Watch the recorded event at: http://info.datameer.com/Slideshare-
Economics-SQL-Hadoop.html
As organizations clamor to utilize their new investments in Hadoop ecosystems AND leverage their existing analytical infrastructures, many rush to integrate SQL as a data access layer to leverage existing skill sets and get started faster.
However, this approach relegates Hadoop to a data management and processing platform rather than the storage and compute engine optimized for analytical workloads it was purpose-built to be.
These slides by EMA and Datameer, will discuss the technical limitations of SQL on Hadoop and propose alternative ways to fully maximize Hadoop investments.
You will understanding:
*how SQL negates the inherent benefits of Hadoop
*why technological paradigm changes can sometimes be good
*use cases when SQL on Hadoop makes sense
Finding fraud in large, diverse data setsChris Selland
The document discusses how big data analytics can be used for fraud detection and prevention. It notes that fraud costs businesses and governments billions annually. Modern technology allows analyzing large transaction data sets to detect patterns and anomalies indicative of fraud. The HP Vertica platform is presented as a solution for businesses to more effectively analyze transaction data in real-time to stay ahead of constantly changing fraud patterns. Case studies of using Vertica to detect healthcare and credit card fraud are also mentioned.
Bio-IT Asia 2013: Informatics & Cloud - Best Practices & Lessons LearnedChris Dagdigian
This document provides an overview of best practices and lessons learned for life science informatics and cloud computing. It discusses three common design patterns for using the cloud: 1) legacy HPC systems replicated in the cloud, 2) Hadoop implementations, and 3) workflows rewritten to leverage cloud capabilities. The document emphasizes that cloud infrastructure allows for more agility than local datacenters in adapting to changing research needs. It also notes some challenges like developing cloud policies and procedures. An example case study describes simulating nuclear magnetic resonance probeheads using Amazon Web Services and hybrid Linux/Windows configurations.
The document discusses Oracle Database 12c and its capabilities for cloud computing, database as a service, and big data. It highlights features like Oracle Multitenant that allow for more efficient consolidation on clouds and simpler provisioning of database as a service. It also describes Oracle's approach to integrating Hadoop and Oracle Database for big data and analytics.
The document discusses how organizations can leverage big data. It notes that the amount of data being produced is growing dramatically and will continue to do so. It outlines four ways that organizations can benefit from big data: getting fast answers to new questions, creating a centralized data reservoir, predicting outcomes more accurately, and accelerating data-driven actions. It provides examples of companies that have achieved benefits like increased revenue and customer satisfaction through big data analytics. Finally, it argues that Oracle offers an integrated platform for organizations to fully leverage big data within their business analytics.
How big data tranform your business? Data Science Thailand Meet up #6Data Science Thailand
How Big Data Transform Your Business?
โกเมษ จันทวิมล
Komes Chandavimol
komes@datascienceth.com
Data Science Thailand Meet up #6 - Chiang Mai University
Briefing room: An alternative for streaming data collectionmark madsen
Knowing what’s happening in your enterprise right now can mark the difference between success and failure. The key is to have a rich view of activity, such that analysts and others can explore in a fully multidimensional fashion. Benefiting from such a detailed perspective can help professionals identify the exact nature of problems or opportunities, thus enabling precise actions that make a difference quickly.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a nexus of innovations for analyzing network traffic can help companies stay on top of their game. He’ll be briefed by Erik Giesa of ExtraHop, who will showcase his company’s stream analytics technology for wire data, which provides real-time, multidimensional views of network traffic. He’ll share success stories of how ExtraHop has solved otherwise intractable problems and enabled a new level of root-cause analysis.
Instant Visualizations in Every Step of AnalysisDatameer
Surveys reveal that concerns about data quality can create barriers for companies deploying Analytics and BI initiatives.
How can you readily identify and correct data quality issues at every step of your big data analysis to ensure accurate insights into customer behavior? In this webcast, we'll discuss how IT and business users can leverage self-service visualizations to quickly spot and correct data anomalies throughout the analytic process.
In this webinar, you will learn how to:
-Continuously visualize a profile of your data to identify inconsistencies, incompleteness and duplicates in your data
-Visualize machine learning and data mining, including clustering, decision tree analysis, column correlations and recommendations
-Create self-service visualizations for business and IT users
Finding fraud in large, diverse data setsChris Selland
The document discusses how big data analytics can be used for fraud detection and prevention. It notes that fraud costs businesses and governments billions annually. Modern technology allows analyzing large transaction data sets to detect patterns and anomalies indicative of fraud. The HP Vertica platform is presented as a solution for businesses to more effectively analyze transaction data in real-time to stay ahead of constantly changing fraud patterns. Case studies of using Vertica to detect healthcare and credit card fraud are also mentioned.
Bio-IT Asia 2013: Informatics & Cloud - Best Practices & Lessons LearnedChris Dagdigian
This document provides an overview of best practices and lessons learned for life science informatics and cloud computing. It discusses three common design patterns for using the cloud: 1) legacy HPC systems replicated in the cloud, 2) Hadoop implementations, and 3) workflows rewritten to leverage cloud capabilities. The document emphasizes that cloud infrastructure allows for more agility than local datacenters in adapting to changing research needs. It also notes some challenges like developing cloud policies and procedures. An example case study describes simulating nuclear magnetic resonance probeheads using Amazon Web Services and hybrid Linux/Windows configurations.
The document discusses Oracle Database 12c and its capabilities for cloud computing, database as a service, and big data. It highlights features like Oracle Multitenant that allow for more efficient consolidation on clouds and simpler provisioning of database as a service. It also describes Oracle's approach to integrating Hadoop and Oracle Database for big data and analytics.
The document discusses how organizations can leverage big data. It notes that the amount of data being produced is growing dramatically and will continue to do so. It outlines four ways that organizations can benefit from big data: getting fast answers to new questions, creating a centralized data reservoir, predicting outcomes more accurately, and accelerating data-driven actions. It provides examples of companies that have achieved benefits like increased revenue and customer satisfaction through big data analytics. Finally, it argues that Oracle offers an integrated platform for organizations to fully leverage big data within their business analytics.
How big data tranform your business? Data Science Thailand Meet up #6Data Science Thailand
How Big Data Transform Your Business?
โกเมษ จันทวิมล
Komes Chandavimol
komes@datascienceth.com
Data Science Thailand Meet up #6 - Chiang Mai University
Briefing room: An alternative for streaming data collectionmark madsen
Knowing what’s happening in your enterprise right now can mark the difference between success and failure. The key is to have a rich view of activity, such that analysts and others can explore in a fully multidimensional fashion. Benefiting from such a detailed perspective can help professionals identify the exact nature of problems or opportunities, thus enabling precise actions that make a difference quickly.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain how a nexus of innovations for analyzing network traffic can help companies stay on top of their game. He’ll be briefed by Erik Giesa of ExtraHop, who will showcase his company’s stream analytics technology for wire data, which provides real-time, multidimensional views of network traffic. He’ll share success stories of how ExtraHop has solved otherwise intractable problems and enabled a new level of root-cause analysis.
Instant Visualizations in Every Step of AnalysisDatameer
Surveys reveal that concerns about data quality can create barriers for companies deploying Analytics and BI initiatives.
How can you readily identify and correct data quality issues at every step of your big data analysis to ensure accurate insights into customer behavior? In this webcast, we'll discuss how IT and business users can leverage self-service visualizations to quickly spot and correct data anomalies throughout the analytic process.
In this webinar, you will learn how to:
-Continuously visualize a profile of your data to identify inconsistencies, incompleteness and duplicates in your data
-Visualize machine learning and data mining, including clustering, decision tree analysis, column correlations and recommendations
-Create self-service visualizations for business and IT users
The New Database Frontier: Harnessing the CloudInside Analysis
The Briefing Room with Rick Sherman and MarkLogic
Live Webcast on May 13, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9cd8eec52f7968721fdcd922e4f70369
The number of data types and sources is increasing almost daily anymore, which poses serious challenges for analytics and discovery. With many of these data sets in the Cloud, analysts are realizing that merging such public resources with internal information assets can be quite problematic. Solutions like virtualization and federation can get the job done, but another option is to employ a database that can natively connect to all these external sources.
Register for this episode of The Briefing Room to hear veteran Analyst Rick Sherman as he explains how the changing needs of the user are driving database innovation. He’ll be briefed by Ken Krupa of MarkLogic, who will tout his company’s NoSQL document database. He’ll discuss the importance of expanding the definition of what it means to be a database, and he’ll show how MarkLogic’s ability to tap into more sources than ever creates a scale-out data nerve center, thus delivering faster and better insights.
Visit InsideAnlaysis.com for more information.
How to do Data Science Without the ScientistDatameer
1. The document discusses making data science and analytics easier and more accessible for non-experts. It outlines challenges with traditional tools like requiring coding skills, long cycle times, and siloed data and applications.
2. The author proposes simplifying data integration and preparation, generating rather than writing code, and moving computation to the data to address these challenges.
3. A demo is presented of Datameer's product which aims to simplify and speed up the analytics process for users without advanced data science skills.
How to Avoid Pitfalls in Big Data Analytics WebinarDatameer
Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts.
Watch this webinar to learn how to navigate the most common obstacles of big data analytics.
Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics.
In this webinar, we will show you how to:
• Find the balance between infrastructure and business use cases
• Overcome challenges of using multiple tools that address big data analytics
• Leverage all your resources (data scientists, IT and analysts) most effectively
The document discusses how big data and analytics can transform businesses. It notes that the volume of data is growing exponentially due to increases in smartphones, sensors, and other data producing devices. It also discusses how businesses can leverage big data by capturing massive data volumes, analyzing the data, and having a unified and secure platform. The document advocates that businesses implement the four pillars of data management: mobility, in-memory technologies, cloud computing, and big data in order to reduce the gap between data production and usage.
This webinar discussed taking a proactive approach to machine learning, analytics, and data engineering in the cloud. It highlighted the benefits of such an approach for both business stakeholders and technologists, including improved agility, reduced complexity, and harmonized metadata and security across workloads. The webinar also covered some common pitfalls of "immediate gratification" cloud implementations, such as siloed data and vendor lock-in. Finally, it provided examples of how major companies are using Cloudera on the cloud to power data-driven initiatives and customer insights.
Customer Case Studies of Self-Service Big Data AnalyticsDatameer
Self-service tools empowers business users to rapidly gather, analyze and visualize data from broad, diverse data sources. Analyzing these sources provides new answers and new business opportunities for those smart enough to answer the new questions.
Free your IT staff from the need to respond to routine report requests. Business users can now rely on the rapid delivery of advanced self-service BI and data visualization capabilities to solve complex problems and capitalize on new opportunities.
From these slides, you will learn:
-Customer examples and return on investment from self-service big data analytics
-How business analysts can take advantage of Machine Learning
-Best practices in self-service big data analytics
InfoSphere BigInsights is IBM's distribution of Hadoop that:
- Enhances ease of use and usability for both technical and non-technical users.
- Includes additional tools, technologies, and accelerators to simplify developing and running analytics on Hadoop.
- Aims to help users gain business insights from their data more quickly through an integrated platform.
Big Data LDN 2017: The New Dominant Companies Are Running on DataMatt Stubbs
The document discusses solutions for deriving value from data through data integration and analytics. It describes three approaches companies have taken: 1) Building a custom machine learning platform like Uber's Michelangelo. 2) Developing custom integrations for a large multinational corporation with many technologies. 3) Implementing a cloud-first enterprise data stack for a 360-degree view of customers. The cloud-first approach provides benefits like scalability, collaboration, and reduced maintenance costs.
The new dominant companies are running on data SnapLogic
The cost of Digital Transformation is dropping rapidly. The technologies and methodologies are evolving to open up new opportunities for new and established corporations to drive business. We will examine specific examples of how and why a combination of robust infrastructure, cloud first and machine learning can take your company to the next level of value and efficiency.
Rich Dill, SnapLogic's enterprise solutions architect, at Big Data LDN 2017.
To disrupt and innovate, you need access to data. All of your data. The challenge for many organisations is that the data they need is locked away in a variety of silos. And there's perhaps no bigger silo than one of the most a widely deployed business application: SAP. Bringing together all your data for analytics and machine learning unlocks new insights and business value. Together, Cloudera and Datavard hold the key to breaking SAP data out of its silo, providing access to unlimited and untapped opportunities that currently lay hidden.
The document discusses opportunities for enriching a data warehouse with Hadoop. It outlines challenges with ETL and analyzing large, diverse datasets. The presentation recommends integrating Hadoop and the data warehouse to create a "data reservoir" to store all potentially valuable data. Case studies show companies using this approach to gain insights from more data, improve analytics performance, and offload ETL processing to Hadoop. The document advocates developing skills and prototypes to prove the business value of big data before fully adopting Hadoop solutions.
Information Builders provides the industry’s most scalable software solutions for data management and analytics. We help organizations operationalize and monetize their data through insights that drive action. Our integrated platform for BI, analytics, data integration, and data quality, combined with our proven expertise, delivers value faster, with less risk. We believe data and analytics are the drivers of digital transformation, and we’re on a mission to help our customers capitalize on new opportunities in the connected world. Information Builders is headquartered in New York, NY, with global offices, and remains one of the largest privately held companies in the industry.
Modern Data Integration Expert Session Webinar ibi
William McKnight, President of McKnight Consulting Group and Information Builders’ Jake Freivald discuss the tools needed for a successful modern data integration.
Building the Enterprise Data Lake - Important Considerations Before You Jump InSnapLogic
This document discusses considerations for building an enterprise data lake. It begins by introducing the presenters and stating that the session will not focus on SQL. It then discusses how the traditional "crab" model of data delivery does not scale and how organizations have shifted to industrialized data publishing. The rest of the document discusses important aspects of data lake architecture, including how different types of data like sensor data require new approaches. It emphasizes that the data lake requires a distributed service architecture rather than a monolithic structure. It also stresses that the data lake consists of three core subsystems for acquisition, management, and access, and that these depend on underlying platform services.
What is the value of big data? How does a user get that value?
Before, analysts would have to wait months relying on IT for a new report or make changes to an existing one. Now, analysts are able to shrink that time down to days or even minutes. On top of that, analysts can ask questions that were not possible before. In this webinar, we’ll show you how this analysis is possible and the value that has been achieved by customers.
In this session, you will learn:
How analysts get value out of big data
How to visualize data at every step of analysis
How analysts can do big data analytics without IT, in one product
This document discusses an approach to enterprise metadata integration using a multilayer metadata model. Key points include:
- Status dashboards provide facts from technical, operational, application, and quality metadata layers
- A graph database allows for context exploration across the entire cluster
- The integration of metadata from multiple sources provides a more holistic view of business knowledge
Datameer 6 is completely re-imagining the user experience for modern BI, helping you deliver new insights faster and more results to your data-hungry business. During this one-hour webinar, we demonstrated all that's new with Datameer 6 and how you can:
Discover answers to a new range of business questions using an iterative, exploratory approach
Find answers faster and deliver more insights with a new faster analytic workflow
Utilize Spark to speed analytic processing time without needing to know technical details
Watch this on-demand webinar, with special guest speaker, Sean Anderson, Senior Product Marketing Manager, from Cloudera, who discusses Cloudera's view of the Hadoop data processing stack and how the market place is benefiting from Spark.
Standing Up an Effective Enterprise Data Hub -- Technology and BeyondCloudera, Inc.
Federal organizations increasingly are focused on creating environments that enable more data-driven decisions. Yet ensuring that all data is considered and is current, complete, and accurate is a tall order for most. To make data analytics meaningful to support real-world transformation, agency staff need business tools that provide user-friendly dashboards, on-demand reporting, and methods to manage efficiently the rise of voluminous and varied data sets and types commonly associated with big data. In most cases, existing systems are insufficient to support these requirements. Enter the enterprise data hub (EDH), a software architecture specifically designed to be a unified platform that can economically store unlimited data and enable diverse access to it at scale. Plan to attend this discussion to understand the key considerations to making an EDH the architectural center of your agency’s modern data strategy.
Transform Banking with Big Data and Automated Machine Learning 9.12.17Cloudera, Inc.
Banks are rich in valuable data and can build and maintain a competitive advantage by identifying and executing on high-value machine learning projects leveraging the rich data available.This webinar will describe use cases fit for big data and machine learning in the banking sector (commercial, consumer, regulatory, and markets) and the impact they can have for your organization.
3 things to learn:
* How to create a next generation data platform and why it is important
* How to monetize big data using predictive modeling and machine learning
* What is needed for automated machine learning as a sustainable, cost-effective, and efficient solution
This webinar featuring Claudia Imhoff, President of Intelligent Solutions & Founder of the Boulder BI Brain Trust (BBBT), Matt Schumpert, Director of Product Management and Azita Martin, CMO at Datameer, will highlight the latest technology trends in extending BI with big data analytics and the top high impact use cases.
Attendees will hear about:
-- The extended architecture for today's modern analytics environment
-- The Internet of Things (IoT) and big data
-- The evolution of analytics – from descriptive to prescriptive
-- High impact use cases as a result of the changing analytics world
Getting Started with Big Data for Business ManagersDatameer
Big Data has become critical to the enterprise because of the massive amount of untapped data sources, and the potential to gain new insights that were previously not possible. So, how to get started with Big Data and Hadoop becomes a question more pertinent than ever before.
Listen to leading analyst at Ovum, Tony Baer, as he discusses answers to the key questions around how to:
Approach Big Data and associated business challenges
-- Identify what types of new insights can be revealed by Big Data
-- Staff for this undertaking and implement the technology necessary to be successful
-- Take the first steps toward getting started with Big Data on Hadoop
The New Database Frontier: Harnessing the CloudInside Analysis
The Briefing Room with Rick Sherman and MarkLogic
Live Webcast on May 13, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=9cd8eec52f7968721fdcd922e4f70369
The number of data types and sources is increasing almost daily anymore, which poses serious challenges for analytics and discovery. With many of these data sets in the Cloud, analysts are realizing that merging such public resources with internal information assets can be quite problematic. Solutions like virtualization and federation can get the job done, but another option is to employ a database that can natively connect to all these external sources.
Register for this episode of The Briefing Room to hear veteran Analyst Rick Sherman as he explains how the changing needs of the user are driving database innovation. He’ll be briefed by Ken Krupa of MarkLogic, who will tout his company’s NoSQL document database. He’ll discuss the importance of expanding the definition of what it means to be a database, and he’ll show how MarkLogic’s ability to tap into more sources than ever creates a scale-out data nerve center, thus delivering faster and better insights.
Visit InsideAnlaysis.com for more information.
How to do Data Science Without the ScientistDatameer
1. The document discusses making data science and analytics easier and more accessible for non-experts. It outlines challenges with traditional tools like requiring coding skills, long cycle times, and siloed data and applications.
2. The author proposes simplifying data integration and preparation, generating rather than writing code, and moving computation to the data to address these challenges.
3. A demo is presented of Datameer's product which aims to simplify and speed up the analytics process for users without advanced data science skills.
How to Avoid Pitfalls in Big Data Analytics WebinarDatameer
Big data analytics is revolutionizing the way businesses are collecting, storing, and more importantly, analyzing data. However, the adoption of a big data analytics solution has its share of failures and false starts.
Watch this webinar to learn how to navigate the most common obstacles of big data analytics.
Datameer and MapR have worked with customers to identify and solve the common pitfalls organizations face when deploying Hadoop-based analytics.
In this webinar, we will show you how to:
• Find the balance between infrastructure and business use cases
• Overcome challenges of using multiple tools that address big data analytics
• Leverage all your resources (data scientists, IT and analysts) most effectively
The document discusses how big data and analytics can transform businesses. It notes that the volume of data is growing exponentially due to increases in smartphones, sensors, and other data producing devices. It also discusses how businesses can leverage big data by capturing massive data volumes, analyzing the data, and having a unified and secure platform. The document advocates that businesses implement the four pillars of data management: mobility, in-memory technologies, cloud computing, and big data in order to reduce the gap between data production and usage.
This webinar discussed taking a proactive approach to machine learning, analytics, and data engineering in the cloud. It highlighted the benefits of such an approach for both business stakeholders and technologists, including improved agility, reduced complexity, and harmonized metadata and security across workloads. The webinar also covered some common pitfalls of "immediate gratification" cloud implementations, such as siloed data and vendor lock-in. Finally, it provided examples of how major companies are using Cloudera on the cloud to power data-driven initiatives and customer insights.
Customer Case Studies of Self-Service Big Data AnalyticsDatameer
Self-service tools empowers business users to rapidly gather, analyze and visualize data from broad, diverse data sources. Analyzing these sources provides new answers and new business opportunities for those smart enough to answer the new questions.
Free your IT staff from the need to respond to routine report requests. Business users can now rely on the rapid delivery of advanced self-service BI and data visualization capabilities to solve complex problems and capitalize on new opportunities.
From these slides, you will learn:
-Customer examples and return on investment from self-service big data analytics
-How business analysts can take advantage of Machine Learning
-Best practices in self-service big data analytics
InfoSphere BigInsights is IBM's distribution of Hadoop that:
- Enhances ease of use and usability for both technical and non-technical users.
- Includes additional tools, technologies, and accelerators to simplify developing and running analytics on Hadoop.
- Aims to help users gain business insights from their data more quickly through an integrated platform.
Big Data LDN 2017: The New Dominant Companies Are Running on DataMatt Stubbs
The document discusses solutions for deriving value from data through data integration and analytics. It describes three approaches companies have taken: 1) Building a custom machine learning platform like Uber's Michelangelo. 2) Developing custom integrations for a large multinational corporation with many technologies. 3) Implementing a cloud-first enterprise data stack for a 360-degree view of customers. The cloud-first approach provides benefits like scalability, collaboration, and reduced maintenance costs.
The new dominant companies are running on data SnapLogic
The cost of Digital Transformation is dropping rapidly. The technologies and methodologies are evolving to open up new opportunities for new and established corporations to drive business. We will examine specific examples of how and why a combination of robust infrastructure, cloud first and machine learning can take your company to the next level of value and efficiency.
Rich Dill, SnapLogic's enterprise solutions architect, at Big Data LDN 2017.
To disrupt and innovate, you need access to data. All of your data. The challenge for many organisations is that the data they need is locked away in a variety of silos. And there's perhaps no bigger silo than one of the most a widely deployed business application: SAP. Bringing together all your data for analytics and machine learning unlocks new insights and business value. Together, Cloudera and Datavard hold the key to breaking SAP data out of its silo, providing access to unlimited and untapped opportunities that currently lay hidden.
The document discusses opportunities for enriching a data warehouse with Hadoop. It outlines challenges with ETL and analyzing large, diverse datasets. The presentation recommends integrating Hadoop and the data warehouse to create a "data reservoir" to store all potentially valuable data. Case studies show companies using this approach to gain insights from more data, improve analytics performance, and offload ETL processing to Hadoop. The document advocates developing skills and prototypes to prove the business value of big data before fully adopting Hadoop solutions.
Information Builders provides the industry’s most scalable software solutions for data management and analytics. We help organizations operationalize and monetize their data through insights that drive action. Our integrated platform for BI, analytics, data integration, and data quality, combined with our proven expertise, delivers value faster, with less risk. We believe data and analytics are the drivers of digital transformation, and we’re on a mission to help our customers capitalize on new opportunities in the connected world. Information Builders is headquartered in New York, NY, with global offices, and remains one of the largest privately held companies in the industry.
Modern Data Integration Expert Session Webinar ibi
William McKnight, President of McKnight Consulting Group and Information Builders’ Jake Freivald discuss the tools needed for a successful modern data integration.
Building the Enterprise Data Lake - Important Considerations Before You Jump InSnapLogic
This document discusses considerations for building an enterprise data lake. It begins by introducing the presenters and stating that the session will not focus on SQL. It then discusses how the traditional "crab" model of data delivery does not scale and how organizations have shifted to industrialized data publishing. The rest of the document discusses important aspects of data lake architecture, including how different types of data like sensor data require new approaches. It emphasizes that the data lake requires a distributed service architecture rather than a monolithic structure. It also stresses that the data lake consists of three core subsystems for acquisition, management, and access, and that these depend on underlying platform services.
What is the value of big data? How does a user get that value?
Before, analysts would have to wait months relying on IT for a new report or make changes to an existing one. Now, analysts are able to shrink that time down to days or even minutes. On top of that, analysts can ask questions that were not possible before. In this webinar, we’ll show you how this analysis is possible and the value that has been achieved by customers.
In this session, you will learn:
How analysts get value out of big data
How to visualize data at every step of analysis
How analysts can do big data analytics without IT, in one product
This document discusses an approach to enterprise metadata integration using a multilayer metadata model. Key points include:
- Status dashboards provide facts from technical, operational, application, and quality metadata layers
- A graph database allows for context exploration across the entire cluster
- The integration of metadata from multiple sources provides a more holistic view of business knowledge
Datameer 6 is completely re-imagining the user experience for modern BI, helping you deliver new insights faster and more results to your data-hungry business. During this one-hour webinar, we demonstrated all that's new with Datameer 6 and how you can:
Discover answers to a new range of business questions using an iterative, exploratory approach
Find answers faster and deliver more insights with a new faster analytic workflow
Utilize Spark to speed analytic processing time without needing to know technical details
Watch this on-demand webinar, with special guest speaker, Sean Anderson, Senior Product Marketing Manager, from Cloudera, who discusses Cloudera's view of the Hadoop data processing stack and how the market place is benefiting from Spark.
Standing Up an Effective Enterprise Data Hub -- Technology and BeyondCloudera, Inc.
Federal organizations increasingly are focused on creating environments that enable more data-driven decisions. Yet ensuring that all data is considered and is current, complete, and accurate is a tall order for most. To make data analytics meaningful to support real-world transformation, agency staff need business tools that provide user-friendly dashboards, on-demand reporting, and methods to manage efficiently the rise of voluminous and varied data sets and types commonly associated with big data. In most cases, existing systems are insufficient to support these requirements. Enter the enterprise data hub (EDH), a software architecture specifically designed to be a unified platform that can economically store unlimited data and enable diverse access to it at scale. Plan to attend this discussion to understand the key considerations to making an EDH the architectural center of your agency’s modern data strategy.
Transform Banking with Big Data and Automated Machine Learning 9.12.17Cloudera, Inc.
Banks are rich in valuable data and can build and maintain a competitive advantage by identifying and executing on high-value machine learning projects leveraging the rich data available.This webinar will describe use cases fit for big data and machine learning in the banking sector (commercial, consumer, regulatory, and markets) and the impact they can have for your organization.
3 things to learn:
* How to create a next generation data platform and why it is important
* How to monetize big data using predictive modeling and machine learning
* What is needed for automated machine learning as a sustainable, cost-effective, and efficient solution
This webinar featuring Claudia Imhoff, President of Intelligent Solutions & Founder of the Boulder BI Brain Trust (BBBT), Matt Schumpert, Director of Product Management and Azita Martin, CMO at Datameer, will highlight the latest technology trends in extending BI with big data analytics and the top high impact use cases.
Attendees will hear about:
-- The extended architecture for today's modern analytics environment
-- The Internet of Things (IoT) and big data
-- The evolution of analytics – from descriptive to prescriptive
-- High impact use cases as a result of the changing analytics world
Getting Started with Big Data for Business ManagersDatameer
Big Data has become critical to the enterprise because of the massive amount of untapped data sources, and the potential to gain new insights that were previously not possible. So, how to get started with Big Data and Hadoop becomes a question more pertinent than ever before.
Listen to leading analyst at Ovum, Tony Baer, as he discusses answers to the key questions around how to:
Approach Big Data and associated business challenges
-- Identify what types of new insights can be revealed by Big Data
-- Staff for this undertaking and implement the technology necessary to be successful
-- Take the first steps toward getting started with Big Data on Hadoop
Understand Your Customer Buying Journey with Big Data Datameer
Imagine being able to use insights about your customer acquisition journey to design campaigns that improve conversion rates. What if you could identify points of failure along the customer acquisition path or during product usage?
Today more than ever the role of the marketing and product executives have become data-driven. Business executives who leverage data to understand prospect and customer behavior have gained an edge over their peers. Big Data analytics is the key to unlocking insights from your customer behavior data and empowers you to combine and analyze all of your customer interaction data to drive customer acquisition and loyalty.
Analyzing Unstructured Data in Hadoop WebinarDatameer
Unstructured data is growing 62% per year faster than structured data. According to Gartner, data volumes are set to grow 800% in aggregate over the next 5 years, and 80% of it will be unstructured data.
This on-demand webinar will highlight and discuss:
How applying big data analytics to unstructured data can help you gain richer, deeper and more accurate insights to gain competitive advantages
The sources of unstructured data which include email, social media platforms, CRM systems, call center platforms (including notes and speech-to-text transcripts), and web scrapes
How monitoring the communications of your customers and prospects enables you to make time-sensitive decisions and jump on new business opportunities
In Big Data projects, analysts often spend 80% of their time preparing data for analysis. In addition, users don’t have a good understanding of their data quality.
Today there are multiple tools that assist with integration, data preparation, analysis and visualization. However, data quality continues to be one of the biggest challenges businesses face when deploying big data analytics.
People need to profile their data and ensure data quality to get accurate insights and make informed business decisions. Join Datameer as we address these pain points with visualizations at every step.
This webinar will highlight and showcase:
-How visual data profiling reduces the guesswork in the data wrangling process
-Enhanced interactive data mining capabilities reduce time to insight
-A demonstration of the new 4.0 features and functions
Datameer offers the first data analytics solution built on Hadoop that helps business users access, analyze and use massive amounts of data. Hadoop provides key breakthroughs over traditional solutions by enabling linear scalability from 1 to 4000 servers, overcoming limitations of storage and compute through use of commodity hardware and open source software, and not requiring rigid data models or specialized hardware or software.
Datameer offers the first data analytics solution built on Hadoop that helps business users access, analyze and use massive amounts of data. Hadoop provides key breakthroughs over traditional solutions by enabling linear scalability from 1 to 4000 servers, overcoming limitations of storage and compute through use of commodity hardware and open source software, and not requiring rigid data models or specialized hardware or software.
Online Fraud Detection Using Big Data Analytics WebinarDatameer
With the ease and convenience of the internet, shopping online has never been faster and simpler than with a click of a button. But with this convenience, lurks the consequence for online fraud.
Companies and merchants lose valuable time and money to online thieves scamming the web. Learn how to identify patterns with Datameer and Trustev as they demonstrate how to take control of the situation and combat combat against suspicious activity by using big data analytics.
In this webinar, you will take away:
*An understanding of the complexities and challenges of online fraud today
*Best practices for merchants and companies to protect themselves from fraud
*A demonstration of fraud reporting, prevention and prediction
The document describes a proof of concept (POC) technical solution for a real estate company to analyze large amounts of web activity and customer data. The POC proposed loading one year of data from six tables into an Amazon cloud Hadoop environment and using Datameer for data discovery and analytics. The goals were to set up the cloud environment, load the search analytics data, and allow the business to perform analytics with acceptable performance and gain new insights. High-level and detailed descriptions of the technical solution are provided.
How do you protect the data in big data analytics projects?
As big data initiatives focus on volume, velocity or variety of data, often overlooked in the big data project is the security of the data. This is especially important for financial services, healthcare and government or anytime sensitive data is analyzed.
This webinar highlights:
*Hadoop security landscape
*Hadoop encryption, masking, and access control
*Customer examples of securing hadoop environments
You can view the full presentation of this webinar here: http://info.datameer.com/Slideshare-Fighting-Fraud-this-Holiday-Season.html
In 2012, retailers lost $3.5 billion in revenue to online fraud. These losses spike by a substantial estimated 20% during the holiday season.
Join Datameer and Hortonworks in this webinar to learn how Big Data Analytics can be used to identify new fraud schemes during peak fraud season.
In this webinar, you will learn about:
current challenges in identifying fraud
what to look for in a big data solution addressing fraud
how big data analytics can identify credit card fraud
best practices
Complement Your Existing Data Warehouse with Big Data & HadoopDatameer
To view the full webinar, please go to: http://info.datameer.com/Slideshare-Complement-Your-Existing-EDW-with-Hadoop-OnDemand.html
With 40% yearly growth in data volumes, traditional data warehouses have become increasingly expensive and challenging.
Much of today’s new data sources are unstructured, making the structured data warehouse an unsuitable platform for analyses. As a result, organizations now look at Hadoop as a data platform to complement existing BI data warehouses, and a scalable, flexible and cost-effective solution for data storage and analysis.
Join Datameer and Cloudera in this webinar to discuss how Hadoop and big data analytics can help to:
-Get all the data your business needs quickly into one environment
Shorten the time to insight from months to days
Extend the life of your existing data warehouse investments
Enable your business analysts to ask and answer bigger questions
Lean Production Meets Big Data: A Next Generation Use CaseDatameer
The document discusses how big data and analytics can help optimize business processes using lean principles. It provides an overview of lean production concepts like identifying and eliminating waste. Big data is presented as a new approach to gain insights from process data that can help pinpoint improvement opportunities. The speaker demonstrates how Datameer's software allows users to easily analyze data from multiple sources and measure key performance indicators to drive continuous process improvements.
Top 3 Considerations for Machine Learning on Big DataDatameer
This document discusses considerations for machine learning on big data. It provides background on speakers Karen Hsu and Elliott Cordo. It then covers drivers and challenges of big data, including how companies like Amazon and Netflix have leveraged big data analytics. Alternatives to machine learning on big data like data mining, traditional BI, and visualization are discussed. Example use cases and key criteria around ease of use and quality for algorithms like clustering, column dependencies, and decision trees are presented. Best practices for machine learning on big data are provided for clustering, recommendations, and overall analytics processes. The document concludes with a polling question and call to action.
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
How to do Predictive Analytics with Limited DataDatameer
http://www.datameer.com It is frustrating: even with petabytes of data on a Hadoop cluster, one still encounters situations where there’s a lack of key data for a wide variety of big data analytic use cases. You might have billions of clicks on your web site, but only a few users choose to rate a product. There might be millions of text documents on your cluster, but it is too expensive to have someone categorize more than a tiny fraction of them. In principle, this is where predictive modeling could help. For instance, one could learn a model to predict user ratings so you can better serve product recommendations based on those expected ratings. Or, one could create a model to automatically categorize text documents, saving countless hours and dollars. The main problem is that there is only a limited amount of training material (i.e. user ratings, categorized documents) and it is thus hard to generate good models.
As it turns out, recent research on machine learning techniques has found a way to deal effectively with such situations with a technique called semi-supervised learning. These techniques are often able to leverage the vast amount of related, but unlabeled data to generate accurate models. In this talk, we will give an overview of the most common techniques including co-training regularization. We first explain the principles and underlying assumptions of semi-supervised learning and then show how to implement such methods with Hadoop.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Skybuffer AI: Advanced Conversational and Generative AI Solution on SAP Busin...Tatiana Kojar
Skybuffer AI, built on the robust SAP Business Technology Platform (SAP BTP), is the latest and most advanced version of our AI development, reaffirming our commitment to delivering top-tier AI solutions. Skybuffer AI harnesses all the innovative capabilities of the SAP BTP in the AI domain, from Conversational AI to cutting-edge Generative AI and Retrieval-Augmented Generation (RAG). It also helps SAP customers safeguard their investments into SAP Conversational AI and ensure a seamless, one-click transition to SAP Business AI.
With Skybuffer AI, various AI models can be integrated into a single communication channel such as Microsoft Teams. This integration empowers business users with insights drawn from SAP backend systems, enterprise documents, and the expansive knowledge of Generative AI. And the best part of it is that it is all managed through our intuitive no-code Action Server interface, requiring no extensive coding knowledge and making the advanced AI accessible to more users.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
According to 2012 EMA research, Online Archiving, or Hadumping, is the Phase “zero” of most Big Data initiatives
Teaches Internal teams about the data delivery and structure
How to interact with the data
How to apply data to business cases as opposed to simply a technology project
It is the where you start when:
“you don’t know what you don’t know…”
2013 EMA Research shows that over half of Big Data projects have online archiving as an ‘In Operation’ status
In Production or as a Pilot Project with hands on keyboards. Software installed.
Over 4 in 10 respondents say “Economics” are a Business Reason for Online Archiving Use Case.
These organizations are attempting to lower their operational costs
Moving beyond select * requires a standard requires a facility that manages and tracks metadata
Select * tablename is the rough equivalent to cat filename
SQL starts to become truly “special” when you use a query such as
Select t.columnA, s.columnB, s.columnC from tablename t tablename s
Where t.columnZ = s.column.X
NoSQL and specifically Hadoop have focused on the ability to be flexible in data storage often at the expense of metadata management
SQL doesn’t do with an “or” data structure (image on right)
SQL works best with a defined data structure (image on right)
When you ask Hive a question it doesn’t understand…. You get the error message.
In2013 EMA Research Big Data initiatives used the following datasets
Machine generated (JSON, XML, etc) almost 40%
Process mediated (structured) just under 30%
Human sourced (emails, texts,) over 30%
Over 30% of respondents indicate that a lack of self-service data access (SQL) is a challenge to operate a Hadoop platform
Nearly 40% of respondents say a lack of SQL data access is a challenge to operate a NoSQL platform
In each of these instances, it indicates that while you “CAN” perform certain applications on Hadoop, SQL-based data access is a high concern.
Big Data environments aren’t just for EDW replacement as some would say
There are multiple use cases
Operational
Analytical
Exploratory
Nearly 3 of 10 respondents in 2013 research say that they are using Exploratory or Discovery use cases
Just under 50% of respondents say operational costs (staff head count is included) are a challenge to operate a discovery platform.
3 of 10 respondents want to utilize the features and functions of products to speed their skills acquisition. Often times these are features that they feel most comfortable with. Interfaces and processes that they use every day. MS Excel is an example.
Nearly 4 out 10 respondents indicate new skills development is a challenge to operate a discovery platform
When you are using exploratory or discovery use cases, you need flexibility… applying a hard schema (structured) presupposes particular questions AND answers.
Square wooden peg and round wooden hole – not a lot of give.
Being able to apply a schema or structure at the time of query or late binding schema enables the best method of discovery
Flexible schema at the time of processing…. Sausage grinder
2013 EMA research says
Over 30% of respondents use late binding schemas when processing data
Nearly a third use multiple approaches
Over 10% don’t apply a schema at all…
“Only” about one third of Respondents are using external technical resources to bridge their skills gaps. This comes from the costs associated with the outside consultants vs existing staff
“Free as in Speech” or “Free as in Beer”… Big Data is “Free as a Free Puppy”
Over 40% of respondents say Economics are a Business Reason for Online Archiving Use Case
Back to Metadata….
Over one third of respondents indicate shortage of technical metadata a challenge to operate a discovery platform. Applying that technical metadata layer takes a manual effort and thus additional headcount. When you link this to ‘only’ a 1% increase in big data budget from 2013 to 2014 for Hadoop implementations, it is important to put the best use for hadoop platforms.
36% implementation time to implement is a challenge to operate a hadoop platform
43% say operational costs are a challenge to operate a discovery platform (link to a 1% increase in big data operational budget from 2013 to 2014)
Over one third of respondents say they lack the skills to manage multi-structured data platforms as an obstacle to implement (Top answer)