SQL Saturday #438
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This document provides an agenda for a presentation on new features in SQL Server 2016. The presentation will demonstrate Columnstore Indexes, In-Memory OLTP, Live Query Statistics, Query Store, Temporal Tables, JSON query results, Always Encrypted, Stretch Database, PolyBase, Row Level Security, Dynamic Data Masking, and High Availability features. It will include demos of each. The presentation aims to help attendees unleash the power of the SQL Server 2016 database engine.
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Using existing language skillsets to create large-scale, cloud-based analytics
Using existing language skillsets to create large-scale, cloud-based analyticsMicrosoft Tech Community
This document discusses how to use Python for analytics with Azure Data Lake. Currently, Python can be used via an extension library to run Python code in a reducer context. Going forward, Python will be able to run natively on vertices, allowing Python code to be used to build extractors, processors, outputters, reducers, appliers, and combiners. This will enable fully leveraging Python for analytics tasks like transforming data, creating new columns, and deleting columns.Experience sql server on l inux and docker
Microsoft SQL Server provides a full-featured database for Linux that offers high performance, security and flexibility across languages and platforms at a lower cost compared to other commercial databases. It has the most consistent data platform with industry-leading performance on Linux and Windows and supports machine learning and artificial intelligence capabilities. SQL Server on Linux allows customers to deploy the database on their choice of Linux distribution for both traditional and container-based workloads.
What's new in SQL Server 2016
The document summarizes new features in SQL Server 2016 SP1, organized into three categories: performance enhancements, security improvements, and hybrid data capabilities. It highlights key features such as in-memory technologies for faster queries, always encrypted for data security, and PolyBase for querying relational and non-relational data. New editions like Express and Standard provide more built-in capabilities. The document also reviews SQL Server 2016 SP1 features by edition, showing advanced features are now more accessible across more editions.
Sql 2016
This document summarizes new features in SQL Server 2016 CTP including improved performance, in-memory OLTP for more applications, native JSON support, always encrypted for data security, PolyBase for querying relational and non-relational data, built-in advanced analytics, stretch databases to Microsoft Azure, and consistency between on-premises and cloud deployments. Key enhancements are around in-memory processing, security, availability, analytics, and hybrid cloud solutions.
An introduction to column store indexes and batch mode
This document discusses column store databases and how they work. It explains that column store databases store data by column rather than row to better utilize modern CPU architectures. It describes how column stores use compression techniques like run-length encoding and dictionaries. It also demonstrates how batch processing and sorting data can improve performance of queries against column stores by keeping more data in CPU caches.
Hypertable
Hypertable is an open source Bigtable clone that manages massive sparse tables with timestamped cell versions using a single primary key index. It is used by companies like Zvents and Baidu to process large amounts of data at scales of billions of cells per day and petabytes of data. Hypertable scales horizontally on commodity hardware and provides high performance through techniques like block caching, bloom filters, and access group optimizations. It is written in C++ for efficiency and provides client APIs in multiple languages.
Hypertable Nosql
Hypertable is an open source Bigtable clone that manages massive sparse tables with timestamped cell versions using a single primary key index. It is used by companies like Zvents and Baidu to process large amounts of data at scales of billions of cells per day and petabytes of data. Hypertable scales horizontally on commodity hardware and provides high performance through techniques like block caching, bloom filters, and access group optimizations. It is written in C++ for efficiency and provides client APIs in multiple languages.
android sqlite
The document discusses using SQLite as the database for Android applications, including its history, advantages for mobile use, features, architecture, and examples of creating SQLite databases and performing basic CRUD operations in Android code. It provides an overview of SQLite's lightweight and portable design that makes it suitable for embedded systems like mobile devices.
Brk2051 sql server on linux and docker
This session shows an overview of the features and architecture of SQL Server on Linux and Containers. It covers install, config, performance, security, HADR, Docker containers, and tools. Find the demos on http://aka.ms/bobwardms
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)
Data Lakes have become a new tool in building modern data warehouse architectures. In this presentation we will introduce Microsoft's Azure Data Lake offering and its new big data processing language called U-SQL that makes Big Data Processing easy by combining the declarativity of SQL with the extensibility of C#. We will give you an initial introduction to U-SQL by explaining why we introduced U-SQL and showing with an example of how to analyze some tweet data with U-SQL and its extensibility capabilities and take you on an introductory tour of U-SQL that is geared towards existing SQL users.
slides for SQL Saturday 635, Vancouver BC, Aug 2017
U-SQL - Azure Data Lake Analytics for Developers
This document introduces U-SQL, a language for big data analytics on Azure Data Lake Analytics. U-SQL unifies SQL with imperative coding, allowing users to process both structured and unstructured data at scale. It provides benefits of both declarative SQL and custom code through an expression-based programming model. U-SQL queries can span multiple data sources and users can extend its capabilities through C# user-defined functions, aggregates, and custom extractors/outputters. The document demonstrates core U-SQL concepts like queries, joins, window functions, and the metadata model, highlighting how U-SQL brings together SQL and custom code for scalable big data analytics.
[JSS2015] In memory and operational analytics
Les index columnstore sont apparus avec SQL Server 2012 et bon nombre de limitations ou d'améliorations ont vu le jour avec SQL Server 2014 et bientôt SQL Server 2016. Il en va de même pour les tables In-Memory à partir de SQL Server 2014. Découvrez lors de cette session comment SQL Server 2016 répond aux besoins d'analyse opérationnelle en temps réel en introduisant et en mixant ces 2 technologies In-Memory
Jss 2015 in memory and operational analytics
This document contains summaries of presentations and information about the #JSS2015 conference on SQL Server 2015 organized by GUSS. It provides information on speakers David Barbarin and Frédéric Pichaut and topics to be covered including columnstore architecture, columnstore improvements in SQL 2016, in-memory OLTP architecture and improvements, and remaining unsupported in-memory features.
Introducing U-SQL (SQLPASS 2016)
U-SQL is a language for big data processing that unifies SQL and C#/custom code. It allows for processing of both structured and unstructured data at scale. Some key benefits of U-SQL include its ability to natively support both declarative queries and imperative extensions, scale to large data volumes efficiently, and query data in place across different data sources. U-SQL scripts can be used for tasks like complex analytics, machine learning, and ETL workflows on big data.
Machine Learning Data Lineage with MLflow and Delta Lake
This document discusses machine learning data lineage using Delta Lake. It introduces Richard Zang and Denny Lee, then outlines the machine learning lifecycle and challenges of model management. It describes how MLflow Model Registry can track model versions, stages, and metadata. It also discusses how Delta Lake allows data to be processed continuously and incrementally in a data lake. Delta Lake uses a transaction log and file format to provide ACID transactions and allow optimistic concurrency control for conflicts.
Modernizing Mission-Critical Apps with SQL Server
Learn about the features that can help you modernize your mission critical applications, where security and performance can go hand in hand. From the wide range of SQL Server features available, we will take a closer look at In-Memory performance, Automatic Tuning, Advanced Security Features like Always Encrypted, Polybase and integration with Machine Learning through R and Python.
Taming the Data Science Monster with A New ‘Sword’ – U-SQL
The document introduces Azure Data Lake and the U-SQL language. U-SQL unifies SQL for querying structured and unstructured data, C# for custom code extensibility, and distributed querying across cloud data sources. Some key features discussed include its declarative query model, built-in and user-defined functions and operators, assembly management, and table definitions. Examples demonstrate complex analytics over JSON and CSV files using U-SQL.
Migrate or modernize your database applications using Azure SQL Database Mana...
Data Platform Summit 2019 is a community initiative by eDominer Systems. The agenda included presentations on Azure SQL Database Managed Instance, migration to the cloud with Azure SQL Database, and a demo. Azure SQL Database Managed Instance provides fully managed SQL Server instances in Azure with built-in intelligence and security. It offers several options for migrating SQL Server workloads to the cloud.
ETL 2.0 Data Engineering for developers
The document summarizes the Databricks analytics platform, which provides a unified environment powered by Apache Spark. It integrates with Azure services and provides features like interactive collaboration, native security integration, and one-click setup. It also discusses capabilities like schema management, data consistency, elastic scalability, and automatic upgrades.
Sterling for Windows Phone 7
The document describes Sterling DB, a lightweight NoSQL object-oriented database for .NET, Silverlight, and Windows Phone 7 applications. It provides concise summaries of Sterling DB's key features, which include recursively serializing complex object graphs, supporting LINQ queries, and allowing storage of objects using their existing class structures without requiring code modification. The document also provides an example of defining a recipe database and tables in Sterling DB.
Similar to SQL Saturday #438 (20)
Using existing language skillsets to create large-scale, cloud-based analytics
Using existing language skillsets to create large-scale, cloud-based analytics
An introduction to column store indexes and batch mode
An introduction to column store indexes and batch mode
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)
Introduction to Azure Data Lake and U-SQL for SQL users (SQL Saturday 635)
Machine Learning Data Lineage with MLflow and Delta Lake
Machine Learning Data Lineage with MLflow and Delta Lake
Taming the Data Science Monster with A New ‘Sword’ – U-SQL
Taming the Data Science Monster with A New ‘Sword’ – U-SQL
Migrate or modernize your database applications using Azure SQL Database Mana...
Migrate or modernize your database applications using Azure SQL Database Mana...
SQL Saturday #438
- 1. Unleash SQL Server 2016 Database Engine By Hamid J. Fard Microsoft Certified Master: SQL Server 2008 Microsoft Certified Solutions Master: Charter – Data Platform CIW Database Design Specialist
- 2. Agenda Columnstore Indexe In-Memory OLTP Live Query Statistics (Demo) Query Store (Demo) Temporal Tables (Demo) JSON Format Query Result (Demo) Always Encrypted (Demo) Stretch DB (Demo) Polybase (Demo) Row Level Security (Demo) Dynamic Data Masking (Demo) High Availability (Demo)
- 3. Columnstore Index Batch execution for single-threaded queries. Snapshot isolation and read-committed snapshot isolation. Specify columnstore index when creating a table. AlwaysOn readable secondary supports updateable columnstore indexes. Updateable nonclustered columnstore index on heap or btree. Btree index on a clustered columnstore index. Columnstore index on a memory-optimized table. *** Nonclustered columnstore index definition supports using a filtered condition.
- 4. In-Memory OLTP ALTER operations for memory-optimized tables & natively compiled stored procedures. Use MARS connections to access memory-optimized tables and natively compiled stored procedures. Support for natively compiled, scalar user-defined functions. Complete support for collations (No more limited to code page 1252). Memory-Optimized Table size increase to 2TB.
- 6. Query Store
- 8. JSON Format Query Result
- 10. Stretch DB
- 11. Polybase
- 14. High Availability – AlwaysOn
- 16. Thank You Evaluate this session http://www.sqlsaturday.com/438/sessions/sessionevaluation.aspx Address: Fard Solutions Sdn Bhd, 1-1C, Inclubator 1, Technology Park Malaysia, Bukit Jalil, 57000 Kuala Lumpur, Malaysia. Website: www.Fard-Solutions.com Email: Hamid@Fard-Solutions.com