BM Cloudant is a NoSQL Database-as-a-Service. Discover how you can outsource the data layer of your mobile or web application to Cloudant to provide high availability, scalability and tools to take you to the next level.
Data persistence using pouchdb and couchdbDimgba Kalu
A presentation on data persistence using pouchdb and couchdb. This is a basic way of building offline data repository and efficient data synchronization
Analyze and visualize non-relational data with DocumentDB + Power BISriram Hariharan
The session will show how to do Analyze and visualize non-relational data with DocumentDB + Power BI. We are in the midst of a paradigm shift on how we store and analyze data. Unstructured or flexible schema data represents a large portion of data within an organization. Everyone is obsessed to turn this data into meaningful business information. Unstructured data analytics do not need to be time consuming and complex. Come learn how to analyze and visualize unstructured data in DocumentDB.
MongoDB has been conceived for the cloud age. Making sure that MongoDB is compatible and performant around cloud providers is mandatory to achieve complete integration with platforms and systems. Azure is one of biggest IaaS platforms available and very popular amongst developers that work on Microsoft Stack.
MongoDB & Hadoop - Understanding Your Big DataMongoDB
Big Data is the evolution of supercomputing for commercial enterprise and governments. Originally the domain of companies operating at Internet scale, today Big Data connects organizations of all sizes with discovery about their patterns, and insights into their business.
But understanding the differences between the plethora of new technologies can be daunting. Graph / columnar / key value store / document are all called NoSQL, but which is best? How does Hadoop play in this ecosystem - its low cost and high efficiency have made it very popular, but how does it fit?
In this webinar, we will explore:
The full spectrum of Big Data
Hadoop and MongoDB: friends or frenemies?
Differences between Systems of Record and Systems of Engagement
MongoDB customer examples of Systems of Engagement
BM Cloudant is a NoSQL Database-as-a-Service. Discover how you can outsource the data layer of your mobile or web application to Cloudant to provide high availability, scalability and tools to take you to the next level.
Data persistence using pouchdb and couchdbDimgba Kalu
A presentation on data persistence using pouchdb and couchdb. This is a basic way of building offline data repository and efficient data synchronization
Analyze and visualize non-relational data with DocumentDB + Power BISriram Hariharan
The session will show how to do Analyze and visualize non-relational data with DocumentDB + Power BI. We are in the midst of a paradigm shift on how we store and analyze data. Unstructured or flexible schema data represents a large portion of data within an organization. Everyone is obsessed to turn this data into meaningful business information. Unstructured data analytics do not need to be time consuming and complex. Come learn how to analyze and visualize unstructured data in DocumentDB.
MongoDB has been conceived for the cloud age. Making sure that MongoDB is compatible and performant around cloud providers is mandatory to achieve complete integration with platforms and systems. Azure is one of biggest IaaS platforms available and very popular amongst developers that work on Microsoft Stack.
MongoDB & Hadoop - Understanding Your Big DataMongoDB
Big Data is the evolution of supercomputing for commercial enterprise and governments. Originally the domain of companies operating at Internet scale, today Big Data connects organizations of all sizes with discovery about their patterns, and insights into their business.
But understanding the differences between the plethora of new technologies can be daunting. Graph / columnar / key value store / document are all called NoSQL, but which is best? How does Hadoop play in this ecosystem - its low cost and high efficiency have made it very popular, but how does it fit?
In this webinar, we will explore:
The full spectrum of Big Data
Hadoop and MongoDB: friends or frenemies?
Differences between Systems of Record and Systems of Engagement
MongoDB customer examples of Systems of Engagement
Implementing and Visualizing Clickstream data with MongoDBMongoDB
Having recently implemented a new framework for the real-time collection, aggregation and visualization of web and mobile generated Clickstream traffic (realizing daily click-stream volumes of 1M+ events), this walkthrough is about the motivations, throughout-process and key decisions made, as well as an in depth look at the implementation of how to buildout a data-collection, analytics and visualization framework using MongoDB. Technologies covered in this presentation (as well as MongoDB) are Java, Spring, Django and Pymongo.
Azure DocumentDB for Healthcare IntegrationBizTalk360
In this session,
You will learn what the series is about, and see what we want to accomplish.
For this session you will be learning about Azure DocumentDB, its features and capabilities.
You will learn how to create a DocumentDB database and configure it to support CRUD operations.
You will also learn about the two API’s provided for DocumentDB
You will learn how DocumentDB can be leveraged as a repository for HL7 documents
We will take a look at using DocumentDB with both API and Logic apps
Design Patterns for Building 360-degree Views with HBase and KijiHBaseCon
Speaker: Jonathan Natkins (WibiData)
Many companies aspire to have 360-degree views of their data. Whether they're concerned about customers, users, accounts, or more abstract things like sensors, organizations are focused on developing capabilities for analyzing all the data they have about these entities. This talk will introduce the concept of entity-centric storage, discuss what it means, what it enables for businesses, and how to develop an entity-centric system using the open-source Kiji framework and HBase. It will also compare and contrast traditional methods of building a 360-degree view on a relational database versus building against a distributed key-value store, and why HBase is a good choice for implementing an entity-centric system.
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQueryChris Schalk
This is an introductory presentation given at DevFest Madrid 2010 by Google Developer Advocate Chris Schalk. It introduces new Google cloud technologies: Google Storage, Google Prediction API and BigQuery.
MongoDB Evenings Dallas: What's the Scoop on MongoDB & HadoopMongoDB
What's the Scoop on MongoDB & Hadoop
Jake Angerman, Sr. Solutions Architect, MongoDB
MongoDB Evenings Dallas
March 30, 2016 at the Addison Treehouse, Dallas, TX
Here I talk about examples and use cases for Big Data & Big Data Analytics and how we accomplished massive-scale sentiment, campaign and marketing analytics for Razorfish using a collecting of database, Big Data and analytics technologies.
Rapid Development and Performance By Transitioning from RDBMSs to MongoDB
Modern day application requirements demand rich & dynamic data structures, fast response times, easy scaling, and low TCO to match the rapidly changing customer & business requirements plus the powerful programming languages used in today's software landscape.
Traditional approaches to solutions development with RDBMSs increasingly expose the gap between the modern development languages and the relational data model, and between scaling up vs. scaling horizontally on commodity hardware. Development time is wasted as the bulk of the work has shifted from adding business features to struggling with the RDBMSs.
MongoDB, the premier NoSQL database, offers a flexible and scalable solution to focus on quickly adding business value again.
In this session, we will provide:
- Overview of MongoDB's capabilities
- Code-level exploration of the MongoDB programming model and APIs and how they transform the way developers interact with a database
- Update of the exciting features in MongoDB 3.0
MongoDB Evenings DC: Get MEAN and Lean with Docker and KubernetesMongoDB
Get MEAN and Lean with Docker and Kubernetes
Vadim Polyakov, Director of Enterprise Application Architecture, Inovalon
MongoDB Evenings DC
April 12, 2016 at 1776
Webinar: Enterprise Data Management in the Era of MongoDB and Data LakesMongoDB
With so much talk of how Big Data is revolutionizing the world and how a data lake with Hadoop and/or Spark will solve all your data problems, it is hard to tell what is hype, reality, or somewhere in-between.
In working with dozens of enterprises in varying stages of their enterprise data management (EDM) strategy, MongoDB enterprise architect, Matt Kalan, sees the same challenges and misunderstandings arise again and again.
In this session, he will explain common challenges in data management, what capabilities are necessary, and what the future state of architecture looks like. MongoDB is uniquely capable of filling common gaps in the data lake strategy.
This session also includes a live Q&A portion during which you are encouraged to ask questions of our team.
The integration between Spring Framework and MongoDB tends to be somewhat unknown. This presentation shows the different projects that compose Spring ecosystem, Springdata, Springboot, SpringIO etc and how to merge between the pure JAVA projects to massive enterprise systems that require the interaction of these systems together.
Hadoop Reporting and Analysis - JaspersoftHortonworks
Hadoop is deployed for a variety of uses, including web analytics, fraud detection, security monitoring, healthcare, environmental analysis, social media monitoring, and other purposes.
Hands-On: Managing Slowly Changing Dimensions Using TD WorkflowTreasure Data, Inc.
In this hands-on webinar we'll explore the data warehousing concept of Slowly Changing Dimensions (SCDs) and common use cases for managing SCDs when dealing with customer data. This webinar will demonstrate different methods for tracking SCDs in a data warehouse, and how Treasure Data Workflow can be used to create robust data pipelines to handle these processes.
The Big Data Analytics Ecosystem at LinkedInrajappaiyer
LinkedIn has several data driven products that improve the experience of its users -- whether they are professionals or enterprises. Supporting this is a large ecosystem of systems and processes that provide data and insights in a timely manner to the products that are driven by it.
This talk provides an overview of the various components of this ecosystem which are:
- Hadoop
- Teradata
- Kafka
- Databus
- Camus
- Lumos
etc.
Implementing and Visualizing Clickstream data with MongoDBMongoDB
Having recently implemented a new framework for the real-time collection, aggregation and visualization of web and mobile generated Clickstream traffic (realizing daily click-stream volumes of 1M+ events), this walkthrough is about the motivations, throughout-process and key decisions made, as well as an in depth look at the implementation of how to buildout a data-collection, analytics and visualization framework using MongoDB. Technologies covered in this presentation (as well as MongoDB) are Java, Spring, Django and Pymongo.
Azure DocumentDB for Healthcare IntegrationBizTalk360
In this session,
You will learn what the series is about, and see what we want to accomplish.
For this session you will be learning about Azure DocumentDB, its features and capabilities.
You will learn how to create a DocumentDB database and configure it to support CRUD operations.
You will also learn about the two API’s provided for DocumentDB
You will learn how DocumentDB can be leveraged as a repository for HL7 documents
We will take a look at using DocumentDB with both API and Logic apps
Design Patterns for Building 360-degree Views with HBase and KijiHBaseCon
Speaker: Jonathan Natkins (WibiData)
Many companies aspire to have 360-degree views of their data. Whether they're concerned about customers, users, accounts, or more abstract things like sensors, organizations are focused on developing capabilities for analyzing all the data they have about these entities. This talk will introduce the concept of entity-centric storage, discuss what it means, what it enables for businesses, and how to develop an entity-centric system using the open-source Kiji framework and HBase. It will also compare and contrast traditional methods of building a 360-degree view on a relational database versus building against a distributed key-value store, and why HBase is a good choice for implementing an entity-centric system.
Intro to new Google cloud technologies: Google Storage, Prediction API, BigQueryChris Schalk
This is an introductory presentation given at DevFest Madrid 2010 by Google Developer Advocate Chris Schalk. It introduces new Google cloud technologies: Google Storage, Google Prediction API and BigQuery.
MongoDB Evenings Dallas: What's the Scoop on MongoDB & HadoopMongoDB
What's the Scoop on MongoDB & Hadoop
Jake Angerman, Sr. Solutions Architect, MongoDB
MongoDB Evenings Dallas
March 30, 2016 at the Addison Treehouse, Dallas, TX
Here I talk about examples and use cases for Big Data & Big Data Analytics and how we accomplished massive-scale sentiment, campaign and marketing analytics for Razorfish using a collecting of database, Big Data and analytics technologies.
Rapid Development and Performance By Transitioning from RDBMSs to MongoDB
Modern day application requirements demand rich & dynamic data structures, fast response times, easy scaling, and low TCO to match the rapidly changing customer & business requirements plus the powerful programming languages used in today's software landscape.
Traditional approaches to solutions development with RDBMSs increasingly expose the gap between the modern development languages and the relational data model, and between scaling up vs. scaling horizontally on commodity hardware. Development time is wasted as the bulk of the work has shifted from adding business features to struggling with the RDBMSs.
MongoDB, the premier NoSQL database, offers a flexible and scalable solution to focus on quickly adding business value again.
In this session, we will provide:
- Overview of MongoDB's capabilities
- Code-level exploration of the MongoDB programming model and APIs and how they transform the way developers interact with a database
- Update of the exciting features in MongoDB 3.0
MongoDB Evenings DC: Get MEAN and Lean with Docker and KubernetesMongoDB
Get MEAN and Lean with Docker and Kubernetes
Vadim Polyakov, Director of Enterprise Application Architecture, Inovalon
MongoDB Evenings DC
April 12, 2016 at 1776
Webinar: Enterprise Data Management in the Era of MongoDB and Data LakesMongoDB
With so much talk of how Big Data is revolutionizing the world and how a data lake with Hadoop and/or Spark will solve all your data problems, it is hard to tell what is hype, reality, or somewhere in-between.
In working with dozens of enterprises in varying stages of their enterprise data management (EDM) strategy, MongoDB enterprise architect, Matt Kalan, sees the same challenges and misunderstandings arise again and again.
In this session, he will explain common challenges in data management, what capabilities are necessary, and what the future state of architecture looks like. MongoDB is uniquely capable of filling common gaps in the data lake strategy.
This session also includes a live Q&A portion during which you are encouraged to ask questions of our team.
The integration between Spring Framework and MongoDB tends to be somewhat unknown. This presentation shows the different projects that compose Spring ecosystem, Springdata, Springboot, SpringIO etc and how to merge between the pure JAVA projects to massive enterprise systems that require the interaction of these systems together.
Hadoop Reporting and Analysis - JaspersoftHortonworks
Hadoop is deployed for a variety of uses, including web analytics, fraud detection, security monitoring, healthcare, environmental analysis, social media monitoring, and other purposes.
Hands-On: Managing Slowly Changing Dimensions Using TD WorkflowTreasure Data, Inc.
In this hands-on webinar we'll explore the data warehousing concept of Slowly Changing Dimensions (SCDs) and common use cases for managing SCDs when dealing with customer data. This webinar will demonstrate different methods for tracking SCDs in a data warehouse, and how Treasure Data Workflow can be used to create robust data pipelines to handle these processes.
The Big Data Analytics Ecosystem at LinkedInrajappaiyer
LinkedIn has several data driven products that improve the experience of its users -- whether they are professionals or enterprises. Supporting this is a large ecosystem of systems and processes that provide data and insights in a timely manner to the products that are driven by it.
This talk provides an overview of the various components of this ecosystem which are:
- Hadoop
- Teradata
- Kafka
- Databus
- Camus
- Lumos
etc.
Teradata Aster: Big Data Discovery Made Easy
Brad Elo, VP, Aster Data, Teradata
ANALYTICS AND VISUALIZATION FOR THE FINANCIAL ENTERPRISE CONFERENCE
June 25, 2013 The Langham Hotel Boston, MA
Trending use cases have pointed out the complementary nature of Hadoop and existing data management systems—emphasizing the importance of leveraging SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve distributed analytic processing. Many vendors have provided interfaces between SQL systems and Hadoop but have not been able to semantically integrate these technologies while Hive, Pig and SQL processing islands proliferate. This session will discuss how Teradata is working with Hortonworks to optimize the use of Hadoop within the Teradata Analytical Ecosystem to ingest, store, and refine new data types, as well as exciting new developments to bridge the gap between Hadoop and SQL to unlock deeper insights from data in Hadoop. The use of Teradata Aster as a tightly integrated SQL-MapReduce® Discovery Platform for Hadoop environments will also be discussed.
Big Data Day LA 2015 - Event Driven Architecture for Web Analytics by Peyman ...Data Con LA
As integrated web analytics evolves to both a service oriented and event based model, there will be higher emphasis on moving toward event based analytics. Business analytics is moving from purely counts of analytics to time-series, relationship and usage analytics. Examples of web analytics that can take advantage of this architecture are conversions analytics or cross channel marketing.
The advantage of storing raw event data is that you have maximum flexibility for analysis. For example, you can trace the sequence of pages that one person visited over the course of their session. You can’t do that if you’ve squashed all the events into e.g. counters. That sort of analysis is really important for some offline processing tasks, such as training a recommender system (“people who bought X also bought Y”, that sort of thing). For such use cases, it’s best to simply keep all the raw events, so that you can later feed them all into your shiny new machine learning system.
In this session we are going to elaborate on using Kafka, an Event Processing framework (e.g. Storm or Spark Streaming) and either Hadoop or EDW for building an Event Driven Architecture.
Teradata - Presentation at Hortonworks Booth - Strata 2014Hortonworks
Hortonworks and Teradata have partnered to provide a clear path to Big Analytics via stable and reliable Hadoop for the enterprise. The Teradata® Portfolio for Hadoop is a flexible offering of products and services for customers to integrate Hadoop into their data architecture while taking advantage of the world-class service and support Teradata provides.
Building Confidence in Big Data - IBM Smarter Business 2013 IBM Sverige
Success with big data comes down to confidence. Without confidence in the underlying data, decision makers may not trust and act on analytic insight. You need confidence in your data – that it’s correct, trusted, and protected through automated integration, visual context, and agile governance. You need confidence in your ability to accelerate time to value, with fast deployments of big data appliances. Learn how clients have succeeded with big data by building confidence in their data, ability to deploy, and skills. Presenter: David Corrigan, Big Data specialist, IBM. Mer från dagen på http://bit.ly/sb13se
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
Content1. Introduction2. What is Big Data3. Characte.docxdickonsondorris
Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
Introduction
• Big Data may well be the Next Big Thing in the IT
world.
• Big data burst upon the scene in the first decade of the
21st century.
• The first organizations to embrace it were online and
startup firms. Firms like Google, eBay, LinkedIn, and
Facebook were built around big data from the
beginning.
• Like many new information technologies, big data can
bring about dramatic cost reductions, substantial
improvements in the time required to perform a
computing task, or new product and service offerings.
• ‘Big Data’ is similar to ‘small data’, but bigger in
size
• but having data bigger it requires different
approaches:
– Techniques, tools and architecture
• an aim to solve new problems or old problems in a
better way
• Big Data generates value from the storage and
processing of very large quantities of digital
information that cannot be analyzed with
traditional computing techniques.
What is BIG DATA?
What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• machine to machine processes exchange data between
billions of devices
• infrastructure and sensors generate massive log data in real-
time
• on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.
• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data stru.
Grow smarter project kista watson summit 2018_tommy auoja-1IBM Sverige
Avicii på Tele2 arena, Drake på Globen och AIK - Luleå på Hovet bäddar för en trång lördagseftermiddag i Globenområdet... (SVT Nyheter, 1 mars 2014) ...och problemen kvarstår än idag
Talare: Tommy Auoja, Kundansvarig för Offentlig Sektor, Kontaktperson i EU projektet GrowSmarter, IBM
Presentation från Watson Kista Summit 2018
Bemanningsplanering axfood och houston finalIBM Sverige
Automatiserad budgetering – låt matematiken göra grovgörat för att säkerställa en optimerad bemanning
Talare: Niklas Westerholm, Axfood & Robert Moberg, Chief Analyst, Houston Analytics
Presentation från Watson Kista Summit 2018
File share and sync (bara) är så 2017!
Att dela filer bekvämt och säkert var bara början. Box har gått vidare till att integrera delade filer i applikationer och processflöden, och revolutionera både internt och externt arbete. Hur kan det revolutionera för dig?
Talare: Jan Hygstedt, Director Nordic, Box
Presentation från Watson Kista Summit 2018
Watson kista summit 2018 en bättre arbetsdag för de många människornaIBM Sverige
Först tvingades vi anpassa oss efter datorerna. Sedan använde vi dem för att samarbeta med varandra. Nu är det dags för datorerna att förstå oss. Vad innebär det för vår arbetsvardag?
Talare och moderator: Peter Bjellerup, Executive Consultant - Social Business, Collaboration & Knowledge Sharing, IBM
Presentation från Watson Kista Summit 2018
Iwcs and cisco watson kista summit 2018 v2IBM Sverige
Samarbeta både över tid och i realtid
Cisco Spark och IBM Connections – tillsammans! Kombinera ledaren för konversationer i realtid – text, video, individuellt och i team med branschledaren sedan sju år för internt samarbete, transparens och nätverk.
Talare: Bo Holtemann, Solution Specialist, IBM Collaboration Solutions
Presentation från Watson Kista Summit 2018
This presentation by Morris Kleiner (University of Minnesota), was made during the discussion “Competition and Regulation in Professions and Occupations” held at the Working Party No. 2 on Competition and Regulation on 10 June 2024. More papers and presentations on the topic can be found out at oe.cd/crps.
This presentation was uploaded with the author’s consent.
Sharpen existing tools or get a new toolbox? Contemporary cluster initiatives...Orkestra
UIIN Conference, Madrid, 27-29 May 2024
James Wilson, Orkestra and Deusto Business School
Emily Wise, Lund University
Madeline Smith, The Glasgow School of Art
Have you ever wondered how search works while visiting an e-commerce site, internal website, or searching through other types of online resources? Look no further than this informative session on the ways that taxonomies help end-users navigate the internet! Hear from taxonomists and other information professionals who have first-hand experience creating and working with taxonomies that aid in navigation, search, and discovery across a range of disciplines.
0x01 - Newton's Third Law: Static vs. Dynamic AbusersOWASP Beja
f you offer a service on the web, odds are that someone will abuse it. Be it an API, a SaaS, a PaaS, or even a static website, someone somewhere will try to figure out a way to use it to their own needs. In this talk we'll compare measures that are effective against static attackers and how to battle a dynamic attacker who adapts to your counter-measures.
About the Speaker
===============
Diogo Sousa, Engineering Manager @ Canonical
An opinionated individual with an interest in cryptography and its intersection with secure software development.
Acorn Recovery: Restore IT infra within minutesIP ServerOne
Introducing Acorn Recovery as a Service, a simple, fast, and secure managed disaster recovery (DRaaS) by IP ServerOne. A DR solution that helps restore your IT infra within minutes.