This document discusses running data analytic workloads in the cloud using Cloudera Altus. It introduces Altus, which provides a platform-as-a-service for analyzing and processing data at scale in public clouds. The document outlines Altus features like low cost per-hour pricing, end-user focus, and cloud-native deployment. It then describes hands-on examples using Altus Data Engineering for ETL and the Altus Analytic Database for exploration and analytics. Workload analytics capabilities are also introduced for troubleshooting and optimizing jobs.
Time and again, research shows organisations are held back in their digital transformation because of a lack of skills. A recent IDC survey shows it's the case for nearly half of all organisations when it comes to specialist big data and data science skills. As an organisation, how do you know you're hiring the right people to close the gap? As an individual, how do you prove you know what you're doing?
Cloudera Data Science Workbench: sparklyr, implyr, and More - dplyr Interfac...Cloudera, Inc.
You like to use R, and you need to use big data. dplyr, one of the most popular packages for R, makes it easy to query large data sets in scalable processing engines like Apache Spark and Apache Impala.
But there can be pitfalls: dplyr works differently with different data sources—and those differences can bite you if you don’t know what you’re doing.
Ian Cook is a data scientist, an R contributor, and a curriculum developer at Cloudera University. In this webinar, Ian will show you exactly what you need to know about sparklyr (from RStudio) and the package implyr (from Cloudera). He will show you how to write dplyr code that works across these different interfaces. And, he will solve mysteries:
Do I need to know SQL to use dplyr?
When is a “tbl” not a “tibble”?
Why is 1 not always equal to 1?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
3 things to learn:
Do I need to know SQL to use dplyr?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
Unlock Hadoop Success with Cloudera Navigator OptimizerCloudera, Inc.
Cloudera Navigator Optimizer analyzes existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop.
Data Science and Machine Learning for the EnterpriseCloudera, Inc.
Overview of Machine Learning and how the Cloudera Data Science Workbench provides full access to data while supporting IT SLAs. The presentation includes details on Fast Forward Labs and The Value of Interpretability in Models.
Multi-Tenant Operations with Cloudera 5.7 & BTCloudera, Inc.
One benefit of Apache Hadoop is the ability to power multiple workloads, across many different users and departments, all within a single, shared cluster. Hear how BT is doing this today and learn about new features in Cloudera Manager to provide better visibility for multi-tenant operations.
Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for productionCloudera, Inc.
It’s no secret that Apache Spark is becoming the successor to MapReduce for data processing in Hadoop. With it’s easy development, flexible API, and performance benefits, Spark is a powerful data processing engine that has quickly gained popularity within the community. On the other hand Hive continues to be the most widely used data warehouse/ETL engine with large scale adoption across enterprises. Therefore, it’s imperative to enable Spark as the underlying execution engine for Hive to seamlessly allow existing and future Hive workloads to leverage the advantages of Spark.
With the recent release of Cloudera 5.7, we have delivered on this goal by adding support for Hive-on-Spark. Data engineers and ETL developers can now transition from MR to Spark for their Hive workloads seamlessly thereby benefitting from the advantages of Spark without any disruption on their end.
Join Santosh Kumar, Senior Product Manager at Cloudera, and Rui Li, Apache Hive committer and engineer at Intel, as we discuss:
An Introduction to Spark and its advantages over MR
An introduction of Hive-on-Spark: Goals and Design Principles
Migrating to HoS and a live demo
Configuring and tuning for batch workloads
What’s next for both tools
How to build leakproof stream processing pipelines with Apache Kafka and Apac...Cloudera, Inc.
When Kafka stream processing pipelines fail, they can leave users panicked about data loss when restarting their application. Jordan Hambleton and Guru Medasani explain how offset management provides users the ability to restore the state of the stream throughout its lifecycle, deal with unexpected failure, and improve accuracy of results.
Hadoop 3.0 has been years in the making, and now it's finally arriving. Andrew Wang and Daniel Templeton offer an overview of new features, including HDFS erasure coding, YARN Timeline Service v2, YARN federation, and much more, and discuss current release management status and community testing efforts dedicated to making Hadoop 3.0 the best Hadoop major release yet.
Time and again, research shows organisations are held back in their digital transformation because of a lack of skills. A recent IDC survey shows it's the case for nearly half of all organisations when it comes to specialist big data and data science skills. As an organisation, how do you know you're hiring the right people to close the gap? As an individual, how do you prove you know what you're doing?
Cloudera Data Science Workbench: sparklyr, implyr, and More - dplyr Interfac...Cloudera, Inc.
You like to use R, and you need to use big data. dplyr, one of the most popular packages for R, makes it easy to query large data sets in scalable processing engines like Apache Spark and Apache Impala.
But there can be pitfalls: dplyr works differently with different data sources—and those differences can bite you if you don’t know what you’re doing.
Ian Cook is a data scientist, an R contributor, and a curriculum developer at Cloudera University. In this webinar, Ian will show you exactly what you need to know about sparklyr (from RStudio) and the package implyr (from Cloudera). He will show you how to write dplyr code that works across these different interfaces. And, he will solve mysteries:
Do I need to know SQL to use dplyr?
When is a “tbl” not a “tibble”?
Why is 1 not always equal to 1?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
3 things to learn:
Do I need to know SQL to use dplyr?
When should you collect(), collapse(), and compute()?
How can you use dplyr to combine data stored in different systems?
Unlock Hadoop Success with Cloudera Navigator OptimizerCloudera, Inc.
Cloudera Navigator Optimizer analyzes existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop.
Data Science and Machine Learning for the EnterpriseCloudera, Inc.
Overview of Machine Learning and how the Cloudera Data Science Workbench provides full access to data while supporting IT SLAs. The presentation includes details on Fast Forward Labs and The Value of Interpretability in Models.
Multi-Tenant Operations with Cloudera 5.7 & BTCloudera, Inc.
One benefit of Apache Hadoop is the ability to power multiple workloads, across many different users and departments, all within a single, shared cluster. Hear how BT is doing this today and learn about new features in Cloudera Manager to provide better visibility for multi-tenant operations.
Faster Batch Processing with Cloudera 5.7: Hive-on-Spark is ready for productionCloudera, Inc.
It’s no secret that Apache Spark is becoming the successor to MapReduce for data processing in Hadoop. With it’s easy development, flexible API, and performance benefits, Spark is a powerful data processing engine that has quickly gained popularity within the community. On the other hand Hive continues to be the most widely used data warehouse/ETL engine with large scale adoption across enterprises. Therefore, it’s imperative to enable Spark as the underlying execution engine for Hive to seamlessly allow existing and future Hive workloads to leverage the advantages of Spark.
With the recent release of Cloudera 5.7, we have delivered on this goal by adding support for Hive-on-Spark. Data engineers and ETL developers can now transition from MR to Spark for their Hive workloads seamlessly thereby benefitting from the advantages of Spark without any disruption on their end.
Join Santosh Kumar, Senior Product Manager at Cloudera, and Rui Li, Apache Hive committer and engineer at Intel, as we discuss:
An Introduction to Spark and its advantages over MR
An introduction of Hive-on-Spark: Goals and Design Principles
Migrating to HoS and a live demo
Configuring and tuning for batch workloads
What’s next for both tools
How to build leakproof stream processing pipelines with Apache Kafka and Apac...Cloudera, Inc.
When Kafka stream processing pipelines fail, they can leave users panicked about data loss when restarting their application. Jordan Hambleton and Guru Medasani explain how offset management provides users the ability to restore the state of the stream throughout its lifecycle, deal with unexpected failure, and improve accuracy of results.
Hadoop 3.0 has been years in the making, and now it's finally arriving. Andrew Wang and Daniel Templeton offer an overview of new features, including HDFS erasure coding, YARN Timeline Service v2, YARN federation, and much more, and discuss current release management status and community testing efforts dedicated to making Hadoop 3.0 the best Hadoop major release yet.
Part 2: Cloudera’s Operational Database: Unlocking New Benefits in the CloudCloudera, Inc.
3 Things to Learn About:
*On-premises versus the cloud
*Design & benefits of real-time operational data in the cloud
*Best practices and architectural considerations
Simplifying Hadoop with RecordService, A Secure and Unified Data Access Path ...Cloudera, Inc.
SFHUG presentation from February 2, 2016. One of the key values of the Hadoop ecosystem is its flexibility. There is a myriad of components that make up this ecosystem, allowing Hadoop to tackle otherwise intractable problems. However, having so many components provides a significant integration, implementation, and usability burden. Features that ought to work in all the components often require sizable per-component effort to ensure correctness across the stack.
Lenni Kuff explores RecordService, a new solution to this problem that provides an API to read data from Hadoop storage managers and return them as canonical records. This eliminates the need for components to support individual file formats, handle security, perform auditing, and implement sophisticated IO scheduling and other common processing that is at the bottom of any computation.
Lenni discusses the architecture of the service and the integration work done for MapReduce and Spark. Many existing applications on those frameworks can take advantage of the service with little to no modification. Lenni demonstrates how this provides fine grain (column level and row level) security, through Sentry integration, and improves performance for existing MapReduce and Spark applications by up to 5×. Lenni concludes by discussing how this architecture can enable significant future improvements to the Hadoop ecosystem.
About the speaker: Lenni Kuff is an engineering manager at Cloudera. Before joining Cloudera, he worked at Microsoft on a number of projects including SQL Server storage engine, SQL Azure, and Hadoop on Azure. Lenni graduated from the University of Wisconsin-Madison with degrees in computer science and computer engineering.
One Hadoop, Multiple Clouds - NYC Big Data MeetupAndrei Savu
The slide deck I presented at NYC Big Data Meetup just before Strata + Hadoop World 2015. It goes into details on what's different about running Hadoop in the cloud, main use case and some lessons learned from working with customers.
Discover the origins of big data, discuss existing and new projects, share common use cases for those projects, and explain how you can modernize your architecture using data analytics, data operations, data engineering and data science.
Big Data Fundamentals is your prerequisite to building a modern platform for machine learning and analytics optimized for the cloud.
We’ll close out with a live Q&A with some of our technical experts as well.
Stretch your brain with a packed agenda:
Open source software
Data storage
Data ingestion
Data analytics
Data engineering
IoT and life after Lambda architectures
Data science
Cybersecurity
Cluster management
Big data in the cloud
Success stories
Part 1: Lambda Architectures: Simplified by Apache KuduCloudera, Inc.
3 Things to Learn About:
* The concept of lambda architectures
* The Hadoop ecosystem components involved in lambda architectures
* The advantages and disadvantages of lambda architectures
Doug Cutting discusses:
- A brief history of Spark and its rise in popularity across developers and enterprises
- Spark's advantages over MapReduce
- The One Platform Initiative and the roadmap for Spark
- The future of data processing in Hadoop
Introduction to Machine Learning on Apache Spark MLlib by Juliet Hougland, Se...Cloudera, Inc.
Spark MLlib is a library for performing machine learning and associated tasks on massive datasets. With MLlib, fitting a machine-learning model to a billion observations can take only a few lines of code, and leverage hundreds of machines. This talk will demonstrate how to use Spark MLlib to fit an ML model that can predict which customers of a telecommunications company are likely to stop using their service. It will cover the use of Spark's DataFrames API for fast data manipulation, as well as ML Pipelines for making the model development and refinement process easier.
Risk Management for Data: Secured and GovernedCloudera, Inc.
Cloudera Tech Day Presentation by Eddie Garcia, Chief Security Architect, Cloudera. Protecting enterprise data is an increasingly complex challenge given the diversity and sophistication of threat actors and their cyber-tactics. In this session, participants will hear a comprehensive introduction to Hadoop Security, including the “three A’s” for secure operating environments: Authentication, Authorization, and Audit. In addition, the presenter will cover strategies to orchestrate data security, encryption, and compliance, and will explain the Cloudera Security Maturity Model for Hadoop. Attendees will leave with a greater understanding of how effective INFOSEC relies on an enterprise big data governance and risk management approach.
Extreme Sports & Beyond: Exploring a new frontier in data with GoProCloudera, Inc.
GoPro is a powerful global brand, thanks in large part to its innovative cameras and accessories that capture moments other cameras just miss: surfing in Maui, skiing in Tahoe, recording your child’s first steps. And today, the company is nearly as well known for its user-generated social and content networks.
Join us for this special webinar hosted by Tableau, Trifacta, and Cloudera—featuring GoPro. We’ll dive into GoPro’s data strategy and architecture, from ingest and processing to data prep and reporting, all on AWS.
Data Science at Scale Using Apache Spark and Apache HadoopCloudera, Inc.
Learn about the skills and tools a data scientist needs and how to start training to be one.
There's so much noise about what a data scientist is or isn't that it can be challenging to identify the skills needed to start training a team or becoming one yourself. What exactly is a data scientist and where do you start?
Cloudera's Director of Data Science, Sean Owen, will start by walking through the different skills data scientist should have and why businesses need them. Afterwards, Tom Wheeler, Cloudera's Principal Curriculum Developer, will introduce the latest data science course developed by Cloudera University designed to help people take their first steps to becoming a data scientist.
Discusses what to consider when writing a facial recognition application and how to scale it on multiple nodes using Spark. The approach discusses tools like OpenCV and dlib for traditional approaches and Tensorflow for inference to create embeddings\features.
Hadoop for the Data Scientist: Spark in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
This is the presentation from Bangalore Big Data November Meetup given by Davin Chaiken, AltiScale.
technology.inmobi.com/events/bigdata-meetup
Talk Outline:
- Altiscale Company Introduction and Perspective
- Altiscale Architecture
- Use Cases: Performance, Job Analysis, Scheduling
- Infinite Hadoop
- Challenges to the Hadoop Community
Part 3: Models in Production: A Look From Beginning to EndCloudera, Inc.
3 Things to Learn About:
-How to uplevel your existing analytics stack with a collaborative environment that supports the latest open source languages and libraries.
-How to get better use of your core data management investments while opening up new supported tools for data science.
-How to expand data science outside of silo’d environments and enable self-service data science access.
This deck covers key considerations and provides advice for enterprises looking to run production-scale Cloudera on AWS. We touch on everything from security to governance to selecting the right instance type for your Hadoop workload (Spark, Impala, Search, etc).
Multidisziplinäre Analyseanwendungen auf einer gemeinsamen Datenplattform ers...Cloudera, Inc.
Maschinelles Lernen und Analyseanwendungen explodieren im Unternehmen und ermöglichen Anwendungsfällen in Bereichen wie vorbeugende Wartung, Bereitstellung neuer, wünschenswerter Produktangebote für Kunden zum richtigen Zeitpunkt und Bekämpfung von Insider-Bedrohungen für Ihr Unternehmen.
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformCloudera, Inc.
Machine learning and analytics applications are exploding in the enterprise; driving use cases for preventative maintenance, delivering new desirable product offers to customers at the right time, and combating insider threats to your business.
But each of these high-value use cases rely on a variety of data analysis capabilities working in concert to combine data from different sources into a single coherent picture. Cloudera SDX delivers a “shared data experience” that makes applications easier to develop, less expensive to deploy and more consistently secure.
3 things to learn:
* Why multi-function applications are difficult to build and secure
* How shared catalog, governance, management, and security applied consistently everywhere can deliver a “shared data experience”
* How enterprise customers are building new, high-value applications with SDX
Part 2: Cloudera’s Operational Database: Unlocking New Benefits in the CloudCloudera, Inc.
3 Things to Learn About:
*On-premises versus the cloud
*Design & benefits of real-time operational data in the cloud
*Best practices and architectural considerations
Simplifying Hadoop with RecordService, A Secure and Unified Data Access Path ...Cloudera, Inc.
SFHUG presentation from February 2, 2016. One of the key values of the Hadoop ecosystem is its flexibility. There is a myriad of components that make up this ecosystem, allowing Hadoop to tackle otherwise intractable problems. However, having so many components provides a significant integration, implementation, and usability burden. Features that ought to work in all the components often require sizable per-component effort to ensure correctness across the stack.
Lenni Kuff explores RecordService, a new solution to this problem that provides an API to read data from Hadoop storage managers and return them as canonical records. This eliminates the need for components to support individual file formats, handle security, perform auditing, and implement sophisticated IO scheduling and other common processing that is at the bottom of any computation.
Lenni discusses the architecture of the service and the integration work done for MapReduce and Spark. Many existing applications on those frameworks can take advantage of the service with little to no modification. Lenni demonstrates how this provides fine grain (column level and row level) security, through Sentry integration, and improves performance for existing MapReduce and Spark applications by up to 5×. Lenni concludes by discussing how this architecture can enable significant future improvements to the Hadoop ecosystem.
About the speaker: Lenni Kuff is an engineering manager at Cloudera. Before joining Cloudera, he worked at Microsoft on a number of projects including SQL Server storage engine, SQL Azure, and Hadoop on Azure. Lenni graduated from the University of Wisconsin-Madison with degrees in computer science and computer engineering.
One Hadoop, Multiple Clouds - NYC Big Data MeetupAndrei Savu
The slide deck I presented at NYC Big Data Meetup just before Strata + Hadoop World 2015. It goes into details on what's different about running Hadoop in the cloud, main use case and some lessons learned from working with customers.
Discover the origins of big data, discuss existing and new projects, share common use cases for those projects, and explain how you can modernize your architecture using data analytics, data operations, data engineering and data science.
Big Data Fundamentals is your prerequisite to building a modern platform for machine learning and analytics optimized for the cloud.
We’ll close out with a live Q&A with some of our technical experts as well.
Stretch your brain with a packed agenda:
Open source software
Data storage
Data ingestion
Data analytics
Data engineering
IoT and life after Lambda architectures
Data science
Cybersecurity
Cluster management
Big data in the cloud
Success stories
Part 1: Lambda Architectures: Simplified by Apache KuduCloudera, Inc.
3 Things to Learn About:
* The concept of lambda architectures
* The Hadoop ecosystem components involved in lambda architectures
* The advantages and disadvantages of lambda architectures
Doug Cutting discusses:
- A brief history of Spark and its rise in popularity across developers and enterprises
- Spark's advantages over MapReduce
- The One Platform Initiative and the roadmap for Spark
- The future of data processing in Hadoop
Introduction to Machine Learning on Apache Spark MLlib by Juliet Hougland, Se...Cloudera, Inc.
Spark MLlib is a library for performing machine learning and associated tasks on massive datasets. With MLlib, fitting a machine-learning model to a billion observations can take only a few lines of code, and leverage hundreds of machines. This talk will demonstrate how to use Spark MLlib to fit an ML model that can predict which customers of a telecommunications company are likely to stop using their service. It will cover the use of Spark's DataFrames API for fast data manipulation, as well as ML Pipelines for making the model development and refinement process easier.
Risk Management for Data: Secured and GovernedCloudera, Inc.
Cloudera Tech Day Presentation by Eddie Garcia, Chief Security Architect, Cloudera. Protecting enterprise data is an increasingly complex challenge given the diversity and sophistication of threat actors and their cyber-tactics. In this session, participants will hear a comprehensive introduction to Hadoop Security, including the “three A’s” for secure operating environments: Authentication, Authorization, and Audit. In addition, the presenter will cover strategies to orchestrate data security, encryption, and compliance, and will explain the Cloudera Security Maturity Model for Hadoop. Attendees will leave with a greater understanding of how effective INFOSEC relies on an enterprise big data governance and risk management approach.
Extreme Sports & Beyond: Exploring a new frontier in data with GoProCloudera, Inc.
GoPro is a powerful global brand, thanks in large part to its innovative cameras and accessories that capture moments other cameras just miss: surfing in Maui, skiing in Tahoe, recording your child’s first steps. And today, the company is nearly as well known for its user-generated social and content networks.
Join us for this special webinar hosted by Tableau, Trifacta, and Cloudera—featuring GoPro. We’ll dive into GoPro’s data strategy and architecture, from ingest and processing to data prep and reporting, all on AWS.
Data Science at Scale Using Apache Spark and Apache HadoopCloudera, Inc.
Learn about the skills and tools a data scientist needs and how to start training to be one.
There's so much noise about what a data scientist is or isn't that it can be challenging to identify the skills needed to start training a team or becoming one yourself. What exactly is a data scientist and where do you start?
Cloudera's Director of Data Science, Sean Owen, will start by walking through the different skills data scientist should have and why businesses need them. Afterwards, Tom Wheeler, Cloudera's Principal Curriculum Developer, will introduce the latest data science course developed by Cloudera University designed to help people take their first steps to becoming a data scientist.
Discusses what to consider when writing a facial recognition application and how to scale it on multiple nodes using Spark. The approach discusses tools like OpenCV and dlib for traditional approaches and Tensorflow for inference to create embeddings\features.
Hadoop for the Data Scientist: Spark in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
This is the presentation from Bangalore Big Data November Meetup given by Davin Chaiken, AltiScale.
technology.inmobi.com/events/bigdata-meetup
Talk Outline:
- Altiscale Company Introduction and Perspective
- Altiscale Architecture
- Use Cases: Performance, Job Analysis, Scheduling
- Infinite Hadoop
- Challenges to the Hadoop Community
Part 3: Models in Production: A Look From Beginning to EndCloudera, Inc.
3 Things to Learn About:
-How to uplevel your existing analytics stack with a collaborative environment that supports the latest open source languages and libraries.
-How to get better use of your core data management investments while opening up new supported tools for data science.
-How to expand data science outside of silo’d environments and enable self-service data science access.
This deck covers key considerations and provides advice for enterprises looking to run production-scale Cloudera on AWS. We touch on everything from security to governance to selecting the right instance type for your Hadoop workload (Spark, Impala, Search, etc).
Multidisziplinäre Analyseanwendungen auf einer gemeinsamen Datenplattform ers...Cloudera, Inc.
Maschinelles Lernen und Analyseanwendungen explodieren im Unternehmen und ermöglichen Anwendungsfällen in Bereichen wie vorbeugende Wartung, Bereitstellung neuer, wünschenswerter Produktangebote für Kunden zum richtigen Zeitpunkt und Bekämpfung von Insider-Bedrohungen für Ihr Unternehmen.
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformCloudera, Inc.
Machine learning and analytics applications are exploding in the enterprise; driving use cases for preventative maintenance, delivering new desirable product offers to customers at the right time, and combating insider threats to your business.
But each of these high-value use cases rely on a variety of data analysis capabilities working in concert to combine data from different sources into a single coherent picture. Cloudera SDX delivers a “shared data experience” that makes applications easier to develop, less expensive to deploy and more consistently secure.
3 things to learn:
* Why multi-function applications are difficult to build and secure
* How shared catalog, governance, management, and security applied consistently everywhere can deliver a “shared data experience”
* How enterprise customers are building new, high-value applications with SDX
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.
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.
Big data journey to the cloud 5.30.18 asher bartchCloudera, Inc.
We hope this session was valuable in teaching you more about Cloudera Enterprise on AWS, and how fast and easy it is to deploy a modern data management platform—in your cloud and on your terms.
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.
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.
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.
Cloudera GoDataFest Deploying Cloudera in the CloudGoDataDriven
Cloud can offer flexibility and agility to your clusters. Learn more about how to deploy Cloudera in the cloud, the best practices for long-running clusters and transient clusters. And see how easy it is to spin up both kind of clusters in the cloud with Altus and Director.
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.
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.
High-Performance Analytics in the Cloud with Apache ImpalaCloudera, Inc.
With more and more data being generated and stored in the cloud, you need a modern data platform that can extend to any environment so you can derive value from all your data. Cloudera Enterprise is the leading enterprise Hadoop platform for cloud deployments. It’s the easiest way to manage and secure Hadoop data across any cloud environment and includes component-level support for cloud-native object stores. This makes the platform uniquely suited to handle transient jobs like ETL and BI analytics, as well as persistent workloads like stream processing and advanced analytics.
With the recent release of Cloudera 5.8, Apache Impala (incubating) has added support for Amazon S3, enabling business analysts to get instant insights from all data through high-performance exploratory analytics and BI.
3 Things to learn:
Join David Tishgart, Director of Product Marketing, and James Curtis, Senior Analyst Data Platforms & Analytics at 451 Research, as they discuss:
* Best practices for analytic workloads in the cloud
* A live demo and real-world use cases
* What’s next for Cloudera and the cloud
Cloudera Altus: Big Data in the Cloud Made EasyCloudera, Inc.
Cloudera Altus makes it easier for data engineers, ETL developers, and anyone who regularly works with raw data to process that data in the cloud efficiently and cost effectively. In this webinar we introduce our new platform-as-a-service offering and explore challenges associated with data processing in the cloud today, how Altus abstracts cluster overhead to deliver easy, efficient data processing, and unique features and benefits of Cloudera Altus.
Gartner Data and Analytics Summit: Bringing Self-Service BI & SQL Analytics ...Cloudera, Inc.
For self-service BI and exploratory analytic workloads, the cloud can provide a number of key benefits, but the move to the cloud isn’t all-or-nothing. Gartner predicts nearly 80 percent of businesses will adopt a hybrid strategy. Learn how a modern analytic database can power your business-critical workloads across multi-cloud and hybrid environments, while maintaining data portability. We'll also discuss how to best leverage the increased agility cloud provides, while maintaining peak performance.
Self-service Big Data Analytics on Microsoft AzureCloudera, Inc.
In this presentation Microsoft will join Cloudera to introduce a new Platform-as-a-Service (PaaS) offering that helps data engineers use on-demand cloud infrastructure to speed the creation and operation of data pipelines that power sophisticated, data-driven applications - without onerous administration.
Cloud-Native Machine Learning: Emerging Trends and the Road AheadDataWorks Summit
Big data platforms are being asked to support an ever increasing range of workloads and compute environments, including large-scale machine learning and public and private clouds. In this talk, we will discuss some emerging capabilities around cloud-native machine learning and data engineering, including running machine learning and Spark workloads directly on Kubernetes, and share our vision of the road ahead for ML and AI in the cloud.
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.
It’s becoming clear that enterprises need more than one cloud. Hybrid enables enterprises to optimize how their business works – public cloud for elasticity and scale, multi-cloud for redundancy and choice, and on-premises for performance and privacy. Cloudera delivers a hybrid cloud solution that works where enterprises work, with the agility, security and governance enterprise IT needs, and the self-service analytics business people and enterprise data professionals demand. In this session, we will talk about how Cloudera helps deliver hybrid solutions for enterprises and will run a hands-on Cloudera PaaS demo to exhibit:
- Altus Environment Setup
- Configure Altus SDX
- Spin-up transient clusters with Altus
- Execute workload on Altus Data Engineering clusters
- Run interactive queries on object store with Altus Data Warehouse
- Job Analytics with Workload Experience Manager (WXM)
Speaker: Junaid Rao, Senior Cloud Sales Engineer, Cloudera
Optimize your cloud strategy for machine learning and analyticsCloudera, Inc.
Join industry superstars Mike Olson (Cloudera CSO and co-founder) and Jim Curtis (451 Research senior analyst) as they outline the best practices for cloud-based machine learning and analytics in this “can’t miss” webinar.
Hot topics include:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
Success stories, cautionary tales, and lessons learned
James will share 451 Research findings and offer insights learned from surveying both the vendor landscape and enterprise practitioners.
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Mike will regale you with his vision for the future of multi-disciplinary machine learning and analytics in hybrid- and multi-cloud environments
3 things to learn:
Why enterprises are moving their analytics to the public cloud
How to select the best cloud deployment model
Design tricks that make cloud economics work
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.
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.
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.
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.
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?
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.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”