Are your customers looking to build diverse machine learning (ML) solutions on Accumulo? In this talk, we walk through three production ML use cases that require users to interact with Accumulo in very different ways - Supply Chain Risk, Cyber Monitoring, and Forensic Document Search. We discuss the surprising result that we are able to support all of these use cases using the a common set of Accumulo tables.
Les objets connectés : de nombreux cas d'usage Jedha Bootcamp
Aujourd'hui, les objets connectés sont partout et nous entourent sans même s'en apercevoir : téléphones, transports, musique, montres, "The Internet of Things" (IoT) a pris une part importante dans notre vie. En nous montrant des cas d'usages des entreprises telles que la NASA, Airbus, Red bull et d'autres, Sean nous expliquera comment ils fonctionnent et comment sont gérées toutes ces données récoltées.
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.
Visualize some of Austin's open source data using Elasticsearch with Kibana. ObjectRocket's Steve Croce presented this talk on 10/13/17 at the DBaaS event in Austin, TX.
Marcel Kornacker, Software Enginner at Cloudera - "Data modeling for data sci...Dataconomy Media
Marcel Kornacker, the Software Enginner at Cloudera presented "Data modeling for data science: Simplify your workload with complex types", as part of the Big Data, Frankfurt v 2.0 meetup organised on the 12th of May 2016 at the headquarters of codecentric AG.
Les objets connectés : de nombreux cas d'usage Jedha Bootcamp
Aujourd'hui, les objets connectés sont partout et nous entourent sans même s'en apercevoir : téléphones, transports, musique, montres, "The Internet of Things" (IoT) a pris une part importante dans notre vie. En nous montrant des cas d'usages des entreprises telles que la NASA, Airbus, Red bull et d'autres, Sean nous expliquera comment ils fonctionnent et comment sont gérées toutes ces données récoltées.
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.
Visualize some of Austin's open source data using Elasticsearch with Kibana. ObjectRocket's Steve Croce presented this talk on 10/13/17 at the DBaaS event in Austin, TX.
Marcel Kornacker, Software Enginner at Cloudera - "Data modeling for data sci...Dataconomy Media
Marcel Kornacker, the Software Enginner at Cloudera presented "Data modeling for data science: Simplify your workload with complex types", as part of the Big Data, Frankfurt v 2.0 meetup organised on the 12th of May 2016 at the headquarters of codecentric AG.
Time Series data is proliferating with literally every step that we take, just think about things like Fit Bit bracelets that track your every move and financial trading data all of which is timestamped.
Time series data requires high performance reads and writes even with a huge number of data sources. Both speed and scale are integral to success, which makes for a unique challenge for your database.
A time series NoSQL data model requires flexibility to support unstructured, and semi-structured data as well as the ability to write range queries to analyze your time series data. So how can you tackle speed, scale and flexibility all at once?
Join Professional Services Architect Drew Kerrigan and Developer Advocate Matt Brender for a discussion of:
Examples of time series data sets, from IoT to Finance to jet engines
What makes time series queries different from other database queries
How to model your dataset to answer the right questions about your data
How to store, query and analyze a set of time series data points
Learn how a NoSQL database model and Riak TS can help you address the unique challenges of time series data.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and ...YASH Technologies
During the implementation of OpenTSDB, YASH tuned applications and databases to maximize system performance,
distributed the storage of monitored data, and eliminated destructive down-sampling.
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
Control Consultants, Inc, (CCI) developed a robust Niagara metering dashboard to address Niagara metering dashboard and aggregation needs! The metering dashboard is for use on any Niagara device and can be used with Honeywell meters, E-mon D-mon Kw meters or any generic pulse meter.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and C...YASH Technologies
YASH tuned applications and databases to maximize system performance, distributed the storage of monitored data, and eliminated destructive down-sampling.
Slides from Riak TS talk by Rob Genova at Seattle Scalability meetup Feb 24, 2016
Look out for Riak TS 1.3 next qrt and uncorkd sample code repo (a play on untappd)
http://www.meetup.com/Seattle-Scalability-Meetup/events/225955122/
Best Practices: How to Analyze IoT Sensor Data with InfluxDBInfluxData
InfluxDB is the purpose-built time series platform. Its high ingest capability makes it perfect for collecting, storing and analyzing time-stamped data from sensors — down to the nanosecond. The InfluxDB platform has everything developers need: the data collection agent, the database, visualization tools, and data querying and scripting language. Join this webinar as Brian Gilmore provides a product overview; he will also deep-dive with some helpful tips and ticks. Stick around for a live demo and Q&A time.
Join this webinar as Brian Gilmore dives into:
The basics of time series data and applications
A platform overview — learn about InfluxDB, Telegraf, and Flux
InfluxDB use case examples — start collecting data at the edge and use your preferred IoT protocol (i.e. MQTT)
In this training webinar, we will walk you through the basics of InfluxDB – the purpose-built time series database. InfluxDB has everything you need from a time series platform in a single binary – a multi-tenanted time series database, UI and dashboarding tools, background processing and monitoring agent. This one-hour session will include the training and time for live Q&A.
What you will learn
Core concepts of time series databases
An overview of the InfluxDB platform
How to ingesting and query data in InfluxDB
This session defines what time series data is (and isn’t), how the problem domain time series differs from more traditional data workloads like full-text search and examines how InfluxData is differentiated from other proposed solutions. This session also includes a review of the most common use cases and a brief demo of InfluxDB.
Elasticity and security are enabling enterprises to move highly regulated workloads to the AWS Cloud. However, given the sensitivity around this protected customer data, what newly released services can be implemented to remain secure and compliant? Find out in this session for Chief Security, Risk and Compliance Officers.
Speaker: Dave Walker, Security Solutions Architect, Amazon Web Services
How does LightsOutPlanning support you in the challenge of digital supply chain? Take the chance to leverage your data via smart algorithms and machine learning to be on the edge of real time planning in order to make sound decisions.
Discover the key features of the tool; the virtual twin (1), the E2E optimizer (2), the smart parameters module (3), the E2E visibility (4) & the control tower (5).
Start your digital supply chain planning journey today!
Presentation at Logipharma event 2018 by bluecrux.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
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Time Series data is proliferating with literally every step that we take, just think about things like Fit Bit bracelets that track your every move and financial trading data all of which is timestamped.
Time series data requires high performance reads and writes even with a huge number of data sources. Both speed and scale are integral to success, which makes for a unique challenge for your database.
A time series NoSQL data model requires flexibility to support unstructured, and semi-structured data as well as the ability to write range queries to analyze your time series data. So how can you tackle speed, scale and flexibility all at once?
Join Professional Services Architect Drew Kerrigan and Developer Advocate Matt Brender for a discussion of:
Examples of time series data sets, from IoT to Finance to jet engines
What makes time series queries different from other database queries
How to model your dataset to answer the right questions about your data
How to store, query and analyze a set of time series data points
Learn how a NoSQL database model and Riak TS can help you address the unique challenges of time series data.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and ...YASH Technologies
During the implementation of OpenTSDB, YASH tuned applications and databases to maximize system performance,
distributed the storage of monitored data, and eliminated destructive down-sampling.
This is a run-through at a 200 level of the Microsoft Azure Big Data Analytics for the Cloud data platform based on the Cortana Intelligence Suite offerings.
Control Consultants, Inc, (CCI) developed a robust Niagara metering dashboard to address Niagara metering dashboard and aggregation needs! The metering dashboard is for use on any Niagara device and can be used with Honeywell meters, E-mon D-mon Kw meters or any generic pulse meter.
Enhanced Data Visualization provided for 200,000 Machines with OpenTSDB and C...YASH Technologies
YASH tuned applications and databases to maximize system performance, distributed the storage of monitored data, and eliminated destructive down-sampling.
Slides from Riak TS talk by Rob Genova at Seattle Scalability meetup Feb 24, 2016
Look out for Riak TS 1.3 next qrt and uncorkd sample code repo (a play on untappd)
http://www.meetup.com/Seattle-Scalability-Meetup/events/225955122/
Best Practices: How to Analyze IoT Sensor Data with InfluxDBInfluxData
InfluxDB is the purpose-built time series platform. Its high ingest capability makes it perfect for collecting, storing and analyzing time-stamped data from sensors — down to the nanosecond. The InfluxDB platform has everything developers need: the data collection agent, the database, visualization tools, and data querying and scripting language. Join this webinar as Brian Gilmore provides a product overview; he will also deep-dive with some helpful tips and ticks. Stick around for a live demo and Q&A time.
Join this webinar as Brian Gilmore dives into:
The basics of time series data and applications
A platform overview — learn about InfluxDB, Telegraf, and Flux
InfluxDB use case examples — start collecting data at the edge and use your preferred IoT protocol (i.e. MQTT)
In this training webinar, we will walk you through the basics of InfluxDB – the purpose-built time series database. InfluxDB has everything you need from a time series platform in a single binary – a multi-tenanted time series database, UI and dashboarding tools, background processing and monitoring agent. This one-hour session will include the training and time for live Q&A.
What you will learn
Core concepts of time series databases
An overview of the InfluxDB platform
How to ingesting and query data in InfluxDB
This session defines what time series data is (and isn’t), how the problem domain time series differs from more traditional data workloads like full-text search and examines how InfluxData is differentiated from other proposed solutions. This session also includes a review of the most common use cases and a brief demo of InfluxDB.
Elasticity and security are enabling enterprises to move highly regulated workloads to the AWS Cloud. However, given the sensitivity around this protected customer data, what newly released services can be implemented to remain secure and compliant? Find out in this session for Chief Security, Risk and Compliance Officers.
Speaker: Dave Walker, Security Solutions Architect, Amazon Web Services
How does LightsOutPlanning support you in the challenge of digital supply chain? Take the chance to leverage your data via smart algorithms and machine learning to be on the edge of real time planning in order to make sound decisions.
Discover the key features of the tool; the virtual twin (1), the E2E optimizer (2), the smart parameters module (3), the E2E visibility (4) & the control tower (5).
Start your digital supply chain planning journey today!
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We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
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- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
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Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
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AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review