Hortonwork’s Hadoop Powered EDW (Enterprise Data Warehouse) Optimization Solution with Syncsort DMX-h enables organizations to liberate data from across the enterprise, quickly create and populate the data lake, and deliver actionable insights.
Customer case studies across a variety of industries will bring to life how organizations are using this solution to gain bigger insights from their enterprise data – securely and cost-effectively – with faster time to time value.
EDW Optimization: A Modern Twist on an Old FavoriteHortonworks
BI and Big Data veterans Carter Shanklin, Sr. Director of Product at Hortonworks and Josh Klahr,, VP of Product at AtScale will deliver this interactive session covering insights, real-world experiences, and answering questions from the online audience. They’ll share real customer stories across industries and pain points to bring to life how you can use EDW Optimization today to drive insights across any and all of your enterprise data – quickly, simply, securely, and widely.
Streamline Apache Hadoop Operations with Apache Ambari and SmartSenseHortonworks
Apache Ambari 2.5 helps customers simplify the experience for provisioning, managing, monitoring, securing and troubleshooting Hadoop deployments. Find out how the combination of Ambari and SmartSense delivers a path to success to help IT get Hadoop up and running effectively. The end result – you get the full business impact management and benefits of Big Data for your organization.
https://hortonworks.com/webinar/streamline-apache-hadoop-operations-apache-ambari-smartsense/
2015 02 12 talend hortonworks webinar challenges to hadoop adoptionHortonworks
Hadoop is no longer optional. Companies of all sizes are in various phases of their own Big Data journey. Whether you are just starting to explore the platform or have multiple clusters up and running, everyone is presented with a similar challenge - developing their internal skillset. Hadoop specialists are hard to find. Hand coding is too prone to error when it comes to storing, integrating or analyzing your data. However, it doesn’t need to be this difficult.
In this recorded webinar, Talend and Hortonworks help you learn how to unify all your data in Hadoop, with no specialized Big Data skills.
Find the recording here. www.talend.com/resources/webinars/challenges-to-hadoop-adoption-if-you-can-dream-it-you-can-build-it
This webinar covers: How Hadoop opens a new world of analytic applications, How to bridge the skills gap with our Big Data solutions, Experience a real-world, simple technical demo
Get Started with Big Data in the Cloud ASAPHortonworks
Learn how can your organization get beyond this hurdle so it can focus on a “lift and reshape” cloud strategy that will enable you to take full advantage of the benefits of your cloud deployment.
Accelerating the Value of Big Data Analytics for P&C Insurers with Hortonwork...Hortonworks
As the Big Data Analytics and the Apache Hadoop ecosystem has matured and gained increasing traction in established industries with faster adoption in the insurance market than originally anticipated, it is clear that the potential benefits for data management and business intelligence are staggering. At the same time, many big data programs have stalled or failed to deliver on their aspirational value proposition, resulting in a substantial gap between expectations of analytics consumers and the ability of big data analytics programs to deliver. Join Hortonworks and Clarity as we review the common needs of Property and Casualty (P&C) Insurers and how to unlock the true value of big data analytics:
Information agility – Centralization of data and decentralization of analysis
Expanded capability – Conventional analysis combined with real-time analytics demands
Reduced expense – Lower costs through cheaper storage while maintaining scalability
We will discuss a modern data architecture that constitutes a mature, enterprise strength Hadoop framework for P&C Insurers that answers the need for governance processes across the enterprise stack. We will cover how a modern data architecture allows organizations to collect, store, analyze and manipulate massive quantities of data on their own terms—regardless of the source of that data - accelerating the real lifetime value of big data and Hadoop analytics for claims, customer sentiment and telematics.
Global Data Management – a practical framework to rethinking enterprise, oper...DataWorks Summit
Global data management is not a newly coined term. However, what it stands for is actually widening in scope particularly around data-in-motion and data-at-rest. Significant technology trends such as IoT, cloud, AI/ML, blockchain, and streaming data have given rise to excessive data volumes and also innovative use cases. The scope for global data management now extends all the way from ingestion, processing, storage, governance, security to analysis. With a good number of endpoints served through the cloud and major application footprints remaining on-premisess, it is pertinent to have a global data management strategy that supports hybrid models and more specifically, a multi-cloud model.
Many modern businesses struggle to balance the demands of rapidly innovating through new technologies like machine learning with the need to keep data safe and secure, all while responding to a constantly changing regulatory landscape. This puts data stewards, data engineers, architects, data scientists, and analysts under intense pressure as they must contend with existing and new applications, multiple logical and physical data stores and sources, diverse data types, and data spread across several deployment environments.
Attend this session led by Matt Aslett, Research Director at 451 Research and Dinesh Chandrasekhar, Director, Hortonworks to learn more about creating a framework for your enterprise that offers guidance on how to think about global data management—priorities, responsibilities, key stakeholders, compliance, and growth.
Speakers
Dinesh Chandrasekhar, Hortonworks, Director Product Marketing
Matt Aslett, 451 Research, Research Director, Data platforms and Analytics
Slides from the joint webinar. Learn how Pivotal HAWQ, one of the world’s most advanced enterprise SQL on Hadoop technology, coupled with the Hortonworks Data Platform, the only 100% open source Apache Hadoop data platform, can turbocharge your Data Science efforts.
Together, Pivotal HAWQ and the Hortonworks Data Platform provide businesses with a Modern Data Architecture for IT transformation.
EDW Optimization: A Modern Twist on an Old FavoriteHortonworks
BI and Big Data veterans Carter Shanklin, Sr. Director of Product at Hortonworks and Josh Klahr,, VP of Product at AtScale will deliver this interactive session covering insights, real-world experiences, and answering questions from the online audience. They’ll share real customer stories across industries and pain points to bring to life how you can use EDW Optimization today to drive insights across any and all of your enterprise data – quickly, simply, securely, and widely.
Streamline Apache Hadoop Operations with Apache Ambari and SmartSenseHortonworks
Apache Ambari 2.5 helps customers simplify the experience for provisioning, managing, monitoring, securing and troubleshooting Hadoop deployments. Find out how the combination of Ambari and SmartSense delivers a path to success to help IT get Hadoop up and running effectively. The end result – you get the full business impact management and benefits of Big Data for your organization.
https://hortonworks.com/webinar/streamline-apache-hadoop-operations-apache-ambari-smartsense/
2015 02 12 talend hortonworks webinar challenges to hadoop adoptionHortonworks
Hadoop is no longer optional. Companies of all sizes are in various phases of their own Big Data journey. Whether you are just starting to explore the platform or have multiple clusters up and running, everyone is presented with a similar challenge - developing their internal skillset. Hadoop specialists are hard to find. Hand coding is too prone to error when it comes to storing, integrating or analyzing your data. However, it doesn’t need to be this difficult.
In this recorded webinar, Talend and Hortonworks help you learn how to unify all your data in Hadoop, with no specialized Big Data skills.
Find the recording here. www.talend.com/resources/webinars/challenges-to-hadoop-adoption-if-you-can-dream-it-you-can-build-it
This webinar covers: How Hadoop opens a new world of analytic applications, How to bridge the skills gap with our Big Data solutions, Experience a real-world, simple technical demo
Get Started with Big Data in the Cloud ASAPHortonworks
Learn how can your organization get beyond this hurdle so it can focus on a “lift and reshape” cloud strategy that will enable you to take full advantage of the benefits of your cloud deployment.
Accelerating the Value of Big Data Analytics for P&C Insurers with Hortonwork...Hortonworks
As the Big Data Analytics and the Apache Hadoop ecosystem has matured and gained increasing traction in established industries with faster adoption in the insurance market than originally anticipated, it is clear that the potential benefits for data management and business intelligence are staggering. At the same time, many big data programs have stalled or failed to deliver on their aspirational value proposition, resulting in a substantial gap between expectations of analytics consumers and the ability of big data analytics programs to deliver. Join Hortonworks and Clarity as we review the common needs of Property and Casualty (P&C) Insurers and how to unlock the true value of big data analytics:
Information agility – Centralization of data and decentralization of analysis
Expanded capability – Conventional analysis combined with real-time analytics demands
Reduced expense – Lower costs through cheaper storage while maintaining scalability
We will discuss a modern data architecture that constitutes a mature, enterprise strength Hadoop framework for P&C Insurers that answers the need for governance processes across the enterprise stack. We will cover how a modern data architecture allows organizations to collect, store, analyze and manipulate massive quantities of data on their own terms—regardless of the source of that data - accelerating the real lifetime value of big data and Hadoop analytics for claims, customer sentiment and telematics.
Global Data Management – a practical framework to rethinking enterprise, oper...DataWorks Summit
Global data management is not a newly coined term. However, what it stands for is actually widening in scope particularly around data-in-motion and data-at-rest. Significant technology trends such as IoT, cloud, AI/ML, blockchain, and streaming data have given rise to excessive data volumes and also innovative use cases. The scope for global data management now extends all the way from ingestion, processing, storage, governance, security to analysis. With a good number of endpoints served through the cloud and major application footprints remaining on-premisess, it is pertinent to have a global data management strategy that supports hybrid models and more specifically, a multi-cloud model.
Many modern businesses struggle to balance the demands of rapidly innovating through new technologies like machine learning with the need to keep data safe and secure, all while responding to a constantly changing regulatory landscape. This puts data stewards, data engineers, architects, data scientists, and analysts under intense pressure as they must contend with existing and new applications, multiple logical and physical data stores and sources, diverse data types, and data spread across several deployment environments.
Attend this session led by Matt Aslett, Research Director at 451 Research and Dinesh Chandrasekhar, Director, Hortonworks to learn more about creating a framework for your enterprise that offers guidance on how to think about global data management—priorities, responsibilities, key stakeholders, compliance, and growth.
Speakers
Dinesh Chandrasekhar, Hortonworks, Director Product Marketing
Matt Aslett, 451 Research, Research Director, Data platforms and Analytics
Slides from the joint webinar. Learn how Pivotal HAWQ, one of the world’s most advanced enterprise SQL on Hadoop technology, coupled with the Hortonworks Data Platform, the only 100% open source Apache Hadoop data platform, can turbocharge your Data Science efforts.
Together, Pivotal HAWQ and the Hortonworks Data Platform provide businesses with a Modern Data Architecture for IT transformation.
How Universities Use Big Data to Transform EducationHortonworks
Student performance data is increasingly being captured as part of software-based and online classroom exercises and testing. This data can be augmented with behavioral data captured from sources such as social media, student-professor meeting notes, blogs, student surveys, and so forth to discover new insights to improve student learning. The results transcend traditional IT departments to focus on issues like retention, research, and the delivery of content and courses through new modalities.
Hortonworks is partnering with Microsoft to show you how the Hortonworks Data Platform (HDP) running on the Microsoft stack enables you to develop a “single view of a student”.
Insurance companies of all sizes are challenged to keep up with emerging technologies that deliver a competitive advantage. Recording: https://www.brighttalk.com/webcast/9573/192877
Big data holds the key to greater customer insight and stronger customer relationships. But risk of sensitive data exposure — and compliance violations — keeps many insurers from pursuing big data initiatives and reaping the rewards of business-driven analytics. Join Dataguise and Hortonworks for this live webinar to learn how you can free your organization from traditional information security constraints and unlock the power of your most valuable business assets.
• What do you need to know about PII/PHI privacy before embarking on big data initiatives?
• Why do so many big data initiatives fail before they’ve even begun—and what can you do about it?
• How can IT security organizations help data scientists extract more business value from their data?
• How are leading insurance companies leveraging big data to gain competitive advantage?
Top 5 Strategies for Retail Data AnalyticsHortonworks
It’s an exciting time for retailers as technology is driving a major disruption in the market. Whether you are just beginning to build a retail data analytics program or you have been gaining advanced insights from your data for quite some time, join Eric and Shish as we explore the trends, drivers and hurdles in retail data analytics
5 Steps to Create a Company Culture that Embraces the Power of DataHortonworks
A business culture that relies on gut checks and feelings for business decisions is a hard hurdle to overcome. Company culture is often the biggest barrier to moving a company toward data-driven decisions. There's a way to get there, when driven by company leaders. Here's how you do that:
1. Get comfortable with softer data sets
2. Must come from top-down
3. A structure where goals are clear
4. Right role for technology
5. Clear stewardship around data
The Next Generation of Big Data AnalyticsHortonworks
Apache Hadoop has evolved rapidly to become a leading platform for managing and processing big data. If your organization is examining how you can use Hadoop to store, transform, and refine large volumes of multi-structured data, please join us for this session where we will discuss, the emergence of "big data" and opportunities for deriving business value, the evolution of Apache Hadoop and future directions, essential components required in a Hadoop-powered platform, and solution architectures that integrate Hadoop with existing data discovery and data warehouse platforms.
The Power of your Data Achieved - Next Gen ModernizationHortonworks
Fueled by ever-changing customer behaviors and an increasing number of industry disruptions, the modern enterprise requires analytics to stay ahead of the game. Today’s data warehouse needs continuous enhancements to address new requirements for advanced analytics, real-time streaming data, Big Data, and unstructured data. The focus should be on developing a forward-looking, future-proof view and holistically addressing the combination of forces that are impacting the existing operational model.
see the recording: http://youtu.be/qdhF1sfef10
Ofer Medelvitch, Director of Data Science of Hortonworks and Michael Zeller, Founder and CEO of Zementis present key learnings as to what drives successful implementations of big data analytics projects. Their knowledge comes from working with dozens of companies from small cloud-based start-ups to some of the largest companies in the world.
Enterprise Data Science at Scale Meetup - IBM and Hortonworks - Oct 2017 Hortonworks
View the recording of the meet up, including the live demos, here: https://www.youtube.com/watch?v=uaJWB3K8lkg
Data science holds tremendous potential for organizations to uncover new insights and drivers of revenue and profitability. Big Data has brought the promise of doing data science at scale to enterprises, however this promise also comes with challenges for data scientists to continuously learn and collaborate. Data Scientists have many tools at their disposal such as notebooks like Juypter and Apache Zeppelin & IDEs such as RStudio with languages like R, Python, Scala and frameworks like Apache Spark. Given all the choices how do you best collaborate to build your model and then work through the development lifecycle to deploy it from test into production?
Why Data Science on Big Data?
In this meetup you will cover the attributes of a modern data science platform that empowers data scientists to build models using all the data in their data lake and foster continuous learning and collaboration. We will show a demo of Apache Zeppelin, Apache Spark, Apache Livy and Apache Hadoop with the focus on integration, security and model deployment and management.
Data Science at Scale DEMO
The demo will cover the Data Science life cycle: develop model in team environment, train the model with all the data on a Hadoop cluster, deploy model into production. The model will be a Spark ML model
Practical ML with Apache Spark
To deliver machine learning solutions data scientists not only need to fit models but also do familiar tasks data collection & wrangling, labelling, feature extraction and transformation, model tuning and evaluation, etc. Apache Spark provide provides a unified solution for all this under the same framework.
For example, one can use Spark SQL to generate training data from different sources and then pass it directly to MLlib for feature engineering and model tuning, instead of using Hive/Pig for the first half and then downloading the data to a single machine to train models in R. The latter is actually very common in practice but painful to maintain. Spark MLlib makes life easier for data scientists and machine learning engineers so that they can focus on building better ML models and applications.
We will discuss the underlying principles required to develop practical machine learning and data science pipelines and show some hands-on experience using Apache Spark to solve typical machine learning and data science problem. We will also have a short discussion about how Spark MLlib faces challenges from other machine learning libraries such as TensorFlow and XGBoost.
Hortonworks and Clarity Solution Group Hortonworks
Many organizations are leveraging social media to understand consumer sentiment and opinions about brands and products. Analytics in this area, however, is in its infancy and does not always provide a compelling result for effective business impact. Learn how consumer organizations can benefit by integrating social data with enterprise data to drive more profitable consumer relationships. This webinar is presented by Hortonworks and Clarity Solution Group, and will focus on the evolution of Hadoop, the clear advantage of Hortonworks distribution, and business challenges solved by “Consumer720.”
Hortonworks DataFlow (HDF) 3.1 - Redefining Data-In-Motion with Modern Data A...Hortonworks
Join the Hortonworks product team as they introduce HDF 3.1 and the core components for a modern data architecture to support stream processing and analytics.
You will learn about the three main themes that HDF addresses:
Developer productivity
Operational efficiency
Platform interoperability
https://hortonworks.com/webinar/series-hdf-3-1-redefining-data-motion-modern-data-architectures/
Eliminating the Challenges of Big Data Management Inside HadoopHortonworks
Your Big Data strategy is only as good as the quality of your data. Today, deriving business value from data depends on how well your company can capture, cleanse, integrate and manage data. During this webinar, we discussed how to eliminate the challenges to Big Data management inside Hadoop.
Go over these slides to learn:
· How to use the scalability and flexibility of Hadoop to drive faster access to usable information across the enterprise.
· Why a pure-YARN implementation for data integration, quality and management delivers competitive advantage.
· How to use the flexibility of RedPoint and Hortonworks to create an enterprise data lake where data is captured, cleansed, linked and structured in a consistent way.
Dynamic Column Masking and Row-Level Filtering in HDPHortonworks
As enterprises around the world bring more of their sensitive data into Hadoop data lakes, balancing the need for democratization of access to data without sacrificing strong security principles becomes paramount. In this webinar, Srikanth Venkat, director of product management for security & governance will demonstrate two new data protection capabilities in Apache Ranger – dynamic column masking and row level filtering of data stored in Apache Hive. These features have been introduced as part of HDP 2.5 platform release.
Hortonworks and Red Hat Webinar_Sept.3rd_Part 1Hortonworks
As the enterprise's big data program matures and Apache Hadoop becomes more deeply embedded in critical operations, the ability to support and operate it efficiently and reliably becomes increasingly important. To aid enterprise in operating modern data architecture at scale, Red hat and Hortonworks have collaborated to integrate Hortonworks Data Platform with Red Hat's proven platform technologies. Join us in this interactive 3-part webinar series, as we'll demonstrate how Red Hat JBoss Data Virtualization can integrate with Hadoop through Hive and provide users easy access to data.
Enterprise Apache Hadoop: State of the UnionHortonworks
So what's in store for 2014? This deck was from Shaun Connolly's (VP of Strategy, Hortonworks) State of the Union webinar.
In this deck, you'll find:
- Reflection on Enterprise Hadoop Market in 2013
- The latest releases and innovations within the open source community
- Highlights of what's in store for Apache Hadoop and Big Data in 2014
Almost every week, news of a proprietary or customer data breach hits the news wave. While attackers have increased the level of sophistication in their tactics, so too have organizations advanced in their ability to build a robust, data-driven defense. Join Hortonworks and Sqrrl to learn how a Modern Data Architecture with Hortonworks Data Platform (HDP) and Sqrrl Enterprise enables intuitive exploration, discovery, and pattern recognition over your big cybersecurity data.
In this webinar you will learn:
--How Apache Hadoop makes it the perfect fit to accumulate cybersecurity data and diagnose the latest attacks
--The effective ways for pinpointing and reasoning about correlated events within your data, and assessing your network security posture.
--How a Modern Data Architecture that includes the power of Hadoop with Hortonworks Data Platform with the massive, secure, entity-centric data models in Sqrrl Enterprise can discover hidden patterns and detect anomalies within your data using linked data analysis.
IDC Retail Insights - What's Possible with a Modern Data Architecture?Hortonworks
This is Greg Girard's presentation from the September 22, 2014 Hortonworks webinar “What’s Possible with a Modern Data Architecture?”. Greg is program director for omni-channel analytics strategies at IDC Retail Insights. He provides targeted, fact-based guidance to retailers for the application of analytics across the enterprise.
Data in Motion - Data at Rest - Hortonworks a Modern ArchitectureMats Johansson
Presentation at Data Innovation Summit 2016 in Stockholm
How to build a modern data architecture supporting data in motion and data at rest with Hortonworks Data Flow and Data Platform.
How Universities Use Big Data to Transform EducationHortonworks
Student performance data is increasingly being captured as part of software-based and online classroom exercises and testing. This data can be augmented with behavioral data captured from sources such as social media, student-professor meeting notes, blogs, student surveys, and so forth to discover new insights to improve student learning. The results transcend traditional IT departments to focus on issues like retention, research, and the delivery of content and courses through new modalities.
Hortonworks is partnering with Microsoft to show you how the Hortonworks Data Platform (HDP) running on the Microsoft stack enables you to develop a “single view of a student”.
Insurance companies of all sizes are challenged to keep up with emerging technologies that deliver a competitive advantage. Recording: https://www.brighttalk.com/webcast/9573/192877
Big data holds the key to greater customer insight and stronger customer relationships. But risk of sensitive data exposure — and compliance violations — keeps many insurers from pursuing big data initiatives and reaping the rewards of business-driven analytics. Join Dataguise and Hortonworks for this live webinar to learn how you can free your organization from traditional information security constraints and unlock the power of your most valuable business assets.
• What do you need to know about PII/PHI privacy before embarking on big data initiatives?
• Why do so many big data initiatives fail before they’ve even begun—and what can you do about it?
• How can IT security organizations help data scientists extract more business value from their data?
• How are leading insurance companies leveraging big data to gain competitive advantage?
Top 5 Strategies for Retail Data AnalyticsHortonworks
It’s an exciting time for retailers as technology is driving a major disruption in the market. Whether you are just beginning to build a retail data analytics program or you have been gaining advanced insights from your data for quite some time, join Eric and Shish as we explore the trends, drivers and hurdles in retail data analytics
5 Steps to Create a Company Culture that Embraces the Power of DataHortonworks
A business culture that relies on gut checks and feelings for business decisions is a hard hurdle to overcome. Company culture is often the biggest barrier to moving a company toward data-driven decisions. There's a way to get there, when driven by company leaders. Here's how you do that:
1. Get comfortable with softer data sets
2. Must come from top-down
3. A structure where goals are clear
4. Right role for technology
5. Clear stewardship around data
The Next Generation of Big Data AnalyticsHortonworks
Apache Hadoop has evolved rapidly to become a leading platform for managing and processing big data. If your organization is examining how you can use Hadoop to store, transform, and refine large volumes of multi-structured data, please join us for this session where we will discuss, the emergence of "big data" and opportunities for deriving business value, the evolution of Apache Hadoop and future directions, essential components required in a Hadoop-powered platform, and solution architectures that integrate Hadoop with existing data discovery and data warehouse platforms.
The Power of your Data Achieved - Next Gen ModernizationHortonworks
Fueled by ever-changing customer behaviors and an increasing number of industry disruptions, the modern enterprise requires analytics to stay ahead of the game. Today’s data warehouse needs continuous enhancements to address new requirements for advanced analytics, real-time streaming data, Big Data, and unstructured data. The focus should be on developing a forward-looking, future-proof view and holistically addressing the combination of forces that are impacting the existing operational model.
see the recording: http://youtu.be/qdhF1sfef10
Ofer Medelvitch, Director of Data Science of Hortonworks and Michael Zeller, Founder and CEO of Zementis present key learnings as to what drives successful implementations of big data analytics projects. Their knowledge comes from working with dozens of companies from small cloud-based start-ups to some of the largest companies in the world.
Enterprise Data Science at Scale Meetup - IBM and Hortonworks - Oct 2017 Hortonworks
View the recording of the meet up, including the live demos, here: https://www.youtube.com/watch?v=uaJWB3K8lkg
Data science holds tremendous potential for organizations to uncover new insights and drivers of revenue and profitability. Big Data has brought the promise of doing data science at scale to enterprises, however this promise also comes with challenges for data scientists to continuously learn and collaborate. Data Scientists have many tools at their disposal such as notebooks like Juypter and Apache Zeppelin & IDEs such as RStudio with languages like R, Python, Scala and frameworks like Apache Spark. Given all the choices how do you best collaborate to build your model and then work through the development lifecycle to deploy it from test into production?
Why Data Science on Big Data?
In this meetup you will cover the attributes of a modern data science platform that empowers data scientists to build models using all the data in their data lake and foster continuous learning and collaboration. We will show a demo of Apache Zeppelin, Apache Spark, Apache Livy and Apache Hadoop with the focus on integration, security and model deployment and management.
Data Science at Scale DEMO
The demo will cover the Data Science life cycle: develop model in team environment, train the model with all the data on a Hadoop cluster, deploy model into production. The model will be a Spark ML model
Practical ML with Apache Spark
To deliver machine learning solutions data scientists not only need to fit models but also do familiar tasks data collection & wrangling, labelling, feature extraction and transformation, model tuning and evaluation, etc. Apache Spark provide provides a unified solution for all this under the same framework.
For example, one can use Spark SQL to generate training data from different sources and then pass it directly to MLlib for feature engineering and model tuning, instead of using Hive/Pig for the first half and then downloading the data to a single machine to train models in R. The latter is actually very common in practice but painful to maintain. Spark MLlib makes life easier for data scientists and machine learning engineers so that they can focus on building better ML models and applications.
We will discuss the underlying principles required to develop practical machine learning and data science pipelines and show some hands-on experience using Apache Spark to solve typical machine learning and data science problem. We will also have a short discussion about how Spark MLlib faces challenges from other machine learning libraries such as TensorFlow and XGBoost.
Hortonworks and Clarity Solution Group Hortonworks
Many organizations are leveraging social media to understand consumer sentiment and opinions about brands and products. Analytics in this area, however, is in its infancy and does not always provide a compelling result for effective business impact. Learn how consumer organizations can benefit by integrating social data with enterprise data to drive more profitable consumer relationships. This webinar is presented by Hortonworks and Clarity Solution Group, and will focus on the evolution of Hadoop, the clear advantage of Hortonworks distribution, and business challenges solved by “Consumer720.”
Hortonworks DataFlow (HDF) 3.1 - Redefining Data-In-Motion with Modern Data A...Hortonworks
Join the Hortonworks product team as they introduce HDF 3.1 and the core components for a modern data architecture to support stream processing and analytics.
You will learn about the three main themes that HDF addresses:
Developer productivity
Operational efficiency
Platform interoperability
https://hortonworks.com/webinar/series-hdf-3-1-redefining-data-motion-modern-data-architectures/
Eliminating the Challenges of Big Data Management Inside HadoopHortonworks
Your Big Data strategy is only as good as the quality of your data. Today, deriving business value from data depends on how well your company can capture, cleanse, integrate and manage data. During this webinar, we discussed how to eliminate the challenges to Big Data management inside Hadoop.
Go over these slides to learn:
· How to use the scalability and flexibility of Hadoop to drive faster access to usable information across the enterprise.
· Why a pure-YARN implementation for data integration, quality and management delivers competitive advantage.
· How to use the flexibility of RedPoint and Hortonworks to create an enterprise data lake where data is captured, cleansed, linked and structured in a consistent way.
Dynamic Column Masking and Row-Level Filtering in HDPHortonworks
As enterprises around the world bring more of their sensitive data into Hadoop data lakes, balancing the need for democratization of access to data without sacrificing strong security principles becomes paramount. In this webinar, Srikanth Venkat, director of product management for security & governance will demonstrate two new data protection capabilities in Apache Ranger – dynamic column masking and row level filtering of data stored in Apache Hive. These features have been introduced as part of HDP 2.5 platform release.
Hortonworks and Red Hat Webinar_Sept.3rd_Part 1Hortonworks
As the enterprise's big data program matures and Apache Hadoop becomes more deeply embedded in critical operations, the ability to support and operate it efficiently and reliably becomes increasingly important. To aid enterprise in operating modern data architecture at scale, Red hat and Hortonworks have collaborated to integrate Hortonworks Data Platform with Red Hat's proven platform technologies. Join us in this interactive 3-part webinar series, as we'll demonstrate how Red Hat JBoss Data Virtualization can integrate with Hadoop through Hive and provide users easy access to data.
Enterprise Apache Hadoop: State of the UnionHortonworks
So what's in store for 2014? This deck was from Shaun Connolly's (VP of Strategy, Hortonworks) State of the Union webinar.
In this deck, you'll find:
- Reflection on Enterprise Hadoop Market in 2013
- The latest releases and innovations within the open source community
- Highlights of what's in store for Apache Hadoop and Big Data in 2014
Almost every week, news of a proprietary or customer data breach hits the news wave. While attackers have increased the level of sophistication in their tactics, so too have organizations advanced in their ability to build a robust, data-driven defense. Join Hortonworks and Sqrrl to learn how a Modern Data Architecture with Hortonworks Data Platform (HDP) and Sqrrl Enterprise enables intuitive exploration, discovery, and pattern recognition over your big cybersecurity data.
In this webinar you will learn:
--How Apache Hadoop makes it the perfect fit to accumulate cybersecurity data and diagnose the latest attacks
--The effective ways for pinpointing and reasoning about correlated events within your data, and assessing your network security posture.
--How a Modern Data Architecture that includes the power of Hadoop with Hortonworks Data Platform with the massive, secure, entity-centric data models in Sqrrl Enterprise can discover hidden patterns and detect anomalies within your data using linked data analysis.
IDC Retail Insights - What's Possible with a Modern Data Architecture?Hortonworks
This is Greg Girard's presentation from the September 22, 2014 Hortonworks webinar “What’s Possible with a Modern Data Architecture?”. Greg is program director for omni-channel analytics strategies at IDC Retail Insights. He provides targeted, fact-based guidance to retailers for the application of analytics across the enterprise.
Data in Motion - Data at Rest - Hortonworks a Modern ArchitectureMats Johansson
Presentation at Data Innovation Summit 2016 in Stockholm
How to build a modern data architecture supporting data in motion and data at rest with Hortonworks Data Flow and Data Platform.
In 2017, more and more corporations are looking to reduce operational overheads in their enterprise data warehouse (EDW) installations. Hortonworks just launched Industry’s first turn key EDW Optimization solution together with our partners Syncsort and AtScale. Join Hortonworks’ CTO Scott Gnau to learn more about this exciting solution and its 3 use cases.
Slides from joint webinar. Pivotal HAWQ, one of the world’s most advanced enterprise SQL on Hadoop technology, coupled with the Hortonworks Data Platform, the only 100% open source Apache Hadoop data platform, can turbocharge your Data Science efforts.
Together, Pivotal HAWQ and the Hortonworks Data Platform provide businesses with a Modern Data Architecture for IT transformation.
What's new in Hortonworks DataFlow 3.0 by Andrew PsaltisData Con LA
Abstract:- Hortonworks DataFlow (HDF) is built with the vision of creating a platform that enables enterprises to build dataflow management and streaming analytics solutions that collect, curate, analyze and act on data in motion across the datacenter and cloud. Do you want to be able to provide a complete end-to-end streaming solution, from an IoT device all the way to a dashboard for your business users with no code? Come to this session to learn how this is now possible with HDF 3.0.
Delivering Real-Time Streaming Data for Healthcare Customers: ClearsenseHortonworks
For years, the healthcare industry has had problems of data scarcity and latency. Clearsense solved the problem by building an open-source Hortonworks Data Platform (HDP) solution while providing decades worth of clinical expertise. Clearsense is delivering smart, real-time streaming data, to its healthcare customers enabling mission-critical data to feed clinical decisions.
https://hortonworks.com/webinar/delivering-smart-real-time-streaming-data-healthcare-customers-clearsense/
Predicting Customer Experience through Hadoop and Customer Behavior GraphsHortonworks
Enhancing a customer experience has become essential for communication service providers to effectively manage customer churn and build a strong, long lasting relationship with their customers. This has become increasingly challenging as customer interactions occur across multiple channels. Understanding customer behavior and how it applies across channels is the key to ensuring the best level of experience is achieved by each customer.
In this webinar Hortonworks and Apigee discuss how service providers can capture and visualize customer behavior across customer interaction points like call center events (IVR and chat) and combine it with network data, to predict customer calls and patterns of digital channel abandonment using Hadoop and predictive analysis and visualization tools..
We will identify ways to develop a 360 degree view across a customer’s household through an HDP Data Lake and visualize customer interaction patterns and predict expected behavior using Apigee Insights to identify and initiate the Next-Best-Action for a customer to ensure a superior level of customer experience.
Using Apache® NiFi to Empower Self-Organising TeamsSebastian Carroll
Even though many organisations are moving to Agile methods, data transport architectures continue to be change-resistant. Given that data is now key to many teams and organisation can we really practice agility if we can't control the data we rely on? Apache NiFi can help alleviate this by giving the control to the teams and placing the decisions into the hands of those most capable of making them.
A Comprehensive Approach to Building your Big Data - with Cisco, Hortonworks ...Hortonworks
Companies in every industry look for ways to explore new data types and large data sets that were previously too big to capture, store and process. They need to unlock insights from data such as clickstream, geo-location, sensor, server log, social, text and video data. However, becoming a data-first enterprise comes with many challenges.
Join this webinar organized by three leaders in their respective fields and learn from our experts how you can accelerate the implementation of a scalable, cost-efficient and robust Big Data solution. Cisco, Hortonworks and Red Hat will explore how new data sets can enrich existing analytic applications with new perspectives and insights and how they can help you drive the creation of innovative new apps that provide new value to your business.
Eliminating the Challenges of Big Data Management Inside HadoopHortonworks
Your Big Data strategy is only as good as the quality of your data. Today, deriving business value from data depends on how well your company can capture, cleanse, integrate and manage data. During this webinar, we discuss how to eliminate the challenges to Big Data management inside Hadoop.
Johns Hopkins - Using Hadoop to Secure Access Log EventsHortonworks
In this webinar, we talk with experts from Johns Hopkins as they share techniques and lessons learned in real-world Apache Hadoop implementation.
https://hortonworks.com/webinar/johns-hopkins-using-hadoop-securely-access-log-events/
Learn how when an organizations combine HP and Vertica Analytics Platform and Hortonworks, they can quickly explore and analyze broad variety of data types to transform to actionable information that allows them to better understand how their customers and site visitors interact with their business, offline and online.
Similar to How Customers are Optimizing their EDW for Fast, Secure, and Effective Insights (20)
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
IoT Predictions for 2019 and Beyond: Data at the Heart of Your IoT StrategyHortonworks
Forrester forecasts* that direct spending on the Internet of Things (IoT) will exceed $400 Billion by 2023. From manufacturing and utilities, to oil & gas and transportation, IoT improves visibility, reduces downtime, and creates opportunities for entirely new business models.
But successful IoT implementations require far more than simply connecting sensors to a network. The data generated by these devices must be collected, aggregated, cleaned, processed, interpreted, understood, and used. Data-driven decisions and actions must be taken, without which an IoT implementation is bound to fail.
https://hortonworks.com/webinar/iot-predictions-2019-beyond-data-heart-iot-strategy/
Getting the Most Out of Your Data in the Cloud with CloudbreakHortonworks
Cloudbreak, a part of Hortonworks Data Platform (HDP), simplifies the provisioning and cluster management within any cloud environment to help your business toward its path to a hybrid cloud architecture.
https://hortonworks.com/webinar/getting-data-cloud-cloudbreak-live-demo/
Catch a Hacker in Real-Time: Live Visuals of Bots and Bad GuysHortonworks
Cybersecurity today is a big data problem. There’s a ton of data landing on you faster than you can load, let alone search it. In order to make sense of it, we need to act on data-in-motion, use both machine learning, and the most advanced pattern recognition system on the planet: your SOC analysts. Advanced visualization makes your analysts more efficient, helps them find the hidden gems, or bombs in masses of logs and packets.
https://hortonworks.com/webinar/catch-hacker-real-time-live-visuals-bots-bad-guys/
We have introduced several new features as well as delivered some significant updates to keep the platform tightly integrated and compatible with HDP 3.0.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-2-release-raises-bar-operational-efficiency/
Curing Kafka Blindness with Hortonworks Streams Messaging ManagerHortonworks
With the growth of Apache Kafka adoption in all major streaming initiatives across large organizations, the operational and visibility challenges associated with Kafka are on the rise as well. Kafka users want better visibility in understanding what is going on in the clusters as well as within the stream flows across producers, topics, brokers, and consumers.
With no tools in the market that readily address the challenges of the Kafka Ops teams, the development teams, and the security/governance teams, Hortonworks Streams Messaging Manager is a game-changer.
https://hortonworks.com/webinar/curing-kafka-blindness-hortonworks-streams-messaging-manager/
Interpretation Tool for Genomic Sequencing Data in Clinical EnvironmentsHortonworks
The healthcare industry—with its huge volumes of big data—is ripe for the application of analytics and machine learning. In this webinar, Hortonworks and Quanam present a tool that uses machine learning and natural language processing in the clinical classification of genomic variants to help identify mutations and determine clinical significance.
Watch the webinar: https://hortonworks.com/webinar/interpretation-tool-genomic-sequencing-data-clinical-environments/
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/
In this exclusive Premier Inside Out, you will hear from Druid committer Slim Bouguerra, Staff Software Engineer and Product Manager Will Xu. These Hortonworkers will explain the vision of these components, review new features, share some best practices and answer your questions.
View the webinar here: https://hortonworks.com/webinar/hortonworks-premier-apache-druid/
Accelerating Data Science and Real Time Analytics at ScaleHortonworks
Gaining business advantages from big data is moving beyond just the efficient storage and deep analytics on diverse data sources to using AI methods and analytics on streaming data to catch insights and take action at the edge of the network.
https://hortonworks.com/webinar/accelerating-data-science-real-time-analytics-scale/
TIME SERIES: APPLYING ADVANCED ANALYTICS TO INDUSTRIAL PROCESS DATAHortonworks
Thanks to sensors and the Internet of Things, industrial processes now generate a sea of data. But are you plumbing its depths to find the insight it contains, or are you just drowning in it? Now, Hortonworks and Seeq team to bring advanced analytics and machine learning to time-series data from manufacturing and industrial processes.
Blockchain with Machine Learning Powered by Big Data: Trimble Transportation ...Hortonworks
Trimble Transportation Enterprise is a leading provider of enterprise software to over 2,000 transportation and logistics companies. They have designed an architecture that leverages Hortonworks Big Data solutions and Machine Learning models to power up multiple Blockchains, which improves operational efficiency, cuts down costs and enables building strategic partnerships.
https://hortonworks.com/webinar/blockchain-with-machine-learning-powered-by-big-data-trimble-transportation-enterprise/
Making Enterprise Big Data Small with EaseHortonworks
Every division in an organization builds its own database to keep track of its business. When the organization becomes big, those individual databases grow as well. The data from each database may become silo-ed and have no idea about the data in the other database.
https://hortonworks.com/webinar/making-enterprise-big-data-small-ease/
Driving Digital Transformation Through Global Data ManagementHortonworks
Using your data smarter and faster than your peers could be the difference between dominating your market and merely surviving. Organizations are investing in IoT, big data, and data science to drive better customer experience and create new products, yet these projects often stall in ideation phase to a lack of global data management processes and technologies. Your new data architecture may be taking shape around you, but your goal of globally managing, governing, and securing your data across a hybrid, multi-cloud landscape can remain elusive. Learn how industry leaders are developing their global data management strategy to drive innovation and ROI.
Presented at Gartner Data and Analytics Summit
Speaker:
Dinesh Chandrasekhar
Director of Product Marketing, Hortonworks
HDF 3.1 pt. 2: A Technical Deep-Dive on New Streaming FeaturesHortonworks
Hortonworks DataFlow (HDF) is the complete solution that addresses the most complex streaming architectures of today’s enterprises. More than 20 billion IoT devices are active on the planet today and thousands of use cases across IIOT, Healthcare and Manufacturing warrant capturing data-in-motion and delivering actionable intelligence right NOW. “Data decay” happens in a matter of seconds in today’s digital enterprises.
To meet all the needs of such fast-moving businesses, we have made significant enhancements and new streaming features in HDF 3.1.
https://hortonworks.com/webinar/series-hdf-3-1-technical-deep-dive-new-streaming-features/
Unlock Value from Big Data with Apache NiFi and Streaming CDCHortonworks
Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It provides an end-to-end platform that can collect, curate, analyze, and act on data in real-time, on-premises, or in the cloud with a drag-and-drop visual interface. It’s being used across industries on large amounts of data that had stored in isolation which made collaboration and analysis difficult.
Join industry experts from Hortonworks and Attunity as they explain how Apache NiFi and streaming CDC technology provides a distributed, resilient platform for unlocking the value of data in new ways.
4 Essential Steps for Managing Sensitive DataHortonworks
Data is growing in data lakes, so are security and compliance risks. These risks stem from storing and processing sensitive data. In this webinar, we will go through a 4 step process to proactively discover and manage sensitive data within big data environments.
https://hortonworks.com/webinar/4-essential-steps-managing-sensitive-data-data-lake/
Exploring the Heated-and Completely Unnecessary- Data Lake DebateHortonworks
When it comes to the data lakes and data warehouses, there’s no shortage of controversy: Is one better than the other? The real answer is, there’s no need for heated debate—a data lake actually complements the data warehouse.
Integrating a data lake with your EDW is really just an evolution of architecture that can provide you with a cross-environment that allows you to explore data creatively to yield great business insights. However, there’s a trick to making it work: EDW optimization.
https://hortonworks.com/webinar/exploring-heated-completely-unnecessary-data-lake-debate/
In this webinar, we will hear from Mark McKinney, Director – Enterprise Data Analytics at Sprint about the business drivers, key success factors, and challenges faced while undertaking Sprint’s data modernization journey. You will hear how Sprint set about establishing a Hadoop data lake, ingested data from multiple environments, and overcame key skill shortages. You will also hear from Diyotta and Hortonworks about best practices for modernizing your data architecture to support transformational business initiatives.
https://hortonworks.com/webinar/sprints-data-modernization-journey/
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
8. 910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing the Modern Data Architecture Isn’t Easy
8
Source: Wikibon Big Data Analytics Adoption Survey, 2014-2015
Syncsort Confidential andProprietary - do not copy or distribute
Of the selected technology-related
barriers to realizing the full value of your
Big Data Analytics, please rank the top 3.
9. 910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing the Modern Data Architecture Isn’t Easy
9
Additional Challenges
• Long, drawn-out development cycles
Source: Wikibon Big Data Analytics Adoption Survey, 2014-2015
9Syncsort Confidential andProprietary - do not copy or distribute
Of the selected technology-related
barriers to realizing the full value of your
Big Data Analytics, please rank the top 3.
10. 910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing the Modern Data Architecture Isn’t Easy
10
Additional Challenges
• Long, drawn-out development cycles
• Rapidly changing technology presents a
moving target, forcing constant re-design
Source: Wikibon Big Data Analytics Adoption Survey, 2014-2015
10Syncsort Confidential andProprietary - do not copy or distribute
Of the selected technology-related
barriers to realizing the full value of your
Big Data Analytics, please rank the top 3.
11. 910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing the Modern Data Architecture Isn’t Easy
11
Additional Challenges
• Long, drawn-out development cycles
• Rapidly changing technology presents a
moving target, forcing constant re-design
• Difficulty accessing and integrating legacy
and new data sources including the
mainframe
Source: Wikibon Big Data Analytics Adoption Survey, 2014-2015
11Syncsort Confidential andProprietary - do not copy or distribute
Of the selected technology-related
barriers to realizing the full value of your
Big Data Analytics, please rank the top 3.
12. 910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing the Modern Data Architecture Isn’t Easy
12
Additional Challenges
• Long, drawn-out development cycles
• Rapidly changing technology presents a
moving target, forcing constant re-design
• Difficulty accessing and integrating legacy
and new data sources including the
mainframe
• Functionality gaps in security, governance,
and compliance
Source: Wikibon Big Data Analytics Adoption Survey, 2014-2015
12Syncsort Confidential andProprietary - do not copy or distribute
Of the selected technology-related
barriers to realizing the full value of your
Big Data Analytics, please rank the top 3.
13. 910
920
930
940
950
960
970
980
990
1,000
Difficulty
transforming data
into a suitable
form for analysis.
Difficulty
integrating
Big Data with
existing
infrastructure.
Difficulty
merging
multiple,
disparate
data sources.
Lack of skilled
Big Data
practitioners.
Difficulty
maintaining
application
performance for
large volume of
concurentusers.
Implementing the Modern Data Architecture Isn’t Easy
13
Additional Challenges
• Long, drawn-out development cycles
• Rapidly changing technology presents a
moving target, forcing constant re-design
• Difficulty accessing and integrating legacy
and new data sources including the
mainframe
• Functionality gaps in security, governance,
and compliance
Source: Wikibon Big Data Analytics Adoption Survey, 2014-2015
13Syncsort Confidential andProprietary - do not copy or distribute
Big data integration
is complicated!
Of the selected technology-related
barriers to realizing the full value of your
Big Data Analytics, please rank the top 3.
25. Get Your Database data into Hadoop, At the Press of a Button
25
• Pull multiple data sources and funnel into your data lake
• Extract and map whole DB schemas in one invocation
• Extract from multiple data sources: DB2/z, Netezza, Oracle,
Teradata,…
• One-step data movement, auto-generating jobs, auto-generating
Hive target tables, and update Hive statistics
• Process multiple funnels in parallel on your edge node or from
data nodes
‒ Leverages DMX-h high speed data engine via DTL
‒ Generated applications can be imported into GUI
• In-flight transformations
‒ Filtering, funnel dependency ordering, mixed source/target, data type
filtering, table exclusion/inclusion
25Syncsort Confidential andProprietary - do not copy or distribute
DMX
DataFunnel™
27. Insurance: Easy Access to ALL Data for Better Analytics
27
• Challenge: Needed hard-to-access operational data for advanced
analytics
• Solution:
• Quickly load ~1000 database tables into HDP with the click of a
button
• Access & integrate complex Mainframe VSAM files, data from
DB2/z, Oracle & SQL Server
• Track changes & keep data up to date
• Benefits:
• Insight: Better and faster analytics
• Agility: Reclaim development time; single tool to ingest, detect changes and populate the data lake
• Compliance: Build audit trails, keep HDP data lake current
• Productivity: No need for deep understanding of Hadoop