Databases


Artificial Intelligence tunes Azure SQL Databases

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Over the last few years, SnelStart has worked closely with the SQL Server product team to leverage the Azure SQL Database platform to improve performance and reduce DevOps costs. Automatic tuning focuses on each database individually, monitors its workload pattern, and applies tuning recommendations to each individual database based on its unique workload. Since enabling automatic tuning, the SQL Database service has executed 3345 tuning actions on 1410 unique databases and improving 1730 unique queries across these databases. Microsoft is enabling automatic tuning on all internal workloads, including Microsoft IT, to reduce the DevOps cost and improve the performance across applications that are relying on Azure SQL Database.


MID-ATLANTIC PERMANENTE Medical Group: Data Consultant

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Consistently supports compliance and the Principles of Responsibility (Kaiser Permanente's Code of Conduct) by maintaining the privacy and confidentiality of information, protecting the assets of the organization, acting with ethics and integrity, reporting non-compliance, and adhering to applicable federal, state and local laws and regulations, accreditation and licenser requirements (if applicable), and Kaiser Permanente's policies and procedures. QUALIFICATIONS Bachelor of Arts degree in economics, finance, health care administration, public health administration, statistics, mathematics, operations research, physical or biological sciences, or related field required, or equivalent work experience in lieu of a degree. Consistently supports compliance and the Principles of Responsibility (Kaiser Permanente's Code of Conduct) by maintaining the privacy and confidentiality of information, protecting the assets of the organization, acting with ethics and integrity, reporting non-compliance, and adhering to applicable federal, state and local laws and regulations, accreditation and licenser requirements (if applicable), and Kaiser Permanente's policies and procedures. Consistently supports compliance and the Principles of Responsibility (Kaiser Permanente's Code of Conduct) by maintaining the privacy and confidentiality of information, protecting the assets of the organization, acting with ethics and integrity, reporting non-compliance, and adhering to applicable federal, state and local laws and regulations, accreditation and licenser requirements (if applicable), and Kaiser Permanente's policies and procedures.


A Simple Introduction To Data Structures: Part One – Linked Lists

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When many developers first realize how important data structures are (after trying to write a system that processes millions of records in seconds) they are often presented with books or articles that were written for people with computer science degrees from Stanford. The second field (the Pointer field) is storing the location in memory to the next node (memory location 2000). Hopefully, this was a quick and simple introduction to why data structures are important to learn and shed some light on when and why Linked List are an important starting point for data structures. If you can think of any better ways of explaining Linked Lists or why data structures are important to understand, leave them in the comments!


Data Virtualization: Unlocking Data for AI and Machine Learning

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Hybrid Execution allows you to "push" queries to a remote system, such as to SQL Server, and access the referential data. However, one can imagine a use case where lots of ETL processing happens in HDInsight clusters and the structured results are published to SQL Server for downstream consumption (for instance, by reporting tools). Note the linear increase in execution time with SQL Server only (blue line) versus when HDInsight is used with SQL Server to scale out the query execution (orange and grey lines). With much larger real-world datasets in SQL Server, which typically runs multiple queries competing for resources, more dramatic performance gains can be expected.


Optimization tips and tricks on Azure SQL Server for Machine Learning Services

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By using memory-optimized tables, resume features are stored in main memory and disk IO could be significantly reduced. If the database engine server detects more than 8 physical cores per NUMA node or socket, it will automatically create soft-NUMA nodes that ideally contain 8 cores. We then further created 4 SQL resource pools and 4 external resource pools [7] to specify the CPU affinity of using the same set of CPUs in each node. We can create resource governance for R services on SQL Server [8] by routing those scoring batches into different workload groups (Figure.


Predicting Hospital Length of Stay using SQL Server R Services

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Last week, my Microsoft colleagues Bharath Sankaranarayan and Carl Saroufim presented a live webinar showing how you can predict a patient's length of stay at a hospital using SQL Server R Services. The webinar is based on the Machine Learning Solution Template Predicting Length of Stay in Hospitals, which we covered here on the blog back in March. The webinar will take you through the process of using Microsoft R Server (included in the VM) to import the data and upload it to SQL Server. To help the administration manage hospital resources, the Hospital Length of Stay solution estimates the number of days the patient is expected to stay before discharge.


Optimization tips and tricks on Azure SQL Server for Machine Learning Services

#artificialintelligence

By using memory-optimized tables, resume features are stored in main memory and disk IO could be significantly reduced. If the database engine server detects more than 8 physical cores per NUMA node or socket, it will automatically create soft-NUMA nodes that ideally contain 8 cores. We then further created 4 SQL resource pools and 4 external resource pools [7] to specify the CPU affinity of using the same set of CPUs in each node. We can create resource governance for R services on SQL Server [8] by routing those scoring batches into different workload groups (Figure.


R and Python drive SQL Server 2017 into machine learning 7wData

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But it was SQL Server's new machine learning tools that grabbed my attention. SQL Server 2016 added support for embedded R code, and SQL Server 2017 continues that evolution by improving its support for R and adding Python. R remains clearly focused on statistical analysis, while Python adds statistical tools to a popular and flexible scripting language. With Python inside SQL Server, you can bring existing data and code together.


Python power comes to SQL Server 2017

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The most conventional application of Python with SQL Server is to execute Python scripts as normal, with SQL Server as a data source. Microsoft has also made it possible to embed Python code directly in SQL Server databases by including the code as a T-SQL stored procedure. These behaviors, and the RevoScalePy package, are essentially Python versions of features Microsoft built for SQL Server back when it integrated the R language into the database. Installation also includes packages from the Anaconda distribution of Python, widely used in data science, and Microsoft's RevoScalePy package, a set of data analysis functions that can take advantage of SQL Server's in-memory and column-store index features.


SQL Server 2017 (CTP 2.0)- 'first RDBMS with built-in AI'

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Community Technology Preview (CTP) 2.0 is the first production-quality preview of SQL Server 2017, and it is available on both Windows and Linux. In this preview, Microsoft added a number of new capabilities, including the ability to run advanced analytics using Python in a parallelized and highly scalable way, the ability to store and analyze graph data, and other capabilities that help you manage SQL Server for high performance and uptime, including the Adaptive Query Processing family of intelligent database features and resumable online indexing.