you have some basic familiarity with Python, Pandas and Jupyter.
On your machine, you will need all of the following installed:
Python 2 or 3 with Pip
Amit shows two separate methods for retrieving data, so check it out.
The ADL Tools for VSCode integrate well with ADLA. Azure Data Lake includes the capabilities required to make it easy for developers, data scientists, and analysts to store data of any size, shape, and speed, and do all types of processing and analytics across platforms and languages. U-SQL on ADLA offers Job as a Service with the Microsoft invented U-SQL language. Customers do not have to manage deployment of clusters, but can simply submit their jobs to ADLA, an analytics platform managed by Microsoft.
Click through for the full announcement.
Another database annoucment today from Microsoft. The first globally distributed, multi-model database service. Microsoft describe Azure Cosmos DB as containing a write optimized, resource governed, schema-agnostic database engine that natively supports multiple data models: key-value, documents, graphs, and columnar.
The troll in me really wants this to be SQL Server, but it is instead a very much beefed-up DocumentDB.
You have two core choices on logins. First, you have to create a SQL login at the server level for both Azure SQL Database and Azure SQL Data Warehouse. You can’t remove this or disable it (to my knowledge, and I’ve tried), so make the password a good one (and don’t lose it). You can then create other SQL logins, but this is not a recommended best practice. In fact, I wouldn’t do it at all unless I was forced because of some third party product (few of which currently support Azure anyway).
The next choice, the preferred choice, is to set up Azure Active Directory. With Azure AD you get all the functionality you’re used to with your local AD. Further, you can federate Azure AD with your local AD to control and manage the logins from within your network. You also get multi-factor authentication with Azure AD. We are talking real security here. Read through the documentation on setting up authentication to get it right. You can do the whole thing using Powershell commands, so there’s no excuse on automating it.
There aren’t as many security-related toggles as in an on-prem product, but Grant demonstrates what is available.
For costs, it allows an unlimited number of users since it is priced by aggregate capacity (see Power BI Premium calculator). Users who need to create content in Power BI will still require a $10/month Power BI Pro seat, but there is no per-seat charge for consumption.
For scale, it runs on dedicated hardware giving capacity exclusively allocated to an organization for increased performance (no noisy neighbors). Organizations can choose to apply their dedicated capacity broadly, or allocate it to assigned workspaces based on the number of users, workload needs or other factors—and scale up or down as requirements change.
They’re throttling down Power BI Free, making it really just for personal use, but I think the Premium tier will help with pricing for adoption.
Topology submissions can fail due to many reasons:
- JDK is not installed or is not in the Path
- Required java dependencies are not included
- Incompatible java jar dependencies. Example: Storm-eventhub-spouts-9.jar is incompatible with Storm 1.0.1. If you submit a jar with that dependency, topolopgy submission will fail.
- Duplicate names for topologies
/var/log/hdinsight-scpwebapi/hdinsight-scpwebapi.out file on active headnode will contain the error details.
At one point, I was big on Storm and really wanted a .NET client for Storm to take off. Nowadays, I’d rather use Spark Streaming or Kafka Streams for the same kind of streaming data work.
Now you can start poking around and seeing what’s in the Dashboard. Since I opted to not put any handles in for analysis of FROM and TO, the first two tabs in the workbook (Outbound Tweets and Inbound Tweets) will not have any information, this is normal.
But then we get to tab #3 – Author Hashtag Graph. The gray dots are hashtags and the green dots are accounts that have tweeted. You can see that I made a tweet that had 2 hashtags – #osmf2017 and #mvpbuzz. And boy was @TexasMusicDude busy tweeting up a storm – and using lots of other hashtags in conjunction with his tweets. Other hashtags that were popular appear to be #CampGround, #ShinyRibs, #TexasMusic, #DreamFolk and #Strings. Along the bottom you can see the day/timeline and the quantity of tweets at what time of day. If you click on any of the nodes, the information about what time the tweet(s) took place is highlighted in the timeline. It’s very interactive.
It does require an Azure subscription, but it looks very useful as a model for an advanced set of dashboards as well as a campaign management tool.