Oracle Database 12.1.0.2 introduced several new features including approximate count distinct, full database caching, pluggable database (PDB) improvements like cloning and state management, JSON support, data redaction, SQL query row limits and offsets, invisible columns, SQL text expansion, calling PL/SQL from SQL, session level sequences, and extended data types support.
Oracle Database 12c Release 2 - New Features On Oracle Database Exadata Expre...Alex Zaballa
This document summarizes new features in Oracle Database 12c Release 2 running on Oracle Database Exadata Express Cloud Service. Key features include longer identifier names up to 128 bytes, native support for JSON, improved functions for data conversion errors and LISTAGG, online conversion of non-partitioned tables to partitioned tables, read-only partitions, and approximate query processing. The presentation provides demonstrations of several new features.
This presentation is an INTRODUCTION to intermediate MySQL query optimization for the Audience of PHP World 2017. It covers some of the more intricate features in a cursory overview.
Developers’ mDay 2021: Bogdan Kecman, Oracle – MySQL nekad i sadmCloud
The document summarizes the evolution of MySQL from its first release in 1995 to version 8.0 released in 2018. It highlights key features and functionality added over time, including improved performance, Unicode support, spatial data types, window functions, common table expressions, and high availability solutions. The document also briefly mentions Oracle's HeatWave and ColumnStore technologies for handling OLAP/OLTP workloads on MySQL.
This presentation explains all of the new features that are relevant for developers in Oracle 12c. It's been out for a couple of years, but many companies haven't updated to 12c. So, if you're looking to update soon, or are just interested in what the new features are, look at this presentation.
The full post is available at http://www.completeitprofessional.com/oracle-12c-new-features-for-developers
Oracle Data Redaction allows protecting data shown to users in real time without changing applications. It applies redaction at query execution through policies that define which data to redact for which users. Redaction occurs just before returning results and does not alter stored data. Methods include full, partial, random redaction. It introduces minimal overhead but does not prevent privileged users like DBAs from accessing raw data.
Oracle Data Redaction - UKOUG - TECH14Alex Zaballa
The document summarizes a presentation on Oracle Data Redaction given at the UKOUG Technology Conference & Exhibition 2014. It describes how data redaction works in Oracle Database 12c to protect sensitive data at query time without changing applications or stored data. Examples are provided of different redaction methods and how data redaction can be used with views, groups, and other database features. Performance overhead of data redaction is generally between 2-10% depending on the method used.
View, Store Procedure & Function and Trigger in MySQL - ThaiptFramgia Vietnam
MySQL allows users to create views, stored procedures, functions, and triggers. Views are saved SELECT queries that can be executed to query tables or other views. Stored procedures and functions allow application logic to be stored in a database. Triggers automatically run SQL statements in response to changes made to data in a table, such as after insert, update or delete operations. These features help with security, maintenance, and reducing application code. However, they can also increase server overhead.
PostgreSQL (or Postgres) began its life in 1986 as POSTGRES, a research project of the University of California at Berkeley.
PostgreSQL isn't just relational, it's object-relational.it's object-relational. This gives it some advantages over other open source SQL databases like MySQL, MariaDB and Firebird.
Oracle Database 12c includes many new features across SQL, PL/SQL, database management, partitioning, patching, compression, Data Guard, and pluggable databases. Key features include increased datatype size limits, identity columns, implicit result sets in PL/SQL, adaptive plans, row pattern matching, pluggable databases that can be plugged into and unplugged from container databases, and many enhancements to compression, partitioning, Data Guard, and patching functionality.
Confoo 2021 - MySQL Indexes & HistogramsDave Stokes
Confoo 2021 presentation on MySQL Indexes, Histograms, and other ways to speed up your queries. This slide deck has slides that may not have been included in the presentation that were omitted due to time constraints
Design and develop with performance in mind
Establish a tuning environment
Index wisely
Reduce parsing
Take advantage of Cost Based Optimizer
Avoid accidental table scans
Optimize necessary table scans
Optimize joins
Use array processing
Consider PL/SQL for “tricky” SQL
This document provides an overview of working with databases and MySQL. It discusses database concepts like tables, records, fields, primary keys, and relationships. It also covers MySQL topics such as creating and selecting databases, defining tables, adding/retrieving/updating/deleting records, and modifying user privileges. The goal is to teach the basics of working with databases and the MySQL database management system.
Oracle Data Redaction is a new feature in Oracle Database 12c that enables the protection of data shown to users in real time without requiring changes to applications. It applies redaction at query execution time, so the stored data remains unchanged. Redaction policies are defined that specify what data to redact for which users. The feature is useful but has some limitations, such as not preventing privileged users like DBAs from accessing protected data. It also incurs a small performance overhead for queries against tables with redaction policies.
The document provides an overview of various techniques for optimizing database and application performance. It discusses fundamentals like minimizing logical I/O, balancing workload, and serial processing. It also covers the cost-based optimizer, column constraints and indexes, SQL tuning tips, subqueries vs joins, and non-SQL issues like undo storage and data migrations. Key recommendations include using column constraints, focusing on serial processing per table, and not over-relying on statistics to solve all performance problems.
MySQL Replication Evolution -- Confoo Montreal 2017Dave Stokes
MySQL Replication has evolved since the early days with simple async master/slave replication with better security, high availability, and now InnoDB Cluster
MySQL 8 -- A new beginning : Sunshine PHP/PHP UK (updated)Dave Stokes
MySQL 8 has many new features and this presentation covers the new data dictionary, improved JSON functions, roles, histograms, and much more. Updated after SunshinePHP 2018 after feedback
PHP UK 2020 Tutorial: MySQL Indexes, Histograms And other ways To Speed Up Yo...Dave Stokes
Slow query? Add an index or two! But things are suddenly even slower! Indexes are great tools to speed data lookup but have overhead issues. Histograms don’t have that overhead but may not be suited. And how you lock rows also effects performance. So what do you do to speed up queries smartly?
Oracle Database 12c - New Features for Developers and DBAsAlex Zaballa
This document summarizes a presentation about new features in Oracle Database 12c for developers and DBAs. It introduces JSON support, data redaction, SQL query row limits and offsets, invisible columns, extended data types, session level sequences, and more. Demo sections are included to illustrate several of the new features.
You can also see these on ipad , iphone , ipod , tablet & android only on Voot , Youtube & Viu & one thing also my name is Aditya Singh Jadoun & I am a child . My school name is Jayshree Periwal High School & I am an indian , my father's name is Munesh Singh Jadoun . End of This line . Now see my fantastic , amazing , ultimate & cool presentation .
SQL Server 2012 introduced columnstore indexes which provide significant performance improvements for data warehouse and analytics queries against large datasets. Columnstore indexes store data by column rather than by row, allowing queries to access only the relevant columns needed. This results in lower I/O and higher data compression compared to row storage. Columnstore indexes also use a new batch processing execution mode which can further improve query performance by processing many rows at once in memory rather than row-by-row. Columnstore indexes require the table to be read-only but provide an easy way to boost query performance for analytics workloads by 10-100x without needing separate data marts or cubes.
Column store indexes and batch processing mode (nx power lite)Chris Adkin
This document discusses SQL Server performance tuning with a focus on leveraging CPU caches through column store compression. It explains how column store compression can bridge the performance gap between IO subsystems and modern processors by breaking data through levels of compression to pipeline batches into CPU caches. Examples are provided showing significant performance improvements from column store compression and clustering over row-based storage and no compression.
The document discusses indexes in SQL Server. It describes internal and external fragmentation that can occur in indexes. Internal fragmentation is unused space between records within a page, while external fragmentation is when page extents are not stored contiguously on disk. It provides examples of identifying fragmentation using system views and the dm_db_index_physical_stats dynamic management function. It also covers best practices for index types, such as numeric and date fields making good candidates while character fields are less efficient. Composite indexes, fill factor, and rebuilding vs. reorganizing indexes are also discussed.
This document provides information about an upcoming presentation on Columnstore Indexes in SQL Server 2014. It notes that the presentation will be recorded so that those who could not attend live can view it later. It requests that anyone with issues about being recorded should leave immediately, and remaining will be taken as consent to the recording. It also states the presentation will be free and will begin in 1 minute.
This talk at the Percona Live MySQL Conference and Expo describes open source column stores and compares their capabilities, correctness and performance.
SQL 2016 Mejoras en InMemory OLTP y Column Store IndexEduardo Castro
Vemos las mejoras que presenta SQL Server 2016 en los temas de InMemory OLTP y también los cambios en Column Store Index, y su importancia en la mejora de desempeño.
Saludos,
Ing. Eduardo Castro, PhD
Microsoft SQL Server MVP
SQL Server 2016 introduces new capabilities to help improve performance, security, and analytics:
- Operational analytics allows running analytics queries concurrently with OLTP workloads using the same schema. This provides minimal impact on OLTP and best performance.
- In-Memory OLTP enhancements include greater Transact-SQL coverage, improved scaling, and tooling improvements.
- The new Query Store feature acts as a "flight data recorder" for databases, enabling quick performance issue identification and resolution.
The Top Skills That Can Get You Hired in 2017LinkedIn
We analyzed all the recruiting activity on LinkedIn this year and identified the Top Skills employers seek. Starting Oct 24, learn these skills and much more for free during the Week of Learning.
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Modernizing Your Database with SQL Server 2019 discusses SQL Server 2019 features that can help modernize a database, including:
- The Hybrid Buffer Pool which supports persistent memory to improve performance on read-heavy workloads.
- Memory-Optimized TempDB Metadata which stores TempDB metadata in memory-optimized tables to avoid certain blocking issues.
- Intelligent Query Processing features like Adaptive Query Processing, Batch Mode processing on rowstores, and Scalar UDF Inlining which improve query performance.
- Approximate Count Distinct, a new function that provides an estimated count of distinct values in a column faster than a precise count.
- Lightweight profiling, enabled by default, which provides query plan
2° Ciclo Microsoft CRUI 3° Sessione: l'evoluzione delle piattaforme tecnologi...Jürgen Ambrosi
L’obiettivo è quello di fare una panoramica dello stato dell’arte sulle tecnologie a supporto dei database. Alcuni esempi sono la tecnologia in-memory integrata con le funzionalità di analisi operative in tempo reale e della tecnologia Always Encrypted per la protezione dei dati utilizzati in locale o durante gli spostamenti. La tecnologia in-memory consente di migliorare di 30 volte le performance delle transazioni utilizzando hardware standard di settore. Inoltre i Big Data e l'analisi sono diventati un importante fattore di differenziazione competitivo, ma la gestione delle enormi quantità di dati correlate a un tempo di attività 24 ore su 24 continua a essere una sfida per l'IT. Oggi è più importante che mai soddisfare a livello aziendale l'esigenza di prestazioni, disponibilità e sicurezza efficace per gestire carichi di lavoro mission-critical a un costo contenuto. Le soluzioni Microsoft fissano un nuovo standard nelle performance mission-critical.
OOW16 - Oracle Database 12c - The Best Oracle Database 12c New Features for D...Alex Zaballa
Oracle Database 12c introduces many new features for developers and DBAs. These include native support for JSON, data redaction capabilities, improved SQL query functionality using row limits and offsets, and new PL/SQL features like calling functions from SQL. The presentation provides demonstrations of these new features.
This document provides an overview of new features in SQL Server 2005, including SQLCLR which allows writing functions, procedures and triggers in .NET languages. It discusses how to install and debug SQLCLR assemblies, and create user-defined data types and aggregates that can extend the functionality of SQL Server. Key enhancements to T-SQL are also summarized, such as common table expressions, ranking commands, and exception handling.
This document summarizes the key points from a presentation on SQL Server 2016. It discusses in-memory and columnstore features, including performance gains from processing data in memory instead of on disk. New capabilities for real-time operational analytics are presented that allow analytics queries to run concurrently with OLTP workloads using the same data schema. Maintaining a columnstore index for analytics queries is suggested to improve performance.
This document summarizes new features in SQL Server 2016. It discusses improvements to columnstore indexes, in-memory OLTP, the query store, temporal tables, always encrypted, stretch database, live query statistics, row level security, and dynamic data masking. It provides links to documentation and demos for these features. It also suggests what may be included in future CTP releases and lists resources for learning more about SQL Server 2016.
This document provides an overview of In-Memory OLTP and other SQL Server 2016 features such as Stretch Database, Always Encrypted, Dynamic Data Masking, and Query Store. It discusses how In-Memory OLTP can significantly improve database application performance through its memory-optimized tables and natively compiled stored procedures. It also summarizes capabilities for several high availability and security features introduced in SQL Server 2016.
Oracle Database 12c - The Best Oracle Database 12c Tuning Features for Develo...Alex Zaballa
Oracle Database 12c includes many new tuning features for developers and DBAs. Some key features include:
- Multitenant architecture allows multiple pluggable databases to consolidate workloads on a single database instance for improved utilization and administration.
- In-memory column store enables real-time analytics on frequently accessed data held entirely in memory for faster performance.
- New SQL syntax like FETCH FIRST for row limiting and offsetting provides more readable and intuitive replacements for previous techniques.
- Adaptive query optimization allows queries to utilize different execution plans like switching between nested loops and hash joins based on runtime statistics for improved performance.
Web Cloud Computing SQL Server - Ferrara Universityantimo musone
The document provides a summary of an individual's background and experience. It includes the following information in Italian:
1. The individual graduated from the University of Ferrara in 2014 and is an engineer from the University of Naples. They have worked at Avanade since 2006 as a Technical Architect focusing on Cloud and Mobile.
2. They speak at events as a Microsoft Student Partner and are a co-founder of the Fifth Element Project.
3. Their areas of expertise include applications, storage, servers, networking, operating systems, databases, virtualization, runtimes, middleware, and infrastructure as a service, platform as a service and software as a service.
4. They provide a link to
The document discusses various techniques for optimizing SQL Server performance, including handling index fragmentation, optimizing files and partitioning tables, effective use of SQL Profiler and Performance Monitor, a methodology for performance troubleshooting, and a 10 step process for performance optimization. Some key points covered are determining and resolving index fragmentation, partitioning tables across multiple file groups, capturing traces with SQL Profiler and Performance Monitor counters to diagnose issues, and ensuring proper indexing through query execution plans and the SQL Server tuning advisor.
The document discusses various techniques for managing performance and concurrency in SQL Server databases. It covers new features in SQL Server 2008/R2 such as read committed snapshot isolation, partition-level lock escalation, filtered indexes, and bulk loading. It also discusses tools for monitoring performance like the Utility Control Point and Performance Monitor. The document uses case studies to demonstrate how these techniques can be applied.
Whats New on SAP HANA SPS 11 Core Database CapabilitiesSAP Technology
Dynamic range partitioning allows tables in SAP HANA to automatically add new partitions when the size of an "OTHERS" partition grows too large. This helps keep partitions balanced and queries optimized. Result caching now caches the results of SQL views, calculated views, and CDS views to improve performance of common queries on complex views. Flexible tables in HANA can now automatically detect optimal data types for dynamic columns and promote column types when needed.
Oracle Database 12c - New Features for Developers and DBAsAlex Zaballa
Oracle Database 12c includes over 500 new features designed to support cloud computing, big data, security, and availability. Key features include support for up to 4096 pluggable databases, hot cloning without placing the source database in read-only mode, sharding capabilities, in-memory column storage, application containers, improved resource management isolation, and AWR support on Active Data Guard databases. Other notable features include enhanced JSON support, data redaction for security, row limits and offsets for queries, invisible columns, SQL text expansion, PL/SQL from SQL, session-level sequences, extended data types up to 32K, multiple indexes on the same columns, READ privileges without row locking ability, session private statistics for global temporary tables,
SQL Server 2008 Development for ProgrammersAdam Hutson
The document outlines a presentation by Adam Hutson on SQL Server 2008 development for programmers, including an overview of CRUD and JOIN basics, dynamic versus compiled statements, indexes and execution plans, performance issues, scaling databases, and Adam's personal toolbox of SQL scripts and templates. Adam has 11 years of database development experience and maintains a blog with resources for SQL topics.
MySQL is an open-source relational database management system that works on many platforms. It provides multi-user access to support many storage engines and is backed by Oracle. SQL is the core of a relational database which is used for accessing and managing the database. The different subsets of SQL are DDL, DML, DCL, and TCL. MySQL has many features including ease of management, robust transactional support, high performance, low total cost of ownership, and scalability.
SSIS provides capabilities for ETL operations using a control flow and data flow engine. It allows importing and exporting data, integrating heterogeneous data sources, and supporting BI solutions. Key concepts include packages, control flow, data flow, variables, and event handlers. SSIS can be optimized for scalability through techniques like parallelism, avoiding blocking transformations, and leveraging SQL for aggregations. Performance can be monitored using tools like SQL Server logs, WMI, and MOM. SSIS is interoperable with data sources like Oracle, Excel, and flat files.
Tony Jambu (obscure) tools of the trade for tuning oracle sq lsInSync Conference
This document provides an overview of various tools that can be used for tuning Oracle SQL statements. It discusses tuning methodology, generating explain plans and traces, and tools like SQL*Plus autotrace, DBMS_XPLAN, TRCA trace analyzer, and SQLTXPLAIN. Demo examples are provided for many of the tools to analyze SQL performance.
Este documento describe la evolución de los grandes datos y la analítica, incluyendo el aumento de fuentes de datos, la comprensión de su valor, y la disminución de costos de hardware. También resume los componentes clave de Hadoop como HDFS, MapReduce, Hive y otros para el procesamiento y análisis de grandes cantidades de datos.
Creando tu primer ambiente de AI en Azure ML y SQL ServerEduardo Castro
Este documento proporciona una introducción a cómo crear el primer entorno de inteligencia artificial en Azure. Explica brevemente los beneficios de la inteligencia artificial y el aprendizaje automático para los negocios. Luego describe algunos de los servicios principales de Azure que pueden usarse para analizar datos, desarrollar modelos de aprendizaje automático y implementar soluciones de IA, como Azure Machine Learning, Databricks y HDInsight.
El documento describe las diferentes características y capacidades de seguridad disponibles en Azure SQL Database y Azure SQL Data Warehouse. Incluye gráficos que muestran el número de vulnerabilidades abordadas desde 2010 hasta 2018 y describe opciones como cifrado de datos en tránsito y en reposo, autenticación multifactor, firewalls, detección de amenazas, auditoría y más. El objetivo es ayudar a los clientes a proteger y auditar sus datos de manera segura en la nube.
Este documento describe cómo integrar Azure Synapse con MLflow para habilitar el seguimiento de experimentos de aprendizaje automático y el registro y despliegue de modelos en Azure Machine Learning. Explica cómo configurar los cuadernos de Azure Synapse para usar MLflow conectado a un área de trabajo de Azure Machine Learning, registrar modelos entrenados en Synapse en el registro de modelos de Azure ML y desplegarlos para su uso.
SQL Server can be installed on Windows Server 2022. Eduardo Castro provides a demonstration of how to install SQL Server on the latest Windows server operating system. His demonstration is available at a GitHub link that tracks an issue regarding documentation on installing SQL Server with Windows Server 2022.
El documento describe las nuevas características de SQL Server 2022, incluyendo la integración bidireccional con Azure SQL para replicación de datos, Azure Synapse Link para transferencia automática de cambios a Synapse Analytics, integración con Azure Purview para detección y clasificación de datos, mejoras en rendimiento a través de Query Store y optimización de planes, y mejoras en seguridad, disponibilidad y resolución de conflictos de réplicas.
SQL Server 2022 está habilitado para Azure para recuperación ante desastres, análisis y seguridad. Ofrece nuevas innovaciones como inteligencia de consultas integrada para mejorar el rendimiento, compatibilidad con almacenamiento de objetos y funciones extendidas de T-SQL para nuevos escenarios.
Machine Learning con Azure Managed InstanceEduardo Castro
En esta presentación mostramos las opciones para implementar Machine Learning dentro de Azure, así como las formas de configurar y utilizar Python dentro de Azure Managed Instance
El documento describe las nuevas características de SQL Server 2022, incluyendo la integración bidireccional con Azure SQL para replicación de datos, Azure Synapse Link para transferencia automática de cambios a Synapse Analytics, integración con Azure Purview para detección y clasificación de datos, mejoras en rendimiento a través de Query Store y optimización de planes, nuevas funciones de seguridad como ledger inmutable, y automatización de conflictos de réplicas en entornos de múltiples escrituras.
SQL Server can be installed on Windows Server 2022. Eduardo Castro provides a demonstration of how to install SQL Server on the latest Windows server operating system. His demonstration is available at a GitHub link that tracks an issue regarding documentation on installing SQL Server with Windows Server 2022.
Este documento presenta una introducción a Apache Spark y Azure Databricks. Explica que Spark es un motor de procesamiento de datos a gran escala de código abierto que incluye características como Spark SQL, aprendizaje automático, procesamiento de flujos y grafos. Luego describe cómo Azure Databricks es una plataforma unificada para análisis que utiliza Spark y ofrece mejor rendimiento, procesamiento de grandes volúmenes de datos y arquitectura de clústeres. Finalmente, incluye una demostración de las capacidades de
Este documento proporciona una introducción a los pronósticos con SQL Server 2019, discutiendo métodos como promedios móviles, suavizado exponencial, proyección de tendencias y regresión lineal. También describe cómo SQL Server 2019 permite a los científicos de datos y desarrolladores interactuar directamente con los datos y realizar análisis avanzados dentro de la base de datos, lo que puede aplicarse a soluciones como detección de fraude, pronósticos de ventas y mantenimiento predictivo.
Data warehouse con azure synapse analyticsEduardo Castro
Azure Synapse is the evolution of Azure SQL Data Warehouse, combining big data, data storage and data integration into a single service for end-to-end cloud scale analytics. It provides unlimited analytics with unparalleled speed to gain insights. Azure Synapse brings together enterprise data warehousing and big data analytics to give a unified experience with the advantages of both worlds.
Que hay de nuevo en el Azure Data Lake Storage Gen2Eduardo Castro
Este documento proporciona una actualización sobre las novedades de Azure Data Lake Storage. Incluye mejoras en el rendimiento, escalabilidad de costos, seguridad, soporte para almacenamiento de blobs y sistemas de archivos jerárquicos, y una vista previa de las integraciones con Azure Event Grid y Azure Synapse Analytics.
Azure Synapse Analytics es un servicio de análisis que combina big data, almacenamiento de datos e integración de datos en un solo servicio con escalabilidad en la nube. Ofrece análisis de datos end-to-end con tiempos de respuesta en segundos utilizando SQL, Python, R y otros lenguajes. Incluye características como ingesta de datos, almacenamiento de datos, análisis SQL, machine learning integrado y más.
Este documento presenta los Servicios Cognitivos de Microsoft, que proporcionan APIs de visión, habla, lenguaje y análisis de datos para permitir que las aplicaciones tengan capacidades como reconocimiento facial, detección de emociones, extracción de frases clave y comprensión del lenguaje natural. Los servicios cognitivos se pueden integrar fácilmente en aplicaciones y ayudan a los equipos de datos a resolver problemas en áreas como la atención médica, la seguridad y el comercio minorista.
Script de paso a paso de configuración de Secure EnclavesEduardo Castro
El documento proporciona instrucciones para configurar un equipo HGS como host protegido y otro equipo con SQL Server para usar enclaves seguros con Always Encrypted. Se explica cómo instalar el servicio de protección de host en HGS, configurar el dominio HGS, configurar la atestación de claves y obtener la dirección IP de HGS. Luego, se indica cómo configurar el equipo SQL Server como host protegido, generar y registrar su clave de host, e indicarle dónde debe realizar la atestación. Finalmente, se habilitan los en
Introducción a conceptos de SQL Server Secure EnclavesEduardo Castro
Este documento describe varias técnicas de cifrado de datos, incluido el cifrado de datos en reposo, en uso y en tránsito. Se centra en particular en Always Encrypted, una solución que permite cifrar datos sensibles en las columnas de una base de datos de forma que se mantengan las consultas enriquecidas. Explica cómo los datos cifrados se almacenan de forma segura utilizando claves maestras de columna almacenadas externamente, y cómo las aplicaciones pueden recuperar datos desencriptados de forma segura mediante el uso de encl
Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems.
While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure.
So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
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This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact
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.
The Challenge of Interpretability in Generative AI Models.pdfSara Kroft
Navigating the intricacies of generative AI models reveals a pressing challenge: interpretability. Our blog delves into the complexities of understanding how these advanced models make decisions, shedding light on the mechanisms behind their outputs. Explore the latest research, practical implications, and ethical considerations, as we unravel the opaque processes that drive generative AI. Join us in this insightful journey to demystify the black box of artificial intelligence.
Dive into the complexities of generative AI with our blog on interpretability. Find out why making AI models understandable is key to trust and ethical use and discover current efforts to tackle this big challenge.
Redefining Cybersecurity with AI CapabilitiesPriyanka Aash
In this comprehensive overview of Cisco's latest innovations in cybersecurity, the focus is squarely on resilience and adaptation in the face of evolving threats. The discussion covers the imperative of tackling Mal information, the increasing sophistication of insider attacks, and the expanding attack surfaces in a hybrid work environment. Emphasizing a shift towards integrated platforms over fragmented tools, Cisco introduces its Security Cloud, designed to provide end-to-end visibility and robust protection across user interactions, cloud environments, and breaches. AI emerges as a pivotal tool, from enhancing user experiences to predicting and defending against cyber threats. The blog underscores Cisco's commitment to simplifying security stacks while ensuring efficacy and economic feasibility, making a compelling case for their platform approach in safeguarding digital landscapes.
Self-Healing Test Automation Framework - HealeniumKnoldus Inc.
Revolutionize your test automation with Healenium's self-healing framework. Automate test maintenance, reduce flakes, and increase efficiency. Learn how to build a robust test automation foundation. Discover the power of self-healing tests. Transform your testing experience.
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.
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.
Keynote : AI & Future Of Offensive SecurityPriyanka Aash
In the presentation, the focus is on the transformative impact of artificial intelligence (AI) in cybersecurity, particularly in the context of malware generation and adversarial attacks. AI promises to revolutionize the field by enabling scalable solutions to historically challenging problems such as continuous threat simulation, autonomous attack path generation, and the creation of sophisticated attack payloads. The discussions underscore how AI-powered tools like AI-based penetration testing can outpace traditional methods, enhancing security posture by efficiently identifying and mitigating vulnerabilities across complex attack surfaces. The use of AI in red teaming further amplifies these capabilities, allowing organizations to validate security controls effectively against diverse adversarial scenarios. These advancements not only streamline testing processes but also bolster defense strategies, ensuring readiness against evolving cyber threats.
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations.
The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.
2. References
What's new in SQL11 by Roger Noble
DAT303 SQL Server “Denali” High Availability: The next generation high availability
solution
http://blogs.technet.com/b/dataplatforminsider/archive/2010/11/12/analysis-
services-roadmap-for-sql-server-denali-and-beyond.aspx
http://prologika.com/CS/blogs/blog/archive/2010/11/15/bism-column-store.aspx
http://msdn.microsoft.com/en-us/library/ms174518.aspx
http://www.jenstirrup.com/2010/11/project-crescent-in-denali-bism-summary.html
http://blogs.msdn.com/b/ssdt/archive/2010/11/08/welcome.aspx
http://msdn.microsoft.com/en-us/data/gg427686
http://www.msteched.com/2010/Europe/DAT314
http://www.msteched.com/2010/NorthAmerica/BIE304
http://ecn.channel9.msdn.com/o9/te/Europe/2010/pptx/bin303.pptx
DAT303 SQL Server “Denali” High Availability
3. References
• What’s new BOL - http://msdn.microsoft.com/en-us/library/bb500435(v=SQL.110).aspx
• HADR - http://msdn.microsoft.com/en-us/library/ff877884(SQL.110).aspx &
http://www.brentozar.com/archive/2010/11/sql-server-denali-database-mirroring-rocks/
• Atlanta - https://www.microsoftatlanta.com/
• T-SQL - Tobias Ternström
• Sequence Generators - http://msdn.microsoft.com/en-us/library/ff878058(SQL.110).aspx &
http://www.sergeyv.com/blog/archive/2010/11/09/sql-server-sequence-generators.aspx
• Contained Databases - http://sqlblog.com/blogs/aaron_bertrand/archive/2010/11/16/sql-server-v-next-
denali-contained-databases.aspx
• Data Quality Services - Denise Draper
• Column Store - http://download.microsoft.com/download/8/C/1/8C1CE06B-DE2F-40D1-9C5C-
3EE521C25CE9/Columnstore Indexes for Fast DW QP SQL Server 11.pdf
• UDM & BISM - http://blogs.technet.com/b/dataplatforminsider/archive/2010/11/12/analysis-services-
roadmap-for-sql-server-denali-and-beyond.aspx &
http://prologika.com/CS/blogs/blog/archive/2010/11/13/business-intelligence-semantic-model-the-
good-the-bad-and-the-ugly.aspx
• SSIS - Matt Masson
• SSIS Resolve References - http://www.sqlservercentral.com/blogs/dknight/archive/2010/11/15/ssis-
denali-resolve-references.aspx
• BISM vs SSAS - http://cwebbbi.wordpress.com/2010/11/14/pass-summit-day-2-the-aftermath/
• Crescent - http://www.jenstirrup.com/2010/11/project-crescent-when-is-it-best_1327.html
4. T-SQL Enhancements
Robust Discovery of Result Set Metadata (replacing
SET FMTONLY)
Improved Error Handling
Introduces the THROW, which allows us to re-throw an
exception caught in an exception handling block
FileTable
Sequence Generators
Paging Construct
Support for UTF-16
Collations can be used with the data types: nchar,
nvarchar, and sql_variant
5. T-SQL Result Sets
Suppose that you need to write code against SQL Server
that uses result sets returned from stored procedures
and dynamic batches
You need a guarantee that the result sets will have very
specific metadata. if the shape of the result is different
than what you expect, you need it to fail.
http://www.sqlmag.com/blogs/puzzled-by-t-sql/tabid/1023/entryid/76198/Denali-T-SQL-
at-a-Glance-EXECUTE-WITH-RESULT-SETS.aspx
6. T-SQL Result Sets
You can specify the new option with the EXECUTE
statement when executing a stored procedure or a
dynamic batch
EXECUTE <batch_or_proc> WITH <result_sets_option>;
7. T-SQL Result Sets
RESULT SETS UNDEFINED
this is the default, meaning that never mind what’s the
shape of the result sets
RESULT SETS NONE
you have a guarantee that no result set will be returned. If
a result set is returned, an error is generated and the
batch terminates
8. T-SQL Result Sets
RESULT SETS <definition>
Specify the metadata of one or more result sets, and get a
guarantee that the result sets and their number will
match the metadata defined in the RESULT SETS clause
10. SQL Server Denali THROW
command
The THROW command can be invoked in two main
ways:
Without any parameters within the CATCH block of a
TRY/CATCH construct. This will essentially re-throw
the original error.
With parameters to throw a user defined error.
12. T-SQL Enhancements File Table
FileTable
• A merging of the FILESTREAM and HierarchyID
• Can store files and folders
• FileTables are accessed via windows share
• Created table has predefined schema
CREATE TABLE DocumentStore AS FileTable WITH
FileTable_Directory Document
FILESTREAM_ON FILESTREAMGroup1;
13. T-SQL Enhancements Sequence Generator
SEQUENCE
• Generates a predicatble number of values
• Global
• Can be
ascending, descending, minimum, maximum and
cycle
• Supports getting a range via
sp_sequence_get_range
14. T-SQL Enhancements Sequence Generator
CREATE SEQUENCE ComWinSchema.IdSequence
AS INT
START WITH 10000 INCREMENT BY 1;
GO
INSERT INTO ComunidadMembers (MemberId, Name)
VALUES (NEXT VALUE FOR ComWinSchema.IdSequence, 'Juan');
INSERT INTO ComunidadAdmin (AdminId, Name)
VALUES (NEXT VALUE FOR ComWinSchema.IdSequence, ‘Juan');
15. T-SQL Enhancements Paging
-- Before Denali
WITH a AS (
SELECT
ROW_NUMBER() OVER (ORDER BY Name)
AS RowNum ...
)
SELECT * FROM a
WHERE a BETWEEN 11 AND 20
ORDER BY RN ORDER BY RowNum;
http://www.davidhayden.com/blog/dave/archive/2005/12/30/2652.aspx
16. T-SQL Enhancements Paging
-- In Denali
SELECT...
ORDER BY ...
OFFSET 10 ROWS
FETCH NEXT 10 ROWS ONLY
Not a performance improvement, there for ease of use
Perf is similar to using ROW_NUMBER
18. VertiPaq
VertiPaq Storage Engine
Lots of cheap RAM => In-memory storage
Column-oriented data compression > 10:1
Support for column-oriented DAX queries
19. Column Store Indexes
• PowerPivot in SQL Server
• An index is created across columns instead
of rows
• Can give a massive query performance
increase
• Read only
• Reduced IO
• Compressed
• Not a NoSQL implementation / alternative
20. Denali Columnstore Indexes
Denali introduces a new type of index called the
columnstore.
This new type of index is built up on the values across
columns instead of traditional row based indexes.
As data tends to be less unique across a column it
allows the columnstore to efficiently compress and
store data.
21. Denali Columnstore Indexes
The columnstore is currently read-only, however it can be
updated via dropping and recreating the index, or switching in a
partition.
Due to the ability to compress and keep the index in memory
the Columnstore can give massive (10x, 100x, 1000x…) increase in
speed to warehouse queries
Not all queries can benefit and some can regress. In general
typical star join queries found in a Data Warehouse when only a
portion of the columns are selected will get the biggest benefit.
23. Column Store Index
http://download.microsoft.com/download/8/C/1/8C1CE06B-DE2F-40D1-9C5C-3EE521C25CE9/
Columnstore%20Indexes%20for%20Fast%20DW%20QP%20SQL%20Server%2011.pdf
Editor's Notes
The FileTable feature in SQL Server Code-Named “Denali” allows SQL Server-based enterprise applications to store unstructured file system data, such as files and directories, on a special FileTable in a relational database. This allows an application to integrate its storage and data management systems, and provides integrated SQL Server services (such as full-text search) over unstructured data and metadata, along with easy policy management and data administration.
Traditional indexes store data across rowsColumn index stores data across a columnAllows for faster queries + better compressionCannot easily insert, update or delete data
Vertipaq technology that is shared in PowerPivot and Analysis ServicesBetter compression and performance gains due to redundancy across columnsHeavily optimised for star join queries