1. The document discusses using Hadoop as an extension to traditional data warehouses to overcome limitations of scaling and accommodating new data types. Hadoop provides a flexible and cost-effective platform for data transformation and analytics workloads.
2. Cloudera provides tools like Impala and Cloudera Manager to integrate Hadoop with SQL data platforms and better support Hadoop deployments. This allows Hadoop to be more easily used as a data transformation platform and extension to existing data warehouses.
3. Using Hadoop as an extension to data warehouses provides benefits like lower costs, ability to keep archived data active, and more flexible division of analytics and transformation workloads between Hadoop and SQL platforms.
Presentation at Data Summit 2015 in NYC.
Elliott Cordo shared real-world insights across a range of topics, including the evolving best practices for building a data warehouse on Hadoop that also coexists with multiple processing frameworks and additional non-Hadoop storage platforms, the place for massively parallel-processing and relational databases in analytic architectures, and the ways in which the cloud offers the ability to quickly and cost-effectively establish a scalable platform for your Big Data warehouse.
For more information, visit www.casertaconcepts.com
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.
In this document, we will present a very brief introduction to BigData (what is BigData?), Hadoop (how does Hadoop fits the picture?) and Cloudera Hadoop (what is the difference between Cloudera Hadoop and regular Hadoop?).
Please note that this document is for Hadoop beginners looking for a place to start.
Complement Your Existing Data Warehouse with Big Data & HadoopDatameer
To view the full webinar, please go to: http://info.datameer.com/Slideshare-Complement-Your-Existing-EDW-with-Hadoop-OnDemand.html
With 40% yearly growth in data volumes, traditional data warehouses have become increasingly expensive and challenging.
Much of today’s new data sources are unstructured, making the structured data warehouse an unsuitable platform for analyses. As a result, organizations now look at Hadoop as a data platform to complement existing BI data warehouses, and a scalable, flexible and cost-effective solution for data storage and analysis.
Join Datameer and Cloudera in this webinar to discuss how Hadoop and big data analytics can help to:
-Get all the data your business needs quickly into one environment
Shorten the time to insight from months to days
Extend the life of your existing data warehouse investments
Enable your business analysts to ask and answer bigger questions
How to select a modern data warehouse and get the most out of it?Slim Baltagi
In the first part of this talk, we will give a setup and definition of modern cloud data warehouses as well as outline problems with legacy and on-premise data warehouses.
We will speak to selecting, technically justifying, and practically using modern data warehouses, including criteria for how to pick a cloud data warehouse and where to start, how to use it in an optimum way and use it cost effectively.
In the second part of this talk, we discuss the challenges and where people are not getting their investment. In this business-focused track, we cover how to get business engagement, identifying the business cases/use cases, and how to leverage data as a service and consumption models.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Presentation at Data Summit 2015 in NYC.
Elliott Cordo shared real-world insights across a range of topics, including the evolving best practices for building a data warehouse on Hadoop that also coexists with multiple processing frameworks and additional non-Hadoop storage platforms, the place for massively parallel-processing and relational databases in analytic architectures, and the ways in which the cloud offers the ability to quickly and cost-effectively establish a scalable platform for your Big Data warehouse.
For more information, visit www.casertaconcepts.com
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.
In this document, we will present a very brief introduction to BigData (what is BigData?), Hadoop (how does Hadoop fits the picture?) and Cloudera Hadoop (what is the difference between Cloudera Hadoop and regular Hadoop?).
Please note that this document is for Hadoop beginners looking for a place to start.
Complement Your Existing Data Warehouse with Big Data & HadoopDatameer
To view the full webinar, please go to: http://info.datameer.com/Slideshare-Complement-Your-Existing-EDW-with-Hadoop-OnDemand.html
With 40% yearly growth in data volumes, traditional data warehouses have become increasingly expensive and challenging.
Much of today’s new data sources are unstructured, making the structured data warehouse an unsuitable platform for analyses. As a result, organizations now look at Hadoop as a data platform to complement existing BI data warehouses, and a scalable, flexible and cost-effective solution for data storage and analysis.
Join Datameer and Cloudera in this webinar to discuss how Hadoop and big data analytics can help to:
-Get all the data your business needs quickly into one environment
Shorten the time to insight from months to days
Extend the life of your existing data warehouse investments
Enable your business analysts to ask and answer bigger questions
How to select a modern data warehouse and get the most out of it?Slim Baltagi
In the first part of this talk, we will give a setup and definition of modern cloud data warehouses as well as outline problems with legacy and on-premise data warehouses.
We will speak to selecting, technically justifying, and practically using modern data warehouses, including criteria for how to pick a cloud data warehouse and where to start, how to use it in an optimum way and use it cost effectively.
In the second part of this talk, we discuss the challenges and where people are not getting their investment. In this business-focused track, we cover how to get business engagement, identifying the business cases/use cases, and how to leverage data as a service and consumption models.
Data Lakehouse, Data Mesh, and Data Fabric (r1)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. I’ll include use cases so you can see what approach will work best for your big data needs.
Better Together: The New Data Management OrchestraCloudera, Inc.
To ingest, store, process and leverage big data for maximum business impact requires integrating systems, processing frameworks, and analytic deployment options. Learn how Cloudera’s enterprise data hub framework, MongoDB, and Teradata Data Warehouse working in concert can enable companies to explore data in new ways and solve problems that not long ago might have seemed impossible.
Gone are the days of NoSQL and SQL competing for center stage. Visionary companies are driving data subsystems to operate in harmony. So what’s changed?
In this webinar, you will hear from executives at Cloudera, Teradata and MongoDB about the following:
How to deploy the right mix of tools and technology to become a data-driven organization
Examples of three major data management systems working together
Real world examples of how business and IT are benefiting from the sum of the parts
Join industry leaders Charles Zedlewski, Chris Twogood and Kelly Stirman for this unique panel discussion, moderated by BI Research analyst, Colin White.
Cloudera Federal Forum 2014: The Building Blocks of the Enterprise Data HubCloudera, Inc.
Chief Technologist, Office of the CTO at Cloudera Eli Collins, shares the story of the enterprise data hub and how it relates to the enterprise data warehouse.
From Traditional Data Warehouse To Real Time Data WarehouseOsama Hussein
Summarising the 'From Traditional Data Warehouse To Real Time Data Warehouse' paper.
1. S. Bouaziz, A. Nabli and F. Gargouri, "From Traditional Data Warehouse To Real Time Data Warehouse", 2017.
Building the Enterprise Data Lake - Important Considerations Before You Jump InSnapLogic
In this webinar, learn from industry analyst and big data thought leader Mark Madsen about the future of big data and importance of the new Enterprise Data Lake reference architecture.
This webinar also covers what’s important when building a modern, multi-use data infrastructure, the difference between a Hadoop application and a Data Lake infrastructure, and an enterprise data lake reference architecture to get you started.
To learn more, visit: www.snaplogic.com/big-data
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.
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.
Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Hadoop and the Data Warehouse: When to Use Which DataWorks Summit
In recent years, Apache™ Hadoop® has emerged from humble beginnings to disrupt the traditional disciplines of information management. As with all technology innovation, hype is rampant, and data professionals are easily overwhelmed by diverse opinions and confusing messages.
Even seasoned practitioners sometimes miss the point, claiming for example that Hadoop replaces relational databases and is becoming the new data warehouse. It is easy to see where these claims originate since both Hadoop and Teradata® systems run in parallel, scale up to enormous data volumes and have shared-nothing architectures. At a conceptual level, it is easy to think they are interchangeable, but the differences overwhelm the similarities. This session will shed light on the differences and help architects, engineering executives, and data scientists identify when to deploy Hadoop and when it is best to use MPP relational database in a data warehouse, discovery platform, or other workload-specific applications.
Two of the most trusted experts in their fields, Steve Wooledge, VP of Product Marketing from Teradata and Jim Walker of Hortonworks will examine how big data technologies are being used today by practical big data practitioners.
In this presentation at DAMA New York, Joe started by asking a key question: why are we doing this? Why analyze and share all these massive amounts of data? Basically, it comes down to the belief that in any organization, in any situation, if we can get the data and make it correct and timely, insights from it will become instantly actionable for companies to function more nimbly and successfully. Enabling the use of data can be a world-changing, world-improving activity and this session presents the steps necessary to get you there. Joe explained the concept of the "data lake" and also emphasizes the role of a strong data governance strategy that incorporates seven components needed for a successful program.
For more information on this presentation or Caserta Concepts, visit our website at http://casertaconcepts.com/.
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.
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.
Keynote: The Journey to Pervasive AnalyticsCloudera, Inc.
We are in the middle of a data rush. When you are right in the center of a storm, it can seem overwhelming. Where should I start? What do I need to think about? What is the best long-term bet? But don’t forget that more data should mean great news. More data should mean more insight, more guidance, and more strategic direction. However, more data doesn’t automatically rally your entire business around common goals and insights. You need a platform and architecture that can support a thriving, analytic-driven business culture that embraces a pervasive analytics strategy.
Better Together: The New Data Management OrchestraCloudera, Inc.
To ingest, store, process and leverage big data for maximum business impact requires integrating systems, processing frameworks, and analytic deployment options. Learn how Cloudera’s enterprise data hub framework, MongoDB, and Teradata Data Warehouse working in concert can enable companies to explore data in new ways and solve problems that not long ago might have seemed impossible.
Gone are the days of NoSQL and SQL competing for center stage. Visionary companies are driving data subsystems to operate in harmony. So what’s changed?
In this webinar, you will hear from executives at Cloudera, Teradata and MongoDB about the following:
How to deploy the right mix of tools and technology to become a data-driven organization
Examples of three major data management systems working together
Real world examples of how business and IT are benefiting from the sum of the parts
Join industry leaders Charles Zedlewski, Chris Twogood and Kelly Stirman for this unique panel discussion, moderated by BI Research analyst, Colin White.
Cloudera Federal Forum 2014: The Building Blocks of the Enterprise Data HubCloudera, Inc.
Chief Technologist, Office of the CTO at Cloudera Eli Collins, shares the story of the enterprise data hub and how it relates to the enterprise data warehouse.
From Traditional Data Warehouse To Real Time Data WarehouseOsama Hussein
Summarising the 'From Traditional Data Warehouse To Real Time Data Warehouse' paper.
1. S. Bouaziz, A. Nabli and F. Gargouri, "From Traditional Data Warehouse To Real Time Data Warehouse", 2017.
Building the Enterprise Data Lake - Important Considerations Before You Jump InSnapLogic
In this webinar, learn from industry analyst and big data thought leader Mark Madsen about the future of big data and importance of the new Enterprise Data Lake reference architecture.
This webinar also covers what’s important when building a modern, multi-use data infrastructure, the difference between a Hadoop application and a Data Lake infrastructure, and an enterprise data lake reference architecture to get you started.
To learn more, visit: www.snaplogic.com/big-data
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.
A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.
Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.
Hadoop and the Data Warehouse: When to Use Which DataWorks Summit
In recent years, Apache™ Hadoop® has emerged from humble beginnings to disrupt the traditional disciplines of information management. As with all technology innovation, hype is rampant, and data professionals are easily overwhelmed by diverse opinions and confusing messages.
Even seasoned practitioners sometimes miss the point, claiming for example that Hadoop replaces relational databases and is becoming the new data warehouse. It is easy to see where these claims originate since both Hadoop and Teradata® systems run in parallel, scale up to enormous data volumes and have shared-nothing architectures. At a conceptual level, it is easy to think they are interchangeable, but the differences overwhelm the similarities. This session will shed light on the differences and help architects, engineering executives, and data scientists identify when to deploy Hadoop and when it is best to use MPP relational database in a data warehouse, discovery platform, or other workload-specific applications.
Two of the most trusted experts in their fields, Steve Wooledge, VP of Product Marketing from Teradata and Jim Walker of Hortonworks will examine how big data technologies are being used today by practical big data practitioners.
In this presentation at DAMA New York, Joe started by asking a key question: why are we doing this? Why analyze and share all these massive amounts of data? Basically, it comes down to the belief that in any organization, in any situation, if we can get the data and make it correct and timely, insights from it will become instantly actionable for companies to function more nimbly and successfully. Enabling the use of data can be a world-changing, world-improving activity and this session presents the steps necessary to get you there. Joe explained the concept of the "data lake" and also emphasizes the role of a strong data governance strategy that incorporates seven components needed for a successful program.
For more information on this presentation or Caserta Concepts, visit our website at http://casertaconcepts.com/.
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.
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.
Keynote: The Journey to Pervasive AnalyticsCloudera, Inc.
We are in the middle of a data rush. When you are right in the center of a storm, it can seem overwhelming. Where should I start? What do I need to think about? What is the best long-term bet? But don’t forget that more data should mean great news. More data should mean more insight, more guidance, and more strategic direction. However, more data doesn’t automatically rally your entire business around common goals and insights. You need a platform and architecture that can support a thriving, analytic-driven business culture that embraces a pervasive analytics strategy.
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.
Transform Your Business with Big Data and Hortonworks Pactera_US
Customer insight and marketplace predictions are a few of the profitable benefits found in big data technology. Leading companies are using the advanced analytics solution to find new revenue streams, increase customer satisfaction and optimize the supply chain.
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
Bridging the Big Data Gap in the Software-Driven WorldCA Technologies
Implementing and managing a Big Data environment effectively requires essential efficiencies such as automation, performance monitoring and flexible infrastructure management. Discover new innovations that enable you to manage entire Big Data environments with unparalleled ease of use and clear enterprise visibility across a variety of data repositories.
To learn more about Mainframe solutions from CA Technologies, visit: http://bit.ly/1wbiPkl
How to use Hadoop for operational and transactional purposes by RODRIGO MERI...Big Data Spain
Hadoop is an open source framework designed to rapidly ingest, store, and analyze large data sets. Hadoop is well suited for batch processing where immediate interactive analytics are not required. But today, Hadoop does not support the operational and transactional workloads. These workloads consist of a constant flow of transactions requiring low-latency response times for read/write access.
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.
This new solution from Capgemini, implemented in
partnership with Informatica, Cloudera and Appfluent,
optimizes the ratio between the value of data and storage
costs, making it easy to take advantage of new big data
technologies.
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.
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.
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
Pivotal: Hadoop for Powerful Processing of Unstructured Data for Valuable Ins...EMC
Pivotal has setup and operationalized 1000 node Hadoop cluster called the Analytics Workbench. It takes special setup and skills to manage such a large deployment. This session shares how we set it up and how you will manage it.
Objective 1: Understand what it takes to operationalize a 1000-nodeHadoop cluster.
After this session you will be able to:
Objective 2: Understand how to set up and manage the day to day challenges of a large Hadoop deployments.
Objective 3: Have a view to the tools that are necessary to solve the challenges of managing the large Hadoop cluster.
Fundamentals of Big Data, Hadoop project design and case study or Use case
General planning consideration and most necessaries in Hadoop ecosystem and Hadoop projects
This will provide the basis for choosing the right Hadoop implementation, Hadoop technologies integration, adoption and creating an infrastructure.
Building applications using Apache Hadoop with a use-case of WI-FI log analysis has real life example.
Similar to Hadoop: Extending your Data Warehouse (20)
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
This annual program recognizes organizations who are moving swiftly towards the future and building innovative solutions by making what was impossible yesterday, possible today.
The winning organizations' implementations demonstrate outstanding achievements in fulfilling their mission, technical advancement, and overall impact.
The 2021 Data Impact Awards recognize organizations' achievements with the Cloudera Data Platform in seven categories:
Data Lifecycle Connection
Data for Enterprise AI
Cloud Innovation
Security & Governance Leadership
People First
Data for Good
Industry Transformation
2020 Cloudera Data Impact Awards FinalistsCloudera, Inc.
Cloudera is proud to present the 2020 Data Impact Awards Finalists. This annual program recognizes organizations running the Cloudera platform for the applications they've built and the impact their data projects have on their organizations, their industries, and the world. Nominations were evaluated by a panel of independent thought-leaders and expert industry analysts, who then selected the finalists and winners. Winners exemplify the most-cutting edge data projects and represent innovation and leadership in their respective industries.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Extending Cloudera SDX beyond the PlatformCloudera, Inc.
Cloudera SDX is by no means no restricted to just the platform; it extends well beyond. In this webinar, we show you how Bardess Group’s Zero2Hero solution leverages the shared data experience to coordinate Cloudera, Trifacta, and Qlik to deliver complete customer insight.
Federated Learning: ML with Privacy on the Edge 11.15.18Cloudera, Inc.
Join Cloudera Fast Forward Labs Research Engineer, Mike Lee Williams, to hear about their latest research report and prototype on Federated Learning. Learn more about what it is, when it’s applicable, how it works, and the current landscape of tools and libraries.
Analyst Webinar: Doing a 180 on Customer 360Cloudera, Inc.
451 Research Analyst Sheryl Kingstone, and Cloudera’s Steve Totman recently discussed how a growing number of organizations are replacing legacy Customer 360 systems with Customer Insights Platforms.
Build a modern platform for anti-money laundering 9.19.18Cloudera, Inc.
In this webinar, you will learn how Cloudera and BAH riskCanvas can help you build a modern AML platform that reduces false positive rates, investigation costs, technology sprawl, and regulatory risk.
Introducing the data science sandbox as a service 8.30.18Cloudera, Inc.
How can companies integrate data science into their businesses more effectively? Watch this recorded webinar and demonstration to hear more about operationalizing data science with Cloudera Data Science Workbench on Cazena’s fully-managed cloud platform.
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/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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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!
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In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Hadoop: Extending your Data Warehouse
1. 1
Hadoop: Extending Your Data Warehouse
Tony Baer | Principal Analyst, Ovum
Moderated by Matt Brandwein | Product Marketing Manager, Cloudera
May 9, 2013
2. Welcome to the webinar!
• All lines are muted
• Q&A after the presentation
• Ask questions at any time by typing them in the
“Questions” pane on your WebEx panel
• Recording of this webinar will be available
on-demand at cloudera.com
• Join the conversation on Twitter:
@cloudera @TonyBaer #EDWHadoop
2
3. Who is Cloudera?
3
What the Enterprise
Requires
Only 100% open source
Hadoop-based platform
with both batch and real-
time processing engines,
enterprise-ready with
native high availability
Suite of system and data
management software
Comprehensive support
and consulting services
Broadest Hadoop training
and certification programs
Extensive Partner
Ecosystem
Over 600 partners across
hardware, software and
services
The Leader in
Big Data
Management
Deliver a revolutionary
data management
platform powered by
Apache Hadoop
World’s leading
commercial vendor of
Apache Hadoop
Enable organizations to
improve operational
efficiency and Ask
Bigger Questions of all
their data
Customers & Users
Across Industries
More production
deployments than all other
vendors combined
24. Impala: Cloudera’s Design Strategy
24
Storage
Integration
Resource Management
Metadata
Batch
Processing
MAPREDUCE,
HIVE & PIG
…
Interactive
SQL
IMPALA
Math
Machine
Learning, Anal
ytics
HDFS HBase
TEXT, RCFILE, PARQUET, AVRO, ETC. RECORDS
Engines
Complement MapReduce with
interactive MPP SQL engine
One pool of data
One metadata model
One security framework
One set of system resources
100% open source
An Integrated Part of the Hadoop Platform
25. Impala Use Cases
25
Interactive BI/analytics on more data
Asking new questions
Data processing with tight SLAs
Query-able archive w/ full fidelity
Cost-effective, ad hoc query environment that
offloads the data warehouse for:
27. Questions?
27
• Type in the “Questions” panel
• Tweet @cloudera #EDWHadoop
• Recording will be available
on-demand at cloudera.com
• Contact us:
tony.baer@ovum.com
Twitter: @TonyBaer
mbrandwein@cloudera.com
Twitter: @MattBrandwein
Thank you for attending!
Try Cloudera today
cloudera.com/downloads
Learn more about Impala
cloudera.com/impala
Get Hadoop Training
university.cloudera.com
Ready to go?
Check out Cloudera Quickstart
cloudera.com/quickstart
Editor's Notes
The rationale for multi-tiered DW architecture
EDWs are straining to keep up with new demands being placed on them. Data volumes are snowballing and increasing familiar analytic problems are consuming new forms of data.Customer retention– for mass markets, social networks are generating new insights on what customers really think, and who is influencing them. For telcos, preventing customer churn goes well beyond dropped calls. Internet, IM, text, and yes… email… are becoming the bulk of mobile carrier traffic, multiplying the volumes and types of log files that must be dissected to understand the customer experienceOperational efficiency means tapping into the Internet of things – M2M– in addition to traditional OLTP systems for logistics providers to delver goods on time, for airlines to efficiency sequence airport operations, utilities to manage generation & transmission, or mfrs to fine tune operations on the shop floor.Risk mitigation must expand the range of transactions and track externalities to understand their exposure to loss.Data retention requirements – especially in regulated industries – forcing organizations to keep more data longer, forcing hard decisions of what data to keep live.This is breaking the establishing EDW model, optimized for transforming MBytes/GBytes of well-understood structured data. It is breaking the ETL model as data transformations are bursting their batch windows. Internet players such as Facebook discovered this years ago as their nightly batch windows to MySQL DWs were exceeding 24 hrs. the same issue is now crossing over the mainstream enterporises.
Surging data volumes drove the need to flatten BI architecture, shifting data transformation loads onto the target system to a pattern known as Extract/Load/Transform (ELT).The DW was still separate from source systems. But in place of a staging server where data was drawn in, transformed, and then moved to a target, data transform was placed inside the EDW. Emergence of ELT pattern reflected reality of shrinking batch windows, and need to minimize data movements.
The obvious advantage is that data movements are reduced; only a single movement from source to target was needed. The transformation workload was co-located to where the data was stored and analyzed.
Inexpensive data transformation platformCompute cycles cheap because low-cost platform.Well suited for performing batch transformation of data to downstream SQL DWs/data marts. Can replace your ETL staging server.Accommodates all kinds of dataNo need to lay out tables ahead of time because you are loading to a file system.HDFS efficient for sequential loading & processing of all kinds of data.Keep your options openDon’t force-fit data & analyticsLate-binding approach to structuring dataData schema can evolve over time as new sources of data become availableHadoop has multiple options for representing structured data. You can add a Hive metadata layer and/or persist it in HBase tables.Extensibility – Hadoop becoming a platform with multiple personalitiesOriginated with MapReduce processingMany alternatives emerging for different styles of processingStream processing, graph, HPC patterns, etc.Applying data mining algorithms to uncover new insightsNew frameworks emerging rapidlyBest of both worldsSQL convergence via Hive, emerging frameworks such as ImpalaSQL querying for exploring and understanding your data through familiar BI tools;ExtensibilityLow cost of storage allows raw data & transformed data to be kept side-by-sideAbility to accommodate variably structured data allows orgs to gain visibility into data & data sources traditionally outside the reach of SQL DWs
Hadoop can replace your ETL server
Ovum believes that the hot spot for Hadoop development in 2013 is convergence with SQL.Cloudera has been an active player in making Hadoop SQL-friendly. It has long partnered with leading ETL, BI, and Data warehousing platform and tool providers to offer connectivity between Hadoop and SQL platforms. In turn, many of these technology providers are taking connectivity to new levels by extending their offerings to venture beyond interfacing to Hadoop to operating natively within it.Cloudera’s introduction of the Impala open source framework takes Hadoop-SQL convergence to the next level. Impala, an Apache open source project developed by Cloudera, brings interactive SQL query directly to Hadoop. It offers a high-performance, massively parallel processing framework that works against any Hadoop file format. While Impala utilizes the Hive metadata store, it provides a higher-performance alternative to relying on batch-oriented MapReduce and Hive processes.Impala helps business analysts iterate modeling for data that may eventually be migrated to a data warehouse. Low-cost platforms for iteratively discovering and structuring data.
Cloudera Navigator, a new feature of Cloudera Manager, tracks how data is utilized; specifically, it compiles an audit trail detailing what operations were performed against specific pieces of data, by whom, and when. In its initial release, Navigator will track activity against HDFS, Hive, and HBase.
Our design strategy is to tightly integrate and couple Impala within the Hadoop system. Impala (and interactive SQL) is just another application that you bring to your data. It’s integrated with Hadoop’s existing security and resource management frameworks and is completely interoperable with existing data formats and processing engines.One pool of dataStorage platforms (HDFS & HBase)Open data formats (files & records)Shared across multiple processing frameworksOne metadata modelNo synchronization of metadata between 2 different systems (analytical DBMS and Hadoop)Same metadata used by other components within Hadoop itself (Hive, Pig, Impala, etc.)One security frameworkSingle model for all of HadoopDoesn’t require “turning off” any portion of native Hadoop securityOne set of system resourcesOne set of nodes – storage, CPU, memoryOne management consoleIntegrated resource managementScale linearly as capacity or performance needs grow
Interactive BI/Analytics on more dataRaw, full fidelity data – nothing lost through aggregation or ETL/LTNew sources & types – structured/unstructuredHistorical dataAsking new questionsExploration and data discovery for analytics and machine learning – need to find a data set for a model, which requires lots of simple queries to summarize, count, and validate.Hypothesis testing – avoid having to subset and fit the data to a warehouse just to ask a single questionData processing with tight SLAsCost-effective platformMinimize data movementReduce strain on data warehouseQuery-able storageReplace production data warehouse for DR/active archiveStore decades of data cost effectively (for better modeling or data retention mandates) without sacrificing the capability to analyze