To disrupt and innovate, you need access to data. All of your data. The challenge for many organisations is that the data they need is locked away in a variety of silos. And there's perhaps no bigger silo than one of the most a widely deployed business application: SAP. Bringing together all your data for analytics and machine learning unlocks new insights and business value. Together, Cloudera and Datavard hold the key to breaking SAP data out of its silo, providing access to unlimited and untapped opportunities that currently lay hidden.
The General Data Protection Regulation (GDPR) went into effect on May 25, 2018, and this has immediate implications for handling data in your big data, machine learning, and analytics environments. Traditional architectural approaches will need to be adjusted to be compliant with several of the provisions. The good news is that Cloudera can help you!
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.
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.
Spark and Deep Learning Frameworks at Scale 7.19.18Cloudera, Inc.
We'll outline approaches for preprocessing, training, inference, and deployment across datasets (time series, audio, video, text, etc.) that leverage Spark, along with its extended ecosystem of libraries and deep learning frameworks using Cloudera's Data Science Workbench.
How komatsu is driving operational efficiencies using io t and machine learni...Cloudera, Inc.
In this joint webinar, Jason Knuth, data scientist and analytics lead at Komatsu shares how they are analyzing over 17 billion data points every day from connected devices and using machine learning and analytics to improve mining operations.
Cloudera Altus: Big Data in der Cloud einfach gemachtCloudera, Inc.
Neueste Studien zeigen, dass Data Scientisten und Analysten bis zu 80% ihrer Zeit dafür nutzen, Daten zu reinigen und vorzubereiten.
Eine ohnehin schon zeitaufwändige Aufgabe kann in der Cloud noch weiter erschwert werden, da das Cluster Management und Operations die Komplexität noch erhöhen.
Nutzer wünschen sich daher, diese komplexen Workflows zu vereinheitlichen und zu vereinfachen.
Um Big Data und Machine Learning Initiativen voranzutreiben, benötigen Unternehmen eine skalierbare und überall verfügbare Plattform. Diese muss Self-Service ermöglichen und Datensilos eliminieren.
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
The General Data Protection Regulation (GDPR) went into effect on May 25, 2018, and this has immediate implications for handling data in your big data, machine learning, and analytics environments. Traditional architectural approaches will need to be adjusted to be compliant with several of the provisions. The good news is that Cloudera can help you!
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.
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.
Spark and Deep Learning Frameworks at Scale 7.19.18Cloudera, Inc.
We'll outline approaches for preprocessing, training, inference, and deployment across datasets (time series, audio, video, text, etc.) that leverage Spark, along with its extended ecosystem of libraries and deep learning frameworks using Cloudera's Data Science Workbench.
How komatsu is driving operational efficiencies using io t and machine learni...Cloudera, Inc.
In this joint webinar, Jason Knuth, data scientist and analytics lead at Komatsu shares how they are analyzing over 17 billion data points every day from connected devices and using machine learning and analytics to improve mining operations.
Cloudera Altus: Big Data in der Cloud einfach gemachtCloudera, Inc.
Neueste Studien zeigen, dass Data Scientisten und Analysten bis zu 80% ihrer Zeit dafür nutzen, Daten zu reinigen und vorzubereiten.
Eine ohnehin schon zeitaufwändige Aufgabe kann in der Cloud noch weiter erschwert werden, da das Cluster Management und Operations die Komplexität noch erhöhen.
Nutzer wünschen sich daher, diese komplexen Workflows zu vereinheitlichen und zu vereinfachen.
Um Big Data und Machine Learning Initiativen voranzutreiben, benötigen Unternehmen eine skalierbare und überall verfügbare Plattform. Diese muss Self-Service ermöglichen und Datensilos eliminieren.
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
Cloudera training secure your cloudera cluster 7.10.18Cloudera, Inc.
Exclusively through Cloudera OnDemand, Cloudera Security Training introduces you to the tools and techniques that Cloudera's solution architects use to protect the clusters our customers rely on for critical machine learning and analytics workloads. This webinar will give you a sneak peek at our new on-demand security course and show you the immense scope of Cloudera training. From authentication and authorization to encryption, auditing, and everything in between, this course gives you the skills you need to properly secure your Cloudera cluster.
The 5 Biggest Data Myths in Telco: ExposedCloudera, Inc.
More than any business, telecommunications firms have long been dealing with huge, diverse sets of data. Big Data. Data that is unstructured, unwieldy and disorganised, making it difficult to analyse and costly to manage. Your landscape is fiercely competitive and you instinctively know it's exactly that data that would allow you to be more innovative. Data that would set you apart from the competition. You would like to realise its true potential yet you have concerns around security, RoI or integration with existing data management solutions.
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
Workload Experience Manager (XM) gives you the visibility necessary to efficiently migrate, analyze, optimize, and scale workloads running in a modern data warehouse. In this recorded webinar we discuss common challenges running at scale with modern data warehouse, benefits of end-to-end visibility into workload lifecycles, overview of Workload XM and live demo, real-life customer before/after scenarios, and what's next for Workload XM.
Cloudera + Syncsort: Fuel Business Insights, Analytics, and Next Generation T...Precisely
Effective AI and ML projects require a perfect blend of scalable, clean data funneled from a variety of sources across the business. The only problem? Uncleaned data often lives in hard-to-access legacy systems, and it costs time and money to build the right foundation to deliver that data to answer ever-changing questions from business users. Together, Cloudera and Syncsort enable you to build a scalable foundation of data connections to reinvent the data lifecycle of all your projects in the most efficient way possible.
View this webinar on-demand to learn how innovative solutions from Cloudera and Syncsort enable AI and ML success. You will learn:
• Best practices for transforming complex data into clear, actionable insights for AI and ML projects
• How to visually assess the quality of the sources in your data lake and their completeness, consistency, and accuracy
• The value of an Enterprise Data Cloud and the newly unveiled Cloudera Data Platform
• How Syncsort Connect integrates natively with the Cloudera Data Platform
In this webinar, we’ll show you how Cloudera SDX reduces the complexity in your data management environment and lets you deliver diverse analytics with consistent security, governance, and lifecycle management against a shared data catalog.
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.
Cloud Data Warehousing with Cloudera Altus 7.24.18Cloudera, Inc.
This webinar will help you maximize the full potential of the cloud. Understand how to leverage cloud environments for different analytic workloads to empower business analysts and keep IT happy. An intricate, beautiful balance. The learn best practices in design, performance tuning, workload considerations, and hybrid or multi-cloud strategies.
Strategies for Enterprise Grade Azure-based AnalyticsCloudera, Inc.
Over the past decade, big data implementations have been more sophisticated in particular for organizations operationalizing machine learning, analytics and data engineering. The pressures of data-driven cultures, multiple workload applications such as customer care, fraud management and cross-platform marketing are changing the game. Mixing machine learning with business processes and operationalizing analytics with data engineering practices places burdens on IT teams. Making these advanced data environments all work together is an ongoing challenge.
While you can still “swipe and go” to implement data management environments in the cloud for an easy solution, the easy path is often littered with additional costs, higher overhead in terms of maintenance and synchronization. Data savvy organizations are taking a more measured and coordinated approach to their machine learning, analytics and data engineering infrastructures. These proactive approaches speed adoption among business stakeholders and lower administration and governance issues for technologists.
Join John L Myers, managing research director at leading IT analyst firm Enterprise Management Associates (EMA), and Nik Rouda, director of product marketing at Cloudera, to discover how the world of cloud implementations have changed for the better and the future of an enterprise grade cloud environment for your organization using the right resources.
Attend this webinar to learn about:
Drivers for implementing machine learning, analytics and data engineering with a proactive approach
Pitfalls associated with “immediate gratification” implementations
How business stakeholders benefit from proactive approaches
How proactive implementations improve the workloads of technologists
Examples of real world customer implementations
3 things to learn:
Drivers for implementing machine learning, analytics and data engineering with a proactive approach
Pitfalls associated with “immediate gratification” implementations
How business stakeholders benefit from proactive approaches
Delivering improved patient outcomes through advanced analytics 6.26.18Cloudera, Inc.
Rush University Medical Center, along with Cloudera and MetiStream, talk about adopting a comprehensive and interactive analytic platform for improved patient outcomes and better genomic analysis, highlighting examples in both genomics and clinical notes. John Spooner of 451 Research provides context to the discussion and shares market insights that complement the customer stories.
How Cloudera SDX can aid GDPR compliance 6.21.18Cloudera, Inc.
In this webinar, we will cover:
Technical capabilities required in your data platform including metadata classification on ingest, column-level lineage, fine-grained authorization, encryption, and more
How a shared data experience can facilitate the safe handling of metadata
Ways to enable your data platform for GDPR success
Cloudera - The Modern Platform for AnalyticsCloudera, Inc.
This presentation provides an overview of Cloudera and how a modern platform for Machine Learning and Analytics better enables a data-driven enterprise.
GDPR: 20 Million Reasons to Get Ready - Part 2: Living ComplianceCloudera, Inc.
Though the majority of organisations will spend plenty of time preparing for GDPR, it’s crucial they consider actually living the regulation. May 2018 is not the end of the need for compliance, it is the beginning. With preparation putting in the foundation for a data subject hub, organisations now need to focus on efficiency in fulfilling the data subject access rights. In this session, we will go into what it means to live GDPR compliance with topics like self service and what it needs to be secure be design.
Get started with Cloudera's cyber solutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
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.
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.
3 Things to Learn About:
*The IoT ecosystem and data management considerations for IoT
*Top IoT use cases and data architecture strategies for managing the sheer volume and variety of IoT data
*Real-life case studies on how our customers are using Cloudera Enterprise to drive insights and analytics from all of their IoT data
Comment développer une stratégie Big Data dans le cloud public avec l'offre P...Cloudera, Inc.
Le cloud public est une proposition attractive pour les entreprises à la recherche d’agilité dans leurs projets big data, qu’il s’agisse de traiter des données en masse ou d’y exécuter des analyses complexes pour une meilleure prise de décision.
ADV Slides: How to Improve Your Analytic Data Architecture MaturityDATAVERSITY
Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning.
In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others.
Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long.
With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.
Cloudera training secure your cloudera cluster 7.10.18Cloudera, Inc.
Exclusively through Cloudera OnDemand, Cloudera Security Training introduces you to the tools and techniques that Cloudera's solution architects use to protect the clusters our customers rely on for critical machine learning and analytics workloads. This webinar will give you a sneak peek at our new on-demand security course and show you the immense scope of Cloudera training. From authentication and authorization to encryption, auditing, and everything in between, this course gives you the skills you need to properly secure your Cloudera cluster.
The 5 Biggest Data Myths in Telco: ExposedCloudera, Inc.
More than any business, telecommunications firms have long been dealing with huge, diverse sets of data. Big Data. Data that is unstructured, unwieldy and disorganised, making it difficult to analyse and costly to manage. Your landscape is fiercely competitive and you instinctively know it's exactly that data that would allow you to be more innovative. Data that would set you apart from the competition. You would like to realise its true potential yet you have concerns around security, RoI or integration with existing data management solutions.
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
Workload Experience Manager (XM) gives you the visibility necessary to efficiently migrate, analyze, optimize, and scale workloads running in a modern data warehouse. In this recorded webinar we discuss common challenges running at scale with modern data warehouse, benefits of end-to-end visibility into workload lifecycles, overview of Workload XM and live demo, real-life customer before/after scenarios, and what's next for Workload XM.
Cloudera + Syncsort: Fuel Business Insights, Analytics, and Next Generation T...Precisely
Effective AI and ML projects require a perfect blend of scalable, clean data funneled from a variety of sources across the business. The only problem? Uncleaned data often lives in hard-to-access legacy systems, and it costs time and money to build the right foundation to deliver that data to answer ever-changing questions from business users. Together, Cloudera and Syncsort enable you to build a scalable foundation of data connections to reinvent the data lifecycle of all your projects in the most efficient way possible.
View this webinar on-demand to learn how innovative solutions from Cloudera and Syncsort enable AI and ML success. You will learn:
• Best practices for transforming complex data into clear, actionable insights for AI and ML projects
• How to visually assess the quality of the sources in your data lake and their completeness, consistency, and accuracy
• The value of an Enterprise Data Cloud and the newly unveiled Cloudera Data Platform
• How Syncsort Connect integrates natively with the Cloudera Data Platform
In this webinar, we’ll show you how Cloudera SDX reduces the complexity in your data management environment and lets you deliver diverse analytics with consistent security, governance, and lifecycle management against a shared data catalog.
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.
Cloud Data Warehousing with Cloudera Altus 7.24.18Cloudera, Inc.
This webinar will help you maximize the full potential of the cloud. Understand how to leverage cloud environments for different analytic workloads to empower business analysts and keep IT happy. An intricate, beautiful balance. The learn best practices in design, performance tuning, workload considerations, and hybrid or multi-cloud strategies.
Strategies for Enterprise Grade Azure-based AnalyticsCloudera, Inc.
Over the past decade, big data implementations have been more sophisticated in particular for organizations operationalizing machine learning, analytics and data engineering. The pressures of data-driven cultures, multiple workload applications such as customer care, fraud management and cross-platform marketing are changing the game. Mixing machine learning with business processes and operationalizing analytics with data engineering practices places burdens on IT teams. Making these advanced data environments all work together is an ongoing challenge.
While you can still “swipe and go” to implement data management environments in the cloud for an easy solution, the easy path is often littered with additional costs, higher overhead in terms of maintenance and synchronization. Data savvy organizations are taking a more measured and coordinated approach to their machine learning, analytics and data engineering infrastructures. These proactive approaches speed adoption among business stakeholders and lower administration and governance issues for technologists.
Join John L Myers, managing research director at leading IT analyst firm Enterprise Management Associates (EMA), and Nik Rouda, director of product marketing at Cloudera, to discover how the world of cloud implementations have changed for the better and the future of an enterprise grade cloud environment for your organization using the right resources.
Attend this webinar to learn about:
Drivers for implementing machine learning, analytics and data engineering with a proactive approach
Pitfalls associated with “immediate gratification” implementations
How business stakeholders benefit from proactive approaches
How proactive implementations improve the workloads of technologists
Examples of real world customer implementations
3 things to learn:
Drivers for implementing machine learning, analytics and data engineering with a proactive approach
Pitfalls associated with “immediate gratification” implementations
How business stakeholders benefit from proactive approaches
Delivering improved patient outcomes through advanced analytics 6.26.18Cloudera, Inc.
Rush University Medical Center, along with Cloudera and MetiStream, talk about adopting a comprehensive and interactive analytic platform for improved patient outcomes and better genomic analysis, highlighting examples in both genomics and clinical notes. John Spooner of 451 Research provides context to the discussion and shares market insights that complement the customer stories.
How Cloudera SDX can aid GDPR compliance 6.21.18Cloudera, Inc.
In this webinar, we will cover:
Technical capabilities required in your data platform including metadata classification on ingest, column-level lineage, fine-grained authorization, encryption, and more
How a shared data experience can facilitate the safe handling of metadata
Ways to enable your data platform for GDPR success
Cloudera - The Modern Platform for AnalyticsCloudera, Inc.
This presentation provides an overview of Cloudera and how a modern platform for Machine Learning and Analytics better enables a data-driven enterprise.
GDPR: 20 Million Reasons to Get Ready - Part 2: Living ComplianceCloudera, Inc.
Though the majority of organisations will spend plenty of time preparing for GDPR, it’s crucial they consider actually living the regulation. May 2018 is not the end of the need for compliance, it is the beginning. With preparation putting in the foundation for a data subject hub, organisations now need to focus on efficiency in fulfilling the data subject access rights. In this session, we will go into what it means to live GDPR compliance with topics like self service and what it needs to be secure be design.
Get started with Cloudera's cyber solutionCloudera, Inc.
Cloudera empowers cybersecurity innovators to proactively secure the enterprise by accelerating threat detection, investigation, and response through machine learning and complete enterprise visibility. Cloudera’s cybersecurity solution, based on Apache Spot, enables anomaly detection, behavior analytics, and comprehensive access across all enterprise data using an open, scalable platform. But what’s the easiest way to get started?
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.
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.
3 Things to Learn About:
*The IoT ecosystem and data management considerations for IoT
*Top IoT use cases and data architecture strategies for managing the sheer volume and variety of IoT data
*Real-life case studies on how our customers are using Cloudera Enterprise to drive insights and analytics from all of their IoT data
Comment développer une stratégie Big Data dans le cloud public avec l'offre P...Cloudera, Inc.
Le cloud public est une proposition attractive pour les entreprises à la recherche d’agilité dans leurs projets big data, qu’il s’agisse de traiter des données en masse ou d’y exécuter des analyses complexes pour une meilleure prise de décision.
ADV Slides: How to Improve Your Analytic Data Architecture MaturityDATAVERSITY
Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning.
In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others.
Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long.
With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.
The quest for the insight-driven enterprise has spurned a mass exodus to the cloud. But cloud data ecosystems can be very complex with multiple data storage and processing options.
These slides-based on the webinar featuring leading IT analyst firm EMA, Amazon Web Services (AWS), and Trifacta--will help you: understand technology trends that simplify your analytics modernization journey; learn best practices to operationalize data management on AWS; establish operational excellence leveraging AWS data storage and processing; accelerate time-to-value for analytics projects with data preparation on AWS.
Data Integration for Both Self-Service Analytics and IT Users Senturus
See a cloud solution that enables data integration for applications such as Salesforce, NetSuite, Workday, Amazon Redshift and Microsoft Azure. View the webinar video recording and download this deck: http://www.senturus.com/resources/data-integration-tool-for-both-business-and-it-users/.
The rapid growth in self-service business analytics has created tremendous value for organizations, but in many cases has created tension between technical and business users. Technical teams have built solid data warehouses filled with trusted data from source systems such as sales, finance, and operations. Business teams are gaining tremendous insights by analyzing data warehouse information with traditional and new data discovery tools such as Cognos, Business Objects, Tableau, and Power BI.
The Informatica Cloud is a best-of-both-worlds solution that combines data integration for both business and IT users. It allows the following: 1) IT incorporates the business analyst’s data integration routines into the core, trusted data warehouse, 2) Business analysts can do data integration from both cloud-based and on-premise data sources, 3) Business analyst can use the industrial-strength data integration engine that IT teams have loved for years and 4) Integration for apps such as Salesforce, NetSuite, Workday, Amazon Redshift, Microsoft Azure, Marketo, SAP, Oracle and SQL Server.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free recorded webinars, trainings, demos and unbiased product reviews. Take a look and share them with your colleagues and friends: http://www.senturus.com/resources/.
Accelerate Digital Transformation Through AI-powered Cloud Analytics Moderniz...Amazon Web Services
Andrew McIntyre, Director of Strategic ISV Alliances, Informatica
Modernizing your analytics capabilities to deliver rapid new insights is critical to successfully drive data-driven digital transformation. Many organizations find it challenging to connect, understand and deliver the right data to generate new insights. Learn about the latest patterns, solutions and benefits of Informatica's next-generation Enterprise Data Management platform to unleash the power of your data through the modern cloud data infrastructure of AWS. See how you can accelerate AI-driven next-generation analytics by cataloging and integrating structured and unstructured data from hundreds of data sources from multiple on-premises and cloud data sources.
Analytics in a Day Ft. Synapse Virtual WorkshopCCG
Say goodbye to data silos! Analytics in a Day will simplify and accelerate your journey towards the modern data warehouse. Join CCG and Microsoft for a half-day virtual workshop, hosted by James McAuliffe.
AWS Summit Singapore - Accelerate Digital Transformation through AI-powered C...Amazon Web Services
Andrew McIntyre, Director of Strategic ISV Alliances, Informatica
Modernizing your analytics capabilities to deliver rapid new insights is critical to successfully drive data-driven digital transformation. Many organizations find it challenging to connect, understand and deliver the right data to generate new insights. Learn about the latest patterns, solutions and benefits of Informatica's next-generation Enterprise Data Management platform to unleash the power of your data through the modern cloud data infrastructure of AWS. See how you can accelerate AI-driven next-generation analytics by cataloging and integrating structured and unstructured data from hundreds of data sources from multiple on-premises and cloud data sources.
Making advanced data environments all work together is an ongoing challenge. Data savvy organizations are taking a more measured and coordinated approach to their machine learning, analytics and data engineering infrastructures.
These slides--based on the webinar from leading IT analyst firm Enterprise Management Associates (EMA) and Cloudera--delve into how the world of cloud implementations have changed for the better and the future of an enterprise grade cloud environment for your organization using the right resources.
Datumize is a software vendor established in 2014 in Barcelona (Spain) working on data integration technology.
We develop innovative products that allow companies to enjoy actionable insights based on Dark Data - data not stored and therefore not used.
Our secret sauce is a proprietary and powerful data collection engine, Datumize Data Collector (DDC), that gets data from fancy sources that most other vendors do not consider.
Four Key Considerations for your Big Data Analytics StrategyArcadia Data
Learn 4 of the key things to consider as you create your big data analytics strategy from John Meyers (Enterprise Management Associates) and Steve Wooledge (Arcadia Data).
Virtualisation de données : Enjeux, Usages & BénéficesDenodo
Watch full webinar here: https://bit.ly/3oah4ng
Gartner a récemment qualifié la Data Virtualisation comme étant une pièce maitresse des architectures d’intégration de données.
Découvrez :
- Les bénéfices d’une plateforme de virtualisation de données
- La multiplication des usages : Lakehouse, Data Science, Big Data, Data Service & IoT
- La création d’une vue unifiée de votre patrimoine de données sans transiger sur la performance
- La construction d’une architecture d’intégration Agile des données : on-premise, dans le cloud ou hybride
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.
IBM Cloud Pak for Data is a single unified platform which helps to unify and simplify the collection, organization and analysis of data. Enterprises can turn data into insights through an integrated cloud-native architecture. IBM Cloud Pak for Data is extensible, easily customized to unique client data and AI landscapes through an integrated catalog of IBM, open source and third-party microservices add-ons
ICP for Data- Enterprise platform for AI, ML and Data ScienceKaran Sachdeva
IBM Cloud Private for Data, an ultimate platform for all AI, ML and Data Science workloads. Integrated analytics platform based on Containers and micro services. Works with Kubernetes and dockers, even with Redhat openshift. Delivers the variety of business use cases in all industries- FS, Telco, Retail, Manufacturing etc
In the digital world, semi-structured data is as important as transactional, structured data. Both need to be analyzed to create a competitive advantage. Unfortunately, neither the data lake nor the data warehouse are adequate to handle the analysis of both data types.
These slides—based on the webinar from EMA Research and Vertica—delve into the push toward the innovative unified analytics warehouse (UAW), a merging of the data lake and data warehouse.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
Big Data LDN 2018: DATA MANAGEMENT AUTOMATION AND THE INFORMATION SUPPLY CHAI...Matt Stubbs
Date: 14th November 2018
Location: Governance and MDM Theatre
Time: 10:30 - 11:00
Speaker: Mike Ferguson
Organisation: IBS
About: For most organisations today, data complexity has increased rapidly. In the area of operations, we now have cloud and on-premises OLTP systems with customers, partners and suppliers accessing these applications via APIs and mobile apps. In the area of analytics, we now have data warehouse, data marts, big data Hadoop systems, NoSQL databases, streaming data platforms, cloud storage, cloud data warehouses, and IoT-generated data being created at the edge. Also, the number of data sources is exploding as companies ingest more and more external data such as weather and open government data. Silos have also appeared everywhere as business users are buying in self-service data preparation tools without consideration for how these tools integrate with what IT is using to integrate data. Yet new regulations are demanding that we do a better job of governing data, and business executives are demanding more agility to remain competitive in a digital economy. So how can companies remain agile, reduce cost and reduce the time-to-value when data complexity is on the up?
In this session, Mike will discuss how companies can create an information supply chain to manufacture business-ready data and analytics to reduce time to value and improve agility while also getting data under control.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
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Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
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Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
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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.
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.
Multi task learning stepping away from narrow expert models 7.11.18Cloudera, Inc.
Join this webinar as Friederike Schüür covers:
A conceptual introduction to multi-task learning (MTL), how and why it works
A technical deep dive, from MTL random forests to MTL neural networks
Applications of MTL, from structured data to text and images
The benefits of MTL to organizations, from financial services to healthcare and agriculture
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3. # 3
Why you need to bridge SAP and Hadoop to turn your
data into Business Value
4. # 4
SAP and Hadoop – bridging two worlds
Hadoop
Java, Python, PigLatin
Massive clusters for big data processing
Structured & unstructured data
Apache & open source
Distributions (e.g. Cloudera)
Engines (e.g. Spark, Impala)
Fast paced evolution since 2006
Big Data management
SAP
ABAP
Client/Server
classic RDBMS as relational database
Proprietary software
Interfaces and open standards
Business Software
Steady evolution since 1972
Data management
5. # 5
SAP and Hadoop – bridging two worlds
Hadoop
Java, Python, PigLatin
Massive clusters for big data processing
Structured & unstructured data
Apache & open source
Distributions (e.g. Cloudera)
Engines (e.g. Spark, Impala)
Fast paced evolution since 2006
Big Data management
SAP
ABAP
Client/Server
classic RDBMS as relational database
Proprietary software
Interfaces and open standards
Business Software
Steady evolution since 1972
Data management
75% of global GDP is generated by
companies running on SAP®
6. # 6
Data Management Issues
Scalability
Data-Pipelines
Granularity and Velocity
Data-Silos
Extensibility
• Not any more possible to do lifetime sizing of platform during procurement
• HW requirements create limitations to possible growth
• Scale UP comes often with great cost, and scale DOWN is usually
valueless
• Data transformations are I/O intensive operations
• Take lot of time, consume lot of resources
• Limitations on format of data
• Limitations on granularity of data, often only aggregated and cleaned
data are stored
• Raw data are necessary for data science activities
• Too many places for storing data
• No interconnection between company units limits data analyzing
possibilities
• Data analyses requires lot of programing languages
• Limited applications compatibility
7. # 7
From Data management to Big Data management
Data Management Issues
Data Growth
Data Separation
8. # 8
From Data management to Big Data management
Data Management Issues Business Questions to
answer
Data Growth
Data Separation
Cost Reduction
Revenue Increase
10. # 10
“Only 12-18% of all data in BW is
actually used.”
Forrester research
11. # 11
“Only 12-18% of all data in BW is
actually used.”
Forrester research
“In Average 35% of SAP data is
temporary and could be deleted”
Based on 300+ Fitness Tests
12. # 12
3%
5%
5%
5%
9%
11%
15%
15%
32%
Cube D data
Master data
Cube F data
Cube E data
PSA data
Changelog data
Other data
Temporary data
DSO data
0% 5% 10% 15% 20% 25% 30% 35%
Data distribution in SAP BW* * Based on 300+ DataVard BW FitnessTestTM
“Only 12-18% of
all data in BW
is actually
used.”
Forrester research
35 %
Housekeeping
“In Average
35% of SAP data
is temporary
and could be
deleted”
Based on 300+ Fitness Tests
13. # 13
DATA GROWTH WITH & WITHOUT DATATIERING
1290
1710
2250
2925
3803
4943
774 716 754
857
1041
1309
0
1000
2000
3000
4000
5000
6000
2017 2018 2019 2020 2021 2022
Data size without datatiering Data size after datatiering
SAP DATA GROWTH (in GB)
3.6 TB
saving
DATA GROWTH
25% p.a.
SIZE TODAY
1,3 TB
SIZE IN 5 YEARS
4,9 TB
DATATIERING ROI
2 YEARS
24. # 24
From Data management to Big Data management
Data Management Issues Business Questions to
answer
Data Growth
Data Separation
Cost Reduction
Revenue Increase
25. # 25
From Data management to Big Data management
Data Management Issues Big Data Management
Solutions
Business Questions to
answer
Data Growth
Data Separation
Cost Reduction
Revenue Increase
Data Tiering
Data Integration
26. # 26
2. Data Integration use case stream - GLUE
1. Data Tiering use case stream - OUTBOARD
From Data management to Big Data management
Data Growth
Data Separation
Cost Reduction
Revenue Increase
Data Tiering
Data Integration
27. # 27
From Data management to Big Data management
1. Data Tiering use case stream - OUTBOARD
Data Growth Cost Reduction Data Tiering
2. Data Integration use case stream - GLUE
Data Separation Revenue Increase Data Integration
3. Security Analyses use case stream – Data Science
Data Protection Cost Prevention Security Analyses
28. # 28
From Data management to Big Data management
1. Data Tiering use case stream - OUTBOARD
Data Growth Cost Reduction Data Tiering
2. Data Integration use case stream - GLUE
Data Separation Revenue Increase Data Integration
3. Security Analyses use case stream – Data Science
Data Protection Cost Prevention Security Analyses
3. Data Aging or decommission of old system – Data Fridge scenario
Data Aging GDPR/Costs Data Fridge
44. # 44
Who is Datavard
Focus on SAP and Data Management: Business Transformation, SAP ABAP, and Big Data
Software products and consulting services
More than 200 projects p.a.
Customers of all industries, regions and sizes
No “me too” topics
Strong partnership with SAP since 1998
Privately held since 1998, 2018: 245 employees
Germany: Heidelberg (HQ), Hamburg | USA: Philadelphia, Washington DC
Switzerland: Regensdorf | Italy: Milan | Central Europe: Bratislava | Singapore
Explore Optimize Transform Innovate
Hello Everyone I’m…Why you need…
Thx Cloudera
Now...How many of you know about SAP?
Left corner SAP
Right corner – I don’t need probably to talk about that
Now for the purposes of my presentation I would consider
SAP as the essential… expensive
And Hadoop a cheap yet powerful…all kind of data
To justify why I’m here today
There are lot of cool companies…
BUT 75%
Now why would we want to connect…
Because we are in trouble!!!
Lots of trouble
Please I ask you not to read…
3 years old slide, 1TB – 5TB
Jim Rohn said – “You don’t need 5 reasons to fail, one is enough!”
So - let me give you two biggest issues!
Data Growth
1. 2016
2. Expensive systems
Data Separation
More vicious one, mostly for 21st century
Units, Systems, security
1. What we are missing are the business questions? Why?
2. Well, if you have issues not related to your business
3. So what are the simplest but yet most related business questions?...
Two valid business questions – Cost reduction and Revenue Increase
Does that make sense to you? Do you want to solve those two first?
Good so! How to do cost reduction? Or How much trash is in your system
Forrester research: 12% of data is used in reporting
Fitness test – answers how is your system being used?
Datavard find out that in Average 35% of SAP data is temporary and can be deleted
Let me talk money! IF you have spend 10MIL on your 20TB SAP Hana system
You are using 1,2 – 1,8M out of 10
AND! 3,5M you spent on trash – what an investment
This is actually average of the biggest BW systems in the world
Data allocation
Now calculation based on real case scenario from last year
Left side
Comparison of system growth with our Data Tiering solution and Without
Without Data Tiering solution exponential, with hadoop and data tiering solution we are more in the linear world
Saving of ~3M on the SAP Hana in the horizon of several years
Guys from Cloudera – can I spin a hadoop cluster in the Altus for 3Mil?
So ROI is 2YEARS SO does this make sense to you to bridge sap with hadoop in order to do this??
1. Revenue increase - Or to increase value of your data – so you increase value of your business.
2. Fact of life is I cannot quantify in general…
3. And there are a lot of use cases around – but there is a rule of the thumb and it’s not coming from the computer world!
4. It is actually leadership 1.0
Do you know what is the relation between output of group of people and the total of output generated by individuals?
Anyone a People manager?
Of course the output value of the group is higher.
Only the diversity by itself is a value!
Same it’s with your data. Combined data have much bigger value then individually presented.
Let me give you an example
Do you know what is this mountain?
It’s not any particular stock-price
It’s average daily temperature in the month of March in Bratislava – central Europe – Stable continental weather – at least used to be
We have one customer, premium retail shop. I love retails.
Their SAP system is filled with details of inventory… But there used to be nothing about the weather data.
Now let’s imagine that you…
Come at the beginning of March for winter cloth – yeas business done check
Spring cloth is history – winter jacket to tshirt
You come 2nd week when there is 20 degrees more and they have winter jacket in sale?
You want to buy Tshirt – either no buy or next shop in the market OR they tell you come in a week.
When there is -6?
So how can you create strategy when you have no clue on what is going to happen?
Without diversity of the data you will be able to count only your loss in comparison to normal months
You start with a proper platform!
You add core data and another source of data
You want to know how your customers feel about the change
And I recommend smart BI solution on top
Small sanity check
Does that make sense?
1. So how it fits together?
We have our Issue and Business question connection?
So lets add a solution
Data Tiering from Datavard and Cloudera
And Data Integration
What brings us to direct streams and complete answer to why you should bridge SAP with a Hadoop!
Now, I’ve said you don’t need 5 reasons to fail, one is enough
Actually you don’t need to be successful in both areas to justify your big-data platform, one is enough! But do try both!
Or do you need more?
You want another? Data Protection
How that relates to Hadoop?
You have system with 20.000 users writing or reporting on data -> you don’t doo security analyses…
You want more?
Who would I be if I would not mention GDPR!
You want another? Data Aging!
Now does that answer the question why you need to bridge SAP and Hadoop? What do you say?
Exactly it makes complete sense to do it so HOW?
I’ll answer that in case you are interested in a f2f conversation
If you allow me to spend time with you and get answer to few core questions I believe you can greatly benefit
Ovum’s definition of a data lake is a governed repository that becomes the default ingest point for raw data. So here we are: an idyllic data lake side setting.
Data Lakes got a ‘bad rap’ early on because they were just repositories. The tools and technologies for governance and data stewardship were missing or immature.
ADD DATA PRIVACY HIGHLIGHTS
So we introduced Cloudera SDX - or shared data experience – the foundations of Cloudera Enterprise.
SDX makes it possible for companies to run dozens - hundreds - of analytic applications against a common pool of data.
SDX applies a centralized, consistent framework for catalog, security, governance, management, data ingest and more.
It makes it faster, easier, and safer for organizations, teams, people to develop and deploy high-value, multi-function use cases like customer next best offer, clinical prediction, and risk modeling.
SDX cuts through silos to unify data, analytics, management, security, and governance, and empowers self-service
BUSINESS CATALOG SERVICES (NOT JUST HMS) ALL DATA SETS, SCHEMAS, COLLABORATIVE TAGS, BUSINESS CLASSIFICATIONS, TARGETTED FOR EACH USER
SDX is a set of open platform services built for multi-functional or multi-disciplinary analytics that have been optimized for the cloud. This means that we offer a unified security model that helps protect sensitive data with a consistent set of controls, that we offer a consistent governance model that enables self-service secure access to all of your relevant data. Not just one type of data, really to all of it, increasing your ability to be compliant, particularly in a regulatory environment. Next, easy workload management that increases user productivity and boosts job predictability. Next, flexible data ingest and replication. We have a number of core partners that we work with in this arena that help you aggregate a single copy of all of your data, providing you easier debt disaster recovery and that eases migration of data from one place to another. Last but not least, as I mentioned a moment ago, we offer a shared catalog that helps to define and preserve the structure and the business context of all your data, regardless of where it happens to reside. So, SDX is really a core piece of how we at Cloudera separate ourselves from the competition.
Note: The content of this slide is based on the Success Story and video in 2015. The slide was created in NOV, 2016.
Company Background: With £18 billion (about US$30 billion) in revenue in 2014, BT is one of the largest telecommunications providers in the world. The company serves more than 18 million consumers and nearly three million businesses.
Use Case: For BT, the key to achieving sustainable, profitable growth in today's competitive landscape is its ability to broaden and deepen customer relationships. To support this goal, BT is using a Cloudera enterprise data hub (EDH) to accelerate data velocity and fast-track the delivery of new offerings to its customers. This EDH provides the backbone for an operational data store (ODS) that enables BT to break through data silos to ingest, store, and prepare data for myriad operational and analytical uses. Within one year, BT increased data processing velocity by a factor of 15, achieved an ROI between 200 and 250 percent, and is now positioned to take on new projects faster at a lower cost.
Moving its ETL platform to Cloudera enabled BT to accelerate data velocity, processing five times the data in a third of the time.
Following the success of its ETL initiative, BT is now utilizing the Cloudera to help deliver its broadband services. The speed of an individual line is dominated by its length (the distance from network equipment to a customer’s premises), but many other factors can have a significant impact on customer experience. BT uses Cloudera to join network topology (GIS) data with terabytes of DSL performance (time series) and electrical line test data to grade the quality of every line in the network. Using this network analysis, the probability of a successful
outcome of an engineer dispatch can be predicted. This reduces wasted engineer visits and truck rolls
BT’s work with Cloudera is also helping position the company to take advantage of the Internet of Things (IoT). Take its work with BT is part of the MK:Smart initiative for Milton Keynes (MK), a fast-growing town in Buckinghamshire, England. This initiative includes early IoT solutions such as sensors in car parking spaces that broadcast if the spots are vacant or occupied. Citizens and visitors can then use a smartphone app that guides them to the nearest free parking space based on the sensor data. According to BT, the same data ultimately will be used to better inform multi-million pound infrastructure decisions, such as the location and size of future car parks.
IoT and fleet vehicle analytics are also a growing area for BT. The company offers fleet services as a managed service to other utility companies. One of the competitive features that BT can offer is the ability to instrument those vehicles and collect data from them. Ultimately, the company seeks to predict analytics around faults, so it can identify a vehicle failing early, improve the lifetime of that vehicle, and help reduce its overall carbon footprint.
SOLUTION HIGHLIGHTS
Modern Data Platform: Cloudera Enterprise, Data Hub Edition
Key Components: Apache Hive, Apache Impala, Apache Pig, Apache Sentry, Apache Spark, Cloudera Manager, Cloudera Navigator
Industry Use Case: Telecommunications
IMPROVED SERVICE
PROCESS IMPROVEMENT
IT COST REDUCTION
Read more with the published story: http://www.cloudera.com/customers/bt.html
Note: The content of this slide is based on the Success Story in JUNE, 2017. The slide was created in JUNE, 2017.
Company Background:
Podo is a Spanish utilities company, providing electricity to consumers and businesses across Spain.
Use Case:
Podo is revolutionizing the utilities industry, using a cloud-based machine learning and advanced analytics platform from Cloudera and Google to help accurately predict future consumption patterns and provide consumers with fully customized rates.
Data sources:
Historical customer records
IoT data from lights and connected devices
Third party databases for government statistics and property records
Solution
Modern Data Platform: Cloudera Enterprise
Workloads: Analytic Database, Data Engineering and Data Science
Components: Apache Impala (incubating), Apache Spark, Cloudera Manager
Analytic tools: R, Python, Matlab
Cloud: Google Cloud Platform
Industry Use Case:
Customer 360°
Network optimization
Operational analytics
Data monetization
Read more with the published story: https://www.cloudera.com/more/customers/Podo.html?cq_ck=1497466958591
Note: The content of this slide is based on the PCI Solution brief (http://www.cloudera.com/content/dam/cloudera/Resources/PDF/solution-briefs/MasterCard_PCI-Data-Security_SolutionBrief.pdf) in 2015. The slide was created in DEC, 2016.
Company Background: MasterCard’s principal business is to process payments between the banks of merchants and the card issuing banks or credit unions of the purchasers who use the "MasterCard" brand debit and credit cards to make purchases. MasterCard Worldwide has been a publicly traded company since 2006 and had $9.5B in 2014 annual revenue and has 6,700 employees. Prior to its initial public offering, MasterCard Worldwide was a cooperative owned by the 25,000+ financial institutions that issue its branded cards.
Use Case: MasterCard chose Cloudera Enterprise for fraud detection and to optimize their DW infrastructure and later expanded to form a partnership with MC Advisors, the consulting arm of MasterCard. MasterCard requires that any technology handling its applications or payment card data files must have full PCI certification. Receiving this important certification allows MasterCard the opportunity to integrate Hadoop datasets with other environments that are already PCI-certified.
Solution
Modern Data Platform: Cloudera Enterprise
Industry Use Case: Financial Services
Fraud Prevention
Read more with the published solution brief: http://www.cloudera.com/content/dam/cloudera/Resources/PDF/solution-briefs/MasterCard_PCI-Data-Security_SolutionBrief.pdf
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