R 3.6.1 is a minor update to R that fixes a few bugs. As usual with a minor release, this version is backwards-compatible with R 3.6.0 and remains compatible with your installed packages.
Click through for the changes. There is one nice addition around
writeClipboard but otherwise it’s a release where you probably update if you’re bothered by a bug it fixes and otherwise skip.
The big data clusters feature continues to add key capabilities for its initial release in SQL Server 2019. This month, the release extends the Apache Spark™ functionality for the feature by supporting the ability to read and write to data pool external tables directly as well as a mechanism to scale compute separately from storage for compute-intensive workloads. Both enhancements should make it easier to integrate Apache Spark™ workloads into your SQL Server environment and leverage each of their strengths. Beyond Apache Spark™, this month’s release also includes machine learning extensions with MLeap where you can train a model in Apache Spark™ and then deploy it for use in SQL Server through the recently released Java extensibility functionality in SQL Server CTP 3.0. This should make it easier for data scientists to write models in Apache Spark™ and then deploy them into production SQL Server environments for both periodic training and full production against the trained model in a single environment.
Click through to learn more about what has changed.
We are so super excited to announce that after 5 long years, dbatools 1.0 is publicly available!
Our team had some lofty goals and met a vast majority of them . In the end, my personal goal for dbatools 1.0 was to have a tool that is not only useful and fun to use but trusted and stable as well. Mission accomplished: over the years, hundreds of thousands of people have used dbatools and dbatools is even recommended by Microsoft.
Go forth and update.
In those versions, flipping compatibility level uses the new Cardinality Estimator (CE). That new Cardinality Estimator is real hit or miss.
The worst part is that there’s practically no gain to be realized for using higher compatibility levels — that changes with SQL Server 2019.
Read on to see what those new features are. As far as the compatibility level switch goes, there comes a time when you just need to bite the bullet and use the new cardinality estimator. Erik has a few tips to help with that too.
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.
Currently, you might import large amounts of data from another relational database system like Oracle or MongoDB into your SQL Server database. If so, you can use Polybase instead.
Polybase has certainly been improved in SQL Server 2019. For instance, more data sources are now available. Plus, you can even install Polybase on SQL Server 2019 on Linux as well.
Instead of importing the data from the source you can use Polybase to connect to it remotely. Which means that you can run your queries against the source directly instead.
Read on for more in this vein. Here it’s less about the technical capabilities and more about making life easier for other people in the business.
Friends, CTP 3.0 dropped today, and it includes some changes for Query Store in SQL Server 2019! I am so excited!! I’ve downloaded it and have WideWorldImporters installed and have a lot of testing planned, but if you’re impatient, guess what? The documentation is already updated! If you check out the ALTER DATABASE SET page you will see that Query Store now has a new option for QUERY_CAPTURE_MODE: CUSTOM. For those of you with ad hoc workloads, this will help.
Read on to see how it can help.
Big data clusters
– Scale out by supporting deployment configurations with an increased number of SQL Server instances in the compute pool. You can now specify up to 4 instances in the compute pool for optimal performance of your queries against data pool, storage pool, or other external data sources.
– The mssqlctl utility includes updates to ease the big data cluster management experience with enhancements to the login experience. There is also a new command to list the cluster endpoints.
– Persistent volumes abstract the details of how the storage is provided and how it’s consumed. In this release, we’re enhancing the supported storage configurations by enabling you to customize storage classes independently for logs and data. With these changes, we also consolidated the storage configurations for big data components, so that the number of persistent volume claims for a big data cluster has been reduced for a default minimum configuration cluster.
There are a few other changes announced in this CTP. Now that we’re at 3.0, the light is at the end of the tunnel.
#119 When the backups check module reports backup issues for a database but the issue is with a FULL or DIFF and the LOG is ok, we now show just the primary server in the Preferred replicas column as a FULL and DIFF only applies to the Primary – this reduces the number of warnings raised within the report as it will no longer report for all replica nodes if the AG backup preference is set to Prefer secondary or Secondary Only. See Git issue for more details.
Click through for the full change set.
A major update to the open-source R language, R 3.6.0, was released on April 26 and is now available for download for Windows, Mac and Linux. As a major update, it has many new features, user-visible changes and bug fixes. You can read the details in the release announcement, and in this blog post I’ll highlight the most significant ones.
There are some good changes in here.