The document provides contact information for Eric Nelson, a developer evangelist at Microsoft. It includes links to his blogs on MSDN which discuss .NET, Visual Basic, and UK developer events. It also lists his career history including his first computer experiences in the 1980s and his job at Microsoft since 1996.
This document discusses using MapReduce to find the top K records in a distributed dataset based on a specific criteria. It begins by explaining MapReduce and its limitations. It then describes finding the top K records on a single machine by sorting the data and selecting the top K. For MapReduce, each mapper finds the top K records within its split and sends to the reducer. The reducer finds the global top K by sorting all records and selecting the top K overall. An example algorithm and sample data are provided to demonstrate how to implement a MapReduce job to solve this problem.
• Distributed datasets loaded into named columns (similar to relational DBs or
Python DataFrames).
• Can be constructed from existing RDDs or external data sources.
• Can scale from small datasets to TBs/PBs on multi-node Spark clusters.
• APIs available in Python, Java, Scala and R.
• Bytecode generation and optimization using Catalyst Optimizer.
• Simpler DSL to perform complex and data heavy operations.
• Faster runtime performance than vanilla RDDs.
SparkSQL is a Spark component that allows SQL queries to be executed on Spark. It uses Catalyst, which provides an execution planning framework for relational operations like SQL parsing, logical optimization, and physical planning. Catalyst defines logical and physical operators, expressions, data types and provides rule-based optimizations of the logical query plan. The SQL core in SparkSQL converts logical plans to physical plans and enables reading/writing to data sources like Parquet files and in-memory columnar tables.
If you’re familiar with relational databases, designing your app to use a fully-managed NoSQL database service like Amazon DynamoDB may be new to you. In this webinar, we’ll walk you through common NoSQL design patterns for a variety of applications to help you learn how to design a schema, store, and retrieve data with DynamoDB. We will discuss best practices with DynamoDB to develop IoT, AdTech, and gaming apps.
In this talk, we’ll discuss technical designs of support of HBase as a “native” data source to Spark SQL to achieve both query and load performance and scalability: near-precise execution locality of query and loading, fine-tuned partition pruning, predicate pushdown, plan execution through coprocessor, and optimized and fully parallelized bulk loader. Point and range queries on dimensional attributes will benefit particularly well from the techniques. Preliminary test results vs. established SQL-on-HBase technologies will be provided. The speaker will also share the future plan and real-world use cases, particularly in the telecom industry.
Trivadis TechEvent 2016 Big Data Cassandra, wieso brauche ich das? by Jan OttTrivadis
First Steps of an Oracle-expert in the Big Data World. Everyone speaks about Big Data. But what does it mean? This speech focuses on one animal of the Big Data Zoo - Cassandra and answers the following questions:
- Why another database?
- There is Impala and Spark. Why would I need Cassandra?
- New database - do I need to learn a new language?
- How do I get the data in?
- Can I use SQL?
- Is it part of a distribution, for example Cloudera?
Demos will explain the theory.
Planning with Polyalgebra: Bringing Together Relational, Complex and Machine ...Julian Hyde
A talk from given by Julian Hyde and Tomer Shiran at Hadoop Summit, Dublin.
Data scientists and analysts want the best API, DSL or query language possible, not to be limited by what the processing engine can support. Polyalgebra is an extension to relational algebra that separates the user language from the engine, so you can choose the best language and engine for the job. It also allows the system to optimize queries and cache results. We demonstrate how Ibis uses Polyalgebra to execute the same Python-based machine learning queries on Impala, Drill and Spark. And we show how to build Polyalgebra expressions in Calcite and how to define optimization rules and storage handlers.
Explore Amazon DynamoDB capabilities and benefits in detail and learn how to get the most out of your DynamoDB database. We go over best practices for schema design with DynamoDB across multiple use cases, including gaming, IoT, and others. We explore designing efficient indexes, scanning, and querying, and go into detail on a number of recently released features, including DynamoDB Accelerator (DAX), DynamoDB Time-to-Live, and more. We also provide lessons learned from operating DynamoDB at scale, including provisioning DynamoDB for IoT.
R Markdown allows users to:
1. Combine narrative text and code to produce dynamic reports or presentations.
2. Choose output formats like HTML, PDF, Word, and slideshows to share results.
3. Reproduce analyses through embedded R code chunks that can be re-executed.
If you’re already a SQL user then working with Hadoop may be a little easier than you think, thanks to Apache Hive. It provides a mechanism to project structure onto the data in Hadoop and to query that data using a SQL-like language called HiveQL (HQL).
This cheat sheet covers:
-- Query
-- Metadata
-- SQL Compatibility
-- Command Line
-- Hive Shell
The document discusses the different states that a package's contents can be stored in, including as a source, bundle, binary, or installed in an R library or online repository. It also lists several functions that can be used to move a package between these states, such as install.packages(), devtools::install(), and library(). The bottom portion provides a cheat sheet on common parts of an R package like the DESCRIPTION file, namespaces, documentation, data, testing, and more.
Trivadis TechEvent 2016 Polybase challenges Hive relational access to non-rel...Trivadis
In this presentation, Olaf Nimz talks about a proposed marriage between SQL Server and Hadoop, about Building Bridges to HDFS, Distributed query processing and about Sensible Hybrid Scenarios.
A Step by Step Introduction to the MySQL Document StoreDave Stokes
Looking for a fast, flexible NoSQL document store? And one that runs with the power and reliability of MySQL. This is an intro on how to use the MySQL Document Store
ADO.NET is a data access technology that allows applications to connect to and manipulate data from various data sources. It provides a common object model for data access that can be used across different database systems through data providers. The core objects in ADO.NET include the Connection, Command, DataReader, DataAdapter and DataSet. Data can be accessed in ADO.NET using either a connected or disconnected model. The disconnected model uses a DataSet to cache data locally, while the connected model directly executes commands against an open connection.
Apache Calcite: A Foundational Framework for Optimized Query Processing Over ...Julian Hyde
Apache Calcite is an open source framework for building data management systems that allows for optimized query processing over heterogeneous data sources. It uses a flexible relational algebra and extensible adapter-based architecture that allows it to incorporate diverse data sources. Calcite's rule-based optimizer transforms logical query plans into efficient physical execution plans tailored for different data sources. It has been adopted by many projects and companies and is also used in research.
Pivoting Data with SparkSQL by Andrew RaySpark Summit
This document discusses pivoting data with SparkSQL. It begins with an outline of topics to be covered, including what a pivot is, syntax, examples, tips, implementation details, and future work. It then provides examples of using pivots on retail sales and movie rating data to generate reports and features for modeling. It also offers tips on specifying pivot values, handling multiple aggregations, and pivoting multiple columns. The implementation details are discussed along with potential areas of future work, including adding pivot support to additional APIs and languages.
The document discusses Hive, an open source data warehousing system built on Hadoop that allows users to query large datasets using SQL. It describes Hive's data model, architecture, query language features like joins and aggregations, optimizations, and provides examples of how queries are executed using MapReduce. The document also covers Hive's metastore, external tables, data types, and extensibility features.
This document summarizes new features in SQL Server 2008 for .NET developers, including spatial data support, BLOB storage using Filestream, enhancements to T-SQL, new date/time types, improved integration with Visual Studio, and business intelligence tools like Analysis Services, Integration Services, and Reporting Services.
This document summarizes new features in SQL Server 2008 for .NET developers, including spatial data support, BLOB storage using Filestream, enhancements to T-SQL, new date/time types, improved integration with Visual Studio, and business intelligence tools like Analysis Services, Integration Services, and Reporting Services.
This document summarizes new features in SQL Server 2008 for .NET developers, including spatial data support, BLOB storage using Filestream, enhancements to T-SQL, new date/time types, improved integration with Visual Studio, and business intelligence tools like SSAS, SSIS, and SSRS. It provides overviews of key concepts like spatial data types, using Filestream for BLOB storage, table-valued parameters, new date/time functionality, MERGE statements, shorthand notation in T-SQL, Entity Framework, SQL CLR, and Reporting Services.
This document summarizes new features in SQL Server 2008 for developers. It covers new data types like spatial, XML, and CLR types as well as features like table valued parameters, change tracking, and ADO.NET Entity Framework support. It also discusses enhancements to Integration Services, reporting services, and the core SQL Server engine.
The document provides an overview of new features in SQL Server 2005 including enhanced XML support, CLR integration, and Service Broker. XML features allow storing and querying XML data natively using XML data types and indexes. CLR integration allows writing database objects in .NET languages. Service Broker introduces asynchronous messaging capabilities.
Spark SQL Deep Dive @ Melbourne Spark MeetupDatabricks
This document summarizes a presentation on Spark SQL and its capabilities. Spark SQL allows users to run SQL queries on Spark, including HiveQL queries with UDFs, UDAFs, and SerDes. It provides a unified interface for reading and writing data in various formats. Spark SQL also allows users to express common operations like selecting columns, joining data, and aggregation concisely through its DataFrame API. This reduces the amount of code users need to write compared to lower-level APIs like RDDs.
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.
Die Neuheiten in MariaDB 10.2 und MaxScale 2.1MariaDB plc
MariaDB Server 10.2 and MariaDB MaxScale 2.1 introduce several new features for analytics, JSON processing, replication, database compatibility, storage engines, security, administration, and performance. Key additions include window functions, common table expressions, JSON and GeoJSON functions, delayed replication, CHECK constraints, security enhancements, and optimizations to improve scalability, encryption, and query handling.
The document discusses various Microsoft technologies for working with data including:
- Entity Framework which provides an object-relational mapper (ORM) for ADO.NET and allows mapping entities and database tables.
- ADO.NET Data Services which exposes data and methods through RESTful web services using OData protocols and supports various data sources.
- Differences between LINQ to SQL and LINQ to Entities where the latter supports more capabilities but both allow querying data with LINQ.
This document summarizes new features in SQL Server 2008 related to spatial data, T-SQL enhancements, Visual Studio integration, SQL CLR, and Reporting Services. Key points include support for spatial data types like geometry and geography, T-SQL improvements like table value parameters and the MERGE statement, enhanced development tools in Visual Studio for database and reporting projects, the ability to write managed code functions and procedures with SQL CLR, and an updated Reporting Services for web-based reporting.
Simon Elliston Ball – When to NoSQL and When to Know SQL - NoSQL matters Barc...NoSQLmatters
Simon Elliston Ball – When to NoSQL and When to Know SQL
With NoSQL, NewSQL and plain old SQL, there are so many tools around it’s not always clear which is the right one for the job.This is a look at a series of NoSQL technologies, comparing them against traditional SQL technology. I’ll compare real use cases and show how they are solved with both NoSQL options, and traditional SQL servers, and then see who wins. We’ll look at some code and architecture examples that fit a variety of NoSQL techniques, and some where SQL is a better answer. We’ll see some big data problems, little data problems, and a bunch of new and old database technologies to find whatever it takes to solve the problem.By the end you’ll hopefully know more NoSQL, and maybe even have a few new tricks with SQL, and what’s more how to choose the right tool for the job.
SQL is a language used to manage and query relational databases. It allows users to create, modify, retrieve, and delete data from the database. The main components of SQL include DDL for defining database schema, DML for manipulating data, and DQL for querying data. SQL tables store data in rows and columns and can be queried using commands like SELECT, WHERE, GROUP BY and JOIN.
Praveen Srivatsa discusses how SQL Server supports non-relational data like documents, images, and videos through features like XML, CLR, FileStream, and spatial data types. SQL Server can store relational and non-relational data together to enable integrated business scenarios. New data types like HierarchyID and improvements to XML and spatial data types in SQL Server 2008 help developers work with hierarchical and location-based data. SQL Server provides reliability, security, and programming interfaces for working with non-relational data alongside relational data and queries.
SQL stands for Structured Query Language
SQL lets you access and manipulate databases
SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987
Building Applications for SQL Server 2008Dave Bost
The document discusses new features in SQL Server 2008 including spatial data support, geography and geometry data types, table value parameters, date and time enhancements, and the MERGE statement. It also covers SQL CLR, reporting services, entity framework, and Visual Studio integration.
The document discusses various disaster recovery strategies for SQL Server including failover clustering, database mirroring, and peer-to-peer transactional replication. It provides advantages and disadvantages of each approach. It also outlines the steps to configure replication for Always On Availability Groups which involves setting up publications and subscriptions, configuring the availability group, and redirecting the original publisher to the listener name.
esProc is a software for data computing, query and integration within or between sql based database, data warehouse,hadoop, NoSql DB, local file, network file, excel or access. It is widely used in data migration, ETL tasks, complex event programming, big data, database parallel computing, hadoop and report development.
The document provides an overview of the SQL programming language. It describes SQL as a language used to manage and retrieve data from relational databases. It then covers SQL fundamentals including basic SQL commands, data types, operators, and expressions. Examples are provided throughout to illustrate concepts.
This document provides an overview of the SQL programming language. It defines SQL as a language used to manage and retrieve data from relational databases. It describes the basic SQL commands like SELECT, INSERT, UPDATE, DELETE, and explains the different data types that can be used in SQL like numeric, character, and date/time. It also gives examples of basic SQL statements and clauses.
The document discusses extending SQL Server to the cloud using SQL Azure. It describes how applications can use standard SQL client libraries to connect to SQL Azure through a load balancer and gateway that enforces authentication and authorization. It also covers data sync between on-premises and cloud databases, sharding data across on-premises and cloud, and compares features of SQL Azure and Azure Tables.
The document discusses extending SQL Server to the cloud using SQL Azure. It describes how applications can use standard SQL client libraries to connect to SQL Azure through a load balancer and gateway that enforces authentication and authorization. It also covers data sync between on-premises and cloud databases, sharding data across on-premises and cloud, and compares features of SQL Azure and Azure Tables.
Windows Azure Platform in 30mins by ericnelEric Nelson
This document provides an overview of the Windows Azure platform. It discusses how developers can build applications that run code inside hosted services made up of roles. Developers can store data using Windows Azure storage options like SQL Azure, blobs, queues and tables. The document also provides a demo and recommends next steps for learning more about the Windows Azure platform.
The document outlines an agenda for a Microsoft technology event, including presentations on the Windows Azure platform, Windows Phone 7, and SQL Server 2008 R2. It discusses Microsoft's investments in new development tools, programming languages, servers, and platforms. It also covers trends like new devices, deployment options, and customer expectations. Key topics are the user interface with HTML5, Silverlight and WPF, data storage and access, and "the cloud" with the Windows Azure platform. The document encourages attendees to consider these technologies and evaluate which may fit their needs.
Windows Azure Platform in 30mins by ericnelEric Nelson
The document provides an overview of the Windows Azure platform, including how applications are developed locally and deployed to run as roles in the cloud, how data can be stored in Windows Azure storage or SQL Azure, and the different data storage options. It also summarizes key aspects of Azure Table and SQL Azure tables and provides an agenda for the talk, which includes an overview of the platform and compute and data storage options.
10 things ever architect should know about the Windows Azure Platform - ericnelEric Nelson
This document discusses 10 key things that every architect needs to know when working with the Windows Azure platform. It notes that code runs on hosted services made up of roles that can have multiple instances running the same code and configuration. Data is typically stored either in Windows Azure storage or SQL Azure. The dynamic environment means roles have limited control and instances can stop without warning, so architects must account for this volatility and handle state preservation. Overall, the document provides an overview of fundamental concepts an architect needs to understand when designing applications for the Windows Azure platform.
Lap around the Windows Azure Platform - ericnelEric Nelson
This document provides an overview of the Windows Azure platform, including compute, data, and SQL Azure. It begins with assumptions that most attendees are new to the platform. The agenda includes an overview of the platform and its components like compute, data storage, and SQL Azure. It concludes with a summary and next steps information.
Windows Azure Platform: Articles from the Trenches, Volume OneEric Nelson
Developers have been exploring the possibilities opened up by the Windows Azure Platform for Cloud Computing. This book pulls together great articles from many of those developers who have been active with the Windows Azure Platform to hopefully help others become successful. There are twenty articles in this first volume covering everything from getting started to implementing best practices for elastic applications.
The document discusses SQL Azure and Windows Azure Storage. SQL Azure provides a scalable, highly available relational database in the cloud using T-SQL and SQL Server. Windows Azure Storage offers a highly scalable file storage system. SQL Azure is limited to 10GB per database but aims to provide a full relational experience, while Storage has a maximum of 100TB but uses REST APIs instead of SQL. The document demonstrates SQL Azure functionality and provides pricing and configuration details.
Building An Application For Windows Azure And Sql AzureEric Nelson
This document provides an overview of building applications for Windows Azure and SQL Azure:
1) It discusses the Windows Azure platform and its components including Windows Azure, SQL Azure database, and AppFabric.
2) It demonstrates how to develop applications using roles, storage, and SQL Azure database and deploy them to Windows Azure.
3) It provides pricing information for Windows Azure and SQL Azure services.
Entity Framework 4 In Microsoft Visual Studio 2010Eric Nelson
The document summarizes the key features and improvements of ADO.NET Entity Framework 4.0. It addresses many of the pain points of earlier versions by improving tools, adding support for model-first development, POCO classes, lazy loading, and better handling of foreign keys and stored procedures. It also discusses new capabilities for code-first development, self-tracking entities, and improved LINQ support to make the Entity Framework more powerful and flexible.
Windows Azure In 30mins for none technical audienceEric Nelson
- The document discusses Windows Azure, a platform as a service by Microsoft that allows developers to build and host applications in the Microsoft cloud.
- It highlights that Windows Azure provides a familiar development experience using technologies like Visual Studio, SQL Azure and ASP.NET, while also supporting other languages.
- Pricing options are outlined on a pay-as-you-go model based on compute and storage usage, with volume discounts available.
Dev305 Entity Framework 4 Emergency SlidesEric Nelson
This document appears to be slides from a presentation on Entity Framework 4.0 given at TechEd Europe 2009. The slides cover topics including model first development, templated code, complex types, runtime features like deferred loading and functions, POCO development using both roll your own and templated approaches, self tracking entities, and code only scenarios. The slides thank the audience for their patience.
Design Considerations For Storing With Windows AzureEric Nelson
This document provides an overview and lessons learned from using different data storage options in Windows Azure, including Blobs, Tables, SQL Azure, and Queues. It discusses how each one works, best practices for using them, and how they compare to each other. Key takeaways include that Tables are not a relational database, picking the right partition key is important for performance, and SQL Azure has some limitations compared to on-premises SQL Server. The presenter provides a demonstration of the storage features in Windows Azure and encourages understanding how they are different from traditional on-premises options.
What Impact Will Entity Framework Have On ArchitectureEric Nelson
This document discusses the impact that adopting the Entity Framework and Entity Data Model will have on application architecture. It provides an overview of object-relational mapping (ORM) technologies and how they help address the impedance mismatch between object-oriented programming and relational databases. The document outlines several key features and improvements in Entity Framework versions 1.0, 2.0, 3.0 and 4.0, such as better code generation tools, a model-first approach, support for stored procedures and persistence ignorance. It argues that adopting an ORM like Entity Framework can improve developer productivity, code quality and database independence.
The document provides an overview of cloud computing concepts like running and storing applications and data in the cloud. It discusses key cloud services from Amazon and Microsoft including Amazon S3, EC2, Windows Azure, and SQL Data Services. It also summarizes how to develop, deploy, and scale applications on the Windows Azure platform, including using queues to decouple processing and web roles to handle requests. Storage options like blobs, tables, and queues are introduced along with their scalability, availability and programming interfaces.
This document provides information about Microsoft's SQL Data Services (SDS), a relational database service running in the cloud. The summary discusses the key points:
- SDS will provide a highly scalable and available relational data store in the cloud, accessible using familiar SQL Server tools and APIs.
- Initially, SDS will support core SQL Server capabilities but future versions may include additional data platform capabilities.
- SDS uses a symmetrical programming model designed to provide a consistent experience whether using the database on-premises or in the cloud.
- Microsoft is currently working towards commercial availability of SDS integrated with the Windows Azure platform in 2009.
The document discusses the history and future of object-relational mapping (ORM) technologies for .NET applications. It provides an overview of Microsoft's ORM strategies over the years, including LINQ to SQL and the ADO.NET Entity Framework. The Entity Framework is now Microsoft's strategic ORM and supports many databases. The document outlines upcoming improvements to the Entity Framework in areas like modeling, queries, and consumption.
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
It's your unstructured data: How to get your GenAI app to production (and spe...Zilliz
So you've successfully built a GenAI app POC for your company -- now comes the hard part: bringing it to production. Aparavi addresses the challenges of AI projects while addressing data privacy and PII. Our Service for RAG helps AI developers and data scientists to scale their app to 1000s to millions of users using corporate unstructured data. Aparavi’s AI Data Loader cleans, prepares and then loads only the relevant unstructured data for each AI project/app, enabling you to operationalize the creation of GenAI apps easily and accurately while giving you the time to focus on what you really want to do - building a great AI application with useful and relevant context. All within your environment and never having to share private corporate data with anyone - not even Aparavi.
TrustArc Webinar - Innovating with TRUSTe Responsible AI CertificationTrustArc
In a landmark year marked by significant AI advancements, it’s vital to prioritize transparency, accountability, and respect for privacy rights with your AI innovation.
Learn how to navigate the shifting AI landscape with our innovative solution TRUSTe Responsible AI Certification, the first AI certification designed for data protection and privacy. Crafted by a team with 10,000+ privacy certifications issued, this framework integrated industry standards and laws for responsible AI governance.
This webinar will review:
- How compliance can play a role in the development and deployment of AI systems
- How to model trust and transparency across products and services
- How to save time and work smarter in understanding regulatory obligations, including AI
- How to operationalize and deploy AI governance best practices in your organization
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
Choosing the Best Outlook OST to PST Converter: Key Features and Considerationswebbyacad software
When looking for a good software utility to convert Outlook OST files to PST format, it is important to find one that is easy to use and has useful features. WebbyAcad OST to PST Converter Tool is a great choice because it is simple to use for anyone, whether you are tech-savvy or not. It can smoothly change your files to PST while keeping all your data safe and secure. Plus, it can handle large amounts of data and convert multiple files at once, which can save you a lot of time. It even comes with 24*7 technical support assistance and a free trial, so you can try it out before making a decision. Whether you need to recover, move, or back up your data, Webbyacad OST to PST Converter is a reliable option that gives you all the support you need to manage your Outlook data effectively.
Cracking AI Black Box - Strategies for Customer-centric Enterprise ExcellenceQuentin Reul
The democratization of Generative AI is ushering in a new era of innovation for enterprises. Discover how you can harness this powerful technology to deliver unparalleled customer value and securing a formidable competitive advantage in today's competitive market. In this session, you will learn how to:
- Identify high-impact customer needs with precision
- Harness the power of large language models to address specific customer needs effectively
- Implement AI responsibly to build trust and foster strong customer relationships
Whether you're at the early stages of your AI journey or looking to optimize existing initiatives, this session will provide you with actionable insights and strategies needed to leverage AI as a powerful catalyst for customer-driven enterprise success.
Demystifying Neural Networks And Building Cybersecurity ApplicationsPriyanka Aash
In today's rapidly evolving technological landscape, Artificial Neural Networks (ANNs) have emerged as a cornerstone of artificial intelligence, revolutionizing various fields including cybersecurity. Inspired by the intricacies of the human brain, ANNs have a rich history and a complex structure that enables them to learn and make decisions. This blog aims to unravel the mysteries of neural networks, explore their mathematical foundations, and demonstrate their practical applications, particularly in building robust malware detection systems using Convolutional Neural Networks (CNNs).
Retrieval Augmented Generation Evaluation with RagasZilliz
Retrieval Augmented Generation (RAG) enhances chatbots by incorporating custom data in the prompt. Using large language models (LLMs) as judge has gained prominence in modern RAG systems. This talk will demo Ragas, an open-source automation tool for RAG evaluations. Christy will talk about and demo evaluating a RAG pipeline using Milvus and RAG metrics like context F1-score and answer correctness.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
Discovery Series - Zero to Hero - Task Mining Session 1DianaGray10
This session is focused on providing you with an introduction to task mining. We will go over different types of task mining and provide you with a real-world demo on each type of task mining in detail.
"Making .NET Application Even Faster", Sergey Teplyakov.pptxFwdays
In this talk we're going to explore performance improvement lifecycle, starting with setting the performance goals, using profilers to figure out the bottle necks, making a fix and validating that the fix works by benchmarking it. The talk will be useful for novice and seasoned .NET developers and architects interested in making their application fast and understanding how things work under the hood.
NVIDIA at Breakthrough Discuss for Space Exploration
SQL Server 2008 Overview
1. Eric Nelson Developer Evangelist [email_address] – I will reply http://blogs.msdn.com/ericnel - tends to be about .NET and data http://blogs.msdn.com/goto100 - all about Visual Basic http://twitter.com/ericnel - pot luck :-) http://blogs.msdn.com/ukdevevents The ONLY LINK you need!
2. First PC – ZX80 in 1980 (then BBC Micro , Atari 520STFM and Macintosh LC ) First computer job... FORTAN in 1986 Wrote most LOC on... Unix using C Favourite IDE of all time ... GNU Emacs Joined Microsoft DRG in 1996 Early adoption work on ASP, SQL 6.5, MTS ... Went “Back to development” in July 2008 – in VB I Geek to Live (not Live to Geek) I am editor of the UK MSDN Flash I am fascinated by ALT.NET values and practices http://blogs.msdn.com/ukdevevents The ONLY LINK you need!
3. I live near Bath with wife, 2 kids, 1 dog (and many pet graves in the garden) I originally intended to be a naval officer I am very shy...seriously! http://blogs.msdn.com/ukdevevents The ONLY LINK you need!
4. Sign up to the MSDN Flash Feedback, vote etc Technical articles for the UK MSDN Flash 400 to 500 words War stories around VB6 to .NET Your right (or left) arm in the air NB: not all the time
5. Give a flavour of the new “stuff” in SQL Server 2008 for development and an insight into the direction we are taking with data
6. TSQL Enhancements SQL-2006 Major and minor Beyond Relational Filestream Full text search Spatial Semi-structured Futures* Object Relational Management SQL Data Services In one hour
9. Date & Time Date types Table value constructor using VALUES clause MERGE Table Types and Table Value Parameters GROUPING SET SQL language IDE improvements Compound assignment operators Declaring and initializing variables Delighters
10. IDE Improvements Debugging is back! Intellisense has made it in Compound Assignment operators: +=, -=, *=, /= Variable initialization during declaration UPDATE Inventory SET quantity += s.quantity FROM Inventory AS i INNER JOIN Sales AS s ON i.id = s.id DECLAER @v int = 5 ; DECLARE @v1 varchar(10) = ‘xxxxx’;
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12. Insert multiple rows based on values in a single INSERT statement SQL 2006 standard compatible INSERT INTO dbo.Customers(custid, companyname, phone, address) VALUES (1, 'cust 1', '(111) 111-1111', 'address 1'), (2, 'cust 2', '(222) 222-2222', 'address 2'), (3, 'cust 3', '(333) 333-3333', 'address 3'), (4, 'cust 4', '(444) 444-4444', 'address 4'), (5, 'cust 5', '(555) 555-5555', 'address 5');
13. Single statement that combines multiple DML operations Operates on a join between source and target SQL-2006 compliant UPDATE TGT SET TGT.quantity = TGT.quantity + SRC.quantity, TGT.LastTradeDate = SRC.TradeDate FROM dbo.StockHolding AS TGT JOIN dbo.StockTrading AS SRC ON TGT.stock = SRC.stock; INSERT INTO dbo.StockHolding (stock, lasttradedate, quantity) SELECT stock, tradedate, quantity FROM dbo.StockTrading AS SRC WHERE NOT EXISTS (SELECT * FROM dbo.StockHolding AS TGT WHERE TGT.stock = SRC.stock); MERGE INTO dbo.StockHolding AS TGT USING dbo.StockTrading AS SRC ON TGT.stock = SRC.stock WHEN MATCHED AND (t.quantity + s.quantity = 0) THEN DELETE WHEN MATCHED THEN UPDATE SET t.LastTradeDate = s.TradeDate, t.quantity += s.quantity WHEN NOT MATCHED THEN INSERT VALUES (s.Stock,s.TradeDate,s.Quantity) Pre-SQL 2008 SQL 2008
15. A new user defined type - Table Can define indexes and constraints Can be used for declaring table variables Input parameters of Table type on SPs/Functions Optimized to scale and perform better for large data Behaves like BCP inside server In ADO.NET SqlDbType.Structured CREATE TYPE myTableType AS TABLE (id INT, name NVARCHAR(100),qty INT); CREATE PROCEDURE myProc (@tvp myTableType READONLY ) AS …
16. Define multiple groupings in the same query Produces a single result set that is equivalent to a UNION ALL of differently grouped rows SQL 2006 standard compatiable SELECT customerType,Null as TerritoryID,MAX(ModifiedDate) FROM Sales.Customer GROUP BY customerType UNION ALL SELECT Null as customerType,TerritoryID,MAX(ModifiedDate) FROM Sales.Customer GROUP BY TerritoryID order by TerritoryID SELECT customerType,TerritoryID,MAX(ModifiedDate) FROM Sales.Customer GROUP BY GROUPING SETS ((customerType), (TerritoryID)) order by customerType Pre-SQL 2008 SQL 2008
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19. CREATE TABLE Employee { FirstName VARCHAR(10), LastName VARCHAR(10), Birthday DATE , … } SELECT Birthday AS BirthDay FROM Employee INSERT INTO T (datetime2_col) VALUES (‘ 1541 -01-01’) INSERT INTO T (time_col) VALUES (’12:30:29 .1176548 ’) CREATE TABLE online-purchase-order { item-id int, item-name VARCHAR(30), qty int, purchase-time datetimeoffset, … } // For value ‘ 2005-09-08 12:20:19.345 -08:00 ’ INSERT INTO online-purchase-order VALUES (…., ‘ 2005-09-08 12:20:19.345 -08:00’ ,..) Large year range (1~9999) Storage saving Easy programming DATE Large or optional precision (0 ~ 100ns) Easy programming TIME Large year range Large or optional precision DATETIME2 Datetime + time zone offset UTC enabled Easy programming DATETIME OFFSET
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22. SQL Server 2005 SQL Server 2008 HierarchyID Large UDTs Sparse Columns Wide Tables Filtered Indices XML Upgrades User Defined Types XML Data Type and Functions Full Text Indexing Filestream Integrated FTS Fully supported Geometry and Geography data types and Functions Relational Semi Structured Documents & Multimedia Spatial
23. Remote BLOB Storage FILESTREAM Storage SQL BLOB Documents & Multimedia Use File Servers DB Application BLOB Dedicated BLOB Store DB Application BLOB Store BLOBs in Database DB Application BLOB Store BLOBs in DB + File System Application BLOB DB
24. Storage Attribute on VARBINARY(MAX) Works with integrated FTS Unstructured data stored directly in the file system (requires NTFS) Dual Programming Model TSQL (Same as SQL BLOB) Win32 Streaming APIs with T-SQL transactional semantics Advantages Integrated Manageability SQL Server Security Stack Dual model Documents & Multimedia Store BLOBs in DB + File System Application BLOB DB
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26. Full-Text Engine and Indexes fully integrated Catalog, index and stopword lists now inside the database Better performance in many common scenarios Make mixed queries perform and scale Optimizer has knowledge about FT index SELECT * FROM candidates WHERE CONTAINS(resume,’”SQL Server”’) AND ZipCode = ‘98052’ Documents & Multimedia
27. Populating an index of 20million rows of 1k data on identical hardware (time in minutes) 2 min 1 min Documents & Multimedia
28. Proliferation of geographical data GPS Systems, Virtual Earth, Live Search Maps etc New opportunities for spatially aware apps Storage and retrieval of spatial data using standard SQL New Spatial Data Types + methods + indexes geometry - Flat Earth (Planar) geography - Round Earth (Geodetic) Offers full set of Open Geospatial Consortium components Spatial
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30. 1 2 3 4 5 1 3 4 5 2 // Create a Filtered Indexes // Sparse column Create Table Products(Id int, Type nvarchar(16)…, Resolution int SPARSE , ZoomLength int SPARSE ); // Filtered Indices Create Index ZoomIdx on Products(ZoomLength) where Type = ‘Camera’; // HierarchyID CREATE TABLE [dbo].[Folder] ( [FolderNode] HIERARCHYID NOT NULL UNIQUE, [Level] AS [FolderNode].GetLevel() PERSISTED, [Description] NVARCHAR(50) NOT NULL ); HierarchyID Store arbitrary hierarchies of data and efficiently query them Large UDTs No more 8K limit on User Defined Types Sparse Columns Optimized storage for sparsely populated columns Wide Tables Support thousands of sparse columns Filtered Indices Define indices over subsets of data in tables Relational Semi Structured
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34. What is it? Technique for working with relational tables as if they were objects in memory Intention is to hide away the complexity of the underlying tables and give a uniform way of working with data Why use it? Productivity Retain database independence Which ORM? There are many ORMs for .NET developers already in existence. E.g. LLBLGen Pro http://www.llblgen.com/ Nhibernate http://www.hibernate.org/343.html EntitySpaces http://www.entityspaces.net/Portal/Default.aspx
35. LINQ to SQL .NET Framework 3.5, Nov 2007 Only SQL Server Simple Easy to learn and master LINQ to Entities ( ADO.NET Entity Framework ) .NET Framework 3.5 SP1, Aug 2008 “ Any” RDBMS Complex Easy to learn, hard to master Strategic
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38. HTTP (AtomPub) Clients (Tools, Libraries, etc) SQL Data Services ADO.NET Data Services Framework SQL Server (On premises data service) (Cloud data service)
39. Data Access Lib SDS Runtime REST / SOAP Data Access Lib SDS Runtime REST / SOAP Data Access Lib SDS Runtime REST / SOAP Data Access Lib SDS Runtime REST / SOAP Data Access Lib SDS Runtime REST / SOAP Data Access Lib SDS Runtime REST / SOAP Data Access Lib SDS Runtime REST / SOAP Mgmt. Services Distributed Data Fabric SQL Server Mgmt. Services Distributed Data Fabric SQL Server Mgmt. Services Distributed Data Fabric SQL Server Mgmt. Services Distributed Data Fabric SQL Server Mgmt. Services Distributed Data Fabric SQL Server Mgmt. Services Distributed Data Fabric SQL Server Mgmt. Services Distributed Data Fabric SQL Server
46. Drop me an email if I confused you about anything! [email_address] http:// blogs.msdn.com/ UKDevEvents Post event resources for all Microsoft UK developer focused sessions The team Eric Nelson http://blogs.msdn.com/ericnel Mike Ormond http://blogs.msdn.com/mikeormond Mike Taulty http://mtaulty.com