The document discusses the challenges of handling massive data volumes from various sources and the need for big data analytics platforms to manage, store, and analyze this data. It then describes the key requirements of an effective big data analytics solution, such as managing huge data volumes, delivering fast analytics, supporting legacy tools and data scientists, and providing advanced analytics capabilities. The remainder of the document focuses on introducing the HPE Vertica Analytics Platform as a next-generation big data analytics solution that can scale limitlessly, perform analytics very fast, and be deployed on-premises, on Hadoop, or in the cloud.
Hortonworks Oracle Big Data Integration Hortonworks
Slides from joint Hortonworks and Oracle webinar on November 11, 2014. Covers the Modern Data Architecture with Apache Hadoop and Oracle Data Integration products.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Oracle Data Integration Platform is a cornerstone for big data solutions that provides five core capabilities: business continuity, data movement, data transformation, data governance, and streaming data handling. It includes eight core products that can operate in the cloud or on-premise, and is considered the most innovative in areas like real-time/streaming integration and extract-load-transform capabilities with big data technologies. The platform offers a comprehensive architecture covering key areas like data ingestion, preparation, streaming integration, parallel connectivity, and governance.
Teradata Listener™: Radically Simplify Big Data StreamingTeradata
Teradata Listener™ is an intelligent, self-service solution for ingesting and distributing extremely fast moving data streams throughout the analytical ecosystem. Listener
is designed to be the primary ingestion framework for organizations with multiple data streams. Listener reliably delivers data without loss and provides low-latency ingestion for near real-time applications.
The document discusses Teradata's portfolio for Hadoop, including the Teradata Aster Big Analytics Appliance, the Teradata Appliance for Hadoop, a commodity offering with Dell, and support for the Hortonworks Data Platform. It provides consulting, training, support, and managed services for Hadoop. Teradata SQL-H gives business users standard SQL access to data stored in Hadoop through Teradata, allowing queries to run quickly on Teradata while accessing data from Hadoop efficiently through HCatalog.
The document introduces the Teradata Aster Discovery Platform for scalable analytics using analytic algorithms on commodity hardware. It discusses use cases like credit risk analysis, fraud detection, and sentiment analysis. It provides an overview of the discovery process model and instructions for downloading, installing, and using Aster including setting up the Aster Management Console and AsterLens for visualization. It then provides examples of using various Aster analytic functions like k-means clustering, market basket analysis, data unpacking, and nPath analysis for applications in marketing, pricing, and web analytics. It concludes that Aster provides more powerful analytics capabilities than SQL alone for exploring big data.
Expand a Data warehouse with Hadoop and Big Datajdijcks
After investing years in the data warehouse, are you now supposed to start over? Nope. This session discusses how to leverage Hadoop and big data technologies to augment the data warehouse with new data, new capabilities and new business models.
This deck cover Microsoft Analytics Platform System (APS) formerly known as Parallel Data Warehouse (PDW). This is based on massively parallel processing technology and can typically reduce your OLAP workloads by 98%.
APS AU3 is a phenomenal technology based on SQL Server 2014 and costs a fraction of a comparable Netezza or Teradata.
Hortonworks Oracle Big Data Integration Hortonworks
Slides from joint Hortonworks and Oracle webinar on November 11, 2014. Covers the Modern Data Architecture with Apache Hadoop and Oracle Data Integration products.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Oracle Data Integration Platform is a cornerstone for big data solutions that provides five core capabilities: business continuity, data movement, data transformation, data governance, and streaming data handling. It includes eight core products that can operate in the cloud or on-premise, and is considered the most innovative in areas like real-time/streaming integration and extract-load-transform capabilities with big data technologies. The platform offers a comprehensive architecture covering key areas like data ingestion, preparation, streaming integration, parallel connectivity, and governance.
Teradata Listener™: Radically Simplify Big Data StreamingTeradata
Teradata Listener™ is an intelligent, self-service solution for ingesting and distributing extremely fast moving data streams throughout the analytical ecosystem. Listener
is designed to be the primary ingestion framework for organizations with multiple data streams. Listener reliably delivers data without loss and provides low-latency ingestion for near real-time applications.
The document discusses Teradata's portfolio for Hadoop, including the Teradata Aster Big Analytics Appliance, the Teradata Appliance for Hadoop, a commodity offering with Dell, and support for the Hortonworks Data Platform. It provides consulting, training, support, and managed services for Hadoop. Teradata SQL-H gives business users standard SQL access to data stored in Hadoop through Teradata, allowing queries to run quickly on Teradata while accessing data from Hadoop efficiently through HCatalog.
The document introduces the Teradata Aster Discovery Platform for scalable analytics using analytic algorithms on commodity hardware. It discusses use cases like credit risk analysis, fraud detection, and sentiment analysis. It provides an overview of the discovery process model and instructions for downloading, installing, and using Aster including setting up the Aster Management Console and AsterLens for visualization. It then provides examples of using various Aster analytic functions like k-means clustering, market basket analysis, data unpacking, and nPath analysis for applications in marketing, pricing, and web analytics. It concludes that Aster provides more powerful analytics capabilities than SQL alone for exploring big data.
Expand a Data warehouse with Hadoop and Big Datajdijcks
After investing years in the data warehouse, are you now supposed to start over? Nope. This session discusses how to leverage Hadoop and big data technologies to augment the data warehouse with new data, new capabilities and new business models.
This deck cover Microsoft Analytics Platform System (APS) formerly known as Parallel Data Warehouse (PDW). This is based on massively parallel processing technology and can typically reduce your OLAP workloads by 98%.
APS AU3 is a phenomenal technology based on SQL Server 2014 and costs a fraction of a comparable Netezza or Teradata.
Modern data management using Kappa and streaming architectures, including discussion by EBay's Connie Yang about the Rheos platform and the use of Oracle GoldenGate, Kafka, Flink, etc.
Partners 2013 LinkedIn Use Cases for Teradata Connectors for HadoopEric Sun
Teradata Connectors for Hadoop enable high-volume data movement between Teradata and Hadoop platforms. LinkedIn conducted a proof-of-concept using the connectors for use cases like copying clickstream data from Hadoop to Teradata for analytics and publishing dimension tables from Teradata to Hadoop for machine learning. The connectors help address challenges of scalability and tight processing windows for these large-scale data transfers.
This document discusses Oracle Data Integration solutions for tapping into big data reservoirs. It begins with an overview of Oracle Data Integration and how it can improve agility, reduce risk and costs. It then discusses Oracle's approach to comprehensive data integration and governance capabilities including real-time data movement, data transformation, data federation, and more. The document also provides examples of how Oracle Data Integration has been used by customers for big data use cases involving petabytes of data.
This document provides an overview of Apache Atlas and how it addresses big data governance issues for enterprises. It discusses how Atlas provides a centralized metadata repository that allows users to understand data across Hadoop components. It also describes how Atlas integrates with Apache Ranger to enable dynamic security policies based on metadata tags. Finally, it outlines new capabilities in upcoming Atlas releases, including cross-component data lineage tracking and a business taxonomy/catalog.
Software engineering practices for the data science and machine learning life...DataWorks Summit
With the advent of newer frameworks and toolkits, data scientists are now more productive than ever and starting to prove indispensable to enterprises. Typical organizations have large teams of data scientists who build out key analytics assets that are used on a daily basis and an integral part of live transactions. However, there is also quite a lot of chaos and complexities that get introduced because of the state of the industry. Many packages used by data scientists are from open source, and even if they are well curated, there is a growing tendency to pick out the cutting-edge or unstable packages and frameworks to accelerate analytics. Different data scientists may use different versions of runtimes, different Python or R versions, or even different versions of the same packages. Predominantly data scientists work on their laptops and it becomes difficult to reproduce their environments for use by others. Since data science is now a team sport across multiple personas, involving non-practitioners, traditional application developers, execs, and IT operators, how does an enterprise create a platform for productive cross-role collaboration?
Enterprises need a very reliable and repeatable process, especially when it results in something that affects their production environments. They also require a well managed approach that enables the graduation of an asset from development through a testing and staging process to production. Given the pace of businesses nowadays, the process needs to be quite agile and flexible too—even enabling an easy path to reversing a change. Compliance and audit processes require clear lineage and history as well as approval chains.
In the traditional software engineering world, this lifecycle has been well understood and best practices have been followed for ages. But what does it mean when you have non-programmers or users who are not really trained in software engineering philosophies or who perceive all of this as "big process" roadblocks in their daily work ? How do you we engage them in a productive manner and yet support enterprise requirements for reliability, tracking, and a clear continuous integration and delivery practice? The presenters, in this session, will bring up interesting techniques based on their user research, real life customer interviews, and productized best practices. The presenters also invite the audience to share their stories and best practices to make this a lively conversation.
Speaker
Sriram Srinivasan, Senior Technical Staff Member, Analytics Platform Architect, IBM
Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...Data Con LA
The document discusses how an Enterprise Data Lake (EDL) provides a more effective solution for enterprise BI and analytics compared to traditional enterprise data warehouses (EDW). It argues that EDL allows enterprises to retain all datasets, service ad-hoc requests with no latency or development time, and offer a low-cost, low-maintenance solution that supports direct analytics and reporting on data stored in its native format. The document promotes EDL as a mainstream solution that should be part of every mid-sized and large enterprise's standard IT stack.
Alexandre Vasseur - Evolution of Data Architectures: From Hadoop to Data Lake...NoSQLmatters
Come to this deep dive on how Pivotal's Data Lake Vision is evolving by embracing next generation in-memory data exchange and compute technologies around Spark and Tachyon. Did we say Hadoop, SQL, and what's the shortest path to get from past to future state? The next generation of data lake technology will leverage the availability of in-memory processing, with an architecture that supports multiple data analytics workloads within a single environment: SQL, R, Spark, batch and transactional.
Hadoop based data Lakes have become increasingly popular within today’s modern data architectures for their ability to scale, handle data variety and low cost. Many organizations start slow with the data lake initiatives but as they grow bigger, they suffer with challenges on data consistency, quality and security, resulting in losing confidence in their data lake initiatives.
This talk will discuss the need for good data governance mechanisms for Hadoop data lakes and it relationship with productivity and how it helps organizations meet regulatory and compliance requirements. The talk advocates carrying a different mindset for designing and implementing flexible governance mechanisms on Hadoop data lakes.
Hortonworks provides an open source Apache Hadoop data platform for managing large volumes of data. It was founded in 2011 and went public in 2014. Hortonworks has over 800 employees across 17 countries and partners with over 1,350 technology companies. Hortonworks' Data Platform is a collection of Apache projects that provides data management, access, governance, integration, operations and security capabilities. It supports batch, interactive and real-time processing on a shared infrastructure using the YARN resource management system.
Can you Re-Platform your Teradata, Oracle, Netezza and SQL Server Analytic Wo...DataWorks Summit
The document discusses re-platforming existing enterprise business intelligence and analytic workloads from platforms like Oracle, Teradata, SAP and IBM to the Hadoop platform. It notes that many existing analytic workloads are struggling with increasing data volumes and are too costly. Hadoop offers a modern distributed platform that can address these issues through the use of a production-grade SQL database like VectorH on Hadoop. The document provides guidelines for re-platforming workloads and notes potential benefits such as improved performance, reduced costs and leveraging the Hadoop ecosystem.
This document discusses deploying a governed data lake using Hadoop and Waterline Data Inventory. It begins by outlining the benefits of a data lake and differences between data lakes and data warehouses. It then discusses using Hadoop as the platform for the data lake and some challenges around governance, scale, and usability. The document proposes a three phase approach using Waterline Data Inventory to organize, inventory, and open up the data lake. It provides screenshots and descriptions of Waterline's key capabilities like metadata discovery, data profiling, sensitive data identification, governance tools, and self-service catalog. It also includes an overview of Waterline Data as a company.
10 Amazing Things To Do With a Hadoop-Based Data LakeVMware Tanzu
Greg Chase, Director, Product Marketing presents Big Data 10 A
mazing Things to do With A Hadoop-based Data Lake at the Strata Conference + Hadoop World 2014 in NYC.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
The document discusses Informatica's data integration platform and its capabilities for big data and analytics projects. Some key points:
- Informatica is a leading data integration vendor with over 5,000 customers including over 70% of the Global 500.
- The Informatica platform provides capabilities across the entire data lifecycle from ingestion to delivery including data quality, master data management, integration, and analytics.
- It supports a variety of data sources including structured, unstructured, cloud, and big data and can run on-premises or in the cloud.
- Customers report the Informatica platform improves agility, scalability, and operational confidence for data integration projects compared to
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The Microsoft Analytics Platform System (APS) is a turnkey appliance that provides a modern data warehouse with the ability to handle both relational and non-relational data. It uses a massively parallel processing (MPP) architecture with multiple CPUs running queries in parallel. The APS includes an integrated Hadoop distribution called HDInsight that allows users to query Hadoop data using T-SQL with PolyBase. This provides a single query interface and allows users to leverage existing SQL skills. The APS appliance is pre-configured with software and hardware optimized to deliver high performance at scale for data warehousing workloads.
Enterprise Data Warehouse Optimization: 7 Keys to SuccessHortonworks
You have a legacy system that no longer meet the demands of your current data needs, and replacing it isn’t an option. But don’t panic: Modernizing your traditional enterprise data warehouse is easier than you may think.
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
Modern data management using Kappa and streaming architectures, including discussion by EBay's Connie Yang about the Rheos platform and the use of Oracle GoldenGate, Kafka, Flink, etc.
Partners 2013 LinkedIn Use Cases for Teradata Connectors for HadoopEric Sun
Teradata Connectors for Hadoop enable high-volume data movement between Teradata and Hadoop platforms. LinkedIn conducted a proof-of-concept using the connectors for use cases like copying clickstream data from Hadoop to Teradata for analytics and publishing dimension tables from Teradata to Hadoop for machine learning. The connectors help address challenges of scalability and tight processing windows for these large-scale data transfers.
This document discusses Oracle Data Integration solutions for tapping into big data reservoirs. It begins with an overview of Oracle Data Integration and how it can improve agility, reduce risk and costs. It then discusses Oracle's approach to comprehensive data integration and governance capabilities including real-time data movement, data transformation, data federation, and more. The document also provides examples of how Oracle Data Integration has been used by customers for big data use cases involving petabytes of data.
This document provides an overview of Apache Atlas and how it addresses big data governance issues for enterprises. It discusses how Atlas provides a centralized metadata repository that allows users to understand data across Hadoop components. It also describes how Atlas integrates with Apache Ranger to enable dynamic security policies based on metadata tags. Finally, it outlines new capabilities in upcoming Atlas releases, including cross-component data lineage tracking and a business taxonomy/catalog.
Software engineering practices for the data science and machine learning life...DataWorks Summit
With the advent of newer frameworks and toolkits, data scientists are now more productive than ever and starting to prove indispensable to enterprises. Typical organizations have large teams of data scientists who build out key analytics assets that are used on a daily basis and an integral part of live transactions. However, there is also quite a lot of chaos and complexities that get introduced because of the state of the industry. Many packages used by data scientists are from open source, and even if they are well curated, there is a growing tendency to pick out the cutting-edge or unstable packages and frameworks to accelerate analytics. Different data scientists may use different versions of runtimes, different Python or R versions, or even different versions of the same packages. Predominantly data scientists work on their laptops and it becomes difficult to reproduce their environments for use by others. Since data science is now a team sport across multiple personas, involving non-practitioners, traditional application developers, execs, and IT operators, how does an enterprise create a platform for productive cross-role collaboration?
Enterprises need a very reliable and repeatable process, especially when it results in something that affects their production environments. They also require a well managed approach that enables the graduation of an asset from development through a testing and staging process to production. Given the pace of businesses nowadays, the process needs to be quite agile and flexible too—even enabling an easy path to reversing a change. Compliance and audit processes require clear lineage and history as well as approval chains.
In the traditional software engineering world, this lifecycle has been well understood and best practices have been followed for ages. But what does it mean when you have non-programmers or users who are not really trained in software engineering philosophies or who perceive all of this as "big process" roadblocks in their daily work ? How do you we engage them in a productive manner and yet support enterprise requirements for reliability, tracking, and a clear continuous integration and delivery practice? The presenters, in this session, will bring up interesting techniques based on their user research, real life customer interviews, and productized best practices. The presenters also invite the audience to share their stories and best practices to make this a lively conversation.
Speaker
Sriram Srinivasan, Senior Technical Staff Member, Analytics Platform Architect, IBM
Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...Data Con LA
The document discusses how an Enterprise Data Lake (EDL) provides a more effective solution for enterprise BI and analytics compared to traditional enterprise data warehouses (EDW). It argues that EDL allows enterprises to retain all datasets, service ad-hoc requests with no latency or development time, and offer a low-cost, low-maintenance solution that supports direct analytics and reporting on data stored in its native format. The document promotes EDL as a mainstream solution that should be part of every mid-sized and large enterprise's standard IT stack.
Alexandre Vasseur - Evolution of Data Architectures: From Hadoop to Data Lake...NoSQLmatters
Come to this deep dive on how Pivotal's Data Lake Vision is evolving by embracing next generation in-memory data exchange and compute technologies around Spark and Tachyon. Did we say Hadoop, SQL, and what's the shortest path to get from past to future state? The next generation of data lake technology will leverage the availability of in-memory processing, with an architecture that supports multiple data analytics workloads within a single environment: SQL, R, Spark, batch and transactional.
Hadoop based data Lakes have become increasingly popular within today’s modern data architectures for their ability to scale, handle data variety and low cost. Many organizations start slow with the data lake initiatives but as they grow bigger, they suffer with challenges on data consistency, quality and security, resulting in losing confidence in their data lake initiatives.
This talk will discuss the need for good data governance mechanisms for Hadoop data lakes and it relationship with productivity and how it helps organizations meet regulatory and compliance requirements. The talk advocates carrying a different mindset for designing and implementing flexible governance mechanisms on Hadoop data lakes.
Hortonworks provides an open source Apache Hadoop data platform for managing large volumes of data. It was founded in 2011 and went public in 2014. Hortonworks has over 800 employees across 17 countries and partners with over 1,350 technology companies. Hortonworks' Data Platform is a collection of Apache projects that provides data management, access, governance, integration, operations and security capabilities. It supports batch, interactive and real-time processing on a shared infrastructure using the YARN resource management system.
Can you Re-Platform your Teradata, Oracle, Netezza and SQL Server Analytic Wo...DataWorks Summit
The document discusses re-platforming existing enterprise business intelligence and analytic workloads from platforms like Oracle, Teradata, SAP and IBM to the Hadoop platform. It notes that many existing analytic workloads are struggling with increasing data volumes and are too costly. Hadoop offers a modern distributed platform that can address these issues through the use of a production-grade SQL database like VectorH on Hadoop. The document provides guidelines for re-platforming workloads and notes potential benefits such as improved performance, reduced costs and leveraging the Hadoop ecosystem.
This document discusses deploying a governed data lake using Hadoop and Waterline Data Inventory. It begins by outlining the benefits of a data lake and differences between data lakes and data warehouses. It then discusses using Hadoop as the platform for the data lake and some challenges around governance, scale, and usability. The document proposes a three phase approach using Waterline Data Inventory to organize, inventory, and open up the data lake. It provides screenshots and descriptions of Waterline's key capabilities like metadata discovery, data profiling, sensitive data identification, governance tools, and self-service catalog. It also includes an overview of Waterline Data as a company.
10 Amazing Things To Do With a Hadoop-Based Data LakeVMware Tanzu
Greg Chase, Director, Product Marketing presents Big Data 10 A
mazing Things to do With A Hadoop-based Data Lake at the Strata Conference + Hadoop World 2014 in NYC.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
The document discusses Informatica's data integration platform and its capabilities for big data and analytics projects. Some key points:
- Informatica is a leading data integration vendor with over 5,000 customers including over 70% of the Global 500.
- The Informatica platform provides capabilities across the entire data lifecycle from ingestion to delivery including data quality, master data management, integration, and analytics.
- It supports a variety of data sources including structured, unstructured, cloud, and big data and can run on-premises or in the cloud.
- Customers report the Informatica platform improves agility, scalability, and operational confidence for data integration projects compared to
Modern Data Warehousing with the Microsoft Analytics Platform SystemJames Serra
The Microsoft Analytics Platform System (APS) is a turnkey appliance that provides a modern data warehouse with the ability to handle both relational and non-relational data. It uses a massively parallel processing (MPP) architecture with multiple CPUs running queries in parallel. The APS includes an integrated Hadoop distribution called HDInsight that allows users to query Hadoop data using T-SQL with PolyBase. This provides a single query interface and allows users to leverage existing SQL skills. The APS appliance is pre-configured with software and hardware optimized to deliver high performance at scale for data warehousing workloads.
Enterprise Data Warehouse Optimization: 7 Keys to SuccessHortonworks
You have a legacy system that no longer meet the demands of your current data needs, and replacing it isn’t an option. But don’t panic: Modernizing your traditional enterprise data warehouse is easier than you may think.
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
A Austrália é a maior ilha do mundo e uma comunidade com uma área de 7,6 milhões de km2. Sua capital é Camberra e sua população é de aproximadamente 21,5 milhões de habitantes. O país possui uma grande variedade de paisagens, desde desertos no oeste até planaltos no leste, e seu clima varia de temperado a tropical.
El Museo Nacional de Escultura de Valladolid se ubica en el antiguo Colegio de San Gregorio, construido a finales del siglo XV. En el museo se exponen obras maestras de la escultura española de los siglos XV al XIX, incluyendo piezas de Alonso Berruguete, Gregorio Fernández y Juan de Juni. El museo ocupa varias salas del colegio original y muestra la evolución del estilo escultórico a través de los años.
Una red informática conecta dispositivos a través de un medio para intercambiar información. Existen redes de área local (LAN), área metropolitana (MAN) y área extensa (WAN). El diseño de una LAN implica dispositivos hardware como routers, tarjetas de red y switches; instalación y conexión de cables; y configuración de la red mediante direcciones IP, máscaras de subred, puertas de enlace y servidores DNS.
Presentacion distorsiones del mercado laboral UFTRosaduarte1202
1. El documento describe los componentes del mercado de trabajo, incluyendo oferentes y demandantes de empleo, así como distorsiones como el desempleo y subempleo.
2. Explica varios tipos de desempleo como estructural, cíclico, friccional y de demanda, así como sus causas y consecuencias.
3. También define el subempleo, sus tipos y causas, así como las consecuencias del desempleo y subempleo.
Este documento presenta la Ley de Ética Gubernamental de El Salvador. Establece principios como la supremacía del interés público, probidad e igualdad que deben guiar la actuación de los servidores públicos. Describe deberes como el cumplimiento de funciones y prohibiciones como solicitar dádivas. También crea el Tribunal de Ética Gubernamental y comisiones de ética en instituciones públicas para promover la ética y prevenir la corrupción.
Melanie Seewald was born in Bad Schlema, Germany in 1986. She completed her primary and secondary school education in Germany and received vocational training as a butcher's shop assistant from 2001-2004. Since then, she has worked in various roles related to butchery and retail sales, most recently as a textile salesperson since August 2007 at Beckenbauer in Rothenburg, Germany. She has basic English skills and extensive experience in sales and customer contact.
Este documento habla sobre los riesgos laborales, incluyendo accidentes, lesiones y pérdidas económicas que pueden ocurrir en el lugar de trabajo. Describe diferentes tipos de riesgos como físicos, psicosociales, biológicos y químicos, y la importancia de la prevención a través de equipos de protección personal, iluminación adecuada, suelos no resbaladizos y mantenimiento de maquinaria. También menciona inspecciones de las instalaciones, riesgos y equipos realizadas por funcion
presentacion de equipo 5 google drive y one drive chuylopez
OneDrive y Google Drive son servicios de almacenamiento en la nube que permiten a los usuarios guardar archivos como fotos, videos y documentos. OneDrive ofrece 7GB de almacenamiento gratuito, mientras que Google Drive ofrece 15GB que se comparten con Gmail. Ambos servicios tienen ventajas como la edición de archivos en la nube y la organización de contenidos, pero OneDrive se integra mejor con Office mientras que Google Drive facilita la subida de archivos adjuntos de correo.
Este documento discute os elementos formativos do Estado moderno que têm raízes no período medieval tardio, como a noção de soberania. Alguns dos principais pontos são: 1) Conflitos entre monarquias, Império, Papado e poderes locais ajudaram a definir a jurisdição e legitimidade de cada um; 2) Novas instituições como burocracias e tribunais foram cruciais para o desenvolvimento do Estado; 3) A estabilização política na Europa permitiu a consolidação dessas instituições e sentimentos de lealdade ao Estado.
Lei nº 12.232/29.04.2010 - Paulo Gomes de Oliveira FilhoABAPMG
O documento descreve a Lei de Licitações de Serviços Publicitários no Brasil. Resume que (1) a Lei estabelece obrigatoriedade de licitação para contratação de agências de publicidade, (2) define os serviços que podem ser contratados e (3) estabelece normas para modalidades, julgamento e apresentação de propostas nas licitações.
O Palácio de Dolmabahce em Istambul foi o principal centro administrativo do Império Otomano e posteriormente se tornou a residência presidencial da Turquia. Foi construído entre 1843 e 1856 a um custo enorme e é conhecido por sua arquitetura opulenta e coleções de arte, tendo abrigado seis sultões otomanos e o primeiro presidente turco Mustafa Kemal Atatürk.
Popplet es una plataforma para organizar ideas visualmente a través de "burbujas" llamadas popplets que permiten agregar texto, imágenes, videos y más. Ofrece funciones como mapas mentales, líneas de tiempo y trabajo colaborativo. Aunque tiene limitaciones como acceso compartido y número de popplets gratuitos, es una herramienta útil para educación al permitir proyectos grupales, presentaciones y diagrama de ideas.
Este documento proporciona instrucciones sobre cómo usar las diferentes funciones y herramientas de un procesador de texto como Word. Explica cómo dar formato a páginas, aplicar estilos de texto como negritas, insertar imágenes, gráficos y formas, revisar la ortografía, organizar el texto, cambiar el tamaño y estilo de la fuente, agregar hipervínculos, cortar y pegar texto, guardar y cerrar archivos, y más. El objetivo es servir como un manual básico para aprender a utilizar
WP_Impetus_2016_Guide_to_Modernize_Your_Enterprise_Data_Warehouse_JRobertsJane Roberts
The document discusses modernizing enterprise data warehouses to handle big data by migrating workloads to a Hadoop-based data lake. It describes challenges with existing data warehouses and outlines Impetus's automated data warehouse workload migration tool which can help organizations migrate schemas, data, queries and access controls to Hadoop to realize the benefits of big data analytics while protecting existing investments.
The document discusses using Attunity Replicate to accelerate loading and integrating big data into Microsoft's Analytics Platform System (APS). Attunity Replicate provides real-time change data capture and high-performance data loading from various sources into APS. It offers a simplified and automated process for getting data into APS to enable analytics and business intelligence. Case studies are presented showing how major companies have used APS and Attunity Replicate to improve analytics and gain business insights from their data.
Pivotal Big Data Suite is a comprehensive platform that allows companies to modernize their data infrastructure, gain insights through advanced analytics, and build analytic applications at scale. It includes components for data processing, storage, analytics, in-memory processing, and application development. The suite is based on open source software, supports multiple deployment options, and provides an agile approach to help companies transform into data-driven enterprises.
Boost Performance with Scala – Learn From Those Who’ve Done It! Cécile Poyet
Scalding is a scala DSL for Cascading. Run on Hadoop, it’s a concise, functional, and very efficient way to build big data applications. One significant benefit of Scalding is that it allows easy porting of Scalding apps from MapReduce to newer, faster execution fabrics.
In this webinar, Cyrille Chépélov, of Transparency Rights Management, will share how his organization boosted the performance of their Scalding apps by over 50% by moving away from MapReduce to Cascading 3.0 on Apache Tez. Dhruv Kumar, Hortonworks Partner Solution Engineer, will then explain how you can interact with data on HDP using Scala and leverage Scala as a programming language to develop Big Data applications.
Boost Performance with Scala – Learn From Those Who’ve Done It! Cécile Poyet
This document provides information about using Scalding on Tez. It begins with prerequisites for using Scalding on Tez, including having a YARN cluster, Cascading 3.0, and the TEZ runtime library in HDFS. It then discusses setting memory and Java heap configuration flags for Tez jobs run through Scalding. The document provides a mini-howto for using Scalding on Tez in two steps - configuring the build.sbt and assembly.sbt files and setting some job flags. It discusses challenges encountered in practice and provides tips and an example Scalding on Tez application.
Boost Performance with Scala – Learn From Those Who’ve Done It! Hortonworks
This document provides information about using Scalding on Tez. It begins with prerequisites for using Scalding on Tez, including having a YARN cluster, Cascading 3.0, and the TEZ runtime library in HDFS. It then discusses setting memory and Java heap configuration flags for Tez jobs in Scalding. The document provides a mini-tutorial on using Scalding on Tez, covering build configuration, job flags, and challenges encountered in practice like Guava version mismatches and issues with Cascading's Tez registry. It also presents a word count plus example Scalding application built to run on Tez. The document concludes with some tips for debugging Tez jobs in Scalding using Cascading's
Teradata - Presentation at Hortonworks Booth - Strata 2014Hortonworks
Hortonworks and Teradata have partnered to provide a clear path to Big Analytics via stable and reliable Hadoop for the enterprise. The Teradata® Portfolio for Hadoop is a flexible offering of products and services for customers to integrate Hadoop into their data architecture while taking advantage of the world-class service and support Teradata provides.
SphereEx provides enterprises with distributed data service infrastructures and products/solutions to address challenges from increasing database fragmentation. It was founded in 2021 by the team behind Apache ShardingSphere, an open-source project providing data sharding and distributed solutions. SphereEx's products include solutions for distributed databases, data security, online stress testing, and its commercial version provides enhanced capabilities over the open-source version.
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Azure Data Platform Services
HDInsight Clusters in Azure
Data Storage: Apache Hive, Apache Hbase, Azure Data Catalog
Data Transformations: Apache Storm, Apache Spark, Azure Data Factory
Healthcare / Life Sciences Use Cases
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Any data source becomes an SQL Query with all the power of
Apache Spark. Querona is a virtual database that seamlessly connects any data source with Power BI, TARGIT, Qlik, Tableau, Microsoft Excel or others. It lets you build your
own universal data model and share it among reporting tools.
Querona does not create another copy of your data, unless you want to accelerate your reports and use build-in execution engine created for purpose of Big Data analytics. Just write standard SQL query and let Querona consolidate data on the fly, use one of execution engines and accelerate processing no matter what kind and how many sources you have.
Data Warehouse Modernization: Accelerating Time-To-Action MapR Technologies
Data warehouses have been the standard tool for analyzing data created by business operations. In recent years, increasing data volumes, new types of data formats, and emerging analytics technologies such as machine learning have given rise to modern data lakes. Connecting application databases, data warehouses, and data lakes using real-time data pipelines can significantly improve the time to action for business decisions. More: http://info.mapr.com/WB_MapR-StreamSets-Data-Warehouse-Modernization_Global_DG_17.08.16_RegistrationPage.html
Oracle Big Data Appliance and Big Data SQL for advanced analyticsjdijcks
Overview presentation showing Oracle Big Data Appliance and Oracle Big Data SQL in combination with why this really matters. Big Data SQL brings you the unique ability to analyze data across the entire spectrum of system, NoSQL, Hadoop and Oracle Database.
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This document discusses approaches to implementing Hadoop, NoSQL, and analytical databases. It describes:
1) The current landscape of big data databases including Hadoop, NoSQL, and analytical databases that are often used together but come from different vendors with different interfaces.
2) Common uses of transactional databases, Hadoop, NoSQL databases, and analytical databases.
3) The complexity of current implementation approaches that involve multiple coding steps across various tools.
4) How Pentaho provides a unified platform and visual tools to reduce the time and effort needed for implementation by eliminating disjointed steps and enabling non-coders to develop workflows and analytics for big data.
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The document discusses modernizing a data warehouse using the Microsoft Analytics Platform System (APS). APS is described as a turnkey appliance that allows organizations to integrate relational and non-relational data in a single system for enterprise-ready querying and business intelligence. It provides a scalable solution for growing data volumes and types that removes limitations of traditional data warehousing approaches.
1. Data sheet
Handling today’s massive data volumes
In modern data infrastructures, data comes from everywhere: business systems like CRM and ERP,
sensors used to gather machine generated data, tweets and other social media data, Web logs and
data streams, gas and electrical grids, and mobile networks to name a few. With all this data from
so many places, companies often face challenges in simply storing and managing these volumes,
never mind performing analytics on that data.
To manage the volume, velocity, and variety of the data, newer, more innovative Big Data analytics
platforms have emerged to keep up with the sheer size and complexity. While most of the newly
created data is unstructured or semi-structured (email, text, IM, log files), it is the job of these
emerging Big Data analytics technologies to combine the known with the unknown to deliver value
in ways never before possible. From data monetization to customer retention to compliance to traffic
optimization, enterprises that embrace Big Data analytics platforms are changing the dynamics of
industries from retail to health care to telecommunications to energy and beyond.
What are the key technology requirements of a Big Data
analytics platform?
So, just what should you look for in a data analytics solution to address today and tomorrow’s
data challenges? Consider the following:
• Manage huge data volumes: You are likely looking to scale limitlessly to store or manage massive
volumes. Today, the scale may be gigabytes or terabytes. Tomorrow, you may be thinking about
petabytes.
• Deliver fast analytics: Users don’t want to wait for results. Your solution should provide the
scalability to meet service-level agreements (SLAs) and expected timeframes for running a query.
• Embrace legacy tools: If your Big Data analytics relies on extract, transform, load (ETL) tools
or SQL-based visualizations, your analytics platform should provide robust and powerful SQL
and also be certified to work with all of your tools—not just those from your primary vendor.
• Support data scientists: The new breed of data scientists are using tools like Java, Python,
and R to create predictive analytics. The underlying analytical database should support and
accelerate the creation of innovative predictive analytics.
• Advanced analytics: Depending upon your use case, it may be important to look at the depth
of built-in SQL analytical functions offered by the analytics engine. You have to look under the
hood to see exactly what SQL analytics are offered under these volumes, never mind performing
analytics on that data.
HPE Vertica Analytics
Platform overview
The next-generation Big Data analytics platform
• Mature and enterprise-ready
• Maximum scalability
• Blazingly fast performance
• SQL-99 compliant
The HPE Vertica Analytics Platform
Fueled by ever-growing volumes of Big Data
found in many corporations and government
agencies, Hewlett Packard Enterprise offers
the Vertica Analytics Platform, an SQL
analytics solution built from the ground
up to handle massive volumes of data and
delivers blazingly fast Big Data analytics.
The platform is available in the broadest
range of deployment and consumption
models, including on premise, on Hadoop,
and in the cloud.
2. Page 2Data sheet
HPE Vertica Analytics Platform—no limits, no compromises
Conceived by legendary database guru Michael Stonebraker, the HPE Vertica Analytics Platform
is purpose built from the very first line of code for Big Data analytics. Why? Because it is clear that
data warehouses and “business-as-usual” practices are limiting technologies, causing businesses
to make painful compromises. The HPE Vertica Analytics Platform is consciously designed with
speed, scalability, simplicity, and openness at its core and architected to handle analytical workloads
via a distributed compressed columnar architecture. HPE Vertica Analytics Platform provides
blazingly fast speed (queries run 50–1,000X faster), petabyte scale (store 10–30X more data per
server), openness, and simplicity (use any business intelligence [BI]/ETL tools, Hadoop, etc.)—at a
much lower cost than traditional data warehouse solutions.1
The technology that makes HPE Vertica so powerful
HPE Vertica is built from the ground up to handle the challenges of Big Data analytics. With its
massively parallel processing system, it can handle petabyte scale, and has done so in some of the
most demanding use cases in the industry. Because it’s a columnar store and offers compression of
data, it delivers very fast Big Data analytics, taking query times from hours to minutes or minutes to
seconds vs. outdated row-store technologies built for an earlier era. Finally, HPE Vertica provides very
advanced SQL-based analytics from graph analysis to triangle counting to Monte Carlo simulations
and more. It is a full-featured analytics system.
Every release of HPE Vertica is certified and tested with visualization and ETL tools. It supports
popular SQL, and Java Database Connectivity (JDBC)/Open Database Connectivity (ODBC). This
enables users to preserve years of investment and training in these technologies because all popular
SQL programming tools and languages work seamlessly. Leading BI and visualization tools are
tightly integrated, such as Tableau, MicroStrategy, and others and so are all popular ETL tools like
Informatica, Talend, Pentaho, and more.
At the core of the HPE Vertica Analytics Platform is a column-oriented, relational database built
specifically to handle today’s analytic workloads. Unlike commercial and open-source row stores,
which were designed decades ago to support small data, the HPE Vertica Analytics Platform
provides customers with:
• Complete and advanced SQL-based analytical functions to provide powerful SQL analytics
• A clustered approach to storing Big Data, offering superior query and analytic performance
• Better compression, requiring less hardware and storage than comparable data analytics solutions
• Flexibility and scalability to easily ramp up when workloads increase
• Better load throughput and concurrency with querying
• Built-in predictive analytics via Python and Ruby
• Less intervention with a database administrator (DBA) for overhead and tuning
HPE Vertica offers maximum scalability for large-scale Big Data analytics. It is uniquely designed
using a memory-and-disk balanced distributed compressed columnar paradigm, which makes it
exponentially faster than older techniques for modern data analytics workloads.
1
techvalidate.com/tvid/B9F-BA0-073
3. HPE Vertica in the Cloud
– Get up and running quickly in the cloud
– Amazon AWS, Microsoft Azure and VMware
– Flexible, enterprise-class cloud deployment options
HPE Vertica Enterprise
– Columnar storage and advanced compression
– Maximum performance and scalability
– Flex tables for schema on read
HPE Vertica for SQL on Apache Hadoop
– Native support for ORC and more
– Support for industry-leading distributions
– No helper node or single point of failure
Core HPE Vertica SQL engine
– Advanced analytics
– Open ANSI SQL standards ++
– R, Python, Java, Spark, Scala
Page 3Data sheet
The broadest deployment and consumption models
Available on-premise, on Hadoop, or in the cloud, HPE Vertica offers proven Big Data analytics
that can deliver unmatched speed and scale.
• HPE Vertica Enterprise Edition is the modular, on-premise version of Vertica that provides
advanced SQL analytics at limitless scale.
• HPE Vertica in the Cloud enables you to take your enterprise license and install it directly on an
Amazon, Microsoft Azure or VMware® cloud. If you need extra capacity and have no time to stand
up on-premise hardware, this is an attractive option.
• HPE Vertica for SQL on Apache Hadoop® accelerates data exploration and SQL analytics while
running natively on an organization’s preferred Hadoop distribution.
In the Cloud—HPE Vertica software is optimized and pre-configured to run on Amazon, Microsoft
Azure and VMware cloud. HPE Vertica provides users, the agility and extensibility to quickly deploy,
self-provision, and integrate with a wide variety of BI and ETL software tools. With the flexibility
to start small and grow as your business grows, HPE Vertica enables you to transition your data
warehouse to the cloud, to on premises, and back seamlessly. With this level of agility, there’s no
need to compromise. With the flexibility to start small and grow as your business grows, HPE Vertica
enables you to transition your data warehouse to the cloud, to on premises, and back seamlessly.
With this level of agility, there’s no need to compromise.
On premise—The HPE Vertica Analytics Platform is a “shared-nothing,” distributed database
designed to work on clusters of cost-effective, off-the-shelf servers, and its performance is scaled
simply by adding new servers to the cluster. The grid architecture of HPE Vertica Analytics Platform
reduces hardware and scaling costs substantially (by 70 to 90 percent) when compared to traditional
databases that require “big iron” servers with many CPUs and SANs. Clustering also speeds up
performance by parallelizing querying and loading across the nodes in the cluster for higher
throughput.