How to Crunch Petabytes with Hadoop and Big Data Using InfoSphere BigInsights...DATAVERSITY
Do you wonder how to process huge amounts of data in short amount of time? If yes, this session is for you! You will learn why Apache Hadoop and Streams is the core framework that enables storing, managing and analyzing of vast amounts of data. You will learn the idea behind Hadoop's famous map-reduce algorithm and why it is at the heart of solutions that process massive amounts of data with flexible workloads and software based scaling. We explore how to go beyond Hadoop with both real-time and batch analytics, usability, and manageability. For practical examples, we will use IBM InfoSphere BigInsights and Streams, which build on top of open source tooling when going beyond basics and scaling up and out is needed.
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
191017 scamander non invasive data governance - with link to movie with bob s...Ronald Kok
This document outlines an approach to data governance called Non-Invasive Data Governance. It discusses how traditional data governance can be challenging due to complexity, lack of commitment, and cultural issues. Non-Invasive Data Governance seeks to formalize existing best practices and establish authority by joining existing meetings and councils rather than creating new roles. It emphasizes that data stewardship is not a new position but something everyone can contribute to. The presentation encourages starting small by seeking out best practices, formalizing roles and responsibilities, solving data issues, and planning communications.
7 Big Data Challenges and How to Overcome ThemQubole
Implementing a big data project is difficult. Hadoop is complex, and data governance is crucial. Learn common big data challenges and how to overcome them.
Strategy how to change Big Data into useful information and win the business/candidacy, and Big Problem into Big Opportunity in the information exposure era.
The Top 5 Factors to Consider When Choosing a Big Data SolutionDATAVERSITY
This document discusses factors to consider when choosing a big data solution. It defines big data and outlines the key characteristics of velocity, variety, and volume. It also discusses complexity in distributing and managing big data. The document recommends considering how well solutions handle these big data characteristics and highlights how the Apache Cassandra and DataStax Enterprise platform is well-suited for big data workloads.
This document summarizes the results of a survey of 298 data management professionals about big data challenges and opportunities. It finds that big data is now present in all organizations, with 11% managing over a petabyte and another 20% having hundreds of terabytes. While bigger companies are dealing with more data currently, most companies will soon have petabyte-sized stores. However, the business does not fully understand big data's potential value yet. Capitalizing on big data does not require replacing existing infrastructure but integrating it. The survey also finds that relational databases and Hadoop are both important technologies for managing big data now and in the future.
Slide from my talk at Contech Forum 2021. This update from the November 2020 talk on digital equity work in the Bronx and lessons for Information providers in our changing world. This session will look at the progression of the Bronx Digital Equity Coalition and the development of principles for information and technology access that can also apply to information provider communities.
How to Crunch Petabytes with Hadoop and Big Data Using InfoSphere BigInsights...DATAVERSITY
Do you wonder how to process huge amounts of data in short amount of time? If yes, this session is for you! You will learn why Apache Hadoop and Streams is the core framework that enables storing, managing and analyzing of vast amounts of data. You will learn the idea behind Hadoop's famous map-reduce algorithm and why it is at the heart of solutions that process massive amounts of data with flexible workloads and software based scaling. We explore how to go beyond Hadoop with both real-time and batch analytics, usability, and manageability. For practical examples, we will use IBM InfoSphere BigInsights and Streams, which build on top of open source tooling when going beyond basics and scaling up and out is needed.
You probably have heard about Big Data, but ever wondered what it exactly is? And why should you care?
Mobile is playing a large part in driving this explosion in data. The data are also created by the apps and other services in the background. As people are moving towards more digital channels, tons of data are being created. This data can be used in a lot of ways for personal and professional use. Big Data and mobile apps are converging in an enterprise and interacting; transforming the whole mobile ecosystem.
191017 scamander non invasive data governance - with link to movie with bob s...Ronald Kok
This document outlines an approach to data governance called Non-Invasive Data Governance. It discusses how traditional data governance can be challenging due to complexity, lack of commitment, and cultural issues. Non-Invasive Data Governance seeks to formalize existing best practices and establish authority by joining existing meetings and councils rather than creating new roles. It emphasizes that data stewardship is not a new position but something everyone can contribute to. The presentation encourages starting small by seeking out best practices, formalizing roles and responsibilities, solving data issues, and planning communications.
7 Big Data Challenges and How to Overcome ThemQubole
Implementing a big data project is difficult. Hadoop is complex, and data governance is crucial. Learn common big data challenges and how to overcome them.
Strategy how to change Big Data into useful information and win the business/candidacy, and Big Problem into Big Opportunity in the information exposure era.
The Top 5 Factors to Consider When Choosing a Big Data SolutionDATAVERSITY
This document discusses factors to consider when choosing a big data solution. It defines big data and outlines the key characteristics of velocity, variety, and volume. It also discusses complexity in distributing and managing big data. The document recommends considering how well solutions handle these big data characteristics and highlights how the Apache Cassandra and DataStax Enterprise platform is well-suited for big data workloads.
This document summarizes the results of a survey of 298 data management professionals about big data challenges and opportunities. It finds that big data is now present in all organizations, with 11% managing over a petabyte and another 20% having hundreds of terabytes. While bigger companies are dealing with more data currently, most companies will soon have petabyte-sized stores. However, the business does not fully understand big data's potential value yet. Capitalizing on big data does not require replacing existing infrastructure but integrating it. The survey also finds that relational databases and Hadoop are both important technologies for managing big data now and in the future.
Slide from my talk at Contech Forum 2021. This update from the November 2020 talk on digital equity work in the Bronx and lessons for Information providers in our changing world. This session will look at the progression of the Bronx Digital Equity Coalition and the development of principles for information and technology access that can also apply to information provider communities.
Austrade Presentation - Big Data the New Oil (Microsoft draft)Dr Andrew Seit
1. Exponential growth in data created, collected, and stored has led to a 10x increase in "situation awareness" with big data, requiring rethinking of decision-making processes.
2. Big data is not a "silver bullet" but a new problem-solving philosophy that seeks evidence-based decisions to increase the tempo and speed of decisions in both the private sector and government.
3. Big data will have top-line and bottom-line implications for companies and far-reaching effects on the economy through increased use of analytics, machine learning, and data visualization techniques on large datasets.
Whitepaper: Thriving in the Big Data era Manage Data before Data Manages you Intellectyx Inc
Paper Overview -
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Data comes from everywhere and we are generating data more than ever before.
This white paper will explain what Big Data is and provide practical examples, concluding with a message how to put data your data to work.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Artificial Intelligence beyond the hype: Local (Belgian) Machine Learning suc...Patrick Van Renterghem
Presentation on "AI beyond the hype: Local (Belgian) Machine Learning success stories" by Peter Depypere (element61), at the BI & Data Analytics Summit on June 13th, 2019 in Diegem (Belgium)
Big Data : From HindSight to Insight to ForesightSunil Ranka
When it comes to Analytics and Reporting , There is a fine line between HindSight to Insight to Foresight . With the evolution of BigData technology, there is a need in deriving value out of the larger datasets, not available in the past. Even before we can start using the new shiny technologies, there is a need of understanding what is categorized as reporting or business intelligence or Big Data and Analytics. Based on my experience, people struggle to distinguish between reporting, Analytics, and Business Intelligence.
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
Presentation by Ivan Schotsmans (DV Community) at the Data Vault Modelling an...Patrick Van Renterghem
The start of GDPR implementations in Europe was, for most organizations, also the start of rethinking their Data Warehouse strategy. The experience of past implementations gave a better view on the do's and don'ts. One of the important lessons learned was the approach of handling information quality. It's not something you handle on top of your data warehouse. To be successful, information quality goes hand in hand with your data warehouse implementation.
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
General Data Protection Regulation - BDW Meetup, October 11th, 2017Caserta
Caserta Presentation:
General Data Protection Regulation (GDPR) is a business and technical challenge for companies worldwide - and the deadlines are coming fast! American institutions that do business in the EU or have customers from the EU will have their data practices affected. With this in mind, Caserta – joined by Waterline Data, Salt Recruiting, and Squire Patton Boggs – hosted a BDW Meetup on the GDPR, which is perhaps the most controversial data legislation that has been passed to date.
Joe Caserta, Founding President, Caserta, spoke on the basics of the GDPR, how it will impact data privacy around the world, and some techniques geared towards compliance.
Slides: Case Study — How J.B. Hunt is Driving Efficiency with AI and Real-Tim...DATAVERSITY
J.B. Hunt, one of the leading providers of transportation and logistics services in North America, recognizes the criticality of customer responsiveness, service quality, and operational efficiency for its success. However, with its data spread across multiple sources, including legacy mainframe systems, the organization was struggling to meet data requirements from multiple departments. They struggled to troubleshoot operational issues and respond to customers quickly.
Join this webinar to hear about the optimized solution J. B. Hunt implemented, which automates real-time data pipelines for a reliable cloud data lake and provides multiple user groups an in-the-moment view of data without overwhelming internal operational systems. Discover how J.B. Hunt now leverages a modernized data environment to accelerate data delivery and drive various AI and analytics initiatives such as real-time service-pricing, competitive counterbidding, and improving their customer experience.
Learn how you can:
• Ingest data in real-time from legacy mainframe systems, enterprise applications, and more
• Create a reliable cloud data lake to accelerate AI and Analytic Initiatives
• Catalog, prepare, and provision data to empower data consumers
• Drive operational efficiency and customer experience with AI-augmented insights
The document discusses Luminar, an analytics company that uses big data and Hadoop to provide insights about Latino consumers in the US. Luminar collects data from over 2,000 sources and uses that data along with "cultural filters" to identify Latinos and understand their purchasing behaviors. This provides more accurate information than traditional surveys. Luminar implemented a Hadoop system to more quickly analyze this large amount of data and provide valuable insights to marketers and businesses.
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
The document discusses creating value from data and overcoming hype around data science. It summarizes that data science has the potential to create value through customer insights, improved processes, and new products, but realizing this value is challenging. Three key challenges are 1) extracting meaningful information from data, 2) bringing business and IT together in joint data science programs and organizations, and 3) developing data skills and an organizational culture that supports data-driven decision making. Overcoming these challenges is necessary to build mature data science capabilities and unlock the full value of data.
Unlocking Greater Insights with Integrated Data Quality for CollibraPrecisely
Data is arguably your company’s greatest asset, and a thoughtful data governance strategy, along with robust tools like Collibra Data Governance Center (DGC), is essential to getting the most value from that data. However, even the best data governance programs will falter without data quality.
Data governance systems provide a framework for the policies, processes, rules, roles and responsibilities that help you manage your enterprise data. But they don’t give you insight into the characteristics and quality of that data – such as errors, outliers and issues – nor how the data changes over time.
During this webinar, we discuss how seamlessly integrating Trillium DQ with Collibra DGC creates a complete data governance solution that delivers rapid insights into the health of your data, ensuring trust and compliance with organizational policies and plans. We demonstrate how data is automatically exchanged between the tools so users can:
• Quickly establish the rules needed to support policies
• Evaluate their data against those rules on an ongoing basis
• Identify problems or improvements with their data quality to take action
The document discusses alternative data and its importance. It defines alternative data as data derived from non-traditional sources like mobile devices, websites, and sensors. This data can provide insights that complement traditional sources and help with decision-making. The document outlines 8 types of alternative data and 3 ways to access it, including hiring a data scientist, partnering with a third party, or using web scraping software. It provides examples of alternative data's applications in advertising, tracking corporate revenues, risk assessment, and more. Overall, the document promotes alternative data as a valuable new resource for businesses seeking a competitive edge.
The document discusses the need for "Smart Data" over "Big Data". It argues that while Big Data has potential, the technology is constantly evolving and data scientists with the needed skills are scarce and expensive. It advocates for providing business users with self-service analytics capabilities to answer over 90% of their questions quickly without IT assistance. Examples are provided of how self-service analytics helped Coca-Cola and JD Group gain insights from their data more efficiently.
Bigdata for sme-industrial intelligence information-24july2017-finalstelligence
This document discusses how small and medium enterprises (SMEs) can benefit from big data analytics. It defines key concepts like the 5 V's of big data and explains challenges SMEs face in adopting analytics. Common types of analytics like reporting, trend analysis, and predictive modeling are described. The document provides recommendations for simple analytic tools and techniques SMEs can use, such as data exploration, time-series analysis, and regression in Excel. Finally, it discusses how cloud-based solutions can help SMEs overcome barriers to adopting traditional IT solutions and analyzes the big data business landscape in Thailand.
The Hive Think Tank: The Future Of Customer Support - AI Driven AutomationThe Hive
The Hive Think Tank Panel Discussion moderated by Kate Leggett (Forrester) with panelists: Allan Leinwand (ServiceNow), Nitin Narkhede (Wipro), Jason Smale (Zendesk), Dan Turchin (Neva). The future of customer support is AI-driven virtual agents. Soon, we’ll interact conversationally with bots that know who we are, how we’re impacted, and what we need. Soon, the capabilities of virtual agents will far exceed those of today’s best human agents. We’ll receive support that is more reliable than friends, more accurate than social media, and less frustrating than waiting on hold.
The Hive Think Tank: AI in The Enterprise by Venkat SrinivasanThe Hive
This The Hive Think Tank talk by Venkat Srinivasan, CEO of RAGE Frameworks, focuses on successful applications of AI in the Enterprise. We start with a broad and more inclusive definition of AI in the context of enterprise business processes.
We introduce a taxonomy of AI solution methods that broaden the focus beyond a narrow focus on deep learning based on neural nets. In line with the taxonomy, we present several successful AI applications in use today at major corporations across industries including financial services, manufacturing/retail, professional services, logistics. These applications range from commercial lending, contract review, customer service intelligence, market and competitive intelligence, signals for capital markets, regulatory compliance and others.
Austrade Presentation - Big Data the New Oil (Microsoft draft)Dr Andrew Seit
1. Exponential growth in data created, collected, and stored has led to a 10x increase in "situation awareness" with big data, requiring rethinking of decision-making processes.
2. Big data is not a "silver bullet" but a new problem-solving philosophy that seeks evidence-based decisions to increase the tempo and speed of decisions in both the private sector and government.
3. Big data will have top-line and bottom-line implications for companies and far-reaching effects on the economy through increased use of analytics, machine learning, and data visualization techniques on large datasets.
Whitepaper: Thriving in the Big Data era Manage Data before Data Manages you Intellectyx Inc
Paper Overview -
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
Data comes from everywhere and we are generating data more than ever before.
This white paper will explain what Big Data is and provide practical examples, concluding with a message how to put data your data to work.
Why Everything You Know About bigdata Is A LieSunil Ranka
As a big data technologist, you can bet that you have heard it all: every crazy claim, myth, and outright lie about what big data is and what it isn't that you can imagine, and probably a few that you can't.If your company has a big data initiative or is considering one, you should be aware of these false statements and the reasons why they are wrong.
Artificial Intelligence beyond the hype: Local (Belgian) Machine Learning suc...Patrick Van Renterghem
Presentation on "AI beyond the hype: Local (Belgian) Machine Learning success stories" by Peter Depypere (element61), at the BI & Data Analytics Summit on June 13th, 2019 in Diegem (Belgium)
Big Data : From HindSight to Insight to ForesightSunil Ranka
When it comes to Analytics and Reporting , There is a fine line between HindSight to Insight to Foresight . With the evolution of BigData technology, there is a need in deriving value out of the larger datasets, not available in the past. Even before we can start using the new shiny technologies, there is a need of understanding what is categorized as reporting or business intelligence or Big Data and Analytics. Based on my experience, people struggle to distinguish between reporting, Analytics, and Business Intelligence.
Presentation: Big Data – From Strategy to Production - Mario Meir-Huber, Big Data Leader Eastern Europe, Teradata GmbH (AT), at the European Data Economy Workshop taking place back to back to SEMANTiCS2015 on 15 September 2015 in Vienna
Presentation by Ivan Schotsmans (DV Community) at the Data Vault Modelling an...Patrick Van Renterghem
The start of GDPR implementations in Europe was, for most organizations, also the start of rethinking their Data Warehouse strategy. The experience of past implementations gave a better view on the do's and don'ts. One of the important lessons learned was the approach of handling information quality. It's not something you handle on top of your data warehouse. To be successful, information quality goes hand in hand with your data warehouse implementation.
Caserta Concepts, Datameer and Microsoft shared their combined knowledge and a use case on big data, the cloud and deep analytics. Attendes learned how a global leader in the test, measurement and control systems market reduced their big data implementations from 18 months to just a few.
Speakers shared how to provide a business user-friendly, self-service environment for data discovery and analytics, and focus on how to extend and optimize Hadoop based analytics, highlighting the advantages and practical applications of deploying on the cloud for enhanced performance, scalability and lower TCO.
Agenda included:
- Pizza and Networking
- Joe Caserta, President, Caserta Concepts - Why are we here?
- Nikhil Kumar, Sr. Solutions Engineer, Datameer - Solution use cases and technical demonstration
- Stefan Groschupf, CEO & Chairman, Datameer - The evolving Hadoop-based analytics trends and the role of cloud computing
- James Serra, Data Platform Solution Architect, Microsoft, Benefits of the Azure Cloud Service
- Q&A, Networking
For more information on Caserta Concepts, visit our website: http://casertaconcepts.com/
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
General Data Protection Regulation - BDW Meetup, October 11th, 2017Caserta
Caserta Presentation:
General Data Protection Regulation (GDPR) is a business and technical challenge for companies worldwide - and the deadlines are coming fast! American institutions that do business in the EU or have customers from the EU will have their data practices affected. With this in mind, Caserta – joined by Waterline Data, Salt Recruiting, and Squire Patton Boggs – hosted a BDW Meetup on the GDPR, which is perhaps the most controversial data legislation that has been passed to date.
Joe Caserta, Founding President, Caserta, spoke on the basics of the GDPR, how it will impact data privacy around the world, and some techniques geared towards compliance.
Slides: Case Study — How J.B. Hunt is Driving Efficiency with AI and Real-Tim...DATAVERSITY
J.B. Hunt, one of the leading providers of transportation and logistics services in North America, recognizes the criticality of customer responsiveness, service quality, and operational efficiency for its success. However, with its data spread across multiple sources, including legacy mainframe systems, the organization was struggling to meet data requirements from multiple departments. They struggled to troubleshoot operational issues and respond to customers quickly.
Join this webinar to hear about the optimized solution J. B. Hunt implemented, which automates real-time data pipelines for a reliable cloud data lake and provides multiple user groups an in-the-moment view of data without overwhelming internal operational systems. Discover how J.B. Hunt now leverages a modernized data environment to accelerate data delivery and drive various AI and analytics initiatives such as real-time service-pricing, competitive counterbidding, and improving their customer experience.
Learn how you can:
• Ingest data in real-time from legacy mainframe systems, enterprise applications, and more
• Create a reliable cloud data lake to accelerate AI and Analytic Initiatives
• Catalog, prepare, and provision data to empower data consumers
• Drive operational efficiency and customer experience with AI-augmented insights
The document discusses Luminar, an analytics company that uses big data and Hadoop to provide insights about Latino consumers in the US. Luminar collects data from over 2,000 sources and uses that data along with "cultural filters" to identify Latinos and understand their purchasing behaviors. This provides more accurate information than traditional surveys. Luminar implemented a Hadoop system to more quickly analyze this large amount of data and provide valuable insights to marketers and businesses.
This framework helps organizations align Data Strategy with Business Strategy to prioritize goals around the most pressing operational needs. It introduces Data Management & Data Ability Maturity Matrix to visualize the core path of business digital transformation, which is easy to understand and follow. And it provides the standard template for implementation, which can share the flexibility to engage applications of different industries.
The document discusses creating value from data and overcoming hype around data science. It summarizes that data science has the potential to create value through customer insights, improved processes, and new products, but realizing this value is challenging. Three key challenges are 1) extracting meaningful information from data, 2) bringing business and IT together in joint data science programs and organizations, and 3) developing data skills and an organizational culture that supports data-driven decision making. Overcoming these challenges is necessary to build mature data science capabilities and unlock the full value of data.
Unlocking Greater Insights with Integrated Data Quality for CollibraPrecisely
Data is arguably your company’s greatest asset, and a thoughtful data governance strategy, along with robust tools like Collibra Data Governance Center (DGC), is essential to getting the most value from that data. However, even the best data governance programs will falter without data quality.
Data governance systems provide a framework for the policies, processes, rules, roles and responsibilities that help you manage your enterprise data. But they don’t give you insight into the characteristics and quality of that data – such as errors, outliers and issues – nor how the data changes over time.
During this webinar, we discuss how seamlessly integrating Trillium DQ with Collibra DGC creates a complete data governance solution that delivers rapid insights into the health of your data, ensuring trust and compliance with organizational policies and plans. We demonstrate how data is automatically exchanged between the tools so users can:
• Quickly establish the rules needed to support policies
• Evaluate their data against those rules on an ongoing basis
• Identify problems or improvements with their data quality to take action
The document discusses alternative data and its importance. It defines alternative data as data derived from non-traditional sources like mobile devices, websites, and sensors. This data can provide insights that complement traditional sources and help with decision-making. The document outlines 8 types of alternative data and 3 ways to access it, including hiring a data scientist, partnering with a third party, or using web scraping software. It provides examples of alternative data's applications in advertising, tracking corporate revenues, risk assessment, and more. Overall, the document promotes alternative data as a valuable new resource for businesses seeking a competitive edge.
The document discusses the need for "Smart Data" over "Big Data". It argues that while Big Data has potential, the technology is constantly evolving and data scientists with the needed skills are scarce and expensive. It advocates for providing business users with self-service analytics capabilities to answer over 90% of their questions quickly without IT assistance. Examples are provided of how self-service analytics helped Coca-Cola and JD Group gain insights from their data more efficiently.
Bigdata for sme-industrial intelligence information-24july2017-finalstelligence
This document discusses how small and medium enterprises (SMEs) can benefit from big data analytics. It defines key concepts like the 5 V's of big data and explains challenges SMEs face in adopting analytics. Common types of analytics like reporting, trend analysis, and predictive modeling are described. The document provides recommendations for simple analytic tools and techniques SMEs can use, such as data exploration, time-series analysis, and regression in Excel. Finally, it discusses how cloud-based solutions can help SMEs overcome barriers to adopting traditional IT solutions and analyzes the big data business landscape in Thailand.
The Hive Think Tank: The Future Of Customer Support - AI Driven AutomationThe Hive
The Hive Think Tank Panel Discussion moderated by Kate Leggett (Forrester) with panelists: Allan Leinwand (ServiceNow), Nitin Narkhede (Wipro), Jason Smale (Zendesk), Dan Turchin (Neva). The future of customer support is AI-driven virtual agents. Soon, we’ll interact conversationally with bots that know who we are, how we’re impacted, and what we need. Soon, the capabilities of virtual agents will far exceed those of today’s best human agents. We’ll receive support that is more reliable than friends, more accurate than social media, and less frustrating than waiting on hold.
The Hive Think Tank: AI in The Enterprise by Venkat SrinivasanThe Hive
This The Hive Think Tank talk by Venkat Srinivasan, CEO of RAGE Frameworks, focuses on successful applications of AI in the Enterprise. We start with a broad and more inclusive definition of AI in the context of enterprise business processes.
We introduce a taxonomy of AI solution methods that broaden the focus beyond a narrow focus on deep learning based on neural nets. In line with the taxonomy, we present several successful AI applications in use today at major corporations across industries including financial services, manufacturing/retail, professional services, logistics. These applications range from commercial lending, contract review, customer service intelligence, market and competitive intelligence, signals for capital markets, regulatory compliance and others.
Chictopia for Mobile & Social Commerce panel discussionThe Hive
Chictopia is a social network started in 2008 for fashion influencers to share outfit photos, make friends, and connect with brands for sponsorship opportunities. It hosts over 1 million photos contributed by its community of over 100,000 bloggers, whose combined social media reach is 30 million people. Chictopia helps influencers create shoppable, viewable content for brands by matching them based on detailed data about influencers' styles, brands worn, body shapes, and lifestyles.
Big Data App servor by Lance Riedel, CTO, The Hive for The Hive India eventThe Hive
This document describes a big data application server framework for handling large volumes, velocities, and varieties of data to extract value. It discusses use cases like log analytics, security detection, sensor data analytics, and recommendation systems. The document then summarizes several key components of big data platforms, including storage and compute systems like HDFS and MapReduce, data ingestion with Apache Flume, workflow systems like Oozie, schema management with HCatalog and Avro, data access with HBase and SQL querying with Hive and Impala.
Search at Linkedin by Sriram Sankar and Kumaresh PattabiramanThe Hive
The document discusses LinkedIn's search capabilities and infrastructure. It describes LinkedIn's transition from using open source search components like Lucene to developing their own proprietary search stack. The new stack allows for more flexible indexing, live updates, and relevance capabilities powered by machine learning. Search is a core part of LinkedIn's vision of creating economic opportunity by connecting professionals to jobs, talent, and information through their economic graph.
The document discusses factors that enterprises should consider to achieve the fastest time to market for new applications. It identifies the key factors as: leveraging existing technology stacks to reduce integration costs, minimizing architectural impacts to reuse existing design patterns, meeting average scale requirements rather than internet-scale, leveraging existing resources and skills as much as possible to reduce training costs, facilitating business adoption which requires significant human involvement, and designing resilient architectures and stable business models.
The document discusses optimizing mobile apps and the challenges of mobile testing. It introduces LeanPlum, a mobile A/B testing service that allows users to implement their SDK, run tests from a dashboard, and view results. Common challenges of mobile optimization include limited screen space, platform fragmentation, connectivity issues, long app store approval times, different metrics than web, and high user acquisition costs. LeanPlum aims to help users overcome these challenges through easy integration, flexible APIs, and A/B testing capabilities.
Controlled experimentation (A/B testing) allows companies to systematically study the effects of potential product changes or treatments by randomly assigning users to a control group or treatment group. The document discusses how controlled experiments can validate hypotheses with data, determine if a treatment has a causal effect, and provide examples of how A/B testing can be used for website variants, call-to-actions, and personalized recommendations. It also outlines best practices for running controlled experiments such as ensuring identical distributions between control and treatment groups and carefully monitoring each variant.
The document discusses controlled experimentation (A/B testing) as a method to study the effects of treatments on users. It notes that experiments randomly divide users into a control and treatment group, with the only difference being the treatment evaluated. Performance metrics are collected and statistically analyzed to determine if any differences are due to the treatment or random chance. Examples of experiments include variations to website design, mobile calls to action, and personalization algorithms. Key aspects of experimentation platforms include hashing to randomly assign users, detailed logging, metrics dashboards, and ensuring control and treatment groups are identical. The document emphasizes measuring overall impact beyond just segments under treatment.
The document discusses Stinger, an initiative by Hortonworks to improve Hive performance and capabilities. Stinger has three phases aimed to improve Hive performance by 100x, extend Hive SQL for analytics, and support interactive queries. The first phase focuses on reducing jobs and adding SQL and ORCFile format. The second phase adds YARN resource management and Hive on Tez. The third phase adds a buffer cache and cost-based optimizer. Charts show performance improvements from projects like Tez and ORCFile format.
Notes from the (greasy) field by Ranjit Nair - Co-founder and CTO, AltizonThe Hive
The document discusses notes from the field on topics related to the Internet of Things. It summarizes popular perceptions of IoT, highlights the need for a flexible hardware data logger on the edge with diverse interfaces and connectivity, and describes how single board computers are enabling new interfacing, connectivity, compute and storage capabilities at the edge. It also outlines a topology with sensors and actuators connecting to gateways for connectivity to the internet/IP network, and lists components that are typically part of an IoT platform including device management, identity, messaging, data storage, and machine learning.
The Hive "Data Virtualization" Introduction - Jim Green, CEO of Composite Sof...The Hive
This document discusses how business leaders can take advantage of their data assets. It describes how Composite Software allows businesses to leverage "big data", operational databases, and third party data. The document provides several customer examples of how they used Composite's data virtualization platform for agile BI, consolidated risk reporting, data integration, and self-service data access. It also outlines common use cases for data virtualization including supporting multiple data sources and applications, abstracting data to the business level, and modernizing data warehouses to handle multiple repositories and big data.
Opportunites in Big Data by Sumant Mandal, Founder of The Hive for The Hive I...The Hive
Big data is disrupting many industries by generating and analyzing large amounts of data from diverse sources, enabling new products and services. Uber and Airbnb have disrupted transportation and hospitality by leveraging big data, while Waze uses traffic data. Sensors are now everywhere and producing huge amounts of scalable data that can create value by addressing real problems. Companies should capture data from all activities, use diverse sources, solve core issues, and retain data for unanticipated future uses.
The Hive Think Tank: Rocking the Database World with RocksDBThe Hive
Igor Canadi, Facebook
Igor is a software engineer at Facebook where his job is making databases more awesome. He recently graduated from University of Wisconsin-Madison with Masters degree in Computer Science. During his time at UW-M, he worked with prof. Paul Barford in the area of internet measurement and analysis. Igor got his undergraduate degree from University of Zagreb in Croatia. During his undergraduate years, he founded and developed a local non-profit organization that focuses on educating talented high-school students.
Sofia Morana has made significant changes to her magazine, including changing the name to "View Point" to better represent the audience's perspectives. She also completely changed the masthead design to reinforce the new name and audience focus. While keeping the same color scheme, she adjusted the front cover design to match the magazine's new direction. She redesigned the double page spread and contents page layouts based on newly taken photoshoots.
This document discusses SQL support for Hadoop including Apache Hive, Drill, Impala, and others. It notes that MapR provides broad SQL support including faster Impala performance. Drill 1.0 is slated for an alpha release this month. Hadoop BI tools can do more than just SQL queries.
“People analytics” is a frequently used buzzword. But questions remain as to why this is becoming such a prominent challenge for HR. What are leading organizations doing to develop their understanding of how data analytics can drive better people decisions? In this session, learn what you can start doing tomorrow to accelerate and mobilize your people analytics efforts.
Learning Objectives
• Learn the research and trends in data & analytics.
• Learn what is driving the people analytics movement.
• Learn the barriers to entry for companies.
• Learn how to mobilize your efforts in building out your people & analytics capabilities.
Speaker: Diego Gomez, Vice President of Human Capital Management Transformation, Oracle
A look at Big Data over time and its applications to talent acquisition in the present. Big data is a big deal, and will continue to be. HiringSolved takes a look at its applications in business, innovation, and now talent acquisition.
Founding a Data Democracy: How Ivy Tech is Leading a Revolution in Higher Edu...Brendan Aldrich
Is your data reliable, intuitive, interactive, and immediately available to everyone who needs it? This presentation explores how Ivy Tech, the nation's largest singly-accredited community college system, coupled cloud-based and open-source platforms with predictive analytics and sustainable data practices to create a cost-effective governed data democracy that's helping administrators, staff, and faculty access the data they need to drive student success.
This document discusses the value and risks of big data. It begins with defining big data as large and complex data sets that require new technologies to manage and analyze. The document then discusses how big data is used for marketing, recommendations, analytics, and other purposes. It notes both the benefits but also risks of poor data quality and limited governance of big data projects. The document also provides overviews of technologies like Hadoop, MapReduce, Pig, Hive, and NoSQL that support big data. It questions whether social data should be considered a corporate asset and discusses the complexity of understanding big data risks. Overall, the document aims to highlight both the opportunities and governance challenges presented by big data.
Big Data - Bridging Technology and HumansMark Laurance
The document discusses big data and how organizations can leverage it. It defines big data and notes the rapid growth in data. It outlines five ways big data can create value for organizations, including making information more transparent and usable, improving performance through data collection, narrow customer segmentation, improved decision making, and better product development. The document also warns of a potential shortage of analytics talent as organizations seek to take advantage of big data.
Once you’ve made the decision to leverage AI and/or machine learning, now you need to figure out how you will source the training data that is necessary for a fully functioning algorithm. Depending on your use case, you might need a significant amount of training data, and you’ll want to consider how that is labeled and annotated too.
View Applause's webinar with Cognilytica principal analysts Ronald Schmelzer and Kathleen Walch, alongside Kristin Simonini, Applause’s Vice President of Product, as they tackle the modern challenges that today’s companies face with sourcing training data.
This document discusses how technologies like big data and social media are changing product management. It provides examples of how companies like Vitaminwater, eBay, and Netflix use big data and social media to test new products and features. The key points are that these new tools allow for faster and cheaper A/B testing of new products, greater customer engagement during development, and the ability to analyze large amounts of user data to identify trends and spot new opportunities. The future will involve more customer involvement in development through signaling and personal data, and combining behavioral and attribute data from multiple sources.
Hadoop provides a solution for overcoming traditional limitations of data storage and computation by leveraging inexpensive commodity hardware and allowing for easy linear scalability. It enables organizations to unlock value from big data by making large amounts of information transparent and usable at high frequencies. This allows for more precise customer segmentation, improved product development, and data-driven management decisions. However, challenges remain around privacy, security, access to diverse data sources, and developing talent with the right skills to work with big data.
Beyond the Classroom consists of events, workshops and presentations meant to introduce Computer Science students to learning opportunities in addition to their regular classroom experiences. Beyond the Classroom events are free and open to all NHCC CSci students.
This presentation is about Big Data, how it changes the traditional data landscape, how different companies are using it, and which skills are in demand.
The Data Operating System: Changing the Digital Trajectory of HealthcareHealth Catalyst
In 1989, John Reed, the CEO of Citibank and the early pioneer for ATMs, said, “I can see a future in which the data and information that is exchanged in our transactions are worth more than the transactions themselves.” We are at an interesting digital nexus in healthcare. Few of us would argue against the notion that data and digital health will play a bigger and bigger role in the future. But, are we on the right track to deliver on that future? It required $30B in federal incentive money to subsidize the uptake of Electronic Health Records (EHRs). You could argue that the federal incentives stimulated the first major step towards the digitization of health, but few physicians would celebrate its value in comparison to its expense. As the healthcare market consolidates through mergers and acquisitions (M&A), patching disparate EHRs and other information systems together becomes even more important, and challenging. An organization is not integrated until its data is integrated, but costly forklift replacements of these transaction information systems and consolidating them with a single EHR solution is not a viable financial solution.
Hitachi Data Systems provides information technologies and services to help companies improve their IT costs and agility. The document discusses measuring the effectiveness of learning technologies to increase their business impact. It summarizes that by linking business data to learning data and using analytics, companies can materially improve customer satisfaction, revenue, and reduce costs. The rest of the document discusses challenges with traditional learning approaches and how emerging technologies like adaptive learning, machine learning, and competency-based learning powered by standards like xAPI can help address these challenges by delivering personalized and efficient learning at scale.
The Data Operating System: Changing the Digital Trajectory of HealthcareDale Sanders
This is the next evolution in health information exchanges and data warehouses, specifically designed to support analytics, transaction processing, and third party application development, in one platform, the Data Operating System.
Fidel Technologies offers big data services including building solutions using Hadoop, HDFS, Hive, Pig and other technologies. They help clients extract insights from large amounts of data to gain competitive advantages. Fidel analyzes clients' data using techniques like data mining, predictive analytics and visualization. They are open to different engagement models and providing solutions like flagging insider trading or optimizing supply chain management through big data analysis.
Big data is large amounts of unstructured data that require new techniques and tools to analyze. Key drivers of big data growth are increased storage capacity, processing power, and data availability. Big data analytics can uncover hidden patterns to provide competitive advantages and better business decisions. Applications include healthcare, homeland security, finance, manufacturing, and retail. The global big data market is expected to grow significantly, with India's market projected to reach $1 billion by 2015. This growth will increase demand for data scientists and analysts to support big data solutions and technologies like Hadoop and NoSQL databases.
Big data is large amounts of unstructured data that require new techniques and tools to analyze. Key factors enabling big data are increased storage capacity, processing power, and data availability. Big data analytics can uncover hidden patterns to provide competitive advantages and better business decisions. Applications include healthcare, homeland security, finance, manufacturing, and retail. The big data market is growing rapidly, with 4.4 million IT jobs expected by 2015. India's big data market is also growing and may reach $1 billion by 2015. Non-SQL databases and Hadoop are common tools used for big data.
Big Data, NoSQL, NewSQL & The Future of Data ManagementTony Bain
It is an exciting and interesting time to be involved in data. More change of influence has occurred in the database management in the last 18 months than has occurred in the last 18 years. New technologies such as NoSQL & Hadoop and radical redesigns of existing technologies, like NewSQL , will change dramatically how we manage data moving forward.
These technologies bring with them possibilities both in terms of the scale of data retained but also in how this data can be utilized as an information asset. The ability to leverage Big Data to drive deep insights will become a key competitive advantage for many organisations in the future.
Join Tony Bain as he takes us through both the high level drivers for the changes in technology, how these are relevant to the enterprise and an overview of the possibilities a Big Data strategy can start to unlock.
Architecting for Big Data: Trends, Tips, and Deployment OptionsCaserta
Joe Caserta, President at Caserta Concepts addressed the challenges of Business Intelligence in the Big Data world at the Third Annual Great Lakes BI Summit in Detroit, MI on Thursday, March 26. His talk "Architecting for Big Data: Trends, Tips and Deployment Options," focused on how to supplement your data warehousing and business intelligence environments with big data technologies.
For more information on this presentation or the services offered by Caserta Concepts, visit our website: http://casertaconcepts.com/.
My keynote speech at the ISACA IIA Belgium software watch day in October 2014 in Brussels on the value of big data and data analytics for auditors and other assurance professionals
Data Governance, Compliance and Security in Hadoop with ClouderaCaserta
The document discusses data governance, compliance and security in Hadoop. It provides an agenda for an event on this topic, including presentations from Joe Caserta of Caserta Concepts on data governance in big data, and Patrick Angeles of Cloudera on using Cloudera for data governance in Hadoop. The document also includes background information on Caserta Concepts and their expertise in data warehousing, business intelligence and big data analytics.
Similar to Opportunities in Big Data by Arihant Patni (20)
The document discusses the benefits of exercise for both physical and mental health. Regular exercise can improve cardiovascular health, reduce symptoms of depression and anxiety, enhance mood, and boost brain health. Staying physically active aims to reap these rewards and promote overall well-being.
Translating a Trillion Points of Data into Therapies, Diagnostics, and New In...The Hive
This document outlines Atul Butte's extensive conflicts of interest and corporate relationships in the biomedical data and technology industry. It then provides brief summaries of several companies started by Butte's students using public data to develop diagnostics, predict disease, and design new drugs. The document concludes by listing Butte's collaborators and supporters in establishing a large biomedical data institute at UCSF.
Quantum Computing (IBM Q) - Hive Think Tank Event w/ Dr. Bob Sutor - 02.22.18The Hive
The document introduces quantum computing and IBM's efforts in the field, including the IBM Q Experience launched in 2016 which allows users to run algorithms and experiments on quantum computers via the cloud. It discusses IBM's goals of building universal fault-tolerant quantum computers and the IBM Q Network, a global community to advance quantum computing.
The Hive Think Tank: Rendezvous Architecture Makes Machine Learning Logistics...The Hive
Think Tank Event 10/23/2017, hosted by The Hive and presented by Ted Dunning, Chief Application Architect of MapR Technologies and Ellen Friedman of MapR Technologies.
“ High Precision Analytics for Healthcare: Promises and Challenges” by Sriram...The Hive
1) Predictive analytics in healthcare often provides risk scores and predictions but lacks actionable insights on how to prevent outcomes.
2) The right methodology is needed to transform raw data like claims, prescriptions and medical records into meaningful predictions using machine learning algorithms.
3) Accurate predictions require measuring precision down to the individual level while accounting for both patient and provider factors that influence health outcomes.
The Hive Think Tank: Machine Learning Applications in Genomics by Prof. Jian ...The Hive
In this The Hive Think Tank talk, Professor Jian Ma introduces machine learning methods that can be used to help tackle some of the most intriguing questions in genomics and biomedicine. He discusses the research projects in his group to study genome structure and function, including algorithms to unravel complex genomic aberrations in cancer genomes and gene regulatory principles encoded in our genome, by utilizing
probabilistic graphical models and deep neural network techniques. The knowledge obtained from such computational methods can greatly enhance our ability to understand disease genomes.
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...The Hive
This document discusses how India can become a $10 trillion economy by 2030 through technology entrepreneurship and the growth of its startup ecosystem. It notes that India currently has the 3rd largest startup ecosystem in the world with 19,400 startups. If the ecosystem continues growing at 270% over 6 years, it could create $500 billion in market value and employ over 3.5 million people by 2030. This growth will be accelerated by initiatives like Digital India that are building digital infrastructure and opening government data through APIs, fueling innovation and problem solving across sectors to help propel India to its economic goals.
The Hive Think Tank: The Content Trap - Strategist's Guide to Digital ChangeThe Hive
In this The Hive Think Tank talk Harvard Business School Professor of Strategy Prof. Bharat Anand shares his insights on the Digital innovation trends that are shaping the way organizations will act in the future.
In this talk, Professor Anand presents the findings from his forthcoming book. To answer these questions, Anand examines a range of businesses around the world, from Chinese internet giant Tencent to Scandinavian digital trailblazer Schibsted, from The New York Times to The Economist, and from talent management to the future of education.
This document provides an overview and hands-on demonstration of Twitter's Heron stream processing framework. The agenda includes a Heron overview, hands-on experience launching topologies and using Heron tools, and exploring the UI. Instructions are given on installing Heron client and tools binaries. Example topologies are launched using the 'heron submit' command. The Heron tracker and UI are launched to view logical/physical plans, metrics, logs, and exceptions. Additional resources mentioned include the Heron starters repository and user forum.
The Hive Think Tank: Unpacking AI for Healthcare The Hive
In this The Hive Think Tank talk, Ash Damle, CEO of Lumiata takes a deep dive into Lumiata’s core technological engine - the Lumiata Medical Graph, which applies graph-based machine learning to compute the complex relationships between health data in the same way that a physician would, and how this medical AI engine powers personalization and automation within risk and care management.
The Hive Think Tank: Translating IoT into Innovation at Every Level by Prith ...The Hive
In this presentation Prith Banerjee discusses how a sustainable future must become radically more efficient with the way we use energy. He shared how the Internet of Things (IoT) and the convergence of Operational Technology (OT) and Information Technology (IT) are enabling Schneider Electric's innovation at every level, redefining power and automation for a new world of energy which is more electric, decarbonized, decentralized and digitized. Prith shared how, in this new world of energy, Schneider ensures that Life Is On everywhere, for everyone and at every moment. He also shared a set of IoT predictions for the future, based on findings of the company’s recent IoT Survey of 2,500 top business executives.
The Hive Think Tank - The Microsoft Big Data Stack by Raghu Ramakrishnan, CTO...The Hive
Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous, and the default increasingly is to capture and store any and all data, in anticipation of potential future strategic value. These differences in data heterogeneity, scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of very large datasets that are stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools, e.g., for machine learning and stream analytics. These new systems are necessarily based on scale-out architectures for both storage and computation.
Hadoop has become a key building block in the new generation of scale-out systems. On the storage side, HDFS has provided a cost-effective and scalable substrate for storing large heterogeneous datasets. However, as key customer and systems touch points are instrumented to log data, and Internet of Things applications become common, data in the enterprise is growing at a staggering pace, and the need to leverage different storage tiers (ranging from tape to main memory) is posing new challenges, leading to caching technologies, such as Spark. On the analytics side, the emergence of resource managers such as YARN has opened the door for analytics tools to bypass the Map-Reduce layer and directly exploit shared system resources while computing close to data copies. This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit.
While Hadoop is widely recognized and used externally, Microsoft has long been at the forefront of Big Data analytics, with Cosmos and Scope supporting all internal customers. These internal services are a key part of our strategy going forward, and are enabling new state of the art external-facing services such as Azure Data Lake and more. I will examine these trends, and ground the talk by discussing the Microsoft Big Data stack.
The Hive Think Tank - Design Thinking by Bernie Roth, Professor at Stanford U...The Hive
Bernie Roth is a founder of Stanford's d.school and author of The Achievement Habit: how to stop wishing, start doing, and take command of life.
Bernie brings to the d.school a wealth of experience in teaching design, an intimate knowledge of the functioning of Stanford University, and a worldwide reputation as a researcher in kinematics and robotics. Together with Doug Wilde and the late Rolf Faste, Bernie developed the concept of a Creativity Workshop. This has been offered to students, faculty and professionals around the world. These same techniques have been made available to d.school students and are described in his book The Achievement Habit. He has found that these types of learning experiences enhance students’ ability to make meaningful positive difference in their own lives. He is especially pleased that his activities at the d.school have contributed to creating an environment where students and coworkers get the tools and values for realizing the enduring satisfactions that come from assisting others in the human community.
The Hive Think Tank: Machine Learning at Pinterest by Jure LeskovecThe Hive
Machine learning is at the core of Pinterest. Pinterest personalizes and ranks 1B+ pins, 700+ million boards for 100M+ users all over the world, using data gathered from collaborative filtering, user curation, web crawling, and more. At Pinterest we model relationships between pins, handle cold-start problems and deal with real-time recommendations.
In this presentation Jure gave an overview of the problems and effective solutions developed at Pinterest. He focused on systems and effective engineering choices made to enable productive machine learning development and enable multiple engineers effectively develop, test, and deploy machine-learned models.
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
8. Intelligent Healthcare
Ellis Medicine deployed GE’s AgileTrac system in 2010 to
track both assets and patient flow, which helped Ellis tag
and track IV pumps and other clinical equipment, improve
workflow, and save $1.1 million over three years.
Newsroom.gehealthcare.com
15. 4 Things to Remember
• How do you generate and capture data from everything that
you do?
• Use more diverse data, not just more data (use data from
multiple sources)
• Address a real pain point – Think about you core problems
and how data can create value
• Data has value far beyond what you originally anticipate –
don’t throw it away!
16. What is THE HIVE INDIA
• Early stage startup funding and launch entity
• Based in Mumbai/Bangalore
• Focused on analytics, applications and services for Big Data
• Co-founded by Amit and Arihant Patni (ex-Patni Computers) and The
Hive in Silicon Valley
Big Data
Technology
Guidance
Partnerships
US Go-To-
MarketBusiness Design Funding
Team of Proven
Company Builders
17. Combination of Two FORCES
Patni Family
• Leveraged key inflection point in Indian IT
industry in the 70’s and 80’s.
• Post exit of Patni Computers in 2011, investing
in new waves of technology innovation.
• Nirvana Venture Advisors to bet on internet
and mobile in India.
• The Hive to bet on Big Data.
18. Combination of Two FORCES
The Hive Silicon Valley
• Co-creating data-driven businesses along
with the who's who of the tech industry
• Team of proven company builders to launch
companies
• Successful serial entrepreneurs
• Investment team behind $B’s of exits
• In-house Big Data technology team
• Strong Big Data Brand and Ecosystem
19. Big Data Experts
Who’s Who of Big Data Advisors & Investors include:
Proprietary deal flow and expert advice for startups
Harel Kodesh
EVP of Cloud
Paul Maritz
CEO of Pivotal
Dhruba Borthakur
Hadoop
Charles Zedlewski
VP Products
Jure Leskovec
Machine Learning
Satya Nadella
CEO
Raghu Ramakrishnan
CTO,
Info Services
Vanja Josifovski
Search/Matching
Dan Warmenhoven
Chairman
James Lau
Founder
Kumar Malavalli
Founder
Rob Goldman
Dir, Monetization
Jerry Yang
Founder
Raymie Stata
fmr CTO
Tom White
Hadoop
20. The Hive India –Investment Thesis
• Engage with entrepreneurs/young companies
who have
• Deep domain experience
• Desire to develop data-driven solutions
• Strong management teams
• B2B or B2B2C play
• US target market
• Product/Services
21. The Hive India – Value Proposition
Capital
Initial capital and access to sources of future capital
Partner Ecosystem
Access to strategic partners
Business Design
Enhance management bandwidth & help with market validation and
business plan
Clients
Access to clients through our network of partners
Access to Silicon Valley
Technology stack to accelerate product development. Engagement with
thought leaders in the Big Data community.
Unique structure to fund and launch Big Data companies in India