Is Hadoop ready for high-concurrency complex BI and Advanced Analytics? Roaring performance and fast, low-latency execution is possible when an in-memory analytical platform is paired with the Apache Hadoop framework. Join Hortonworks and Kognitio for an informative Web Briefing on putting Hadoop at the center of your modern data architecture—with zero disruption to business users.
The Modern Data Architecture for Advanced Business Intelligence with Hortonwo...Hortonworks
There certainly is no shortage of hype when it comes to the term “Big Data”. One thing we can be sure of is that massive data volumes are driving a new modern data architecture that includes Hadoop in the mix. But what does that architecture look like for Business Intelligence Data Strategy?
Join Hortonworks and MicroStrategy, where we’ll:
• Discuss the modern architecture for Business Intelligence on top of Hadoop as a data source.
• Learn how our joint solution helps enterprises store, process and analyze vast amounts of structured and unstructured data to deliver business insights throughout an organization.
• Discover what new benefits Hadoop 2.0 offers and how the MicroStrategy Analytics platform leverages those new features to improve performance, achieve faster access times, and allow for true interactive visual data discovery.
The Modern Data Architecture for Predictive Analytics with Hortonworks and Re...Revolution Analytics
Hortonworks and Revolution Analytics have teamed up to bring the predictive analytics power of R to Hortonworks Data Platform.
Hadoop, being a disruptive data processing framework, has made a large impact in the data ecosystems of today. Enabling business users to translate existing skills to Hadoop is necessary to encourage the adoption and allow businesses to get value out of their Hadoop investment quickly. R, being a prolific and rapidly growing data analysis language, now has a place in the Hadoop ecosystem.
This presentation covers:
- Trends and business drivers for Hadoop
- How Hortonworks and Revolution Analytics play a role in the modern data architecture
- How you can run R natively in Hortonworks Data Platform to simply move your R-powered analytics to Hadoop
Presentation replay at:
http://www.revolutionanalytics.com/news-events/free-webinars/2013/modern-data-architecture-revolution-hortonworks/
Data Lakes are early in the Gartner hype cycle, but companies are getting value from their cloud-based data lake deployments. Break through the confusion between data lakes and data warehouses and seek out the most appropriate use cases for your big data lakes.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
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
Building a Modern Data Architecture by Ben Sharma at Strata + Hadoop World Sa...Zaloni
When building your data stack, the architecture could be your biggest challenge. Yet it could also be the best predictor for success. With so many elements to consider and no proven playbook, where do you begin to assemble best practices for a scalable data architecture? Ben Sharma, thought leader and coauthor of Architecting Data Lakes, offers lessons learned from the field to get you started.
The Big Data Journey – How Companies Adopt Hadoop - StampedeCon 2016StampedeCon
Hadoop adoption is a journey. Depending on the business the process can take weeks, months, or even years. Hadoop is a transformative technology so the challenges have less to do with the technology and more to do with how a company adapts itself to a new way of thinking about data. There are challenges for companies who have lived with an application driven business for the last two decades to suddenly become data driven. Companies need to begin thinking less in terms of single, silo’d servers and more about “the cluster”.
The concept of the cluster becomes the center of data gravity drawing all the applications to it. Companies, especially the IT organizations, embark on a process of understanding how to maintain and operationalize this environment and provide the data lake as a service to the businesses. They must empower the business by providing the resources for the use cases which drive both renovation and innovation. IT needs to adopt new technologies and new methodologies which enable the solutions. This is not technology for technology sake. Hadoop is a data platform servicing and enabling all facets of an organization. Building out and expanding this platform is the ongoing journey as word gets out to businesses that they can have any data they want and any time. Success is what drives the journey.
The length of the journey varies from company to company. Sometimes the challenges are based on the size of the company but many times the challenges are based on the difficulty of unseating established IT processes companies have adopted without forethought for the past two decades. Companies must navigate through the noise. Sifting through the noise to find those solutions which bring real value takes time. As the platform matures and becomes mainstream, more and more companies are finding it easier to adopt Hadoop. Hundreds of companies have already taken many steps; hundreds more have already taken the first step. As the wave of successful Hadoop adoption continues, more and more companies will see the value in starting the journey and paving the way for others.
The Modern Data Architecture for Advanced Business Intelligence with Hortonwo...Hortonworks
There certainly is no shortage of hype when it comes to the term “Big Data”. One thing we can be sure of is that massive data volumes are driving a new modern data architecture that includes Hadoop in the mix. But what does that architecture look like for Business Intelligence Data Strategy?
Join Hortonworks and MicroStrategy, where we’ll:
• Discuss the modern architecture for Business Intelligence on top of Hadoop as a data source.
• Learn how our joint solution helps enterprises store, process and analyze vast amounts of structured and unstructured data to deliver business insights throughout an organization.
• Discover what new benefits Hadoop 2.0 offers and how the MicroStrategy Analytics platform leverages those new features to improve performance, achieve faster access times, and allow for true interactive visual data discovery.
The Modern Data Architecture for Predictive Analytics with Hortonworks and Re...Revolution Analytics
Hortonworks and Revolution Analytics have teamed up to bring the predictive analytics power of R to Hortonworks Data Platform.
Hadoop, being a disruptive data processing framework, has made a large impact in the data ecosystems of today. Enabling business users to translate existing skills to Hadoop is necessary to encourage the adoption and allow businesses to get value out of their Hadoop investment quickly. R, being a prolific and rapidly growing data analysis language, now has a place in the Hadoop ecosystem.
This presentation covers:
- Trends and business drivers for Hadoop
- How Hortonworks and Revolution Analytics play a role in the modern data architecture
- How you can run R natively in Hortonworks Data Platform to simply move your R-powered analytics to Hadoop
Presentation replay at:
http://www.revolutionanalytics.com/news-events/free-webinars/2013/modern-data-architecture-revolution-hortonworks/
Data Lakes are early in the Gartner hype cycle, but companies are getting value from their cloud-based data lake deployments. Break through the confusion between data lakes and data warehouses and seek out the most appropriate use cases for your big data lakes.
Big data architectures and the data lakeJames Serra
With so many new technologies it can get confusing on the best approach to building a big data architecture. The data lake is a great new concept, usually built in Hadoop, but what exactly is it and how does it fit in? In this presentation I'll discuss the four most common patterns in big data production implementations, the top-down vs bottoms-up approach to analytics, and how you can use a data lake and a RDBMS data warehouse together. We will go into detail on the characteristics of a data lake and its benefits, and how you still need to perform the same data governance tasks in a data lake as you do in a data warehouse. Come to this presentation to make sure your data lake does not turn into a data swamp!
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
Building a Modern Data Architecture by Ben Sharma at Strata + Hadoop World Sa...Zaloni
When building your data stack, the architecture could be your biggest challenge. Yet it could also be the best predictor for success. With so many elements to consider and no proven playbook, where do you begin to assemble best practices for a scalable data architecture? Ben Sharma, thought leader and coauthor of Architecting Data Lakes, offers lessons learned from the field to get you started.
The Big Data Journey – How Companies Adopt Hadoop - StampedeCon 2016StampedeCon
Hadoop adoption is a journey. Depending on the business the process can take weeks, months, or even years. Hadoop is a transformative technology so the challenges have less to do with the technology and more to do with how a company adapts itself to a new way of thinking about data. There are challenges for companies who have lived with an application driven business for the last two decades to suddenly become data driven. Companies need to begin thinking less in terms of single, silo’d servers and more about “the cluster”.
The concept of the cluster becomes the center of data gravity drawing all the applications to it. Companies, especially the IT organizations, embark on a process of understanding how to maintain and operationalize this environment and provide the data lake as a service to the businesses. They must empower the business by providing the resources for the use cases which drive both renovation and innovation. IT needs to adopt new technologies and new methodologies which enable the solutions. This is not technology for technology sake. Hadoop is a data platform servicing and enabling all facets of an organization. Building out and expanding this platform is the ongoing journey as word gets out to businesses that they can have any data they want and any time. Success is what drives the journey.
The length of the journey varies from company to company. Sometimes the challenges are based on the size of the company but many times the challenges are based on the difficulty of unseating established IT processes companies have adopted without forethought for the past two decades. Companies must navigate through the noise. Sifting through the noise to find those solutions which bring real value takes time. As the platform matures and becomes mainstream, more and more companies are finding it easier to adopt Hadoop. Hundreds of companies have already taken many steps; hundreds more have already taken the first step. As the wave of successful Hadoop adoption continues, more and more companies will see the value in starting the journey and paving the way for others.
Creating a Next-Generation Big Data ArchitecturePerficient, Inc.
If you’ve spent time investigating Big Data, you quickly realize that the issues surrounding Big Data are often complex to analyze and solve. The sheer volume, velocity and variety changes the way we think about data – including how enterprises approach data architecture.
Significant reduction in costs for processing, managing, and storing data, combined with the need for business agility and analytics, requires CIOs and enterprise architects to rethink their enterprise data architecture and develop a next-generation approach to solve the complexities of Big Data.
Creating the data architecture while integrating Big Data into the heart of the enterprise data architecture is a challenge. This webinar covered:
-Why Big Data capabilities must be strategically integrated into an enterprise’s data architecture
-How a next-generation architecture can be conceptualized
-The key components to a robust next generation architecture
-How to incrementally transition to a next generation data architecture
The Value of the Modern Data Architecture with Apache Hadoop and Teradata Hortonworks
This webinar discusses why Apache Hadoop most typically the technology underpinning "Big Data". How it fits in a modern data architecture and the current landscape of databases and data warehouses that are already in use.
Alexandre Vasseur - Evolution of Data Architectures: From Hadoop to Data Lake...NoSQLmatters
Come to this deep dive on how Pivotal's Data Lake Vision is evolving by embracing next generation in-memory data exchange and compute technologies around Spark and Tachyon. Did we say Hadoop, SQL, and what's the shortest path to get from past to future state? The next generation of data lake technology will leverage the availability of in-memory processing, with an architecture that supports multiple data analytics workloads within a single environment: SQL, R, Spark, batch and transactional.
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...DATAVERSITY
Join Basho technologies and Databricks, creators of Apache Spark, as we share lessons learned by both organizations in building scalable applications for IoT and time series use cases. We'll be discussing some of the data modeling considerations unique to time series data and some of the key factors developers and architects need to take into consideration as data moves through the pipeline. You'll learn:
Challenges in building apps to leverage data being generated by IoT devices
What you need to think about before you start modeling your IoT data
Shortcuts to success in building IoT apps
The webinar will also give a live demonstration of how to store and retrieve IoT data as well as a demonstration of integrated data store with analytics engine using a live Notebook as a guide.
Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
10 Amazing Things To Do With a Hadoop-Based Data LakeVMware Tanzu
Greg Chase, Director, Product Marketing presents Big Data 10 A
mazing Things to do With A Hadoop-based Data Lake at the Strata Conference + Hadoop World 2014 in NYC.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
Designing Fast Data Architecture for Big Data using Logical Data Warehouse a...Denodo
Companies such as Autodesk are fast replacing the once-true- and-tried physical data warehouses with logical data warehouses/ data lakes. Why? Because they are able to accomplish the same results in 1/6 th of the time and with 1/4 th of the resources.
In this webinar, Autodesk’s Platform Lead, Kurt Jackson,, will describe how they designed a modern fast data architecture as a single unified logical data warehouse/ data lake using data virtualization and contemporary big data analytics like Spark.
Logical data warehouse / data lake is a virtual abstraction layer over the physical data warehouse, big data repositories, cloud, and other enterprise applications. It unifies both structured and unstructured data in real-time to power analytical and operational use cases.
Incorporating the Data Lake into Your Analytic ArchitectureCaserta
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented Incorporating the Data Lake into Your Analytics Architecture.
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
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 Lake for the Cloud: Extending your Hadoop ImplementationHortonworks
As more applications are created using Apache Hadoop that derive value from the new types of data from sensors/machines, server logs, click-streams, and other sources, the enterprise "Data Lake" forms with Hadoop acting as a shared service. While these Data Lakes are important, a broader life-cycle needs to be considered that spans development, test, production, and archival and that is deployed across a hybrid cloud architecture.
If you have already deployed Hadoop on-premise, this session will also provide an overview of the key scenarios and benefits of joining your on-premise Hadoop implementation with the cloud, by doing backup/archive, dev/test or bursting. Learn how you can get the benefits of an on-premise Hadoop that can seamlessly scale with the power of the cloud.
Are you exploring the transition to becoming a cloud broker? Establishing cloud business practices and marketing is one of the most overlooked areas by enterprise IT professionals. This session explores the role marketing and the 4 Ps - Product, Price, Promotion, and Placement - play in multi-cloud and cloud brokerage. Don’t let your technical success die on the vine without exposure!aka the 4 Ps of Multi-cloud
Pervasive analytics through data & analytic centricityCloudera, Inc.
Cloudera and Teradata discuss the best-in-class solution enabling companies to put data and analytics at the center of their strategy, achieve the highest forms of agility, while reducing the costs and complexity of their current environment.
Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...Data Con LA
Why and How has the Big Data based Enterprise Data Lake solution based on No-SQL and SQL technologies has become significantly effective in solving enterprise data challenges than its predecessor EDW which had tried and failed to solve the same problem entirely based on SQL database only.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
In this webinar, we'll:
-Examine the key drivers and use cases for High Availability, performance and scalability for Apache Hadoop.
-Walk through an overview of reference architecture for a Non-Stop Hadoop implementation.
-Show how you can get started with Non-Stop Hadoop with the Hortonworks Data Platform.
Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...Hortonworks
How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services?
Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business.
Join Hortonworks and Informatica as we discuss:
- What is a data lake?
- The modern data architecture for a data lake
- How Hadoop fits into the modern data architecture
- Innovative use-cases for a data lake
Creating a Next-Generation Big Data ArchitecturePerficient, Inc.
If you’ve spent time investigating Big Data, you quickly realize that the issues surrounding Big Data are often complex to analyze and solve. The sheer volume, velocity and variety changes the way we think about data – including how enterprises approach data architecture.
Significant reduction in costs for processing, managing, and storing data, combined with the need for business agility and analytics, requires CIOs and enterprise architects to rethink their enterprise data architecture and develop a next-generation approach to solve the complexities of Big Data.
Creating the data architecture while integrating Big Data into the heart of the enterprise data architecture is a challenge. This webinar covered:
-Why Big Data capabilities must be strategically integrated into an enterprise’s data architecture
-How a next-generation architecture can be conceptualized
-The key components to a robust next generation architecture
-How to incrementally transition to a next generation data architecture
The Value of the Modern Data Architecture with Apache Hadoop and Teradata Hortonworks
This webinar discusses why Apache Hadoop most typically the technology underpinning "Big Data". How it fits in a modern data architecture and the current landscape of databases and data warehouses that are already in use.
Alexandre Vasseur - Evolution of Data Architectures: From Hadoop to Data Lake...NoSQLmatters
Come to this deep dive on how Pivotal's Data Lake Vision is evolving by embracing next generation in-memory data exchange and compute technologies around Spark and Tachyon. Did we say Hadoop, SQL, and what's the shortest path to get from past to future state? The next generation of data lake technology will leverage the availability of in-memory processing, with an architecture that supports multiple data analytics workloads within a single environment: SQL, R, Spark, batch and transactional.
Webinar: Data Modeling and Shortcuts to Success in Scaling Time Series Applic...DATAVERSITY
Join Basho technologies and Databricks, creators of Apache Spark, as we share lessons learned by both organizations in building scalable applications for IoT and time series use cases. We'll be discussing some of the data modeling considerations unique to time series data and some of the key factors developers and architects need to take into consideration as data moves through the pipeline. You'll learn:
Challenges in building apps to leverage data being generated by IoT devices
What you need to think about before you start modeling your IoT data
Shortcuts to success in building IoT apps
The webinar will also give a live demonstration of how to store and retrieve IoT data as well as a demonstration of integrated data store with analytics engine using a live Notebook as a guide.
Data Lakes are meant to support many of the same analytics capabilities of Data Warehouses while overcoming some of the core problems. Yet Data Lakes have a distinctly different technology base. This webinar will provide an overview of the standard architecture components of Data Lakes.
This will include:
The Lab and the factory
The base environment for batch analytics
Critical governance components
Additional components necessary for real-time analytics and ingesting streaming data
10 Amazing Things To Do With a Hadoop-Based Data LakeVMware Tanzu
Greg Chase, Director, Product Marketing presents Big Data 10 A
mazing Things to do With A Hadoop-based Data Lake at the Strata Conference + Hadoop World 2014 in NYC.
Big Data: Architecture and Performance Considerations in Logical Data LakesDenodo
This presentation explains in detail what a Data Lake Architecture looks like, how data virtualization fits into the Logical Data Lake, and goes over some performance tips. Also it includes an example demonstrating this model's performance.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/9Jwfu6.
Designing Fast Data Architecture for Big Data using Logical Data Warehouse a...Denodo
Companies such as Autodesk are fast replacing the once-true- and-tried physical data warehouses with logical data warehouses/ data lakes. Why? Because they are able to accomplish the same results in 1/6 th of the time and with 1/4 th of the resources.
In this webinar, Autodesk’s Platform Lead, Kurt Jackson,, will describe how they designed a modern fast data architecture as a single unified logical data warehouse/ data lake using data virtualization and contemporary big data analytics like Spark.
Logical data warehouse / data lake is a virtual abstraction layer over the physical data warehouse, big data repositories, cloud, and other enterprise applications. It unifies both structured and unstructured data in real-time to power analytical and operational use cases.
Incorporating the Data Lake into Your Analytic ArchitectureCaserta
Joe Caserta, President at Caserta Concepts presented at the 3rd Annual Enterprise DATAVERSITY conference. The emphasis of this year's agenda is on the key strategies and architecture necessary to create a successful, modern data analytics organization.
Joe Caserta presented Incorporating the Data Lake into Your Analytics Architecture.
For more information on the services offered by Caserta Concepts, visit out website at http://casertaconcepts.com/.
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 Lake for the Cloud: Extending your Hadoop ImplementationHortonworks
As more applications are created using Apache Hadoop that derive value from the new types of data from sensors/machines, server logs, click-streams, and other sources, the enterprise "Data Lake" forms with Hadoop acting as a shared service. While these Data Lakes are important, a broader life-cycle needs to be considered that spans development, test, production, and archival and that is deployed across a hybrid cloud architecture.
If you have already deployed Hadoop on-premise, this session will also provide an overview of the key scenarios and benefits of joining your on-premise Hadoop implementation with the cloud, by doing backup/archive, dev/test or bursting. Learn how you can get the benefits of an on-premise Hadoop that can seamlessly scale with the power of the cloud.
Are you exploring the transition to becoming a cloud broker? Establishing cloud business practices and marketing is one of the most overlooked areas by enterprise IT professionals. This session explores the role marketing and the 4 Ps - Product, Price, Promotion, and Placement - play in multi-cloud and cloud brokerage. Don’t let your technical success die on the vine without exposure!aka the 4 Ps of Multi-cloud
Pervasive analytics through data & analytic centricityCloudera, Inc.
Cloudera and Teradata discuss the best-in-class solution enabling companies to put data and analytics at the center of their strategy, achieve the highest forms of agility, while reducing the costs and complexity of their current environment.
Big Data Day LA 2015 - Data Lake - Re Birth of Enterprise Data Thinking by Ra...Data Con LA
Why and How has the Big Data based Enterprise Data Lake solution based on No-SQL and SQL technologies has become significantly effective in solving enterprise data challenges than its predecessor EDW which had tried and failed to solve the same problem entirely based on SQL database only.
This white paper will present the opportunities laid down by
data lake and advanced analytics, as well as, the challenges
in integrating, mining and analyzing the data collected from
these sources. It goes over the important characteristics of
the data lake architecture and Data and Analytics as a
Service (DAaaS) model. It also delves into the features of a
successful data lake and its optimal designing. It goes over
data, applications, and analytics that are strung together to
speed-up the insight brewing process for industry’s
improvements with the help of a powerful architecture for
mining and analyzing unstructured data – data lake.
In this webinar, we'll:
-Examine the key drivers and use cases for High Availability, performance and scalability for Apache Hadoop.
-Walk through an overview of reference architecture for a Non-Stop Hadoop implementation.
-Show how you can get started with Non-Stop Hadoop with the Hortonworks Data Platform.
Modern Data Architecture for a Data Lake with Informatica and Hortonworks Dat...Hortonworks
How do you turn data from many different sources into actionable insights and manufacture those insights into innovative information-based products and services?
Industry leaders are accomplishing this by adding Hadoop as a critical component in their modern data architecture to build a data lake. A data lake collects and stores data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field. A data lake cost-effectively scales to collect and retain massive amounts of data over time, and convert all this data into actionable information that can transform your business.
Join Hortonworks and Informatica as we discuss:
- What is a data lake?
- The modern data architecture for a data lake
- How Hadoop fits into the modern data architecture
- Innovative use-cases for a data lake
Hortonworks Oracle Big Data Integration Hortonworks
Slides from joint Hortonworks and Oracle webinar on November 11, 2014. Covers the Modern Data Architecture with Apache Hadoop and Oracle Data Integration products.
Apache Hadoop and its role in Big Data architecture - Himanshu Barijaxconf
In today’s world of exponentially growing big data, enterprises are becoming increasingly more aware of the business utility and necessity of harnessing, storing and analyzing this information. Apache Hadoop has rapidly evolved to become a leading platform for managing and processing big data, with the vital management, monitoring, metadata and integration services required by organizations to glean maximum business value and intelligence from their burgeoning amounts of information on customers, web trends, products and competitive markets. In this session, Hortonworks' Himanshu Bari will discuss the opportunities for deriving business value from big data by looking at how organizations utilize Hadoop to store, transform and refine large volumes of this multi-structured information. Connolly will also discuss the evolution of Apache Hadoop and where it is headed, the component requirements of a Hadoop-powered platform, as well as solution architectures that allow for Hadoop integration with existing data discovery and data warehouse platforms. In addition, he will look at real-world use cases where Hadoop has helped to produce more business value, augment productivity or identify new and potentially lucrative opportunities.
Hadoop in 2015: Keys to Achieving Operational Excellence for the Real-Time En...MapR Technologies
In this webinar, Carl W. Olofson, Research Vice President, Application Development and Deployment for IDC, and Dale Kim, Director of Industry Solutions for MapR, will provide an insightful outlook for Hadoop in 2015, and will outline why enterprises should consider using Hadoop as a "Decision Data Platform" and how it can function as a single platform for both online transaction processing (OLTP) and real-time analytics.
Combine Apache Hadoop and Elasticsearch to Get the Most of Your Big DataHortonworks
Hadoop is a great platform for storing and processing massive amounts of data. Elasticsearch is the ideal solution for Searching and Visualizing the same data. Join us to learn how you can leverage the full power of both platforms to maximize the value of your Big Data.
In this webinar we'll walk you through:
How Elasticsearch fits in the Modern Data Architecture.
A demo of Elasticsearch and Hortonworks Data Platform.
Best practices for combining Elasticsearch and Hortonworks Data Platform to extract maximum insights from your data.
Fueling AI & Machine Learning: Legacy Data as a Competitive AdvantagePrecisely
The data fueling your AI or machine learning initiatives plays a critical role. Different data sources provide different outcomes. The most important thing a business can do to prepare for success with AI and machine learning is to understand and provide access to all of the data that you can possibly get to. In addition to newer data sources, like IoT and Social Media, what will set your results apart – and give your business a competitive advantage – is powering AI and machine learning with your historical and proprietary data: the data sitting in your mainframe, legacy, and other traditional systems.
View this on-demand webcast with Wikibon Analyst James Kobielus as we discuss:
• Using your historical customer data to train predictive AI/ML models for effective target marketing
• Leveraging social, mobile, and IoT data to give your marketing an extra level of personalization
• Making the most of your legacy and proprietary data while protecting customer privacy and ensuring regulatory compliance
Big Data Made Easy: A Simple, Scalable Solution for Getting Started with HadoopPrecisely
With so many new, evolving frameworks, tools, and languages, a new big data project can lead to confusion and unwarranted risk.
Many organizations have found Data Warehouse Optimization with Hadoop to be a good starting point on their Big Data journey. Offloading ETL workloads from the enterprise data warehouse (EDW) into Hadoop is a well-defined use case that produces tangible results for driving more insights while lowering costs. You gain significant business agility, avoid costly EDW upgrades, and free up EDW capacity for faster queries. This quick win builds credibility and generates savings to reinvest in more Big Data projects.
A proven reference architecture that includes everything you need in a turnkey solution – the Hadoop distribution, data integration software, servers, networking and services – makes it even easier to get started.
Simplifying Real-Time Architectures for IoT with Apache KuduCloudera, Inc.
3 Things to Learn About:
*Building scalable real time architectures for managing data from IoT
*Processing data in real time with components such as Kudu & Spark
*Customer case studies highlighting real-time IoT use cases
Presenter: Ofer Mendelevitch of Hortonworks > Learn the benefits of big data for data scientists, and how Hadoop and HDInsight fit into the modern data architecture and enable data-driven products.
You'll learn:
* What data science actually means
* The term "data products"
* The benefits of using big data for data scientists
* How Hadoop helps data scientists work with big data
* About HDInsight, the big data platform from Microsoft and Hortonworks
Enrich a 360-degree Customer View with Splunk and Apache HadoopHortonworks
What if your organization could obtain a 360 degree view of the customer across offline, online and social and mobile channels? Attend this webinar with Splunk and Hortonworks and see examples of how marketing, business and operations analysts can reach across disparate data sets in Hadoop to spot new opportunities for up-sell and cross-sell. We'll also cover examples of how to measure buyer sentiment and changes in buyer behavior. Along with best practices on how to use data in Hadoop with Splunk to assign customer influence scores that online, call-center, and retail branches can use to customize more compelling products and promotions.
DAMA & Denodo Webinar: Modernizing Data Architecture Using Data Virtualization Denodo
Watch here: https://bit.ly/2NGQD7R
In an era increasingly dominated by advancements in cloud computing, AI and advanced analytics it may come as a shock that many organizations still rely on data architectures built before the turn of the century. But that scenario is rapidly changing with the increasing adoption of real-time data virtualization - a paradigm shift in the approach that organizations take towards accessing, integrating, and provisioning data required to meet business goals.
As data analytics and data-driven intelligence takes centre stage in today’s digital economy, logical data integration across the widest variety of data sources, with proper security and governance structure in place has become mission-critical.
Attend this session to learn:
- Learn how you can meet cloud and data science challenges with data virtualization.
- Why data virtualization is increasingly finding enterprise-wide adoption
- Discover how customers are reducing costs and improving ROI with data virtualization
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.
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.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
36. The Modern Data Architecture
…for in memory Big Data Analytics
More about Kognito and Hortonworks
http://hortonworks.com/partner/kognitio
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http://hortonworks.com/hadoop-tutorial/
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Question & Answer session will be conducted electronically,
using the panel to the right of your screen
Today’s Slides available at: www.slideshare.net/kognitio