Este documento presenta una lista de cámaras de seguridad y DVRs de las marcas Hikvision, AV Tech y TWD, con sus especificaciones y precios. Incluye minidomos, domos, tubos y cámaras de color con resoluciones de hasta 900 TVL, visión nocturna con infrarrojos de hasta 40 metros, protección IP66 e instalación incluida. También se detallan DVRs de 4 a 16 canales con grabación H.264, salida HDMI/VGA y soporte para 1 disco duro de hasta 4
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Top 5 Event Streaming Use Cases for 2021 with Apache KafkaKai Wähner
Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what the predicted TOP 5 Event Streaming Architectures and Use Cases for 2021 are? Check out the following presentation. Learn about edge deployments, hybrid and multi-cloud architectures, service mesh-based microservices, streaming machine learning, and cybersecurity.
On-demand video recording: https://videos.confluent.io/watch/XAjxV3j8hzwCcEKoZVErUJ
Building Pinterest Real-Time Ads Platform Using Kafka Streams confluent
Building Pinterest Real-Time Ads Platform Using Kafka Streams (Liquan Pei + Boyang Chen, Pinterest) Kafka Summit SF 2018
In this talk, we are sharing the experience of building Pinterest’s real-time Ads Platform utilizing Kafka Streams. The real-time budgeting system is the most mission-critical component of the Ads Platform as it controls how each ad is delivered to maximize user, advertiser and Pinterest value. The system needs to handle over 50,000 queries per section (QPS) impressions, requires less than five seconds of end-to-end latency and recovers within five minutes during outages. It also needs to be scalable to handle the fast growth of Pinterest’s ads business.
The real-time budgeting system is composed of real-time stream-stream joiner, real-time spend aggregator and a spend predictor. At Pinterest’s scale, we need to overcome quite a few challenges to make each component work. For example, the stream-stream joiner needs to maintain terabyte size state while supporting fast recovery, and the real-time spend aggregator needs to publish to thousands of ads servers while supporting over one million read QPS. We choose Kafka Streams as it provides milliseconds latency guarantee, scalable event-based processing and easy-to-use APIs. In the process of building the system, we performed tons of tuning to RocksDB, Kafka Producer and Consumer, and pushed several open source contributions to Apache Kafka. We are also working on adding a remote checkpoint for Kafka Streams state to reduce the time of code start when adding more machines to the application. We believe that our experience can be beneficial to people who want to build real-time streaming solutions at large scale and deeply understand Kafka Streams.
Temporal-Joins in Kafka Streams and ksqlDB | Matthias Sax, ConfluentHostedbyConfluent
Joins in Kafka Streams and ksqlDB are a killer-feature for data processing and basic join semantics are well understood. However, in a streaming world records are associated with timestamps that impact the semantics of joins: welcome to the fabulous world of _temporal_ join semantics. For joins, timestamps are as important as the actual data and it is important to understand how they impact the join result.
In this talk we want to deep dive on the different types of joins, with a focus of their temporal aspect. Furthermore, we relate the individual join operators to the overall ""time engine"" of the Kafka Streams query runtime and explain its relationship to operator semantics. To allow developers to apply their knowledge on temporal join semantics, we provide best practices, tip and tricks to ""bend"" time, and configuration advice to get the desired join results. Last, we give an overview of recent, and an outlook to future, development that improves joins even further.
Halal has now become a universal concept. Halal is a term exclusively used in Islam which means permitted or lawful. There are no parties which can claim the food is Halal without complying with Islamic Law. Halal and non-Halal covers all spectrums of Muslim life, not limited to foods and drinks only, but also for safety, animal welfare, social justice and sustainable environment. Halal and Toyyiban which means clean and wholesome portray the symbol of intolerance to hygiene, safety and quality of food that Muslims consumed. ( K. Baharuddin, N. A. Kassim, S K. Nordin & S. Z. Buyong. et al. 2015)
Why My Streaming Job is Slow - Profiling and Optimizing Kafka Streams Apps (L...confluent
Kafka Streams performance monitoring and tuning is important for many reasons, including identifying bottlenecks, achieving greater throughput, and capacity planning. In this talk we’ll share the techniques we used to achieve greater performance and save on compute, storage, and cost. We’ll cover: Identifying design bottlenecks in by reviewing logs, metrics, and serdes. State store access patterns, design, and optimization Using profiling tools such as JMX, YourKit etc. Performance tuning of Kafka and Kafka Streams configuration and properties. JVM optimization for correct heap size and garbage collection strategies. Functional programming and imperative programming trade offs.
Building Pinterest Real-Time Ads Platform Using Kafka Streams confluent
Building Pinterest Real-Time Ads Platform Using Kafka Streams (Liquan Pei + Boyang Chen, Pinterest) Kafka Summit SF 2018
In this talk, we are sharing the experience of building Pinterest’s real-time Ads Platform utilizing Kafka Streams. The real-time budgeting system is the most mission-critical component of the Ads Platform as it controls how each ad is delivered to maximize user, advertiser and Pinterest value. The system needs to handle over 50,000 queries per section (QPS) impressions, requires less than five seconds of end-to-end latency and recovers within five minutes during outages. It also needs to be scalable to handle the fast growth of Pinterest’s ads business.
The real-time budgeting system is composed of real-time stream-stream joiner, real-time spend aggregator and a spend predictor. At Pinterest’s scale, we need to overcome quite a few challenges to make each component work. For example, the stream-stream joiner needs to maintain terabyte size state while supporting fast recovery, and the real-time spend aggregator needs to publish to thousands of ads servers while supporting over one million read QPS. We choose Kafka Streams as it provides milliseconds latency guarantee, scalable event-based processing and easy-to-use APIs. In the process of building the system, we performed tons of tuning to RocksDB, Kafka Producer and Consumer, and pushed several open source contributions to Apache Kafka. We are also working on adding a remote checkpoint for Kafka Streams state to reduce the time of code start when adding more machines to the application. We believe that our experience can be beneficial to people who want to build real-time streaming solutions at large scale and deeply understand Kafka Streams.
Temporal-Joins in Kafka Streams and ksqlDB | Matthias Sax, ConfluentHostedbyConfluent
Joins in Kafka Streams and ksqlDB are a killer-feature for data processing and basic join semantics are well understood. However, in a streaming world records are associated with timestamps that impact the semantics of joins: welcome to the fabulous world of _temporal_ join semantics. For joins, timestamps are as important as the actual data and it is important to understand how they impact the join result.
In this talk we want to deep dive on the different types of joins, with a focus of their temporal aspect. Furthermore, we relate the individual join operators to the overall ""time engine"" of the Kafka Streams query runtime and explain its relationship to operator semantics. To allow developers to apply their knowledge on temporal join semantics, we provide best practices, tip and tricks to ""bend"" time, and configuration advice to get the desired join results. Last, we give an overview of recent, and an outlook to future, development that improves joins even further.
Halal has now become a universal concept. Halal is a term exclusively used in Islam which means permitted or lawful. There are no parties which can claim the food is Halal without complying with Islamic Law. Halal and non-Halal covers all spectrums of Muslim life, not limited to foods and drinks only, but also for safety, animal welfare, social justice and sustainable environment. Halal and Toyyiban which means clean and wholesome portray the symbol of intolerance to hygiene, safety and quality of food that Muslims consumed. ( K. Baharuddin, N. A. Kassim, S K. Nordin & S. Z. Buyong. et al. 2015)
Why My Streaming Job is Slow - Profiling and Optimizing Kafka Streams Apps (L...confluent
Kafka Streams performance monitoring and tuning is important for many reasons, including identifying bottlenecks, achieving greater throughput, and capacity planning. In this talk we’ll share the techniques we used to achieve greater performance and save on compute, storage, and cost. We’ll cover: Identifying design bottlenecks in by reviewing logs, metrics, and serdes. State store access patterns, design, and optimization Using profiling tools such as JMX, YourKit etc. Performance tuning of Kafka and Kafka Streams configuration and properties. JVM optimization for correct heap size and garbage collection strategies. Functional programming and imperative programming trade offs.
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CENTRO COMERCIAL PUERTA GRANDE
Presentación- PLATAFORMA VIRTUAL E-LEARNING .pptxarelisguerra707
PLATAFORMA VIRTUAL E-LEARNING
Las plataformas virtuales de e-learning son sistemas en línea que permiten la enseñanza y el aprendizaje a través de internet. Estas plataformas facilitan la gestión de cursos, la distribución de materiales educativos, la comunicación entre estudiantes y profesores, y el seguimiento del progreso académico. A continuación, se describen algunas características y ejemplos de plataformas de e-learning populares:
Características Comunes de las Plataformas de E-learning
Gestión de Cursos: Permiten la creación, organización y administración de cursos.
Materiales Educativos: Ofrecen acceso a documentos, videos, presentaciones, y otros recursos educativos.
Evaluaciones y Tareas: Facilitan la creación de exámenes, cuestionarios, y la entrega de tareas.
Interacción: Incluyen herramientas para foros de discusión, chats en vivo, videoconferencias, y mensajería.
Seguimiento del Progreso: Proporcionan reportes y análisis del desempeño y progreso de los estudiantes.
Accesibilidad: Pueden ser accesibles desde múltiples dispositivos, incluyendo computadoras, tablets y smartphones.
1. CÓDIGO MARCA FOTO DESCRIPCIÓN S/. PRECIO
HK-
DS2CE5582N
MINI DOMO ICR DIA&NOCHE | Chip DIS 1/3" | 600 TVL
Resolución:600 Tvl • NTSC:720 (H) x 480 (V) • Lente:
3.6mm • Angulode Visión:68.4°
Iluminación:0.1Lux@F1.2 • Velocidad de Obturador:
1/30 a 1/15,000 • BLC
Alimentación:12Vdc +/- 10% • Consumo:1 Watt. No
incluye fuente. Marca HIKVISION
S/. 110.00
HK-
DS2CE55A2N-
IRP
MINI DOMO ICR DIA&NOCHE | Chip DIS 1/3" | 700 TVL |
IR 10 a 20M
Resolución:700 Tvl • NTSC:976 (H) x 496 (V) • Lente:
3.6mm • Angulode Visión:68.4°
Iluminación:0.1Lux@F1.2/ 0Lux IROn (8un.) • Velocidad
de Obturador:1/60 a 1/100,000 • BLC
Alimentación:12Vdc +/- 10% • Consumo:4 Watt. No
incluye fuente. Marca HIKVISION
S/. 120.00
HK-
DS2CE5582N-
IR
MINI DOMO EXTERIOR ICR DIA&NOCHE | Chip DIS 1/3"
| 600 TVL | IR 10 a 20M | IP66
Resolución:600 Tvl • NTSC:720 (H) x 480 (V) • Lente:
3.6mm • Angulode Visión:70.6°
Iluminación:0.1Lux@F1.2/ 0Lux IROn (8un.) • Velocidad
de Obturador:1/30 a 1/15,000 • BLC
Alimentación:12Vdc +/- 10% • Consumo:4 Watt. No
incluye fuente. Marca HIKVISION
S/. 130.00
HK-
DS2CE55A2N-
IRM
DOMO EXTERIOR ICR DIA&NOCHE | Chip DIS 1/3" | 700
TVL | IR 10 a 20M | IP66
Resolución:700 Tvl • NTSC: 976 (H) x 496 (V) • Lente:
3.6mm • Angulode Visión:68.4°
Iluminación:0.1Lux@F1.2/ 0Lux IROn (8un.) • Velocidad
de Obturador:1/60 a 1/100,000 • BLC
Alimentación:12Vdc +/- 10% • Consumo:4 Watt. No
incluye fuente. Marca HIKVISION
S/. 135.00
HK-
DS2CE55A2N-
VFIR3
DOMO EXTERIOR ICR DIA&NOCHE | Chip DIS 1/3" | 700
TVL | VARIFOCAL | IR 30 a 40M | IP66 81.25 229.94
Resolución:700 Tvl • NTSC:976 (H) x 496 (V) • Lente:2.8-
12mm • Angulode Visión:80° A 27.2°
Iluminación:0.1Lux@F1.2/ 0Lux IROn (36un) • Velocidad
de Obturador:1/60 a 1/100,000 • BLC
Alimentación:12Vdc +/- 10% • Consumo:5 Watt. No
incluye fuente. Marca HIKVISION
S/. 280.00
HK-
DS2CE15A2N-
IR
TUBO EXTERIOR ICR DIA&NOCHE | Chip DIS 1/3" | 700
TVL | IR 10 a 20M | IP66 37.25 105.42
Resolución:700 Tvl • NTSC:976 (H) x 496 (V) • Lente:
3.6mm • Angulode Visión:68.4°
Iluminación:0.1Lux@F1.2/ 0Lux IROn (8un.) • Velocidad
de Obturador:1/60 a 1/100,000 • BLC
Alimentación:12Vdc +/- 10% • Consumo:4 Watt. No
incluye fuente. Marca HIKVISION
S/. 140.00
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CONTROLA VEA SU NEGOCIO EN DIRECTO DESDE SU CELULAR, TABLET,
O PC 24 HORAS 365 días
2. ST-
KPC133ZAD
N
DOMO A COLOR Chip CCD 1/3" | Resolución Estándar | IR 10
a 15M |12 Vdc 30.94 87.56
Resolución:Estándar • NTSC:512 (H) x 492 (V) • Lente:
3.6mm@F2.0 • Angulo de Visión:92.6°
Iluminación: 0.05Lux@F2.0 | 0 Lux con IR (14 un.) • V.
Obturador:1/60 a 1/100,000 • ATW
Alimentación:12Vdc +/- 10% • Consumo:170mA. No incluye
fuente. Marca AV TECH
S/. 110.00
ST-
KPC133ZEN
DOMO A COLOR Chip CCD 1/3" | 520 TVL | IR 10 a 15M |12
Vdc Resolución:520 Tvl • NTSC:771 (H) x 492 (V) • Lente:
3.6mm@F2.0 • Angulo de Visión:92°
Iluminación:0.1Lux@F2.0 Color | 0 Lux con IR (21 un.)• V.
Obturador:1/60 a 1/100,000 • ATW
Alimentación:12Vdc +/- 10% • Consumo:220mA. No incluye
fuente. Marca AV TECH
S/. 120.00
ST-
KPC136A
TUBO EXTERIOR Chip CCD 1/3" | Resolución Estándar | IR
10M |12 Vdc | IP67 30.94 87.56
Resolución:Estándar • NTSC:512 (H) x 492 (V) • Lente:
3.6mm@F2.0 • Angulo de Visión:92.6°
Iluminación:0.05Lux@F2.0 | 0 Lux con IR (14 un.) • V.
Obturador:1/60 a 1/100,000 • ATW
Alimentación:12Vdc +/- 10% • Consumo:200mA. No incluye
fuente. Marca AV TECH
S/. 120.00
ST-
KPC132ZEN MINI DOMO A COLOR Chip CCD 1/3" |520 TVL | 12 Vdc
29.63 83.85
Resolución: 520 Tvl • NTSC:771 (H) x 492 (V) • Lente:
3.6mm@F2.0 • Angulo de Visión:92°
Iluminación:0.1Lux@F2.0 • Velocidad de Obturador:1/60 a
1/100,000 • ATW
Alimentación:12Vdc +/- 10% • Consumo:70mA. No incluye
fuente. Marca AV TECH
S/. 110.00
HK-
DS2CE15A2
N-VFIR3
TUBO EXTERIOR ICR DIA&NOCHE | Chip DIS 1/3" | 700 TVL |
VARIFOCAL | IR 30 a 40M | IP66 78.75 222.86
Resolución:700 Tvl • NTSC:976 (H) x 496 (V) • Lente:2.8-
12mm • Angulode Visión:80° A 27.2°
Iluminación:0.1Lux@F1.2/ 0Lux IR On (36un) • Velocidad de
Obturador:1/60 a 1/100,000 • BLC
Alimentación:12Vdc +/- 10% • Consumo:5 Watt. No incluye
fuente. Marca HIKVISION
S/. 280.00
CONTROLA VEA SU NEGOCIO EN DIRECTO DESDE SU CELULAR, TABLET, O
PC 24 HORAS 365 días