Learn the differences between the Basic, Standard, and Premium tiers of Azure SQL Single Database.

Provisioning is one component of maximizing the value of your deployment – licensing is another.

In this video with Matt Gordon, take a walk through the differences between licensing by DTU and by vCore and talk about what may be right for you and your needs.

Containers are taking over, changing the way systems are developed and deployed.

Just imagine if you could deploy SQL Server or even your whole application stack in just minutes. You can do that, using containers! In this session, we’ll get your started on your container journey learning how to deploy SQL Server in Containers.

Resources:

About Anthony Nocentino:

Anthony is the Founder and President of Centino Systems as well as a Pluralsight Author and a Microsoft Data Platform MVP, Linux Expert, and Corporate Problem Solver. Anthony designs solutions, deploys the technology and provides expertise on business system performance, architecture, and security. Anthony has a Bachelors and Masters in Computer Science with research publications in high performance/low latency data access algorithms and spatial database systems.

Microsoft added several features to SQL Server 2019 that can improve performance without changes to code.

One of these is T-SQL Scalar UDF Inlining.

The use of UDFs is a red flag when tuning queries, but, in some cases, the engine will treat these as if the underlining code was added directly to the select statement.

Tune in to this session with Kathi Kellenberger to see when this new feature will help, and, more importantly, when it won’t.

Content index:

  • 0:00 Introduction
  • 1:10 What are UDFs?
  • 1:47 Performance Issues
  • 2:30 UDF Scalar Inlining
  • 3:15 Demos
  • 11:40 Control when inling is used

Microsoft continues to push the envelope on feature capabilities with every release of SQL Server. 

One of the more prominent features that was released with Microsoft SQL Server 2019 is the ability to resume or throttle certain index operations.

Have you ever want to just stop and then resume an index operation picking up where it left off?  Now you can! This feature alone magnifies the data professionals ability to have deeper granular control on how index operations affect their ecosystem as well as their work-life balance.  In this episode, John Morehouse gives a high-level look at how this feature works and how it can be applied seamlessly to your environment.

Resources:

Persistent Log Buffers, sometimes referred to as tail of log caching, uses persistent memory to persist the database log buffer, eliminating bottlenecks that may occur on busy systems waiting for the log buffer to flush to disk.

A process known as log hardening.

Learn more here.

  • [00:00] Intro
  • [00:45] Positioning persistent log buffer
  • [01:13] Persistent memory (PMEM) devices
  • [01:58] Usecase for and benefits of persistent log buffer
  • [02:31] Best practices for SQL Server with PMEM in Windows
  • [03:38] Best practices for SQL Server with PMEM in Linux
  • [04:01] What is persistent log buffer?
  • [04:43] What is forced delayed durability?
  • [05:30] Difference between persistent log buffer and forced delayed durability
  • [06:42] Demo: setting up persistent log buffer
  • [07:54] Wrap-up

This video provides an overview of administration experiences for BDC (Big Data Clusters).

In big data clusters, we ensure that management services embedded with the platform provide fast scale and upgrade operations, automatic logs and metrics collection, enterprise grade secure access and high availability.

Here’s a great collection of Jupyter notebooks that explore all the new features of SQL Server 2019.

Here are some of the ones that caught my attention.

SQL Server 2019 Querying 1 TRILLION rows

  • OneTrillionRowsWarm.ipynb – This notebook shows how SQL Server 2019 reads 9 BILLION rows/second using a 1 trillion row table using a warm cache,
  • OneTrillionRowsCold.ipynb – This notebook shows how SQL Server 2019 performs IO at ~24GB/s using a 1 trillion row table with a cold cache.

Big Data, Machine Learning & Data Virtualization

  • SQL Server Big Data Clusters – Part of our Ground to Cloud workshop. In this lab, you will use notebooks to experiment with SQL Server Big Data Clusters (BDC), and learn how you can use it to implement large-scale data processing and machine learning.
  • Data Virtualization using PolyBase – The notebooks in this SQL Server 2019 workshop cover how to use SQL Server as a hub for data virtualization for sources like OracleSAP HANAAzure CosmosDBSQL Server and Azure SQL Database.
  • Spark with Big Data Clusters – The notebooks in this folder cover the following scenarios:
    • Data Loading – Transforming CSV to Parquet
    • Data Transfer – Spark to SQL using Spark JDBC connector
    • Data Transfer – Spark to SQL using MSSQL Spark connector
    • Configure – Configure a spark session using a notebook
    • Install – Install 3rd party packages
    • Restful-Access – Access Spark in BDC via restful Livy APIs
  • Machine Learning
    • Powerplant Output Prediction – This sample uses the automated machine learning capabilities of the third party H2O package running in Spark in a SQL Server 2019 Big Data Cluster to build a machine learning model that predicts powerplant output.
    • TensorFlow on GPUs in SQL Server 2019 big data cluster – The notebooks in this directory illustrate fitting TensorFlow image classification models using GPU acceleration.