We’re excited to announce the release of SQL Server Management Studio (SSMS) 18.1. It’s been just over a month since we released SSMS 18.0. While we brought in many fantastic capabilities, we also regressed some functionality for some of our users. We are happy to share that we’ve fixed those and are also bringing in some new features along with bug fixes.
The big thing for a lot of people is that database diagrams have returned. I was never the biggest fan of those, but there was enough of an uproar to bring them back.
Here we have a T-SQL statement that we can either create as a stored procedure or just run as T-SQL in a job step. It looks back over the last 10 minutes and looks for job failures. I would recommend scheduling this T-SQL to run every 5 minutes, you will get duplicate entries for a short period of time, but ideally, you shouldn’t get any failures anyway right? Plus once you’re notified, you can turn this off while you work on it or you can specify in the where clause to remove this job until fixed.
Click through for the script and do read the instructions.
One of my favorite recent examples was a company who came to me saying, “We’re spending about $2M per year in the cloud just on our databases alone. Can you help us reduce those costs?” Absolutely: with just a couple of days spent query & index tuning, we chopped their biggest database expenses in half while increasing performance.
At the end of that engagement, the CTO told me, “I thought I’d save money in the cloud by not having a DBA, but what I’m learning is that in the cloud, I actually get a return on my DBA investments.”
I completely agree with this post. The exact tools DBAs use will change, but the role will still be around decades from now. And that’s at the companies which move quickly.
Tables were very narrow with just a few columns and my expectations for data growth were very modest. However, after just a little while I was very surprised when my database showed huge unexpected growth and size of the data became multiple times higher than I’ve expected.
After very little research I’ve found and fixed the problem. In this post I’ll describe how I’ve done it.
Read on to learn how Slava figured this out and how a clustered index fixed the problem.
In my blog post here https://the.agilesql.club/blogs/ed-elliott/2019-06-10/steps-to-automated-database-deployments I described the steps you need to go through so you can build up your confidence that you are capable of deploying databases using automation. I mean, afterall, knowing that it is possible to automate your deployments and having confidence that they will succeed are two very different things.
Even with the best tooling in the world, automated database deployments are still a struggle and there is one key thing that you can do, no matter what tools you choose and that is to make the deployments re-runnable. (Insert discussion here on the word idempotent and how it means re-runnable but sounds far cooler and intellectual). If you make your deployments re-runnable then you can, by their very definiton, re-run them.
Click through for two options. I definitely prefer option number 1 as well.
In this post you will see some recommended tools and best practices that you should apply while doing performance comparison. The recommended performance comparison process has three stages:
1. Compare the environment settings on SQL Server and Managed Instance.
2. Create performance baseline on source SQL Server
3. Compare performance on Managed Instance with the baseline
In the following sections will be described the best practices and the recommended approaches
This is a good bit more involved than installing some product, clicking a few buttons, and comparing numbers.
Today, we are launching Quantum, a high-performance serverless SQL engine, available on Qubole Data Platform, that simplifies SQL access by offering a true serverless deployment option to enable data analysts to query petabyte-scale volumes of data using ANSI-SQL.
Quantum allows teams to realize value from their data much more quickly, and because of its serverless nature, users pay only for queries they run. Data analysts can query object stores on AWS, Azure, Google Cloud, and Oracle Cloud in seconds to achieve faster time to market with far less IT management overhead.
Existing serverless SQL service offerings do not provide users with the ability to use a metastore of their choice. With Quantum, data teams can use their own custom metastore and start using Quantum without recreating schemas or table metadata.
Most existing Qubole customers already use a custom metastore in the cloud. So there’s virtually no ramp up time to reap the benefits of Quantum.
The technical overview is a bit too much marketing for my tastes, but this is a move worth watching.
The future is not clear for either Cloudera and MapR. While there are similarities in the two companies’ positions, there are big differences too.
Cloudera does not have a permanent CEO at the moment, and it still hasn’t shipped the new converged Hadoop distribution, dubbed Cloudera Data Platform (CDP), that will replace the old Cloudera and Hortonworks distributions. During its first quarter ended April 30, Cloudera said customers are holding off investing in the old Hadoop products since they know the new CDP is due by the end of the year. That fact led Cloudera to dramatically lower its revenue expectations for the year, which upset stockholders, who pushed Cloudera’s stock (NYSE: CLDR) down 40% the following day.
The way I’m phrasing it is that the Hadoop ecosystem is strong (with the successes of companies like Databricks and Confluent), but core Hadoop companies are struggling.
Rolf Tesmer explains that machine learning and DevOps aren’t oil and water (or maybe they are and we just need to stir harder):
In talking with various development teams, customers and DevOps engineers, a lot of the potential problems of meshing ML development into an enterprise DevOps process can be boiled down to a few different areas this aims to address…
– ML stack might be different from rest of the application stack
– Testing accuracy of ML model
– ML code is not always version controlled
– Hard to reproduce models (ie explainability)
– Need to re-write featurizing + scoring code into different languages
– Hard to track breaking changes
– Difficult to monitor models & determine when to retrain
So DevOps helps with this, right? Right?
Well er, some of them yes, but not all.
DevOps is not a panacea but it can solve certain types of problems well.
The gripes I hear about fully fixing dynamic SQL are:
– The syntax is hard to remember (setting up and calling parameters)
– It might lead to parameter sniffing issues
I can sympathize with both. Trading one problem for another problem generally isn’t something people get excited about.
But there are good reasons fully to fix it, so read on.