An overview of how TIBCO integrates dynamic, interactive visual applications in Spotfire with predictive and advanced analytics in the R language, using TIBCO Enterprise Runtime for R--our R-compatible, enterprise-grade platform for the R language.
Presented by: Hector Martinez, Staff Solution Consultant, TIBCO Spotfire
TIBCO Spotfire and Teradata: First to Insight, First to Action; Warehousing, Analytics and Visualizations for the High Tech Industry Conference
July 22, 2013 The Four Seasons Hotel Palo Alto, CA
BIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICSTIBCO Spotfire
Presented by: Dr. Bruce Aldridge, Sr. Industry Consultant Hi-Tech Manufacturing, Teradata
TIBCO Spotfire and Teradata: First to Insight, First to Action; Warehousing, Analytics and Visualizations for the High Tech Industry Conference
July 22, 2013 The Four Seasons Hotel Palo Alto, CA
Presented by: Hector Martinez, Staff Solution Consultant, TIBCO Spotfire
TIBCO Spotfire and Teradata: First to Insight, First to Action; Warehousing, Analytics and Visualizations for the High Tech Industry Conference
July 22, 2013 The Four Seasons Hotel Palo Alto, CA
BIG DATA ANALYTICS MEANS “IN-DATABASE” ANALYTICSTIBCO Spotfire
Presented by: Dr. Bruce Aldridge, Sr. Industry Consultant Hi-Tech Manufacturing, Teradata
TIBCO Spotfire and Teradata: First to Insight, First to Action; Warehousing, Analytics and Visualizations for the High Tech Industry Conference
July 22, 2013 The Four Seasons Hotel Palo Alto, CA
The case of vehicle networking financial services accomplished by China MobileDataWorks Summit
As the largest mobile telecom carrier in the world, China Mobile has the world's largest wireless mobile network, based on the existing vehicle networking equipment (CAN-bus, OBD, ADAS, equipment fatigue warning system, GPS, driving recorder, etc.), which can provide vehicle networking service, based on vehicle networking data analysis and provide users risk assessment, vehicle real-time risk monitoring, and comprehensive financial institutions for the vehicle and provide data support for differentiated financial services.
The main contents include the following:
1. Vehicle and drivers data collection: Collecting information of vehicle's mechanical status, driving behavior, and surrounding environment through OBD, ADAS, fatigue warning system, GPS, and other equipment.
2. AI technology application: mainly include the identification of the driver's body state, the wine driving, the fatigue degree, and so on.
3. To improve the accuracy and applicability of the risk assessment model through machine learning.
Speaker
Duan Yunfeng, Chief Designer of China Mobile's big data system, China Mobile Communications Corporation
Stream processing consists of ingesting and processing continuously generated data, often from end users in web applications or from more challenging settings where devices such as servers and sensors generate events at a high rate. Such scenarios often demand the use of a software stack that is able to scale and accommodate changes to the characteristics of the application.
One of the major challenges with processing data streams is adapting to workload variations (e.g., due to daily cycles or the growth of the population of sources). Systems to ingest stream data typically parallelize it by sharding the incoming messages and events according to a routing key. Having the ability to parallelize ingestion is very effective, but future changes to the workload (which are very often unknown beforehand) might make the initial choice for the degree of parallelism inadequate for even short-term spikes. Consequently, the ability to scale by adapting parallelism according to workload while preserving important API properties, such as per-key order, is highly desirable to handle mission-critical workloads.
In this presentation, we explain how to accommodate changes to workloads in and with Pravega, an open source stream store built to ingest and serve stream data. Pravega primarily manipulates and stores segments (append-only byte sequences), forming streams by creating and composing segments, which it uses to enable the scaling of streams. Stream scaling in Pravega is automatic and transparent to the application, but such a change to the ingestion volume might also require the application to follow and scale its resources downstream (e.g., the operators of an Apache Flink job) to accommodate the new ingestion volume. Pravega signals such changes to the application so that it can react accordingly. The cooperation between Pravega and the downstream application is crucial for building an effective stream data pipeline.
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...Databricks
"The modernization of the tobacco industry is resulting in a shift towards a more data-driven approach to trade, operations and the consumer. The need to scale while maintaining margins is paramount, and today’s consumer requires more personalized engagement and value at every interaction to drive sales and revenue.
At Altria, we’re at the forefront of this evolution, leveraging hundreds of terabytes of big data (such as point-of-sale, clickstream, mobile data, and more) and machine learning to improve our ability to make smarter decisions and outpace the competition. This talk recaps our big data journey from a legacy data infrastructure (Teradata), isolated data systems, and the lack of resources which prevented our ability to move quickly and scale, to our current state where we’ve successfully implemented, architected and on-boarded tools and processes in stages of data acquisition, store, prepare, and business intelligence with Azure Data Lake, Azure Databricks, Azure Data factory, APIs Managements, Streaming and Hosting technologies and provided Data Analytics platform.
We’ll discuss the roadblocks we came across, how we overcame them, and how we employed a unified approach to big data and analytics through the fully managed Azure Databricks platform and the Azure suite of tools which allowed us to streamline workflows, improve operational performance, and ultimately introduce new customer experiences that drive engagement and revenue."
Democratizing data science Using spark, hive and druidDataWorks Summit
MZ is re-inventing how the entire world experiences data via our mobile games division MZ Games Studios, our digital marketing division Cognant, and our live data platform division Satori.
Growing need of data science capabilities across the organization requires an architecture that can democratize building these applications and disseminating insight from the outcome of data science applications to the wider organization.
Attend this session to learn about how we built a platform for data science using spark, hive, and druid specifically for our performance marketing division cognant.This platform powers several data science application like fraud detection and bid optimization at large scale.
We will be sharing lessons learned over past 3 years in building this platform by also walking through some of the actual data science applications built on top of this platform.
Attendees from ML engineering and data science background can gain deep insight from our experience of building this platform.
Speakers
Pushkar Priyadarshi, Director of Engineer, Michaine Zone Inc
Igor Yurinok, Staff Software Engineer, MZ
When you look at traditional ERP or management systems, they are usually used to manage the supply chain originating from either the point of Origin or point of destination which all our primarily physical locations. And for these, you have several processes like order to cash, source to pay, physical distribution, production etc.
O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...DataWorks Summit
In order to support the new IRFS 15 global accounting standards, O2 UK needed a new reliable, robust solution that could also be used to trigger a new wave of transformational activities. The main idea was to ensure that the basics were met by building a centralized, highly reconciled financial data hub that in time could be used to support all financial and additional business reporting.
Requirements from business stakeholders were well defined where the data was to be refreshed daily with yesterday’s data being available by 9 a.m. the following day. Full data lineage must be known and reference data and business rules needed to be automated.
O2 working with its service integrator, Accenture, brought together a team to design and develop a new big data architecture. The overall solution was based on a hybrid architecture, where Big Data Open Source technologies were brought together with Ab Initio to meet the overall requirements.
The Financial Data Hub went live in August 2017 and has ensured that O2 meets its compliance obligations as well as having a hybrid big data architecture built for the future.
Speakers
Jonathan Ratcliff, Managing Enterprise Architect, Telefonica UK
Kieran Miller, Applied Intelligence - Digital Business Integration Senior Manager, Accenture
Presented by Jack Norris, SVP Data & Applications at Gartner Symposium 2016.
Jack presents how companies from TransUnion to Uber use event-driven processing to transform their business with agility, scale, robustness, and efficiency advantages.
More info: https://www.mapr.com/company/press-releases/mapr-present-gartner-symposiumitxpo-and-other-notable-industry-conferences
Highly configurable and extensible data processing framework at PubMaticDataWorks Summit
PubMatic is a leading advertisement technology company that processes 500 billion transactions (50 terabytes of data) per day in real-time and batch processing pipeline on a 900-node cluster to power highly efficient machine learning algorithms, provide real time feedback to ad-server for optimization and provide in depth insights on customer inventory and audience.
At PubMatic, scaling with ever growing volume has always been the biggest challenge; we have been optimizing our technology stack for performance and costs. Another challenge is to support the demand for variety reports and analytics by customers and internal stakeholders. Writing custom jobs to provide analytics leads to repetitive efforts and redundancy of business logic in many different jobs.
To solve the above problems, we built a platform that allows creating configuration driven data processing pipeline with high re-usability of business functions. It is also extensible to utilize cutting-edge technologies in the ever-changing big data ecosystem. This platform enables our development teams to build a robust batch data processing pipeline to power analytics dashboards. It also empowers novice users to provide a configuration with fact and dimensions to generate ad-hoc reports in a single data processing job. Framework intelligently identifies and re-uses existing business functions based on user inputs. It also provides an abstraction layer that keeps core business logic un-affected by the any technology changes. This framework is currently powered by Spark, but it can be easily configured with other technologies.
Framework significantly improved time to develop data processing jobs from weeks to few days, it simplified unit testing and QA automation, as well as provided simpler interfaces to the customers and internal stakeholders to generate custom reports.
Speaker
Kunal Umrigar, Sr. Director Engineering Big Data & Analytics, PubMatic
San Antonio’s electric utility making big data analytics the business of the ...DataWorks Summit
Being part of a municipality-owned electric utility offers a unique opportunity to lead in the area of big data analytics. What moves the electric utility of the 7th largest city in the U.S.? The answer is, people. For years, CPS Energy has invested in development of local talent, local technology development, city growth, its employees, and an asset infrastructure that is setting the stage for continued success. At CPS Energy, when such investments are topped by a data infrastructure and applications conducive to creation of business insights, we can justify and prioritize investments. For us, the biggest people opportunities in big data analytics are around operations, customer and employee engagement, and safety. The presenter will provide examples and share how his views have evolved from those of a researcher to global renewable energy consultant to technology innovator and more recently a “harvester of value” from within people, process, and technology assets. Lastly, current and anticipated future states with regards to San Antonio’s electric utility big data enablement platform will be presented...
Speaker
Rolando Vega, Manager of Analytics and Business Insight, CPS Engery
Data and analytics are at the heart of the digital transformation. Implementing a modern data platform can be challenging; moreover, success requires a shift in culture. Andreas will discuss the ways Munich Re drives cultural and technological change within their company, focusing on three key elements: people, processes, and technology. What does it mean to be a data-driven organization? How can we provide self-service analytics to our internal and external customers in an agile way? How do we get the most value out of our big data lake? How does Munich Re balance technology and culture to meet the data demands of their business?
Speaker
Andreas Kohlmaier, Head of Data Engineering, Munich Re
ML, Statistics, and Spark with Databricks for Maximizing Revenue in a Delayed...Databricks
In this talk, we will present how we used Spark, Databricks, Airflow and MLflow to process big data, and build a pipeline of both ML(XGBoost) and statistical models that maximizes our revenues in one of our core products, called the “Offer Wall”. The “Offer wall” is a mobile phone product that is integrated with existing apps, suggesting users to perform tasks in exchange for in-app currency. The problem gets even more interesting when considering the fact that some of the tasks users do take 15 minutes and some may take up to 2 to weeks, forcing us to make revenue determining decisions in an uncertain space all of the time. The solution we developed utilizes Databricks and Spark’s strengths and diversity in machine learning, big data, MLflow and Airflow integrations, allowing us to deliver a production-grade solution with short development time between experiments.
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...Precisely
So you built your Hadoop cluster. How do you get data from hundreds of database tables, streaming Kafka sources, and data shared by 20-year-old COBOL programs, all in there and working together quickly, efficiently and securely? With many customers asking this same question, Hortonworks recently expanded its partnership with Syncsort to provide optimized ETL onboarding for Hadoop. During this talk, we'll discuss how a next-generation ETL tool, built on contributions to the open source community and natively integrated in Hadoop, can drive lasting value for your organization. 1) Seamlessly onboard data from all your enterprise sources – batch and streaming -- into Hadoop for fast and easy analytics. 2) Stay agile and simplify your environment with a "design once, deploy anywhere" approach that minimizes disruption and risk in the face of a rapidly evolving big data ecosystem. 3) Secure, govern and manage your data with full integration with Apache Ambari, Apache Ranger, and more. These benefits come to life with real customer case studies. Learn how a national insurance company and global hotel chain are using Hortonworks HDP and Syncsort DMX-h to get bigger insights from their enterprise data, securely, efficiently, and cost-effectively, without spending hundreds of man-hours.
MapR on Azure: Getting Value from Big Data in the Cloud -MapR Technologies
Public cloud adoption is exploding and big data technologies are rapidly becoming an important driver of this growth. According to Wikibon, big data public cloud revenue will grow from 4.4% in 2016 to 24% of all big data spend by 2026. Digital transformation initiatives are now a priority for most organizations, with data and advanced analytics at the heart of enabling this change. This is key to driving competitive advantage in every industry.
There is nothing better than a real-world customer use case to help you understand how to get value from big data in the cloud and apply the learnings to your business. Join Microsoft, MapR, and Sullexis on November 10th to:
Hear from Sullexis on the business use case and technical implementation details of one of their oil & gas customers
Understand the integration points of the MapR Platform with other Azure services and why they matter
Know how to deploy the MapR Platform on the Azure cloud and get started easily
You will also get to hear about customer use cases of the MapR Converged Data Platform on Azure in other verticals such as real estate and retail.
Speakers
Rafael Godinho
Technical Evangelist
Microsoft Azure
Tim Morgan
Managing Director
Sullexis
Insight Platforms Accelerate Digital TransformationMapR Technologies
Many organizations have invested in big data technologies such as Hadoop and Spark. But these investments only address how to gain deeper insights from more diverse data. They do not address how to create action from those insights.
Forrester has identified an emerging class of software—insight platforms—that combine data, analytics, and insight execution to drive action using a big data fabric.
In this presentation, our guest, Forrester Research VP and Principal Analyst, Brian Hopkins, will:
o Present Forrester's recent research on insight platforms and big data fabrics.
o Provide strategies for getting more value from your big data investments.
MapR will share:
o Examples of leading companies and best practices for creating modern applications.
o How to combine analytics and operations to accelerate digital transformation and create competitive advantage.
The Single Most Important Formula for Business SuccessDataWorks Summit
There are multiple factors that lead to business success in today’s competitive world. Join Hortonworks CTO Scott Gnau as he looks at where data fits into today’s business strategy and how today’s enterprises need to shift their thinking when driving business transformation to deliver the best customer and product experiences.
Using the R Language in BI and Real Time Applications (useR 2015)Lou Bajuk
R provides tremendous value to statisticians and data scientists, however they are often challenged to integrate their work and extend that value to the rest of their organization. This presentation will demonstrate how the R language can be used in Business Intelligence applications (such as Financial Planning and Budgeting, Marketing Analysis, and Sales Forecasting) to put advanced analytics into the hands of a wider pool of decisions makers. We will also show how R can be used in streaming applications (such as TIBCO Streambase) to rapidly build, deploy and iterate predictive models for real-time decisions. TIBCO's enterprise platform for the R language, TIBCO Enterprise Runtime for R (TERR) will be discussed, and examples will include fraud detection, marketing upsell and predictive maintenance.
The case of vehicle networking financial services accomplished by China MobileDataWorks Summit
As the largest mobile telecom carrier in the world, China Mobile has the world's largest wireless mobile network, based on the existing vehicle networking equipment (CAN-bus, OBD, ADAS, equipment fatigue warning system, GPS, driving recorder, etc.), which can provide vehicle networking service, based on vehicle networking data analysis and provide users risk assessment, vehicle real-time risk monitoring, and comprehensive financial institutions for the vehicle and provide data support for differentiated financial services.
The main contents include the following:
1. Vehicle and drivers data collection: Collecting information of vehicle's mechanical status, driving behavior, and surrounding environment through OBD, ADAS, fatigue warning system, GPS, and other equipment.
2. AI technology application: mainly include the identification of the driver's body state, the wine driving, the fatigue degree, and so on.
3. To improve the accuracy and applicability of the risk assessment model through machine learning.
Speaker
Duan Yunfeng, Chief Designer of China Mobile's big data system, China Mobile Communications Corporation
Stream processing consists of ingesting and processing continuously generated data, often from end users in web applications or from more challenging settings where devices such as servers and sensors generate events at a high rate. Such scenarios often demand the use of a software stack that is able to scale and accommodate changes to the characteristics of the application.
One of the major challenges with processing data streams is adapting to workload variations (e.g., due to daily cycles or the growth of the population of sources). Systems to ingest stream data typically parallelize it by sharding the incoming messages and events according to a routing key. Having the ability to parallelize ingestion is very effective, but future changes to the workload (which are very often unknown beforehand) might make the initial choice for the degree of parallelism inadequate for even short-term spikes. Consequently, the ability to scale by adapting parallelism according to workload while preserving important API properties, such as per-key order, is highly desirable to handle mission-critical workloads.
In this presentation, we explain how to accommodate changes to workloads in and with Pravega, an open source stream store built to ingest and serve stream data. Pravega primarily manipulates and stores segments (append-only byte sequences), forming streams by creating and composing segments, which it uses to enable the scaling of streams. Stream scaling in Pravega is automatic and transparent to the application, but such a change to the ingestion volume might also require the application to follow and scale its resources downstream (e.g., the operators of an Apache Flink job) to accommodate the new ingestion volume. Pravega signals such changes to the application so that it can react accordingly. The cooperation between Pravega and the downstream application is crucial for building an effective stream data pipeline.
Journey to Creating a 360 View of the Customer: Implementing Big Data Strateg...Databricks
"The modernization of the tobacco industry is resulting in a shift towards a more data-driven approach to trade, operations and the consumer. The need to scale while maintaining margins is paramount, and today’s consumer requires more personalized engagement and value at every interaction to drive sales and revenue.
At Altria, we’re at the forefront of this evolution, leveraging hundreds of terabytes of big data (such as point-of-sale, clickstream, mobile data, and more) and machine learning to improve our ability to make smarter decisions and outpace the competition. This talk recaps our big data journey from a legacy data infrastructure (Teradata), isolated data systems, and the lack of resources which prevented our ability to move quickly and scale, to our current state where we’ve successfully implemented, architected and on-boarded tools and processes in stages of data acquisition, store, prepare, and business intelligence with Azure Data Lake, Azure Databricks, Azure Data factory, APIs Managements, Streaming and Hosting technologies and provided Data Analytics platform.
We’ll discuss the roadblocks we came across, how we overcame them, and how we employed a unified approach to big data and analytics through the fully managed Azure Databricks platform and the Azure suite of tools which allowed us to streamline workflows, improve operational performance, and ultimately introduce new customer experiences that drive engagement and revenue."
Democratizing data science Using spark, hive and druidDataWorks Summit
MZ is re-inventing how the entire world experiences data via our mobile games division MZ Games Studios, our digital marketing division Cognant, and our live data platform division Satori.
Growing need of data science capabilities across the organization requires an architecture that can democratize building these applications and disseminating insight from the outcome of data science applications to the wider organization.
Attend this session to learn about how we built a platform for data science using spark, hive, and druid specifically for our performance marketing division cognant.This platform powers several data science application like fraud detection and bid optimization at large scale.
We will be sharing lessons learned over past 3 years in building this platform by also walking through some of the actual data science applications built on top of this platform.
Attendees from ML engineering and data science background can gain deep insight from our experience of building this platform.
Speakers
Pushkar Priyadarshi, Director of Engineer, Michaine Zone Inc
Igor Yurinok, Staff Software Engineer, MZ
When you look at traditional ERP or management systems, they are usually used to manage the supply chain originating from either the point of Origin or point of destination which all our primarily physical locations. And for these, you have several processes like order to cash, source to pay, physical distribution, production etc.
O2’s Financial Data Hub: going beyond IFRS compliance to support digital tran...DataWorks Summit
In order to support the new IRFS 15 global accounting standards, O2 UK needed a new reliable, robust solution that could also be used to trigger a new wave of transformational activities. The main idea was to ensure that the basics were met by building a centralized, highly reconciled financial data hub that in time could be used to support all financial and additional business reporting.
Requirements from business stakeholders were well defined where the data was to be refreshed daily with yesterday’s data being available by 9 a.m. the following day. Full data lineage must be known and reference data and business rules needed to be automated.
O2 working with its service integrator, Accenture, brought together a team to design and develop a new big data architecture. The overall solution was based on a hybrid architecture, where Big Data Open Source technologies were brought together with Ab Initio to meet the overall requirements.
The Financial Data Hub went live in August 2017 and has ensured that O2 meets its compliance obligations as well as having a hybrid big data architecture built for the future.
Speakers
Jonathan Ratcliff, Managing Enterprise Architect, Telefonica UK
Kieran Miller, Applied Intelligence - Digital Business Integration Senior Manager, Accenture
Presented by Jack Norris, SVP Data & Applications at Gartner Symposium 2016.
Jack presents how companies from TransUnion to Uber use event-driven processing to transform their business with agility, scale, robustness, and efficiency advantages.
More info: https://www.mapr.com/company/press-releases/mapr-present-gartner-symposiumitxpo-and-other-notable-industry-conferences
Highly configurable and extensible data processing framework at PubMaticDataWorks Summit
PubMatic is a leading advertisement technology company that processes 500 billion transactions (50 terabytes of data) per day in real-time and batch processing pipeline on a 900-node cluster to power highly efficient machine learning algorithms, provide real time feedback to ad-server for optimization and provide in depth insights on customer inventory and audience.
At PubMatic, scaling with ever growing volume has always been the biggest challenge; we have been optimizing our technology stack for performance and costs. Another challenge is to support the demand for variety reports and analytics by customers and internal stakeholders. Writing custom jobs to provide analytics leads to repetitive efforts and redundancy of business logic in many different jobs.
To solve the above problems, we built a platform that allows creating configuration driven data processing pipeline with high re-usability of business functions. It is also extensible to utilize cutting-edge technologies in the ever-changing big data ecosystem. This platform enables our development teams to build a robust batch data processing pipeline to power analytics dashboards. It also empowers novice users to provide a configuration with fact and dimensions to generate ad-hoc reports in a single data processing job. Framework intelligently identifies and re-uses existing business functions based on user inputs. It also provides an abstraction layer that keeps core business logic un-affected by the any technology changes. This framework is currently powered by Spark, but it can be easily configured with other technologies.
Framework significantly improved time to develop data processing jobs from weeks to few days, it simplified unit testing and QA automation, as well as provided simpler interfaces to the customers and internal stakeholders to generate custom reports.
Speaker
Kunal Umrigar, Sr. Director Engineering Big Data & Analytics, PubMatic
San Antonio’s electric utility making big data analytics the business of the ...DataWorks Summit
Being part of a municipality-owned electric utility offers a unique opportunity to lead in the area of big data analytics. What moves the electric utility of the 7th largest city in the U.S.? The answer is, people. For years, CPS Energy has invested in development of local talent, local technology development, city growth, its employees, and an asset infrastructure that is setting the stage for continued success. At CPS Energy, when such investments are topped by a data infrastructure and applications conducive to creation of business insights, we can justify and prioritize investments. For us, the biggest people opportunities in big data analytics are around operations, customer and employee engagement, and safety. The presenter will provide examples and share how his views have evolved from those of a researcher to global renewable energy consultant to technology innovator and more recently a “harvester of value” from within people, process, and technology assets. Lastly, current and anticipated future states with regards to San Antonio’s electric utility big data enablement platform will be presented...
Speaker
Rolando Vega, Manager of Analytics and Business Insight, CPS Engery
Data and analytics are at the heart of the digital transformation. Implementing a modern data platform can be challenging; moreover, success requires a shift in culture. Andreas will discuss the ways Munich Re drives cultural and technological change within their company, focusing on three key elements: people, processes, and technology. What does it mean to be a data-driven organization? How can we provide self-service analytics to our internal and external customers in an agile way? How do we get the most value out of our big data lake? How does Munich Re balance technology and culture to meet the data demands of their business?
Speaker
Andreas Kohlmaier, Head of Data Engineering, Munich Re
ML, Statistics, and Spark with Databricks for Maximizing Revenue in a Delayed...Databricks
In this talk, we will present how we used Spark, Databricks, Airflow and MLflow to process big data, and build a pipeline of both ML(XGBoost) and statistical models that maximizes our revenues in one of our core products, called the “Offer Wall”. The “Offer wall” is a mobile phone product that is integrated with existing apps, suggesting users to perform tasks in exchange for in-app currency. The problem gets even more interesting when considering the fact that some of the tasks users do take 15 minutes and some may take up to 2 to weeks, forcing us to make revenue determining decisions in an uncertain space all of the time. The solution we developed utilizes Databricks and Spark’s strengths and diversity in machine learning, big data, MLflow and Airflow integrations, allowing us to deliver a production-grade solution with short development time between experiments.
Use Cases from Batch to Streaming, MapReduce to Spark, Mainframe to Cloud: To...Precisely
So you built your Hadoop cluster. How do you get data from hundreds of database tables, streaming Kafka sources, and data shared by 20-year-old COBOL programs, all in there and working together quickly, efficiently and securely? With many customers asking this same question, Hortonworks recently expanded its partnership with Syncsort to provide optimized ETL onboarding for Hadoop. During this talk, we'll discuss how a next-generation ETL tool, built on contributions to the open source community and natively integrated in Hadoop, can drive lasting value for your organization. 1) Seamlessly onboard data from all your enterprise sources – batch and streaming -- into Hadoop for fast and easy analytics. 2) Stay agile and simplify your environment with a "design once, deploy anywhere" approach that minimizes disruption and risk in the face of a rapidly evolving big data ecosystem. 3) Secure, govern and manage your data with full integration with Apache Ambari, Apache Ranger, and more. These benefits come to life with real customer case studies. Learn how a national insurance company and global hotel chain are using Hortonworks HDP and Syncsort DMX-h to get bigger insights from their enterprise data, securely, efficiently, and cost-effectively, without spending hundreds of man-hours.
MapR on Azure: Getting Value from Big Data in the Cloud -MapR Technologies
Public cloud adoption is exploding and big data technologies are rapidly becoming an important driver of this growth. According to Wikibon, big data public cloud revenue will grow from 4.4% in 2016 to 24% of all big data spend by 2026. Digital transformation initiatives are now a priority for most organizations, with data and advanced analytics at the heart of enabling this change. This is key to driving competitive advantage in every industry.
There is nothing better than a real-world customer use case to help you understand how to get value from big data in the cloud and apply the learnings to your business. Join Microsoft, MapR, and Sullexis on November 10th to:
Hear from Sullexis on the business use case and technical implementation details of one of their oil & gas customers
Understand the integration points of the MapR Platform with other Azure services and why they matter
Know how to deploy the MapR Platform on the Azure cloud and get started easily
You will also get to hear about customer use cases of the MapR Converged Data Platform on Azure in other verticals such as real estate and retail.
Speakers
Rafael Godinho
Technical Evangelist
Microsoft Azure
Tim Morgan
Managing Director
Sullexis
Insight Platforms Accelerate Digital TransformationMapR Technologies
Many organizations have invested in big data technologies such as Hadoop and Spark. But these investments only address how to gain deeper insights from more diverse data. They do not address how to create action from those insights.
Forrester has identified an emerging class of software—insight platforms—that combine data, analytics, and insight execution to drive action using a big data fabric.
In this presentation, our guest, Forrester Research VP and Principal Analyst, Brian Hopkins, will:
o Present Forrester's recent research on insight platforms and big data fabrics.
o Provide strategies for getting more value from your big data investments.
MapR will share:
o Examples of leading companies and best practices for creating modern applications.
o How to combine analytics and operations to accelerate digital transformation and create competitive advantage.
The Single Most Important Formula for Business SuccessDataWorks Summit
There are multiple factors that lead to business success in today’s competitive world. Join Hortonworks CTO Scott Gnau as he looks at where data fits into today’s business strategy and how today’s enterprises need to shift their thinking when driving business transformation to deliver the best customer and product experiences.
Using the R Language in BI and Real Time Applications (useR 2015)Lou Bajuk
R provides tremendous value to statisticians and data scientists, however they are often challenged to integrate their work and extend that value to the rest of their organization. This presentation will demonstrate how the R language can be used in Business Intelligence applications (such as Financial Planning and Budgeting, Marketing Analysis, and Sales Forecasting) to put advanced analytics into the hands of a wider pool of decisions makers. We will also show how R can be used in streaming applications (such as TIBCO Streambase) to rapidly build, deploy and iterate predictive models for real-time decisions. TIBCO's enterprise platform for the R language, TIBCO Enterprise Runtime for R (TERR) will be discussed, and examples will include fraud detection, marketing upsell and predictive maintenance.
Real time applications using the R LanguageLou Bajuk
My presentation to the Bay Area R User Meetup on 1/28/15, providing a brief overview of real time applications using the R language, based on TIBCO CEP and TERR.
TIBCO Spotfire: Data Science in the EnterpriseTIBCO Spotfire
From Data to Insights in Internet Time
Eric Novik, Internal Analytics Group, TIBCO Spotfire
ANALYTICS AND VISUALIZATION FOR THE FINANCIAL ENTERPRISE CONFERENCE
June 25, 2013 The Langham Hotel Boston, MA
Deploying R in BI and Real time ApplicationsLou Bajuk
Overview of how Spotfire and TERR enables the deployment of R language analytics into Business Intelligence and Real time applications, including several examples. Presented at useR 2014 at UCLA on 7/2/14
Advantages of Spotfire:
1.Easily provide targeted, relevant predictive analytics to business users
2.Increase confidence and effectiveness in decision-making
3.Reduce/Manage Risk
4.Forecast specific behavior, preemptively act on it 5.Anticipate and react to emerging trends
Source URL: https://intellipaat.com/spotfire-training/
Webinar: SAP BW Dinosaur to Agile Analytics PowerhouseAgilexi
Organisations who can quickly harness their corporate data to make optimum decisions will outperform their competitors. Use the corporate data in your SAP Business Warehouse to create significant business value and competitive advantage for your organisation.
View this webinar presentation to learn about:
• The business imperative to go analytics directly with SAP BW
• How you can rapidly, and at low cost, turn SAP BW into an analytics powerhouse
• Comprehensive and market leading integration of SAP BW with TIBCO Spotfire Analytics
See how SAP BW can be used to deliver analytics at speed and power not seen before. Empower your business and delighting your users beautifully presented insights.
The webinar can be viewed online at: http://bit.ly/sapbwanalytics1
Analysis and visualization of microarray experiment data integrating Pipeline...Vladimir Morozov
More 30 public and proprietary microarray experiments have been analyzed using in-house software. Pipeline Pilot workflows are developed to integrate the analysis results into the company gene target Knowledge Sphere platform. The gene expression values are analyzed and plotted via the R connector and custom R scripts. Pipeline Pilot workflows are embedded as Spotfire guides to retrieve gene annotation from NCBI, produce visualizations of differential expression statistics and biological pathway
Presentation given at the Joint Statistical Meetings in Boston in Aug. 2014, on applications of the R language using TERR, in Business Intelligence and Real Time applications
Extend the Reach of R to the Enterprise (for useR! 2013)Lou Bajuk
An overview of how and why we developed TIBCO Enterprise Runtime for R (TERR), and how it helps organizations leverage the power of the R language more widely.
Applying R in BI and Real Time applications EARL London 2015Lou Bajuk
Overview of the challenges of applying R in enterprise analytic applications, and TIBCO's approach to these challenges with Spotfire, TERR and Streambase.
Big Data Day LA 2016/ Big Data Track - Apply R in Enterprise Applications, Lo...Data Con LA
Prototypes are typically re-implemented in another language due to compatibility issues with R in the enterprise, but TIBCO Enterprise Runtime for R (TERR) allows the language to be run on several platforms. Enterprise-level scalability has been brought to the R language, enabling rapid iteration without the need to recode, re-implement and test. This presentation will delve further into these topics, highlighting specific use cases and the true value that can be gained from utilizing R. The session will be followed by a lively, open Q&A discussion.
RubiOne: Apache Spark as the Backbone of a Retail Analytics Development Envir...Databricks
The retail industry has a long history of fierce competition leading to innovations in marketing and operational efficiencies; however, this rapid advancement has not always kept pace with the latest advances in technology. This is evident by the abundance of business analysts at large enterprise retailers who are often constrained more by their own IT departments than by a lack of expertise or problems to solve.
RubiOne was designed as a vertically-integrated big data analytics development environment for retail business analysts and data scientists, with Apache Spark as the cornerstone of the product. It allows retailers to make data-driven decisions going beyond traditional analytics tools such as SQL and Excel. Using Apache Spark as one of the primary tools to query data and perform analytics, issues such as package installation, computational resources, and scalability are seamlessly handled by RubiOne.
In this session, you will learn how Apache Spark can serve as a shared backbone for an entire suite of enterprise services such as credential management, continuous integration, ad-hoc interactive data exploration, and task automation, while still maintaining hard enterprise requirements around security, availability, and cost. Learn from our war stories and best practices around transparently scaling Apache Spark clusters with Kubernetes, managing service and user isolation, and monitoring accurate enough for both debugging and billing. Beyond the technical aspects, we’ll also share our experiences of working with a global enterprise retailer to drive adoption of a modern big data technology stack centered around Apache Spark.
Discover the concept of 'on-the-fly' analysis with TIBCO Spotfire based on effortless of coding program for combining different types of file, cut cost of increasing in DB warehouse while DB growing, and real time analysis for digital era.
The success of any transformation efforts depending on the best practices followed over the transformation and beyond. Enterprise Architecture practice helps to execute the transformation efforts seamlessly. This presentation discover more details.
What Does Artificial Intelligence Have to Do with IT Operations?Precisely
From the early days of IT, organizations have grappled with the challenges of understanding how well their infrastructure is performing in support of the business. They have used a plethora of tools to detect, manage, and resolve problems that are causing disruption of services, but still struggle to achieve a unified, cross-domain understanding of what is happening across their IT infrastructure. Fortunately, over the past few years analytics platforms like Splunk, Elastic, and others have emerged to address requirements around IT Operations Analytics (ITOA). Now today the buzz is around AIOps – Artificial Intelligence Operations. But what is AIOps, and what can it do to help organizations address IT challenges. In this presentation you will get a better understanding of:
What is Artificial Intelligence for IT Operations
What are the required technologies for success at AIOps
What challenges exist for achieving AIOPs
Reusing and Managing R models in an EnterpriseLou Bajuk
My talk from EARL Conference in Boston in Oct. 2017, discussing how TIBCO helps our customers deploy, manage and reuse R models and scripts in their organization, making Data Science accessible to a wider audience.
Making Data Science accessible to a wider audienceLou Bajuk
TIBCO's Lou Bajuk talks about the challenges to making Data Science accessible to a wider audience, and how the TIBCO Analytics platform helps our customers tackle those challenges.
Slides from my 12/10/14 Webinar with the Bloor Group on the importance of an Analytics Platform for delivering value across your organization, and how TIBCO Spotfire meets that need.
As the number of packages available for R continues to grow, maintaining and testing these packages becomes more difficult. This difficulty is compounded as independent implementations of the R language, such as TIBCO Enterprise Runtime for R (TERR), are developed. To address this, we have created a test automation framework for testing packages with both TERR and R. We will describe how the framework automatically creates tests from a package's source files. Issues with testing on multiple platforms will be discussed. Suggestions for improving packages with tests will also be presented.
The Compatibility Challenge:Examining R and Developing TERRLou Bajuk
Slides from Michael Sannella, architect for TIBCO Enterprise Runtime for R (TERR), on the the Compatibility Challenge: Examining R and Developing TERR. Presented at useR 2014
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
2. Extending the Reach of R to the Enterprise
• TIBCO, S+, and embracing R in Spotfire
• Challenges of R for Enterprise applications
• TIBCO Enterprise Runtime for R (TERR)
• Benefits for organizations (and individuals) who use R
• Examples of TERR integration and performance
• Learn more and try it yourself
-2
3. Our Journey to TERR
•
John Chambers developed the S language at Bell Labs
– Starting in the mid 70’s
•
Insightful (Statsci) founded to commercial S as S+ in 1987
– The “plus”: statistical libraries, documentation, and support
– Later focus on commercial users, ease of use, server integration
•
R: development begun by Ross Ihaka and Robert Gentleman at University of
Auckland in mid 90’s
•
Insightful acquired by TIBCO in 2008
– Spotfire (for Data Discovery and Visualization) acquired in 2007
•
Focus shifted to applying Predictive Analytics in Spotfire
– Step 1: Embrace R
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4. Predictive Analytics with Spotfire
Easily provide targeted, relevant predictive analytics to business users to
improve decision making
•
Ensure compliance & proper usage
•
Share best practices and consistent workflows
•
Get the answer & do “What If?” analyses when needed
•
Leverage investments in R, S+, SAS, MATLAB, …
Powerful Predictive Analytics tools for Spotfire analysts
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Integrated into Spotfire workflows
•
Easily create, evaluate, and share Predictive Models
•
Add Forecasts with a single click
Benefits of Predictive Analytics to a spectrum of users
•
Increase confidence & effectiveness in decision-making
–
–
–
Reduce uncertainty
Discover meaningful patterns, important data
Maximize ROI
•
Anticipate and react to emerging trends
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Reduce/manage risk
–
•
Scenario planning, forecasts, fraud detection
Forecast specific behavior, preemptively act on it
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Increase upsell, decrease churn
5. Embracing R
•
Spotfire Statistics Server
–
Integration of R & S+ into Spotfire
applications
•
–
Later added SAS® & MATLAB®
Leverage the interactive visualizations
of Spotfire
•
Contribute to the R community
•
Well received—but our Enterprise
customers need more
–
–
-5
R provides tremendous benefits to
statisticians
But large enterprises are often
challenged to leverage that value
9. Providing Value for individuals who use R
•
Not seeking to displace R from statistician’s
desktops
–
•
Contribute to the R community
–
–
•
As we port from S+ or develop for TERR
• Supports “Develop in Open Source R, Deploy
on TERR”
• E.g., splusTimeSeries, splusTimeDate, sjdbc
TERR Developer Edition
–
–
–
-9
Sponsor useR conferences, contribute to R
Foundation
Contribute bug reports and propose fixes to R core
Contribute packages to CRAN
–
•
Enterprise platform for the deployment and
integration of your work—without having to rewrite
it!
Full version of TERR engine for testing code prior to
deployment
• Compatible with RStudio & ESS Emacs
Free for non-production use
Supported through Community site
10. Example 1: TERR vs. R Raw Performance
One specific example
• Non-optimal, non-vectorized, real-world R script
• For loop with row by row processing
for (i in seq(1,length=nrow(df))) {
…process each customer record…
}
Results
• TERR is ~35x faster for 50K rows, 150x faster for 500K rows
• No code modification required
We are looking for more real-world performance tests!
• On average 2-10x faster than R in microtests
11. Example 2: Spotfire Forecast Tool
•
Forecast Tool
– Easily add Forecasts to
Visualizations by right click menu
– Advanced users can tune settings
– Uses embedded TERR engine
•
Benefits
– Extend the power of Predictive
Analytics for ad hoc analysis to all
Spotfire users
– Easy entry point to Spotfire
Predictive Analytics
12. TERR integration with TIBCO StreamBase
•
Event-Driven analysis in TIBCO Spotfire
Event Analytics
–
•
Apply predictive models in real-time
decision making
–
–
–
–
•
Process monitoring, analysis, and
optimization
Best marketing offer
Customer churn
Predictive Maintenance
Yield optimization
Rapidly develop and iterate models in
production
–
Respond to changing opportunities
and threats
12
13. TIBCO Cloud Compute Grid
•
High performance computing on the cloud
– Available on TIBCO Cloud Marketplace
– TERR, Java and .NET computations
•
Robust DataSynapse GridServer architecture
– Used by Wall Street to manage 10K’s nodes
– Java, .NET, and REST APIs (JSON)
•
Perfect for pure computational work
– Vastly easier to use for applications like Monte Carlo
simulations than Map-Reduce
– Run complex statistical models multiple orders of
magnitude faster than open source R on a single
computer
– Unparalleled scalability without upfront capital
investment
•
Easy to get started
– Uses your Amazon EC2 account
14. Demos
• TERR in Spotfire
– Fraud Detection Application
– Data Functions: using the R language in Spotfire
– Forecast Tool
15. Learn more and Try it yourself
•
TERR Community at TIBCOmmunity.com
–
–
–
–
•
TERR Developer Edition
–
–
•
Full version of TERR engine for testing code prior to deployment
Supported through TIBCOmmunity, download via tap.tibco.com
TIBCO Cloud Compute Grid
–
•
Resources, FAQs, Forums
Details of R coverage
Product documentation & download
More info at spotfire.tibco.com/terr
https://marketplace.cloud.tibco.com
We want your feedback and input!
–
–
–
Real world performance tests
Package & R coverage prioritization
Via TERR Community, or contact me lbajuk@tibco.com or @loubajuk