This document provides instructions on how to create and use shared memory objects in ABAP. It discusses defining a root class with attributes and methods to store and retrieve data, as well as creating a memory area in transaction SHMA. The root class serves as a template for the shared memory area, allowing data to be stored and accessed more quickly than reading from database tables. Methods are demonstrated for initializing the stored data, retrieving all data, and retrieving a single record by material number. Using shared memory objects can improve performance for applications that require frequent, heavy access to largely static reference data.
HBase is a column-oriented NoSQL database that provides random real-time read/write access to big data stored in Hadoop's HDFS. It is modeled after Google's Bigtable and sits on top of HDFS to allow fast access to large datasets. HBase architecture includes HMaster, HRegionServers, ZooKeeper, and HDFS. HMaster manages metadata and load balancing while HRegionServers serve read/write requests directly from clients. ZooKeeper coordinates the cluster and HDFS provides storage. Data is stored in tables divided into regions hosted by HRegionServers.
HBase In Action - Chapter 10 - Operationsphanleson
HBase In Action - Chapter 10: Operations
Learning HBase, Real-time Access to Your Big Data, Data Manipulation at Scale, Big Data, Text Mining, HBase, Deploying HBase
Hbase in action - Chapter 09: Deploying HBasephanleson
Hbase in action - Chapter 09: Deploying HBase
Learning HBase, Real-time Access to Your Big Data, Data Manipulation at Scale, Big Data, Text Mining, HBase, Deploying HBase
Datastage is an ETL tool with client-server architecture. It uses jobs to design data flows from source to target systems. A job contains source definitions, target definitions, and transformation rules. The main Datastage components include the Administrator, Designer, Director, and Manager clients and the Repository, Server, and job execution components. Jobs can be server jobs for smaller data volumes or parallel jobs for larger volumes and use of parallel processing. Stages define sources, targets, and processing in a job. Common stages include files, databases, and transformation stages like Aggregator and Copy.
The document discusses Oracle database memory management. It describes the basic memory structures as software code areas, the system global area (SGA), and the program global area (PGA). It recommends enabling automatic memory management, which allows Oracle to dynamically manage and tune the total instance memory between the SGA and instance PGA. The document provides steps to enable automatic memory management, which involves calculating a MEMORY_TARGET parameter size and restarting the database.
What is Data Warehousing? ,
Who needs Data Warehousing? ,
Why Data Warehouse is required? ,
Types of Systems ,
OLTP
OLAP
Maintenance of Data Warehouse
Data Warehousing Life Cycle
Datastage parallell jobs vs datastage server jobsshanker_uma
The document compares Datastage parallel jobs and server jobs. Parallel jobs can take advantage of parallelism through features like partitioning and pipelining to enhance speed and performance when loading large amounts of data. Parallel jobs run on a multiprocessor system allowing both pipeline parallelism, where data is exchanged between stages as soon as it is available, and partitioning parallelism, where records are divided among nodes. In contrast, server jobs do not have built-in mechanisms for parallelism between stages.
HBase is a column-oriented NoSQL database that provides random real-time read/write access to big data stored in Hadoop's HDFS. It is modeled after Google's Bigtable and sits on top of HDFS to allow fast access to large datasets. HBase architecture includes HMaster, HRegionServers, ZooKeeper, and HDFS. HMaster manages metadata and load balancing while HRegionServers serve read/write requests directly from clients. ZooKeeper coordinates the cluster and HDFS provides storage. Data is stored in tables divided into regions hosted by HRegionServers.
HBase In Action - Chapter 10 - Operationsphanleson
HBase In Action - Chapter 10: Operations
Learning HBase, Real-time Access to Your Big Data, Data Manipulation at Scale, Big Data, Text Mining, HBase, Deploying HBase
Hbase in action - Chapter 09: Deploying HBasephanleson
Hbase in action - Chapter 09: Deploying HBase
Learning HBase, Real-time Access to Your Big Data, Data Manipulation at Scale, Big Data, Text Mining, HBase, Deploying HBase
Datastage is an ETL tool with client-server architecture. It uses jobs to design data flows from source to target systems. A job contains source definitions, target definitions, and transformation rules. The main Datastage components include the Administrator, Designer, Director, and Manager clients and the Repository, Server, and job execution components. Jobs can be server jobs for smaller data volumes or parallel jobs for larger volumes and use of parallel processing. Stages define sources, targets, and processing in a job. Common stages include files, databases, and transformation stages like Aggregator and Copy.
The document discusses Oracle database memory management. It describes the basic memory structures as software code areas, the system global area (SGA), and the program global area (PGA). It recommends enabling automatic memory management, which allows Oracle to dynamically manage and tune the total instance memory between the SGA and instance PGA. The document provides steps to enable automatic memory management, which involves calculating a MEMORY_TARGET parameter size and restarting the database.
What is Data Warehousing? ,
Who needs Data Warehousing? ,
Why Data Warehouse is required? ,
Types of Systems ,
OLTP
OLAP
Maintenance of Data Warehouse
Data Warehousing Life Cycle
Datastage parallell jobs vs datastage server jobsshanker_uma
The document compares Datastage parallel jobs and server jobs. Parallel jobs can take advantage of parallelism through features like partitioning and pipelining to enhance speed and performance when loading large amounts of data. Parallel jobs run on a multiprocessor system allowing both pipeline parallelism, where data is exchanged between stages as soon as it is available, and partitioning parallelism, where records are divided among nodes. In contrast, server jobs do not have built-in mechanisms for parallelism between stages.
Day 1 Data Stage Administrator And Director 11.0kshanmug2
DataStage is a widely used ETL tool that has both an administrator and director component. The administrator allows you to prepare project setup, perform general administration, and assign user roles. The director allows you to monitor, schedule, run jobs, and view job logs. It provides options to validate, run, stop, reset, schedule, and clean up resources for jobs. The administrator and director provide tools to configure and manage DataStage projects and job executions.
This document contains answers to questions about Informatica and data warehousing concepts. It defines key Informatica components like the Designer, Server Manager and Repository Manager. It describes how to create mappings, sessions, transformations and reusable objects. It also covers data warehousing topics such as the differences between OLTP and data warehousing systems, and between views and materialized views in a data warehouse.
DataStage Online Training, Job Oriented Data Stage Training Classes by Real Time Expert for India, USA, Canada, UK, Japan, Singapore , Hyderabad, Bangalore, Pune @ +91 7680813158
This document provides an overview of Hadoop and how it addresses the challenges of big data. It discusses how Hadoop uses a distributed file system (HDFS) and MapReduce programming model to allow processing of large datasets across clusters of computers. Key aspects summarized include how HDFS works using namenodes and datanodes, how MapReduce leverages mappers and reducers to parallelize processing, and how Hadoop provides fault tolerance.
The document discusses installing DataStage and configuring projects. It describes installing the DataStage server first before installing any clients, and provides an overview of the server installation process which includes entering license information and selecting installation directories and options. It also briefly outlines installing the DataStage clients after the server and the different editions available, and notes that projects must be configured and opened before using any of the DataStage tools.
Hadoop, Evolution of Hadoop, Features of HadoopDr Neelesh Jain
Hadoop, Evolution of Hadoop, Features of Hadoop is explained in the presentation as per the syllabus of RGPV, BU and MCU for the students of BCA, MCA and B. Tech.
The document discusses new features in IBM Information Server/DataStage 11.3. Key points include:
- The Hierarchical Data stage was renamed and can now process JSON and includes new REST, JSON parsing, and composition steps.
- The Big Data File stage supports more Hadoop distributions and Greenplum and Master Data Management connector stages were added.
- The Amazon S3 and Microsoft Excel connectors were enhanced.
- Sorting and record delimiting were optimized and Operations Console/Workload Manager are now default features.
The document discusses NoSQL databases and big data frameworks. It defines NoSQL databases as next generation databases that are non-relational, distributed, open-source and horizontally scalable. It describes four main categories of NoSQL databases - document databases, key-value stores, column-oriented databases and graph databases. It also discusses properties of NoSQL databases and provides examples of popular NoSQL databases. The document then discusses big data frameworks like Hadoop and its ecosystem including HDFS, MapReduce, YARN and Hadoop Common. It provides details on how these components work together to process large datasets in a distributed manner.
This document lists Oracle 19c initialization parameters, including their default values and descriptions. Some key parameters include:
DB_BLOCK_SIZE - Sets the database block size, typically 8K for OLTP and 16-32K for OLAP.
DB_CACHE_SIZE - Sets the size of the default buffer cache for standard block sizes. Should be sized to maximize data buffer cache hit ratio.
DB_RECOVERY_FILE_DEST - Sets the default location for control files, redo logs, archived redo logs, flashback logs and RMAN backups in a fast recovery area (FRA).
COMPATIBLE - Database compatibility level. Should not be decreased after upgrade and must be at least three decimal
Shadow paging is a database recovery technique that uses two page tables - a current page table and a shadow page table. During transaction execution, updates are made to copies of pages in the shadow page table rather than directly updating pages. If a crash occurs, the database can be recovered by freeing the modified pages and using the unchanged shadow page table. Shadow paging reduces log overhead during recovery compared to log-based techniques but has disadvantages like increased data fragmentation and higher commit overhead.
Data Storage and Management project ReportTushar Dalvi
This paper aims at evaluating the performance of random reads and random writes the information of HBase and Cassandra and compare the results that we got through various ubuntu operation
DBArtisan and Quest Toad with the DB Admin Module are compared in the document. DBArtisan offers more features that are useful for database administration, including wizard-driven creation of database objects, common editors for objects across platforms, cross-platform and cross-database migration support, SQL logging, PL/SQL formatting and debugging tools, scheduling commands, and advanced performance and capacity monitoring. A case study found DBArtisan provided significant productivity gains and labor cost savings equivalent to four additional full-time employees for a company managing databases on Sybase, SQL Server and Oracle.
This document provides an introduction to SAS analytics training. It begins with introducing the instructor and their qualifications. It then outlines what will be covered in the training, including an introduction to analytics, the top 5 features of SAS, different types of SAS datasets, how to read data into SAS, and how to plot graphs to understand data. It also discusses what SAS is and why it is widely used, highlighting its maturity, certification programs, product support, and role in large enterprises.
This document provides an overview of Tableau for IT managers, covering Tableau's architecture, deployment models, security features, scalability, and data strategy. Tableau has a client-server architecture that allows for highly scalable deployments from simple single-server configurations up to large enterprise clusters. It provides role-based security, data security through user filters, and network security including SSL encryption. Tableau is highly scalable and supports deployments from small teams up to thousands of users at large companies.
This document discusses various data warehousing concepts. It begins by explaining that fact tables can share dimension tables and that typically multiple dimension tables are associated with a single fact table. It then defines ROLAP, MOLAP, and DOLAP architectures for OLAP and discusses how data is stored in each. An MDDB is described as a multidimensional database that stores data in multidimensional arrays, whereas an RDBMS stores data in tables and columns. The differences between OLTP and OLAP systems are outlined. Transformations in ETL are explained as manipulating data from its source form into a simplified form for the data warehouse. Filter transformations are briefly described. Finally, supported default source types for Informatica Power
1. Introduction to the Course "Designing Data Bases with Advanced Data Models...Fabio Fumarola
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
This document outlines a 5-level framework for documenting a business's processes and procedures, with the levels ranging from high-level mission and strategy descriptions to low-level technical procedures. Level 1 provides a mission statement, Level 2 covers business strategy, Level 3 gives a global process overview, Level 4 delves into functional descriptions and subprocesses, and Level 5 contains technical systems procedures and task documentation.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
презентация Microsoft office power pointYulya Tkachuk
The document discusses the history and various meanings of the term "freak". It describes how the term was originally used to refer to physically deformed people in sideshows but now can also refer to those with unusual behaviors. It outlines how the 1960s "freak scene" embraced the term as a badge of honor for those rejecting social norms. The document also explores various ways people intentionally alter their physical appearance through tattoos, piercings, hair dye or cosmetic surgery to consider themselves "made freaks".
Abengoa es una empresa que aplica soluciones tecnológicas innovadoras para el desarrollo sostenible
en los sectores de energía y medioambiente, generando electricidad a partir de recursos renovables,
transformando biomasa en biocombustibles o produciendo agua potable a partir del agua de mar. Articula
su negocio en torno a tres actividades: ingeniería y construcción, infraestructuras de tipo concesional y
producción industrial.
Clojure REPL or cluster deployment with DockerFilippo Vitale
Clojure REPL or cluster deployment with Docker – January 2014 by Filippo Vitale – January 30, 2014
What is Docker and how to use it to spawn a Clojure REPL or for deploy and run a cluster of services.
Este documento describe los mundos virtuales, su historia y Second Life. Explica que los mundos virtuales son simulaciones de entornos tridimensionales relacionados con la inteligencia artificial. Su historia se remonta a los años 60 y 70 cuando se desarrollaron las tecnologías subyacentes. Second Life es un metaverso lanzado en 2003 donde los usuarios pueden interactuar a través de avatares, explorar el mundo virtual, crear objetos y comerciar con propiedades virtuales.
Day 1 Data Stage Administrator And Director 11.0kshanmug2
DataStage is a widely used ETL tool that has both an administrator and director component. The administrator allows you to prepare project setup, perform general administration, and assign user roles. The director allows you to monitor, schedule, run jobs, and view job logs. It provides options to validate, run, stop, reset, schedule, and clean up resources for jobs. The administrator and director provide tools to configure and manage DataStage projects and job executions.
This document contains answers to questions about Informatica and data warehousing concepts. It defines key Informatica components like the Designer, Server Manager and Repository Manager. It describes how to create mappings, sessions, transformations and reusable objects. It also covers data warehousing topics such as the differences between OLTP and data warehousing systems, and between views and materialized views in a data warehouse.
DataStage Online Training, Job Oriented Data Stage Training Classes by Real Time Expert for India, USA, Canada, UK, Japan, Singapore , Hyderabad, Bangalore, Pune @ +91 7680813158
This document provides an overview of Hadoop and how it addresses the challenges of big data. It discusses how Hadoop uses a distributed file system (HDFS) and MapReduce programming model to allow processing of large datasets across clusters of computers. Key aspects summarized include how HDFS works using namenodes and datanodes, how MapReduce leverages mappers and reducers to parallelize processing, and how Hadoop provides fault tolerance.
The document discusses installing DataStage and configuring projects. It describes installing the DataStage server first before installing any clients, and provides an overview of the server installation process which includes entering license information and selecting installation directories and options. It also briefly outlines installing the DataStage clients after the server and the different editions available, and notes that projects must be configured and opened before using any of the DataStage tools.
Hadoop, Evolution of Hadoop, Features of HadoopDr Neelesh Jain
Hadoop, Evolution of Hadoop, Features of Hadoop is explained in the presentation as per the syllabus of RGPV, BU and MCU for the students of BCA, MCA and B. Tech.
The document discusses new features in IBM Information Server/DataStage 11.3. Key points include:
- The Hierarchical Data stage was renamed and can now process JSON and includes new REST, JSON parsing, and composition steps.
- The Big Data File stage supports more Hadoop distributions and Greenplum and Master Data Management connector stages were added.
- The Amazon S3 and Microsoft Excel connectors were enhanced.
- Sorting and record delimiting were optimized and Operations Console/Workload Manager are now default features.
The document discusses NoSQL databases and big data frameworks. It defines NoSQL databases as next generation databases that are non-relational, distributed, open-source and horizontally scalable. It describes four main categories of NoSQL databases - document databases, key-value stores, column-oriented databases and graph databases. It also discusses properties of NoSQL databases and provides examples of popular NoSQL databases. The document then discusses big data frameworks like Hadoop and its ecosystem including HDFS, MapReduce, YARN and Hadoop Common. It provides details on how these components work together to process large datasets in a distributed manner.
This document lists Oracle 19c initialization parameters, including their default values and descriptions. Some key parameters include:
DB_BLOCK_SIZE - Sets the database block size, typically 8K for OLTP and 16-32K for OLAP.
DB_CACHE_SIZE - Sets the size of the default buffer cache for standard block sizes. Should be sized to maximize data buffer cache hit ratio.
DB_RECOVERY_FILE_DEST - Sets the default location for control files, redo logs, archived redo logs, flashback logs and RMAN backups in a fast recovery area (FRA).
COMPATIBLE - Database compatibility level. Should not be decreased after upgrade and must be at least three decimal
Shadow paging is a database recovery technique that uses two page tables - a current page table and a shadow page table. During transaction execution, updates are made to copies of pages in the shadow page table rather than directly updating pages. If a crash occurs, the database can be recovered by freeing the modified pages and using the unchanged shadow page table. Shadow paging reduces log overhead during recovery compared to log-based techniques but has disadvantages like increased data fragmentation and higher commit overhead.
Data Storage and Management project ReportTushar Dalvi
This paper aims at evaluating the performance of random reads and random writes the information of HBase and Cassandra and compare the results that we got through various ubuntu operation
DBArtisan and Quest Toad with the DB Admin Module are compared in the document. DBArtisan offers more features that are useful for database administration, including wizard-driven creation of database objects, common editors for objects across platforms, cross-platform and cross-database migration support, SQL logging, PL/SQL formatting and debugging tools, scheduling commands, and advanced performance and capacity monitoring. A case study found DBArtisan provided significant productivity gains and labor cost savings equivalent to four additional full-time employees for a company managing databases on Sybase, SQL Server and Oracle.
This document provides an introduction to SAS analytics training. It begins with introducing the instructor and their qualifications. It then outlines what will be covered in the training, including an introduction to analytics, the top 5 features of SAS, different types of SAS datasets, how to read data into SAS, and how to plot graphs to understand data. It also discusses what SAS is and why it is widely used, highlighting its maturity, certification programs, product support, and role in large enterprises.
This document provides an overview of Tableau for IT managers, covering Tableau's architecture, deployment models, security features, scalability, and data strategy. Tableau has a client-server architecture that allows for highly scalable deployments from simple single-server configurations up to large enterprise clusters. It provides role-based security, data security through user filters, and network security including SSL encryption. Tableau is highly scalable and supports deployments from small teams up to thousands of users at large companies.
This document discusses various data warehousing concepts. It begins by explaining that fact tables can share dimension tables and that typically multiple dimension tables are associated with a single fact table. It then defines ROLAP, MOLAP, and DOLAP architectures for OLAP and discusses how data is stored in each. An MDDB is described as a multidimensional database that stores data in multidimensional arrays, whereas an RDBMS stores data in tables and columns. The differences between OLTP and OLAP systems are outlined. Transformations in ETL are explained as manipulating data from its source form into a simplified form for the data warehouse. Filter transformations are briefly described. Finally, supported default source types for Informatica Power
1. Introduction to the Course "Designing Data Bases with Advanced Data Models...Fabio Fumarola
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
This document outlines a 5-level framework for documenting a business's processes and procedures, with the levels ranging from high-level mission and strategy descriptions to low-level technical procedures. Level 1 provides a mission statement, Level 2 covers business strategy, Level 3 gives a global process overview, Level 4 delves into functional descriptions and subprocesses, and Level 5 contains technical systems procedures and task documentation.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
презентация Microsoft office power pointYulya Tkachuk
The document discusses the history and various meanings of the term "freak". It describes how the term was originally used to refer to physically deformed people in sideshows but now can also refer to those with unusual behaviors. It outlines how the 1960s "freak scene" embraced the term as a badge of honor for those rejecting social norms. The document also explores various ways people intentionally alter their physical appearance through tattoos, piercings, hair dye or cosmetic surgery to consider themselves "made freaks".
Abengoa es una empresa que aplica soluciones tecnológicas innovadoras para el desarrollo sostenible
en los sectores de energía y medioambiente, generando electricidad a partir de recursos renovables,
transformando biomasa en biocombustibles o produciendo agua potable a partir del agua de mar. Articula
su negocio en torno a tres actividades: ingeniería y construcción, infraestructuras de tipo concesional y
producción industrial.
Clojure REPL or cluster deployment with DockerFilippo Vitale
Clojure REPL or cluster deployment with Docker – January 2014 by Filippo Vitale – January 30, 2014
What is Docker and how to use it to spawn a Clojure REPL or for deploy and run a cluster of services.
Este documento describe los mundos virtuales, su historia y Second Life. Explica que los mundos virtuales son simulaciones de entornos tridimensionales relacionados con la inteligencia artificial. Su historia se remonta a los años 60 y 70 cuando se desarrollaron las tecnologías subyacentes. Second Life es un metaverso lanzado en 2003 donde los usuarios pueden interactuar a través de avatares, explorar el mundo virtual, crear objetos y comerciar con propiedades virtuales.
voip2day 2012 - Asterisk update by Steve SokolVOIP2DAY
The document introduces Asterisk 11, an open source communications software. Key highlights include:
1) Asterisk 11 includes new features like WebSockets and improved WebRTC support to enable real-time communications over web browsers.
2) WebSockets allow SIP to use multiple transports including UDP, TCP, TLS and WebSockets. This enables SIP over WebRTC.
3) The changes pave the way for instantly enabling VoIP capabilities in any web browser without additional software, allowing for unified communications like voice, video and screen sharing over the web.
Social Media, ICT, Mobile & Gezondheid - eHealth, mHealthSacan
Presentatie aan studenten van Fontys minor "Gezondheid en Technologie" in Eindhoven over Social Media, ICT, Mobile in samenhang met gezondheidssector, gezondheid. Welke ziekenhuizen, dokters zijn actief op social media. Informatie en voorbeelden over webcare, apps en ontwikkelingen binnen IT & gezondheid.
iMoroz - мобильное приложение для корпоративного Нового годаEventPlatform
Вовлечь ВСЕХ гостей новогоднего мероприятия в интерактивные конкурсы, викторины и опросы – с помощью очень простого мобильного приложения, которое гости установят на свои мобильные телефоны или планшеты
Techno is a form of electronic dance music that originated in Detroit, Michigan in the late 1980s. It was first referred to as a genre of music in 1988. Many styles of techno now exist but Detroit techno is seen as the foundational style that other subgenres have been built upon.
Los parámetros de operación de una máquina proporcionan información actualizada para fabricantes y usuarios sobre cómo operar las máquinas de manera segura, eficiente y con los mejores rendimientos. Estos parámetros determinan los requisitos y condiciones necesarias para la operación, garantizando la seguridad de los operadores y el óptimo aprovechamiento de la máquina. Es importante seguir los parámetros específicos de cada máquina para operarlas de forma segura y obtener el máximo provecho.
Este documento presenta a Jesús Leal Gutiérrez, autor del libro "La Autonomía del Sujeto Investigador y la Metodología de Investigación". Se incluye una breve biografía de Jesús Leal Gutiérrez y una introducción al libro. Finalmente, se presenta el prólogo escrito por Susana Gómez, quien elogia la obra de Leal Gutiérrez por tratar temas importantes como la autonomía del investigador y diferentes formas de hacer ciencia.
Bulevarde largi, numeroase parcuri şi spaţii verzi, precum şi centrul vechi al Bucureştiului sunt câteva din trăsăturile capitalei României. Acest eBook isi propune sa prezinte câteva clădiri impresionante si cele mai representative atractii ale oraşului incluse intr-o imagine de ansamblu a sa.
Plast is a non-political, denominational youth organization in Ukraine with over 10,000 scouts. It is the oldest and largest scouting organization in Ukraine, with a history of over 100 years. Plast organizes over 150 educational camps annually in areas like sports, arts, and ecology to help scouts apply knowledge gained from weekly classes. Plast cooperates with government agencies and organizations and has supported by the President of Ukraine to promote scouting in the country.
Luxury Apartments, Sky Villas, Flats in Vijayawada | Mid Valley Citymidvalleycity city
Mid Valley City is a with mixed development sprawling over 15.5 acres. It includes plush residential apartments, Luxury Flats,Sky Villas and commercial space of prime land near Mangalagiri Vijayawada.
The goth subculture originated in the UK in the early 1980s from the gothic rock music scene. It has endured longer than many subcultures of that era and has diversified over time. The goth subculture is characterized by a dark aesthetic inspired by gothic literature, horror films, and BDSM culture that is expressed through styles of music, fashion, and art. Some of the main musical genres include gothic rock, deathrock, and darkwave. Fashion includes dark colors, Victorian or medieval-inspired clothing, heavy makeup, and accessories. While the subculture is generally considered non-violent, some high-profile crimes committed by individuals who identified as goth have contributed
This document provides best practices for using key ABAP programming features, including data storage and retrieval, dynamic programming, and administrative issues. It recommends storing persistent data in database tables and using shared objects in shared memory instead of shared buffers. For dynamic programming, it suggests prudent use and preferring dynamic token specification over code generation. It also covers best practices for dynamic data objects, anonymous objects, field symbols, dynamic tokens, RTTI/RTTC, and program generation. Finally, it discusses testing, documenting, and using packages for programs.
Apache Spark is a fast and general engine for large-scale data processing that eBay uses to improve user experiences, provide relevant offers, and optimize performance. Spark provides simple programming abstractions and powerful in-memory caching capabilities to enable high-performance iterative processing of large datasets. At eBay, Spark jobs are commonly run on Hadoop clusters using Yarn and process data stored in HDFS, with many jobs written in Scala. Spark is helping eBay create more value from its data and its use is expanding from experimental to everyday.
The document summarizes findings from a project testing batch processing performance using J2EE. It discusses considerations for batch frameworks, infrastructure, caching, logging, design challenges, and whether to use batch processing. It also outlines the design of the batch process used, including leveraging raw JDBC, Oracle caching, and tools for performance monitoring.
1) The document outlines the tasks, tools, and topics explored by Vipul Divyanshu during a summer internship at India Innovation Labs, including data analytics on a medium-sized database and building a recommender engine.
2) Key tools explored include Mahout for machine learning algorithms, Hadoop for distributed processing, and Rush Analyzer (with KNIME) for data visualization and analytics.
3) Vipul implemented recommendation engines including user-based, item-based, and SlopeOne recommenders and evaluated performance using recommender evaluators.
NoSQL databases are non-relational databases designed for large volumes of data across many servers. They emerged to address scaling and reliability issues with relational databases. While different technologies, NoSQL databases are designed for distribution without a single point of failure and to sacrifice consistency for availability if needed. Examples include Dynamo, BigTable, Cassandra and CouchDB.
Today, many businesses around the world are using an Oracle product and in many of these at the core there is an Oracle Database. Many of us who started as a Database administrator where put in this position because we were good PL/SQL programmers or good Sysadmins, but knew very little of what it took to be a DBA. In this session you will learn the core architecture of an Oracle Database in 12c as well as what it takes to administer and apply this new knowledge the day you go back to your office.
SAP Data Archiving allows organizations to remove old data from their SAP database and store it externally to reduce costs and improve performance. The archiving process involves creating archive files, running delete programs to remove data from the database, and storing the archive files externally. Archiving objects define which data to archive and how. The archive information system then allows users to search and retrieve archived data.
Matteo Moretti discusses scaling PHP applications. He covers scaling the web server, sessions, database, filesystem, asynchronous tasks, and logging. The key aspects are decoupling services, using caching, moving to external services like Redis, S3, and RabbitMQ, and allowing those services to scale automatically using techniques like auto-scaling. Sharding the database is difficult to implement and should only be done if really needed.
The document discusses setting up a MongoDB Atlas cloud database account and adding a MongoDB load/save class to a Result Calculator project. It describes creating a MongoDB Atlas cluster, connecting an application, and adding methods to a MongoDBAccess class to load data from records into MongoDB and save records from MongoDB. Code snippets are provided for implementing MongoDB connection and various load/save methods.
Impact of in-memory technology and SAP HANA (2012 Update)Vitaliy Rudnytskiy
The document is a presentation from September 2012 about the impact of in-memory technology and SAP HANA on businesses, IT, and careers. It discusses how SAP is executing on its in-memory vision and how this is reshaping how businesses and IT use SAP solutions. It also impacts the skills required for different roles. The presentation provides an overview of in-memory concepts, principles, and the SAP HANA platform, and how they can be applied using tools like SAP BusinessObjects. It encourages attendees to learn more about this emerging technology area.
2015 01-17 Lambda Architecture with Apache Spark, NextML ConferenceDB Tsai
Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods. In Lambda architecture, the system involves three layers: batch processing, speed (or real-time) processing, and a serving layer for responding to queries, and each comes with its own set of requirements.
In batch layer, it aims at perfect accuracy by being able to process the all available big dataset which is an immutable, append-only set of raw data using distributed processing system. Output will be typically stored in a read-only database with result completely replacing existing precomputed views. Apache Hadoop, Pig, and HIVE are
the de facto batch-processing system.
In speed layer, the data is processed in streaming fashion, and the real-time views are provided by the most recent data. As a result, the speed layer is responsible for filling the "gap" caused by the batch layer's lag in providing views based on the most recent data. This layer's views may not be as accurate as the views provided by batch layer's views created with full dataset, so they will be eventually replaced by the batch layer's views. Traditionally, Apache Storm is
used in this layer.
In serving layer, the result from batch layer and speed layer will be stored here, and it responds to queries in a low-latency and ad-hoc way.
One of the lambda architecture examples in machine learning context is building the fraud detection system. In speed layer, the incoming streaming data can be used for online learning to update the model learnt in batch layer to incorporate the recent events. After a while, the model can be rebuilt using the full dataset.
Why Spark for lambda architecture? Traditionally, different
technologies are used in batch layer and speed layer. If your batch system is implemented with Apache Pig, and your speed layer is implemented with Apache Storm, you have to write and maintain the same logics in SQL and in Java/Scala. This will very quickly becomes a maintenance nightmare. With Spark, we have an unified development framework for batch and speed layer at scale. In this talk, an end-to-end example implemented in Spark will be shown, and we will
discuss about the development, testing, maintenance, and deployment of lambda architecture system with Apache Spark.
Presentation detailed about capabilities of In memory Analytic using Apache Spark. Apache Spark overview with programming mode, cluster mode with Mosos, supported operations and comparison with Hadoop Map Reduce. Elaborating Apache Spark Stack expansion like Shark, Streaming, MLib, GraphX
Beginner's Guide: Programming with ABAP on HANAAshish Saxena
The focus of this blog is to present an overview of the new programming techniques in ABAP after the introduction of HANA database. The focus will be towards providing a guideline on why and how an ABAP developer should start transitioning its code to use the new coding technique’s.
This document provides an overview of an Oracle DBA walkthrough presentation. It includes a table of contents covering topics like the duties of database administrators, memory and process architecture, instance startup and shutdown, and tools for DBAs. It also introduces the presenter, Akash Pramanik, who is an Oracle DBA by profession and freelance trainer.
The document provides details about experiments to be performed in the Big Data Analytics lab course. It includes 8 experiments: 1) Implementing common data structures in Java like linked lists, stacks, queues, sets and maps. 2) Setting up Hadoop in standalone, pseudo-distributed and fully distributed modes. 3) Performing file management tasks in Hadoop like adding, retrieving and deleting files. 4) Running a basic word count MapReduce program. 5) Writing a MapReduce program to analyze weather data. 6) Implementing matrix multiplication using MapReduce. 7) Installing and using Pig to write Pig Latin scripts to sort, group, join, project and filter data. 8) Installing and using Hive to create, alter
The document provides details about experiments to be performed in the Big Data Analytics lab course. It includes implementing various data structures like linked lists, stacks, queues, sets and maps in Java. It also describes setting up Hadoop in standalone, pseudodistributed and fully distributed modes. Other experiments involve performing file management tasks in Hadoop, running a basic word count MapReduce program, writing MapReduce programs to analyze weather data, implementing matrix multiplication in MapReduce, installing and using Pig and Hive with Hadoop, and solving some real-life big data problems.
The document discusses NHibernate, an open source object-relational mapping framework for .NET. It begins by describing some of the limitations of using ADO.NET datasets for data access and how NHibernate provides a more object-oriented approach. It then provides steps to get started with NHibernate, including configuring NHibernate, defining a domain model, mapping the domain model to database tables, and generating the necessary code.
Near Real Time Indexing Kafka Messages into Apache Blur: Presented by Dibyend...Lucidworks
This document discusses Pearson's use of Apache Blur for distributed search and indexing of data from Kafka streams into Blur. It provides an overview of Pearson's learning platform and data architecture, describes the benefits of using Blur including its scalability, fault tolerance and query support. It also outlines the challenges of integrating Kafka streams with Blur using Spark and the solution developed to provide a reliable, low-level Kafka consumer within Spark that indexes messages from Kafka into Blur in near real-time.
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Cat Plein, Development & Communications Director of Forth, gave this presentation at the Forth and Electrification Coalition CFI Grant Program - Overview and Technical Assistance webinar on June 12, 2024.
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