Apache Ambari: Managing Hadoop and YARNHortonworks
Part of the Hortonworks YARN Ready Webinar Series, this session is about management of Apache Hadoop and YARN using Apache Ambari. This series targets developers and we will feature a demo on Ambari.
Getting Ready to Use Redis with Apache Spark with Dvir VolkSpark Summit
Getting Ready to use Redis with Apache Spark is a technical tutorial designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. To set the context for the session, we start with a quick introduction to Redis and the capabilities Redis provides. We cover the basic data types provided by Redis and cover the module system. Using an ad serving use-case, we look at how Redis can improve the performance and reduce the cost of using complex ML-models in production. Attendees will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will be discussed focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these feature. At the end of the session, developers should feel confident building a prototype/proof-of-concept application using Redis and Spark. Attendees will understand how Redis complements Spark and how to use Redis to serve complex, ML-models with high performance.
Dit is een begeleidende presentatie bij het hoofdstuk 5.2 van het Sleutelboek Computerhardware. Deze presentatie mag vrij worden gebruikt, aangepast en verspreid.
Meer informatie over het Sleutelboek Computerhardware is beschikbaar op www.sleutelboek.eu
IBC 2022 IP Showcase - Timestamps in ST 2110: What They Mean and How to Measu...Kieran Kunhya
Timestamps in ST 2110 are exceptionally important but many ST 2110 tutorials lack the time to go into detail about how they work, how they relate to PTP, and, for example, the differences between RTP timestamps and packet arrival times. This presentation will aim to fill that gap and allow engineers to diagnose problems with timestamps based on examples from real world facilities.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Apache Ambari: Managing Hadoop and YARNHortonworks
Part of the Hortonworks YARN Ready Webinar Series, this session is about management of Apache Hadoop and YARN using Apache Ambari. This series targets developers and we will feature a demo on Ambari.
Getting Ready to Use Redis with Apache Spark with Dvir VolkSpark Summit
Getting Ready to use Redis with Apache Spark is a technical tutorial designed to address integrating Redis with an Apache Spark deployment to increase the performance of serving complex decision models. To set the context for the session, we start with a quick introduction to Redis and the capabilities Redis provides. We cover the basic data types provided by Redis and cover the module system. Using an ad serving use-case, we look at how Redis can improve the performance and reduce the cost of using complex ML-models in production. Attendees will be guided through the key steps of setting up and integrating Redis with Spark, including how to train a model using Spark then load and serve it using Redis, as well as how to work with the Spark Redis module. The capabilities of the Redis Machine Learning Module (redis-ml) will be discussed focusing primarily on decision trees and regression (linear and logistic) with code examples to demonstrate how to use these feature. At the end of the session, developers should feel confident building a prototype/proof-of-concept application using Redis and Spark. Attendees will understand how Redis complements Spark and how to use Redis to serve complex, ML-models with high performance.
Dit is een begeleidende presentatie bij het hoofdstuk 5.2 van het Sleutelboek Computerhardware. Deze presentatie mag vrij worden gebruikt, aangepast en verspreid.
Meer informatie over het Sleutelboek Computerhardware is beschikbaar op www.sleutelboek.eu
IBC 2022 IP Showcase - Timestamps in ST 2110: What They Mean and How to Measu...Kieran Kunhya
Timestamps in ST 2110 are exceptionally important but many ST 2110 tutorials lack the time to go into detail about how they work, how they relate to PTP, and, for example, the differences between RTP timestamps and packet arrival times. This presentation will aim to fill that gap and allow engineers to diagnose problems with timestamps based on examples from real world facilities.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
Tesla, la Nhtsa scagiona il sistema Autopilot: il verbaleAutoblog.it
Nessuna responsabilità del sistema di guida semiautonoma Autopilot nell'incidente mortale che ha coinvolto il 45 enne Joshua Brown a bordo della sua Tesla Model S il 7 maggio nei pressi di Orlando.
Mercato Auto Europa, le vendite nel 2016 Autoblog.it
Nel 2016 il mercato auto del vecchio continente cresce del 6,5%, con l'Italia a +15,8% che tira la volata. Il Gruppo Volkswagen resta leader, per FCA +14,4%
FCA US is disappointed that the EPA has chosen to issue a notice of violation with respect to the emissions control technology employed in the company’s 2014-16 model year light duty 3.0-liter diesel engines.
FCA US looks forward to the opportunity to meet with the EPA’s enforcement division and representatives of the new administration to demonstrate that FCA US’s emissions control strategies are properly justified and thus are not “defeat devices” under applicable regulations and to resolve this matter expeditiously.
1. Tipo carrozzeria Monoscocca
Posti a sedere 5
Esterno
Lunghezza totale con/senza portarga mm 4.555 / 4.540
Larghezza totale con/senza codolini mm 1.840
Larghezza totale (da specchietto a specchietto) mm 2.142
Altezza totale (senza carico) mm 1.670
Passo mm 2.700
Sbalzo anteriore con/senza portarga mm 950 / 935
Sbalzo posteriore mm 905
Carreggiata anteriore mm 1.585
Carreggiata posteriore mm 1.590
Altezza da terra fra gli assi (senza carico) mm 215 (FWD) - 210 (AWD)
Interni
Spazio ant testa senza tetto apribile mm 1.018
Spazio post testa senza tetto apribile mm 991
Spazio anteriore per le spalle mm 1.460
Spazio posteriore per le spalle mm 1.410
Spazio anteriore ai fianchi mm 1.402
Spazio posteriore ai fianchi mm 1.363
Spazio anteriore per le gambe mm 1.041
Spazio posteriore per le gambe mm 997
Spazio post ginocchia mm 66
Bagagliaio
Capacità al copribagagliaio con sedile post alzato (VDA) * L 463
Capacità alla linea di cintura con sedile post alzato* L 503
Capacità al tetto con sedile post abbattuto (VDA) * L 1.620
Altezza dal pavimento al copribagagliaio mm 515
Lunghezza con sedile post alzato mm 982
Lunghezza con sedile post abbattuto mm 1.758
Larghezza fra i passaruota post mm 1.050
Larghezza (al pavimento) mm 1.451
Soglia di accesso portellone, altezza da terra mm 742
Larghezza/altezza apertura portellone mm 1.128 / 827
Dimensioni
58 59
2015 MAZDA CX-5
CARATTERISTICHE TECNICHE
* Compreso vano sottobaule
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MAZDA CX-52015|CARATTERISTICHETECNICHE
3. Sospensione e ruote Sterzo e freni
SKYACTIV-G
2.0
(165 / 160 CV)
SKYACTIV-G
2.5 (192 CV)
n.d. in Italia
SKYACTIV-D
2.2
(150 CV)
SKYACTIV-D
2.2
(175 CV)
Trazione Anteriore (FWD) Integrale (AWD) Integrale (AWD) Anteriore (FWD) Integrale (AWD) Integrale (AWD)
Sospensioni
Sospensioni
anteriori
Montanti Macpherson
Sospensioni
posteriori
Multilink
Diametro
barra stabiliz-
zatrice
(ant / post)
22/18 22/19 22/19 23/18 23/19 23/19
Pneumatici e ruote
Dimensioni
ruote
MT: 17 X 7J
AT: 17 X 7J
19 X 7J
17 X 7J
19 X 7J
17 X 7J
19 X 7J
17 X 7J
17 X 7J
19 X 7J
17 X 7J
19 X 7J
Dimensioni
pneumatici
MT: 225/65R17
AT: 225/65R17
225/55R19
225/65R17
225/55R19
225/65R17
225/55R19
225/65R17
225/65R17
225/55R19
225/65R17
225/55R19
SKYACTIV-G
2.0
(165 / 160 CV)
SKYACTIV-G 2.5
(192 CV)
n.d. in Italia
SKYACTIV-D
2.2
(150 / 175 CV)
Sterzo
Tipo di sterzo Cremagliera e pignone
Tipo di servosterzo EPAS
Rapporto di demoltiplica-
zione sterzo
15,5
Diametro di sterzata fra
marciapiedi
m 11,2
Raggio di sterzata fra
muri (diametro)
m 11,7
Freni
Tipo freni ant Disco (ventilato)
Tipo freni post Disco (pieno)
Diametro ant mm 297
Diametro post mm 303
Diametro pompa
di depressione
pollici 9,0
62 63
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MAZDA CX-52015|CARATTERISTICHETECNICHE
4. Prestazioni e pesi
(Versioni FWD)
Prestazioni e pesi
(Versioni AWD)
SKYACTIV-G 2.0
(165 / 160 CV)
SKYACTIV-D 2.2
(150 / 175 CV)
Trazione FWD FWD FWD FWD
Cambio
6MT
6AT
n.d. in Italia
6MT 6AT
Prestazioni
Velocità max
(con limitatore)
km/h 200 191 202 198
Accelerazione
(0-100 km/h)*
s
9,2(17”)
9,0(19”)
8,9 9,2 10,0
Consumi di carburante
Combinato L/100km 6,0 6,3 4,6 5,3
Extra urbano L/100km 5,1 5,4 4,1 4,7
Urbano L/100km 7,5 7,9 5,4 6,2
Emissioni CO2
(combinato)
g/km 139 147 119 139
Norme emissioni Euro 6 Euro 6 Euro 6 Euro 6
Pesi e carico utile
Peso in o.d.m. min.
(senza conducente)
kg 1.315 1.345 1.420 1.430
Peso in o.d.m. minimo** kg 1.390 1.420 1.495 1.505
Peso max. consentito kg 1.930 1.965 2.050 2.050
Peso consentito
sull'assale ant.
kg 990 1.040 1.115 1.115
Peso consentito
sull'assale post.
kg 1.045 1.040 1.035 1.035
Peso trainabile consentito,
rimorchio privo di freni
kg 690 700 740 750
Peso trainabile consentito,
rimorchio con freni
(pendenza 12%)
kg 1.800 1.800 2.000 2.000
Peso trainabile consentito,
rimorchio con freni
(pendenza 8%)
kg 1.900 1.900 2.000 2.000
Carico max sul tetto kg 100 100 100 100
SKYACTIV-G
2.0
(165 / 160 CV)
SKYACTIV-G
2.5 (192 CV)
n.d. in Italia
SKYACTIV-D
2.2
(150 CV)
SKYACTIV-D
2.2
(175 CV)
Trazione AWD AWD AWD AWD AWD AWD AWD
Cambio 6MT 6AT 6AT 6MT 6AT 6MT 6AT
Prestazioni
Velocità max (con
limitatore)
km/h 197 187 194 197 194 207 204
Accelerazione
(0-100 km/h)*
s 10,5 9,2 8,0 9,4 10,2 8,8 9,4
Consumi di carburante
Combinato L/100km 6,7 6,6 7,2 5,2 5,5 5,2 5,5
Extra urbano L/100km 5,9 5,8 6,0 4,7 4,9 4,7 4,9
Urbano L/100km 8,2 8,0 9,3 6,0 6,4 6,0 6,4
Emissioni CO2
(combinato)
g/km 156 155 165 136 144 136 144
Norme emissioni Euro 6 Euro 6 Euro 6 Euro 6
Pesi e carico utile
Peso in o.d.m. min.
(senza conducente)
kg 1.375 1.405 1.440 1.480 1.495 1.480 1.495
Peso in o.d.m.
minimo**
kg 1.450 1.480 1.515 1.555 1.570 1.555 1.570
Peso max. ammesso kg 2.005 2.035 2.065 2.110 2.125 2.110 2.125
Peso ammesso
sull'assale ant.
kg 1.015 1.050 1.075 1.125 1.145 1.125 1.145
Peso ammesso
sull'assale post.
kg 1.090 1.090 1.095 1.085 1.085 1.085 1.085
Peso trainabile
ammesso, rimorchio
privo di freni
kg 725 735 750 750 750 750 750
Peso trainabile ammes-
so, rimorchio con freni
(pendenza 12%)
kg 1.800 1.800 1.800 2.000 2.000 2.000 2.000
Peso trainabile ammes-
so, rimorchio con freni
(pendenza 8%)
kg 1.900 2.000 2.000 2.100 2.100 2.100 2.100
Carico max sul tetto kg 100 100 100 100 100 100 100
64 65* secondo condizioni di prova di Mazda
** Compresi 75 kg per il conducente
* secondo condizioni di prova di Mazda
** Compresi 75 kg per il conducente
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MAZDA CX-52015|CARATTERISTICHETECNICHE
