Big Data Developer in Madrid @ IBM Client Center Madrid
Introduction to interactive and exploratory analytics of time-series data with Druid. It ended up with a demo of querying data in Druid via Superset.
[Hortonworks] Future Of Data: Madrid - HDF & Data in motionRaúl Marín
First meetup event Future Of Data event where we introduced Hortonworks DataFlow (HDF).
The slides describe what HDF is, and we presented a very simple demo about sentiment analysis of tweets using Apache OpenNLP as the NLP framework to do so.
In this talk, we will give a high-level overview of what is Deep Learning (DL) is and provide a lot of visual examples for an intuitive take-aways from the session – and no worries, no math here!
The session will:
- Illustrate what neural nets are via interactive examples and mini-demos
- Show applications of DL to solve specific real-world problems with State-of-the-Art (SOTA) results
- Demonstrate how latest research in DL can be applied today to areas of vision, language, recommender systems, and tabular data
- Provide pro-tips on how to get started
Speaker: Robert Hryniewicz, AI Evangelist, Hortonworks
Running Enterprise Workloads with an open source Hybrid Cloud Data ArchitectureDataWorks Summit
Cloud is turbocharging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
With the convergence of cloud, IoT, and big data technologies, enterprises increasingly have their data spread across multiple data lakes on-prem and in cloud data lake stores in many geographies and across multiple public cloud vendor platforms, for example, due to regulatory and compliance mandates that limit cross-border data transfer. With the proliferation of data types and sources in this complex landscape, the process of discovery, provisioning, and running relevant workloads on this data to gather insights has become more complex. Additionally, gaining global visibility into the business context, usage, and trustworthiness of data requires a centralized view of all data and metadata, security controls, data access, and monitoring.
All of these challenges create a significant chasm between initial data capture and subsequent data insights generation to drive value creation. Therefore, enterprises now require a “global insight fabric” that can find a happy medium between adequate rules and policies of data governance while providing a trusted environment for users to collaborate and share data responsibly in order to create value.
In this talk, we will outline how Hortonworks DataPlane Service(DPS) can help customers build a global insight fabric that can span storing and analyzing data within data centers to implementing an open source hybrid architecture that takes advantage of cloud's elasticity and new use cases. We will get a personal view of the challenges faced in safely moving data from on-premises data centers into multiple public clouds, safeguarding it through replication, and then applying consistent security and governance policies across diverse environments to deliver trusted data and insights to the business. We will highlight how DataPlane Service can help enterprises with this hybrid architectural journey, and how open source architectures are enabling this transformation across enterprises.
Flink and NiFi, Two Stars in the Apache Big Data ConstellationMatthew Ring
Presented to the Chicago Apache Flink Meetup, Jan. 19, 2016
Goal: To provide a non-exhaustive but interesting demonstration of Apache NiFi and Apache Flink working together. Included a demo of NiFi and Flink together to simulate a simplified trading ecosystem of Brokers and Day Traders, with streaming market data, orders, executions and P/L results.
Promote the Good of the People of the United Kingdom by Maintaining Monetary ...DataWorks Summit
The Bank of England is the central Bank of the United Kingdom, established in 1694. Representatives from the Bank’s Data Analytics & Modelling team will discuss the Bank of England's journey to delivering a Big Data capability and how the Hortonworks HDP platform is helping us deliver on our mission statement of “promote the good of the people of the United Kingdom by maintaining monetary and financial stability". We will explore the challenges we've faced, how we have overcome some of these and those that remain to be conquered. We will also present our strategy for the Bank’s future Big Data platform as we look to scale up further in the coming years.
We will focus in particular on our first successful ‘Big Data’ production system. This exists in response to the financial crises of 2008 and the subsequent push to make the derivative markets safer by reducing systemic risk. In Europe this was delivered through the European Market Infrastructure Regulation (EMIR). We will explain the Bank of England’s role in monitoring UK entities within this important market and describe the significant challenges facing our team in building a data analytics platform to facilitate this
Speakers
Nick Vaughan, Domain SME - Data Analytics & Modelling
Bank of England
Adrian Waddy, Technical Lead
Bank of England
Big Traffic, Big Trouble: Big Data Security AnalyticsDataWorks Summit
With the rise of IoT and the increasing complexity of applications, clouds, networks and infrastructure, the battle to keep your data and your infrastructure safe from attackers is getting harder. As groups of bad actors collaborate, sharing information and offering illegal access, and botnets as a service, terabits of attack can be launched cheaply. Meanwhile, it’s hard to find enough security analysts to catch and prevent these attacks.
This is where community collaboration and open source efforts like Apache Metron come in. Metron presents a comprehensive framework for application and network, security built on Apache Hadoop and open source Streaming Analytics(ie Apache Nifi, Apache Kafka) tool’s highly scalable data management and processing stacks. Advanced features like profiling, machine learning, and visualization work with real-time streaming detection to make your SOC analysts more efficient, while the intrinsic extensibility of open source helps your data scientists get security insights out of the lab and into production fast.
We will discuss and demonstrate how some real-world businesses and managed service providers are using Apache Metron to identify and solve security threats at scale, and some approaches and ideas for how the platform can fit into your security architecture.
In order to deal with customers expecting a seamless omnichannel experience, increased regulations and speed with which innovative fin-techs enter the market, ING has formulated a customer centric strategy based on data and analytics.
Last year we talked about the fact that ING developed a new architecture, the ING Data Lake. And how within ING In parallel the Big Data paradigm, based on Hadoop, appeared and how this was mapped on the Data Lake architecture to make sure Hadoop is leveraged to the maximum.
This year we want to tell you how the international working group helped realizing the advanced analytic pattern on the ING private cloud, without prior management approval.
This presentation will discuss the community strategy, how to stay under the radar, how to surface when actual content is strong enough to force change, open issues and the private cloud challenges ING is dealing with. Join us in this ride from community idea through architecture to private cloud implementation with some organizational challenges along the way.
Overcoming the AI hype — and what enterprises should really focus onDataWorks Summit
Deep learning for all its hype is brittle, non-generalizeable, and its learnings are not readily transferable from one application to another. Since we are unlikely to see anything close to artificial general intelligence in the next few decades., we should instead focus on how enterprises can capitalize on the state of the art in machine learning and re-implement successful algorithms and follow the data science lifecycles that generate highest ROI.
This talk will cover the current state of the art in AI, its limits vs. hype, and discuss concrete steps that enterprises can take to achieve desired ROI by re-implementing production-grade-ready machine learning algorithms, that have been hardened and demonstrated to work very well in specific, constrained domains.
By the end of this talk, attendees should have a better grasp on how to avoid costly and unnecessary investments into yet unproven technologies, be better equipped to navigate the complex space of AI, and understand where to best focus their resources to maximize ROI. ROBERT HRYNIEWICZ, Technical Evangelist, Hortonworks
[Hortonworks] Future Of Data: Madrid - HDF & Data in motionRaúl Marín
First meetup event Future Of Data event where we introduced Hortonworks DataFlow (HDF).
The slides describe what HDF is, and we presented a very simple demo about sentiment analysis of tweets using Apache OpenNLP as the NLP framework to do so.
In this talk, we will give a high-level overview of what is Deep Learning (DL) is and provide a lot of visual examples for an intuitive take-aways from the session – and no worries, no math here!
The session will:
- Illustrate what neural nets are via interactive examples and mini-demos
- Show applications of DL to solve specific real-world problems with State-of-the-Art (SOTA) results
- Demonstrate how latest research in DL can be applied today to areas of vision, language, recommender systems, and tabular data
- Provide pro-tips on how to get started
Speaker: Robert Hryniewicz, AI Evangelist, Hortonworks
Running Enterprise Workloads with an open source Hybrid Cloud Data ArchitectureDataWorks Summit
Cloud is turbocharging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
With the convergence of cloud, IoT, and big data technologies, enterprises increasingly have their data spread across multiple data lakes on-prem and in cloud data lake stores in many geographies and across multiple public cloud vendor platforms, for example, due to regulatory and compliance mandates that limit cross-border data transfer. With the proliferation of data types and sources in this complex landscape, the process of discovery, provisioning, and running relevant workloads on this data to gather insights has become more complex. Additionally, gaining global visibility into the business context, usage, and trustworthiness of data requires a centralized view of all data and metadata, security controls, data access, and monitoring.
All of these challenges create a significant chasm between initial data capture and subsequent data insights generation to drive value creation. Therefore, enterprises now require a “global insight fabric” that can find a happy medium between adequate rules and policies of data governance while providing a trusted environment for users to collaborate and share data responsibly in order to create value.
In this talk, we will outline how Hortonworks DataPlane Service(DPS) can help customers build a global insight fabric that can span storing and analyzing data within data centers to implementing an open source hybrid architecture that takes advantage of cloud's elasticity and new use cases. We will get a personal view of the challenges faced in safely moving data from on-premises data centers into multiple public clouds, safeguarding it through replication, and then applying consistent security and governance policies across diverse environments to deliver trusted data and insights to the business. We will highlight how DataPlane Service can help enterprises with this hybrid architectural journey, and how open source architectures are enabling this transformation across enterprises.
Flink and NiFi, Two Stars in the Apache Big Data ConstellationMatthew Ring
Presented to the Chicago Apache Flink Meetup, Jan. 19, 2016
Goal: To provide a non-exhaustive but interesting demonstration of Apache NiFi and Apache Flink working together. Included a demo of NiFi and Flink together to simulate a simplified trading ecosystem of Brokers and Day Traders, with streaming market data, orders, executions and P/L results.
Promote the Good of the People of the United Kingdom by Maintaining Monetary ...DataWorks Summit
The Bank of England is the central Bank of the United Kingdom, established in 1694. Representatives from the Bank’s Data Analytics & Modelling team will discuss the Bank of England's journey to delivering a Big Data capability and how the Hortonworks HDP platform is helping us deliver on our mission statement of “promote the good of the people of the United Kingdom by maintaining monetary and financial stability". We will explore the challenges we've faced, how we have overcome some of these and those that remain to be conquered. We will also present our strategy for the Bank’s future Big Data platform as we look to scale up further in the coming years.
We will focus in particular on our first successful ‘Big Data’ production system. This exists in response to the financial crises of 2008 and the subsequent push to make the derivative markets safer by reducing systemic risk. In Europe this was delivered through the European Market Infrastructure Regulation (EMIR). We will explain the Bank of England’s role in monitoring UK entities within this important market and describe the significant challenges facing our team in building a data analytics platform to facilitate this
Speakers
Nick Vaughan, Domain SME - Data Analytics & Modelling
Bank of England
Adrian Waddy, Technical Lead
Bank of England
Big Traffic, Big Trouble: Big Data Security AnalyticsDataWorks Summit
With the rise of IoT and the increasing complexity of applications, clouds, networks and infrastructure, the battle to keep your data and your infrastructure safe from attackers is getting harder. As groups of bad actors collaborate, sharing information and offering illegal access, and botnets as a service, terabits of attack can be launched cheaply. Meanwhile, it’s hard to find enough security analysts to catch and prevent these attacks.
This is where community collaboration and open source efforts like Apache Metron come in. Metron presents a comprehensive framework for application and network, security built on Apache Hadoop and open source Streaming Analytics(ie Apache Nifi, Apache Kafka) tool’s highly scalable data management and processing stacks. Advanced features like profiling, machine learning, and visualization work with real-time streaming detection to make your SOC analysts more efficient, while the intrinsic extensibility of open source helps your data scientists get security insights out of the lab and into production fast.
We will discuss and demonstrate how some real-world businesses and managed service providers are using Apache Metron to identify and solve security threats at scale, and some approaches and ideas for how the platform can fit into your security architecture.
In order to deal with customers expecting a seamless omnichannel experience, increased regulations and speed with which innovative fin-techs enter the market, ING has formulated a customer centric strategy based on data and analytics.
Last year we talked about the fact that ING developed a new architecture, the ING Data Lake. And how within ING In parallel the Big Data paradigm, based on Hadoop, appeared and how this was mapped on the Data Lake architecture to make sure Hadoop is leveraged to the maximum.
This year we want to tell you how the international working group helped realizing the advanced analytic pattern on the ING private cloud, without prior management approval.
This presentation will discuss the community strategy, how to stay under the radar, how to surface when actual content is strong enough to force change, open issues and the private cloud challenges ING is dealing with. Join us in this ride from community idea through architecture to private cloud implementation with some organizational challenges along the way.
Overcoming the AI hype — and what enterprises should really focus onDataWorks Summit
Deep learning for all its hype is brittle, non-generalizeable, and its learnings are not readily transferable from one application to another. Since we are unlikely to see anything close to artificial general intelligence in the next few decades., we should instead focus on how enterprises can capitalize on the state of the art in machine learning and re-implement successful algorithms and follow the data science lifecycles that generate highest ROI.
This talk will cover the current state of the art in AI, its limits vs. hype, and discuss concrete steps that enterprises can take to achieve desired ROI by re-implementing production-grade-ready machine learning algorithms, that have been hardened and demonstrated to work very well in specific, constrained domains.
By the end of this talk, attendees should have a better grasp on how to avoid costly and unnecessary investments into yet unproven technologies, be better equipped to navigate the complex space of AI, and understand where to best focus their resources to maximize ROI. ROBERT HRYNIEWICZ, Technical Evangelist, Hortonworks
The newly enacted GDPR regulations which become effective in 2018 require comprehensive protection of personal information of EU subjects. In this paper, we outline a solution that discovers and classifies personal data that is subject to GDPR in Hadoop ecosystem and uses such precise classification to automatically create a robust set of policies for authorization. The solution consists of using Dataguise’s DgSecure sensitive data detection to automatically classify sensitive data assets in Apache Atlas and author comprehensive and robust authorization policies via Apache Ranger. DgSecure is used to detect sensitive data in Hive databases and continuously update the classification in Apache Atlas via tags. Apache Atlas tags are used to create Apache Ranger policies that protect access to sensitive HDFS files, Hive tables, and Hive columns. We demonstrate a workflow where the components of the solution are automated requiring little or no manual intervention to provide protection of such sensitive data in Hadoop clusters.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
With the rise of IoT and complexity of applications, clouds, networks and infrastructure, it is becoming more difficult to protect data and infrastructure from attackers. When groups of bad actors collaborate, share information, provide unauthorized access, and do botnet as a service, attacks in terabit units also start easily. On the other hand, it is also difficult to find enough security analysts to deal with and defend against such attacks.
Here is the emergence of community cooperation like Apache Metron and efforts to open source. Metron provides a comprehensive framework for applications, networks and security built on Apache Hadoop and open source streaming analysis (eg Apache Nifi, Apache Kafka) tools in scalable data management and processing stacks. Extensions such as profiling, machine learning, and visualization work and real-time streaming detection make SOC analysts more efficient, while intrinsic scalability of open source gives data scientists security insight from data laboratories So that it can be quickly incorporated into production.
This section explains how real-world businesses and managed service providers use Apache Metron, identify and resolve security threats on a large scale, and explain methods and ideas for adapting the platform to your security architecture · I will demonstrate.
Data and analytics are at the heart of the digital transformation. Implementing a modern data platform can be challenging; moreover, success requires a shift in culture. Andreas will discuss the ways Munich Re drives cultural and technological change within their company, focusing on three key elements: people, processes, and technology. What does it mean to be a data-driven organization? How can we provide self-service analytics to our internal and external customers in an agile way? How do we get the most value out of our big data lake? How does Munich Re balance technology and culture to meet the data demands of their business?
Speaker
Andreas Kohlmaier, Head of Data Engineering, Munich Re
In this talk, we will give an overview of the deep learning space starting with a brief history. We will distinguish between deep learning hype vs practical real-world applications, cover how deep learning differs from other machine learning algorithms, go over sample neural net architectures, and provide a step-by-step guide on how to get started.
Specifically, we will cover what type of training data is required and how to prepare it with Apache Spark, followed by how to choose a correct neural net architecture, train, and deploy a deep learning model with TensorFlow on Apache Hadoop 3.1.
Finally, we will wrap-up with deep learning challenges and shortcomings, and offer short- and long-term recommendations to successfully train and deploy deep learning models within your organization to maximize return on investment.
This Big Data Hadoop certification program is structured by professionals and experienced course curators to provide you with an in-depth understanding of the Hadoop and Spark Big Data platforms and the frameworks which are used by them. With the help of the Integrated Laboratory session, you will work upon and complete real-world, industry-based projects in this course.
American Water shares how bringing IoT to fleet management can provide value to the customer. In the utilities industry, fleet management plays a major part in the business. The front line is one of the largest parts of the business whether it is the field employees working on mains, or those working on the customers' property. American Water strives to provide the best customer experience and part of that includes improving the effectiveness of our fleet.
Currently, there is no insight or active feedback on the effectiveness of the routes or driving behaviors. As a PoC, American Water leveraged NiFi to track metrics against a simulated truck, showing the initial values in capturing this type of data.
Technologies: NiFi, Druid, Hive
Learn Real Time Hands on Practical Oriented Talend Online Training by Industry Expert.Attend Free Live Interactive Talend Demo Class.Trainer having 11 Years of Working Experience in BI and Data Warehousing Tools.Enhance your Business Intelligence Career with Learning Talend Online Course in QEdge Technologies Hyderabad.
Talend Online Course Overview
Talend But Why?
Talend Cloud Integration
What is Talend
About Talend
Talend Architecture
Talend Course Content
Talend - Learning Objects
Data Integration (DI) Enterprise
Data Integration (DI) Enterprise Administration
Talend Salary Trends
Kudu as Storage Layer to Digitize Credit ProcessesDataWorks Summit
With HDFS and HBase, there are two different storage options available in the Hadoop ecosystem. Both have their strengths and weaknesses. However, neither HDFS nor HBase can be used universally for all kinds of workloads. Usually this leads to complex hybrid architectures. Kudu is a very versatile storage layer which fills this gap and simplifies the architecture of Big Data systems.
A large German bank is using Kudu as storage layer to fasten their credit processes. Within this system, financial transactions of millions of customers are analysed by Spark jobs to categorize transactions and to calculate key figures. In addition to this analytical workload, several frontend applications are using the Kudu Java API to perform random reads and writes in real-time.
The presentation will cover these topics:
- Business and technical requirements
- Data access patterns
- System architecture
- Kudu data modelling
- Kudu architecture for High Availability
- Experiences from development and operations
Speaker: Olaf Hein, Department Head & Principal Consultant
ORDIX AG
Data Acquisition Automation for NiFi in a Hybrid Cloud environment – the Path...DataWorks Summit
Liberty Global is one of the world’s largest international TV and broadband company, operating in multiple European countries, and with tens of millions of TV, broadband internet, telephony and mobile subscribers.
The Data Solutions team's journey started last year with a strategic project that aimed to implement a state of the art Hybrid Cloud Big Data platform. In this talk, the Manager and the Platform Architect are presenting the team’s data acquisition journey which begins with implementing NiFi flows with simple Get-Put pattern and, in its the final iteration, produces a solution capable of generating complex flows automatically, leading the path to the DataOps way of working.
IBM+Hortonworks = Transformation of the Big Data LandscapeHortonworks
Last year IBM and Hortonworks jointly announced a strategic and deep partnership. Join us as we take a close look at the partnership accomplishments and the conjoined road ahead with industry-leading analytics offers.
View the webinar here: https://hortonworks.com/webinar/ibmhortonworks-transformation-big-data-landscape/
Achieving a 360-degree view of manufacturing via open source industrial data ...DataWorks Summit
Continuously improving factory operations is of critical importance to manufacturers. Consider the facts: the total cost of poor quality amounts to a staggering 20% of sales (American Society of Quality), and unplanned downtime costs plants approximately $50 billion per year (Deloitte).
The most pressing questions are: which process variables effect quality and yield and which process variables predict equipment failure? Getting to those answers is providing forward thinking manufacturers a leg up over competitors.
The speakers address the data management challenges facing today's manufacturers, including proprietary systems and siloed data sources, as well as an inability to make sensor-based data usable.
Integrating enterprise data from ERP, MES, maintenance systems, and other sources with real-time operations data from sensors, PLCs, SCADA systems, and historians represents a major first step. But how to get started? What is the value of a data lake? How are AI/ML being applied to enable real time action?
Join us for this educational session, which includes a view into a roadmap for an open source industrial IoT data management platform.
Key Takeaways:
• Understand key use cases commonly undertaken by manufacturing enterprises
• Understand the value of using multivariate manufacturing data sources, as opposed to a single sensor on a piece of equipment
• Understand advances in big data management and streaming analytics that are paving the way to next-generation factory performance
Speakers
Michael Ger, General Manager Manufacturing and Automotive, Hortonworks
Wade Salazar, Solutions Engineer, Hortonworks
All data accessible to all my organization - Presentation at OW2con'19, June...OW2
It is clear that all employees must have access to data wherever they are to make decisions. However, tools that allow to share data just as easily as the best collaborative tools such as a google doc or an office 365 should be used.
Open source driven by the big data ecosystem and a number of large companies have provided solutions to allow organizations to federate data systems and secure their access.
After a quick overview of existing open source solutions, and how such projects can be organized, it will be necessary to detail Dremio implementation, a unique and centralized interface on all your data. Some real feedbacks will conclude the presentation.
Running Enterprise Workloads with an open source Hybrid Cloud Data Architectu...DataWorks Summit
Cloud accelerates corporate IT landscapes with agility and flexibility. Today, discussion of cloud architecture dominates corporate IT. The cloud enables a number of temporary on-demand use cases that revolutionize analytical workload opportunities. But all of this involves the task of running corporate workloads safely and easily in the cloud.
With the convergence of cloud, IoT, and big data technology, enterprises are increasingly using multiple on-premises Data Lake and multiple Public on different geographies, for example due to regulations and compliance requirements restricting cross- It now distributes data to the cloud Data Lake store of the cloud vendor platform. Diffusion of data types and sources in this complex landscape makes the discovery process, provisioning, and getting insight by performing the appropriate workload on this data more complicated. In addition, to obtain business context, usage, and visibility of data trustworthiness worldwide, it is necessary to display all data and metadata, security management, data access, and monitoring in a centralized way .
All these problems create cracks during the creation of data insights to promote initial data capture and subsequent value creation. As a result, companies now look for compromises between appropriate rules and data control policies while providing a trusted environment that allows them to share data and partner with users responsibly to create value We need "Global Insight Fabric".
In this talk, how the Hortonworks DataPlane Service (DPS) analyzes the data in the data center to expand the storage, implement the open source hybrid architecture utilizing cloud flexibility and new use cases, global in Describes how site fabrics can help customers create. Securely migrate data from on-premises data centers to multiple public clouds, protect the data with replication, then apply consistent safety and governance policies to a wide variety of environments to ensure trustworthy data and inn We provide personal views on the challenges we face in providing the site to the business. I will explain how the DetaPlane service can be useful for traveling to this hybrid architecture and how the open source architecture enables the transformation of the entire enterprise.
The newly enacted GDPR regulations which become effective in 2018 require comprehensive protection of personal information of EU subjects. In this paper, we outline a solution that discovers and classifies personal data that is subject to GDPR in Hadoop ecosystem and uses such precise classification to automatically create a robust set of policies for authorization. The solution consists of using Dataguise’s DgSecure sensitive data detection to automatically classify sensitive data assets in Apache Atlas and author comprehensive and robust authorization policies via Apache Ranger. DgSecure is used to detect sensitive data in Hive databases and continuously update the classification in Apache Atlas via tags. Apache Atlas tags are used to create Apache Ranger policies that protect access to sensitive HDFS files, Hive tables, and Hive columns. We demonstrate a workflow where the components of the solution are automated requiring little or no manual intervention to provide protection of such sensitive data in Hadoop clusters.
Using Spark Streaming and NiFi for the next generation of ETL in the enterpriseDataWorks Summit
In recent years, big data has moved from batch processing to stream-based processing since no one wants to wait hours or days to gain insights. Dozens of stream processing frameworks exist today and the same trend that occurred in the batch-based big data processing realm has taken place in the streaming world so that nearly every streaming framework now supports higher level relational operations.
On paper, combining Apache NiFi, Kafka, and Spark Streaming provides a compelling architecture option for building your next generation ETL data pipeline in near real time. What does this look like in an enterprise production environment to deploy and operationalized?
The newer Spark Structured Streaming provides fast, scalable, fault-tolerant, end-to-end exactly-once stream processing with elegant code samples, but is that the whole story?
We discuss the drivers and expected benefits of changing the existing event processing systems. In presenting the integrated solution, we will explore the key components of using NiFi, Kafka, and Spark, then share the good, the bad, and the ugly when trying to adopt these technologies into the enterprise. This session is targeted toward architects and other senior IT staff looking to continue their adoption of open source technology and modernize ingest/ETL processing. Attendees will take away lessons learned and experience in deploying these technologies to make their journey easier.
With the rise of IoT and complexity of applications, clouds, networks and infrastructure, it is becoming more difficult to protect data and infrastructure from attackers. When groups of bad actors collaborate, share information, provide unauthorized access, and do botnet as a service, attacks in terabit units also start easily. On the other hand, it is also difficult to find enough security analysts to deal with and defend against such attacks.
Here is the emergence of community cooperation like Apache Metron and efforts to open source. Metron provides a comprehensive framework for applications, networks and security built on Apache Hadoop and open source streaming analysis (eg Apache Nifi, Apache Kafka) tools in scalable data management and processing stacks. Extensions such as profiling, machine learning, and visualization work and real-time streaming detection make SOC analysts more efficient, while intrinsic scalability of open source gives data scientists security insight from data laboratories So that it can be quickly incorporated into production.
This section explains how real-world businesses and managed service providers use Apache Metron, identify and resolve security threats on a large scale, and explain methods and ideas for adapting the platform to your security architecture · I will demonstrate.
Data and analytics are at the heart of the digital transformation. Implementing a modern data platform can be challenging; moreover, success requires a shift in culture. Andreas will discuss the ways Munich Re drives cultural and technological change within their company, focusing on three key elements: people, processes, and technology. What does it mean to be a data-driven organization? How can we provide self-service analytics to our internal and external customers in an agile way? How do we get the most value out of our big data lake? How does Munich Re balance technology and culture to meet the data demands of their business?
Speaker
Andreas Kohlmaier, Head of Data Engineering, Munich Re
In this talk, we will give an overview of the deep learning space starting with a brief history. We will distinguish between deep learning hype vs practical real-world applications, cover how deep learning differs from other machine learning algorithms, go over sample neural net architectures, and provide a step-by-step guide on how to get started.
Specifically, we will cover what type of training data is required and how to prepare it with Apache Spark, followed by how to choose a correct neural net architecture, train, and deploy a deep learning model with TensorFlow on Apache Hadoop 3.1.
Finally, we will wrap-up with deep learning challenges and shortcomings, and offer short- and long-term recommendations to successfully train and deploy deep learning models within your organization to maximize return on investment.
This Big Data Hadoop certification program is structured by professionals and experienced course curators to provide you with an in-depth understanding of the Hadoop and Spark Big Data platforms and the frameworks which are used by them. With the help of the Integrated Laboratory session, you will work upon and complete real-world, industry-based projects in this course.
American Water shares how bringing IoT to fleet management can provide value to the customer. In the utilities industry, fleet management plays a major part in the business. The front line is one of the largest parts of the business whether it is the field employees working on mains, or those working on the customers' property. American Water strives to provide the best customer experience and part of that includes improving the effectiveness of our fleet.
Currently, there is no insight or active feedback on the effectiveness of the routes or driving behaviors. As a PoC, American Water leveraged NiFi to track metrics against a simulated truck, showing the initial values in capturing this type of data.
Technologies: NiFi, Druid, Hive
Learn Real Time Hands on Practical Oriented Talend Online Training by Industry Expert.Attend Free Live Interactive Talend Demo Class.Trainer having 11 Years of Working Experience in BI and Data Warehousing Tools.Enhance your Business Intelligence Career with Learning Talend Online Course in QEdge Technologies Hyderabad.
Talend Online Course Overview
Talend But Why?
Talend Cloud Integration
What is Talend
About Talend
Talend Architecture
Talend Course Content
Talend - Learning Objects
Data Integration (DI) Enterprise
Data Integration (DI) Enterprise Administration
Talend Salary Trends
Kudu as Storage Layer to Digitize Credit ProcessesDataWorks Summit
With HDFS and HBase, there are two different storage options available in the Hadoop ecosystem. Both have their strengths and weaknesses. However, neither HDFS nor HBase can be used universally for all kinds of workloads. Usually this leads to complex hybrid architectures. Kudu is a very versatile storage layer which fills this gap and simplifies the architecture of Big Data systems.
A large German bank is using Kudu as storage layer to fasten their credit processes. Within this system, financial transactions of millions of customers are analysed by Spark jobs to categorize transactions and to calculate key figures. In addition to this analytical workload, several frontend applications are using the Kudu Java API to perform random reads and writes in real-time.
The presentation will cover these topics:
- Business and technical requirements
- Data access patterns
- System architecture
- Kudu data modelling
- Kudu architecture for High Availability
- Experiences from development and operations
Speaker: Olaf Hein, Department Head & Principal Consultant
ORDIX AG
Data Acquisition Automation for NiFi in a Hybrid Cloud environment – the Path...DataWorks Summit
Liberty Global is one of the world’s largest international TV and broadband company, operating in multiple European countries, and with tens of millions of TV, broadband internet, telephony and mobile subscribers.
The Data Solutions team's journey started last year with a strategic project that aimed to implement a state of the art Hybrid Cloud Big Data platform. In this talk, the Manager and the Platform Architect are presenting the team’s data acquisition journey which begins with implementing NiFi flows with simple Get-Put pattern and, in its the final iteration, produces a solution capable of generating complex flows automatically, leading the path to the DataOps way of working.
IBM+Hortonworks = Transformation of the Big Data LandscapeHortonworks
Last year IBM and Hortonworks jointly announced a strategic and deep partnership. Join us as we take a close look at the partnership accomplishments and the conjoined road ahead with industry-leading analytics offers.
View the webinar here: https://hortonworks.com/webinar/ibmhortonworks-transformation-big-data-landscape/
Achieving a 360-degree view of manufacturing via open source industrial data ...DataWorks Summit
Continuously improving factory operations is of critical importance to manufacturers. Consider the facts: the total cost of poor quality amounts to a staggering 20% of sales (American Society of Quality), and unplanned downtime costs plants approximately $50 billion per year (Deloitte).
The most pressing questions are: which process variables effect quality and yield and which process variables predict equipment failure? Getting to those answers is providing forward thinking manufacturers a leg up over competitors.
The speakers address the data management challenges facing today's manufacturers, including proprietary systems and siloed data sources, as well as an inability to make sensor-based data usable.
Integrating enterprise data from ERP, MES, maintenance systems, and other sources with real-time operations data from sensors, PLCs, SCADA systems, and historians represents a major first step. But how to get started? What is the value of a data lake? How are AI/ML being applied to enable real time action?
Join us for this educational session, which includes a view into a roadmap for an open source industrial IoT data management platform.
Key Takeaways:
• Understand key use cases commonly undertaken by manufacturing enterprises
• Understand the value of using multivariate manufacturing data sources, as opposed to a single sensor on a piece of equipment
• Understand advances in big data management and streaming analytics that are paving the way to next-generation factory performance
Speakers
Michael Ger, General Manager Manufacturing and Automotive, Hortonworks
Wade Salazar, Solutions Engineer, Hortonworks
All data accessible to all my organization - Presentation at OW2con'19, June...OW2
It is clear that all employees must have access to data wherever they are to make decisions. However, tools that allow to share data just as easily as the best collaborative tools such as a google doc or an office 365 should be used.
Open source driven by the big data ecosystem and a number of large companies have provided solutions to allow organizations to federate data systems and secure their access.
After a quick overview of existing open source solutions, and how such projects can be organized, it will be necessary to detail Dremio implementation, a unique and centralized interface on all your data. Some real feedbacks will conclude the presentation.
Running Enterprise Workloads with an open source Hybrid Cloud Data Architectu...DataWorks Summit
Cloud accelerates corporate IT landscapes with agility and flexibility. Today, discussion of cloud architecture dominates corporate IT. The cloud enables a number of temporary on-demand use cases that revolutionize analytical workload opportunities. But all of this involves the task of running corporate workloads safely and easily in the cloud.
With the convergence of cloud, IoT, and big data technology, enterprises are increasingly using multiple on-premises Data Lake and multiple Public on different geographies, for example due to regulations and compliance requirements restricting cross- It now distributes data to the cloud Data Lake store of the cloud vendor platform. Diffusion of data types and sources in this complex landscape makes the discovery process, provisioning, and getting insight by performing the appropriate workload on this data more complicated. In addition, to obtain business context, usage, and visibility of data trustworthiness worldwide, it is necessary to display all data and metadata, security management, data access, and monitoring in a centralized way .
All these problems create cracks during the creation of data insights to promote initial data capture and subsequent value creation. As a result, companies now look for compromises between appropriate rules and data control policies while providing a trusted environment that allows them to share data and partner with users responsibly to create value We need "Global Insight Fabric".
In this talk, how the Hortonworks DataPlane Service (DPS) analyzes the data in the data center to expand the storage, implement the open source hybrid architecture utilizing cloud flexibility and new use cases, global in Describes how site fabrics can help customers create. Securely migrate data from on-premises data centers to multiple public clouds, protect the data with replication, then apply consistent safety and governance policies to a wide variety of environments to ensure trustworthy data and inn We provide personal views on the challenges we face in providing the site to the business. I will explain how the DetaPlane service can be useful for traveling to this hybrid architecture and how the open source architecture enables the transformation of the entire enterprise.
Interactive Analytics at Scale in Apache Hive Using DruidDataWorks Summit
Druid is an open-source analytics data store specially designed to execute OLAP queries on event data. Its speed, scalability and efficiency have made it a popular choice to power user-facing analytic applications, including multiple BI tools and dashboards. However, Druid does not provide important features requested by many of these applications, such as a SQL interface or support for complex operations such as joins. This talk presents our work on extending Druid indexing and querying capabilities using Apache Hive. In particular, our solution allows to index complex query results in Druid using Hive, query Druid data sources from Hive using SQL, and execute complex Hive queries on top of Druid data sources. We describe how we built an extension that brings benefits to both systems alike, leveraging Apache Calcite to overcome the challenge of transparently generating Druid JSON queries from the input Hive SQL queries. We conclude with an experimental evaluation highlighting the performant and powerful integration of these projects.
Speaker
Jesus Camancho Rodriquez, Hortonworks
How is it that one system can query terabytes of data, yet still provide interactive query support? This talk will discuss two of the underlying technologies that allow Apache Hive to support fast query response, both on-premise in HDFS and in cloud object stores such as S3 and WASB.
LLAP was introduced in Hive 2.6. It provides standing processes that securely cache Hive’s columnar data and can do query processing without ever needing to start tasks in Hadoop. We will cover LLAP’s architecture, intended uses cases, and performance numbers for both on-premise and in the cloud.
The second technology is the integration of Hive with Apache Druid. Druid excels at low-latency, interactive queries over streaming data. Its method of storing data makes it very well suited for OLAP style queries. We will cover how Hive can be integrated with Druid to support real-time streaming of data from Kafka and OLAP queries.
Speaker: Alan Gates, Co-Founder, Hortonworks
Druid is an open-source analytics data store specially designed to execute OLAP queries on event data. Its speed, scalability and efficiency have made it a popular choice to power user-facing analytic applications, including multiple BI tools and dashboards. However, Druid does not provide important features requested by many of these applications, such as a SQL interface or support for complex operations such as joins. This talk presents our work on extending Druid indexing and querying capabilities using Apache Hive. In particular, our solution allows to index complex query results in Druid using Hive, query Druid data sources from Hive using SQL, and execute complex Hive queries on top of Druid data sources. We describe how we built an extension that brings benefits to both systems alike, leveraging Apache Calcite to overcome the challenge of transparently generating Druid JSON queries from the input Hive SQL queries. We conclude with a demo highlighting the performant and powerful integration of these projects.
More and more organizations are moving their ETL workloads to a Hadoop based ELT grid architecture. Hadoop`s inherit capabilities, especially it`s ability to do late binding addresses some of the key challenges with traditional ETL platforms. In this presentation, attendees will learn the key factors, considerations and lessons around ETL for Hadoop. Areas such as pros and cons for different extract and load strategies, best ways to batch data, buffering and compression considerations, leveraging HCatalog, data transformation, integration with existing data transformations, advantages of different ways of exchanging data and leveraging Hadoop as a data integration layer. This is an extremely popular presentation around ETL and Hadoop.
Enterprise IIoT Edge Processing with Apache NiFiTimothy Spann
April 5, 2018 IoT Fusion 2018 Conference in Philadelphia, PA hosted by Chariot Solutions. This talk is about Apache NiFi, MiniFi, Python, Deep Learning, NVidia Jetson TX1, Raspberry Pi, Apache MXNet, TensorFlow and how to run things at the edge and process in your big data center. http://iotfusion.net/session/ https://github.com/tspannhw/IoTFusion2018Talk
Enabling the Real Time Analytical EnterpriseHortonworks
Combining IOT, Customer Experience and Real-Time Enterprise Data within Hadoop. What if you could derive real-time insights using ALL of your data? Join us for this webinar and learn how companies are combining “new” real-time data sources (i.e. IOT, Social, Web Logs) with continuously updated enterprise data from SAP and other enterprise transactional systems, providing deep and up-to-the-second analytical insights. This presentation will include a demonstration of how this can be achieved quickly, easily and affordably by utilizing a joint solution from Attunity and Hortonworks.
Lecture to the London S2DS students.
Some fun in highlighting that I'm their polar opposite (no schooling since 17, and focused on operations not science).
Mr. Slim Baltagi is a Systems Architect at Hortonworks, with over 4 years of Hadoop experience working on 9 Big Data projects: Advanced Customer Analytics, Supply Chain Analytics, Medical Coverage Discovery, Payment Plan Recommender, Research Driven Call List for Sales, Prime Reporting Platform, Customer Hub, Telematics, Historical Data Platform; with Fortune 100 clients and global companies from Financial Services, Insurance, Healthcare and Retail.
Mr. Slim Baltagi has worked in various architecture, design, development and consulting roles at.
Accenture, CME Group, TransUnion, Syntel, Allstate, TransAmerica, Credit Suisse, Chicago Board Options Exchange, Federal Reserve Bank of Chicago, CNA, Sears, USG, ACNielsen, Deutshe Bahn.
Mr. Baltagi has also over 14 years of IT experience with an emphasis on full life cycle development of Enterprise Web applications using Java and Open-Source software. He holds a master’s degree in mathematics and is an ABD in computer science from Université Laval, Québec, Canada.
Languages: Java, Python, JRuby, JEE , PHP, SQL, HTML, XML, XSLT, XQuery, JavaScript, UML, JSON
Databases: Oracle, MS SQL Server, MYSQL, PostreSQL
Software: Eclipse, IBM RAD, JUnit, JMeter, YourKit, PVCS, CVS, UltraEdit, Toad, ClearCase, Maven, iText, Visio, Japser Reports, Alfresco, Yslow, Terracotta, Toad, SoapUI, Dozer, Sonar, Git
Frameworks: Spring, Struts, AppFuse, SiteMesh, Tiles, Hibernate, Axis, Selenium RC, DWR Ajax , Xstream
Distributed Computing/Big Data: Hadoop, MapReduce, HDFS, Hive, Pig, Sqoop, HBase, R, RHadoop, Cloudera CDH4, MapR M7, Hortonworks HDP 2.1
Harnessing Data-in-Motion with HDF 2.0, introduction to Apache NIFI/MINIFIHaimo Liu
Introducing the new Hortonworks DataFlow (HDF) release, HDF 2.0. Also provides introduction to the flow management part of the platform, powered by Apache NIFI and MINIFI.
Learn about HDF and how you can easily augment your existing data systems - Hadoop and otherwise. Learn what Dataflow is all about and how Apache NiFi, MiNiFi, Kafka and Storm work together for streaming analytics.
Hortonworks Data in Motion Webinar Series - Part 1Hortonworks
VIEW THE ON-DEMAND WEBINAR: http://hortonworks.com/webinar/introduction-hortonworks-dataflow/
Learn about Hortonworks DataFlow (HDFTM) and how you can easily augment your existing data systems – Hadoop and otherwise. Learn what Dataflow is all about and how Apache NiFi, MiNiFi, Kafka and Storm work together for streaming analytics.
Running Enterprise Workloads with an Open Source Hybrid Cloud Data ArchitectureDataWorks Summit
Cloud is turbocharging the Enterprise IT landscape with agility and flexibility. And now, discussions of cloud architecture dominate Enterprise IT. Cloud is enabling many ephemeral on-demand use cases which is a game-changing opportunity for analytic workloads. But all of this comes with the challenges of running enterprise workloads in the cloud securely and with ease.
With the convergence of cloud, IoT, and big data technologies, enterprises increasingly have their data spread across multiple data lakes on-prem and in cloud data lake stores in many geographies and across multiple public cloud vendor platforms, for example, due to regulatory and compliance mandates that limit cross-border data transfer. With the proliferation of data types and sources in this complex landscape, the process of discovery, provisioning, and running relevant workloads on this data to gather insights has become more complex. Additionally, gaining global visibility into the business context, usage, and trustworthiness of data requires a centralized view of all data and metadata, security controls, data access, and monitoring.
All of these challenges create a significant chasm between initial data capture and subsequent data insights generation to drive value creation. Therefore, enterprises now require a “global insight fabric” that can find a happy medium between adequate rules and policies of data governance while providing a trusted environment for users to collaborate and share data responsibly in order to create value.
In this talk, we will outline how Hortonworks DataPlane Service(DPS) can help customers build a global insight fabric that can span storing and analyzing data within data centers to implementing an open source hybrid architecture that takes advantage of cloud's elasticity and new use cases. We will get a personal view of the challenges faced in safely moving data from on-premises data centers into multiple public clouds, safeguarding it through replication, and then applying consistent security and governance policies across diverse environments to deliver trusted data and insights to the business. We will highlight how DataPlane Service can help enterprises with this hybrid architectural journey, and how open source architectures are enabling this transformation across enterprises.
Speaker: Alan Gates, Co-Founder, Hortonworks
Hortonworks and Platfora in Financial Services - WebinarHortonworks
Big Data Analytics is transforming how banks and financial institutions unlock insights, make more meaningful decisions, and manage risk. Join this webinar to see how you can gain a clear understanding of the customer journey by leveraging Platfora to interactively analyze the mass of raw data that is stored in your Hortonworks Data Platform. Our experts will highlight use cases, including customer analytics and security analytics.
Speakers: Mark Lochbihler, Partner Solutions Engineer at Hortonworks, and Bob Welshmer, Technical Director at Platfora
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Apache Hive is a rapidly evolving project, many people are loved by the big data ecosystem. Hive continues to expand support for analytics, reporting, and bilateral queries, and the community is striving to improve support along with many other aspects and use cases. In this lecture, we introduce the latest and greatest features and optimization that appeared in this project last year. This includes benchmarks covering LLAP, Apache Druid's materialized views and integration, workload management, ACID improvements, using Hive in the cloud, and performance improvements. I will also tell you a little about what you can expect in the future.
Similar to Time-series data analysis and persistence with Druid (20)
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
