Learn about SAS and Cloudera technical integration, how SAS builds on the enterprise data hub, and SAS In-Memory Statistics for Hadoop, machine learning capabilities.
Oracle Openworld Presentation with Paul Kent (SAS) on Big Data Appliance and ...jdijcks
Learn about the benefits of Oracle Big Data Appliance and how it can drive business value underneath applications and tools. This includes a section by Paul Kent, VP Big Data SAS describing how SAS runs well on Oracle Engineered Systems and on Oracle Big Data Appliance specifically.
Predictive Analytics and Machine Learning…with SAS and Apache HadoopHortonworks
In this interactive webinar, we'll walk through use cases on how you can use advanced analytics like SAS Visual Statistics and In-Memory Statistic with Hortonworks’ data platform (HDP) to reveal insights in your big data and redefine how your organization solves complex problems.
The document summarizes the Cask Data Application Platform (CDAP), which provides an integrated framework for building and running data applications on Hadoop and Spark. It consolidates the big data application lifecycle by providing dataset abstractions, self-service data, metrics and log collection, lineage, audit, and access control. CDAP has an application container architecture with reusable programming abstractions and global user and machine metadata. It aims to simplify deploying and operating big data applications in enterprises by integrating technologies like YARN, HBase, Kafka and Spark.
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA and SAP BusinessObjects enabling a broad range of new analytic applications.
The document discusses Teradata's portfolio for Hadoop, including the Teradata Aster Big Analytics Appliance, the Teradata Appliance for Hadoop, a commodity offering with Dell, and support for the Hortonworks Data Platform. It provides consulting, training, support, and managed services for Hadoop. Teradata SQL-H gives business users standard SQL access to data stored in Hadoop through Teradata, allowing queries to run quickly on Teradata while accessing data from Hadoop efficiently through HCatalog.
This is a point of view document showing the various possible techniques to integrate SAP HANA and Hadoop and their pros & cons and the scenarios where each of them is recommended.
This document discusses strategies for successfully utilizing a data lake. It notes that creating a data lake is just the beginning and that challenges include data governance, metadata management, access, and effective use of the data. The document advocates for data democratization through discovery, accessibility, and usability. It also discusses best practices like self-service BI and automated workload migration from data warehouses to reduce costs and risks. The key is to address the "data lake dilemma" of these challenges to avoid a "data swamp" and slow adoption.
Partners 2013 LinkedIn Use Cases for Teradata Connectors for HadoopEric Sun
Teradata Connectors for Hadoop enable high-volume data movement between Teradata and Hadoop platforms. LinkedIn conducted a proof-of-concept using the connectors for use cases like copying clickstream data from Hadoop to Teradata for analytics and publishing dimension tables from Teradata to Hadoop for machine learning. The connectors help address challenges of scalability and tight processing windows for these large-scale data transfers.
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA, Sybase and SAP BusinessObjects enabling a broad range of new analytic applications.
This document discusses architecting Hadoop for adoption and data applications. It begins by explaining how traditional systems struggle as data volumes increase and how Hadoop can help address this issue. Potential Hadoop use cases are presented such as file archiving, data analytics, and ETL offloading. Total cost of ownership (TCO) is discussed for each use case. The document then covers important considerations for deploying Hadoop such as hardware selection, team structure, and impact across the organization. Lastly, it discusses lessons learned and the need for self-service tools going forward.
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Hortonworks
Many enterprises are turning to Apache Hadoop to enable Big Data Analytics and reduce the costs of traditional data warehousing. Yet, it is hard to succeed when 80% of the time is spent on moving data and only 20% on using it. It’s time to swap the 80/20! The Big Data experts at Attunity and Hortonworks have a solution for accelerating data movement into and out of Hadoop that enables faster time-to-value for Big Data projects and a more complete and trusted view of your business. Join us to learn how this solution can work for you.
This document discusses harnessing big data in real-time. It outlines how business requirements are increasingly demanding real-time insights from data. Traditional systems struggle with high latency, complexity, and costs when dealing with big data. The document proposes using SAP HANA and Hadoop together to enable instant analytics on vast amounts of data. It provides examples of using this approach for cancer genome analysis and other use cases to generate personalized and timely results.
This document discusses Tableau's role in big data architectures and its integration with Hadoop. It outlines different workload categories for business intelligence and their considerations for Tableau. Three integration models are described: isolated exploration, live interactive query, and integrated advanced analytics. Capability models are presented for each integration approach regarding suitability for Hadoop. Finally, architecture patterns are shown for isolated exploration, live interactive querying, and an integrated advanced analytics platform using Tableau and Hadoop.
Lowering the entry point to getting going with Hadoop and obtaining business ...DataWorks Summit
SAS is a leader in advanced analytics with over 40 years of experience. They provide tools to manage, explore, develop models, and deploy analytics from, with, and within Hadoop. This allows customers to realize value from Hadoop throughout the entire analytics lifecycle. SAS helps address challenges like Hadoop skills shortages and tools not being optimized for big data. They demonstrated identifying reasons for abandoned shopping carts using Hadoop and SAS analytics tools.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Oracle Data Integration Platform is a cornerstone for big data solutions that provides five core capabilities: business continuity, data movement, data transformation, data governance, and streaming data handling. It includes eight core products that can operate in the cloud or on-premise, and is considered the most innovative in areas like real-time/streaming integration and extract-load-transform capabilities with big data technologies. The platform offers a comprehensive architecture covering key areas like data ingestion, preparation, streaming integration, parallel connectivity, and governance.
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the CloudDataWorks Summit
This document discusses how organizations can leverage data and analytics to power their business models. It provides examples of Fortune 100 companies that are using Attunity products to build data lakes and ingest data from SAP and other sources into Hadoop, Apache Kafka, and the cloud in order to perform real-time analytics. The document outlines the benefits of Attunity's data replication tools for extracting, transforming, and loading SAP and other enterprise data into data lakes and data warehouses.
Hortonworks Data Platform 2.2 includes Apache HBase for fast NoSQL data access. In this 30-minute webinar, we discussed HBase innovations that are included in HDP 2.2, including: support for Apache Slider; Apache HBase high availability (HA); block ache compression; and wire-level encryption.
Ambari Meetup: 2nd April 2013: Teradata Viewpoint Hadoop Integration with AmbariHortonworks
Teradata Viewpoint provides a unified monitoring solution for Teradata Database, Aster, and Hadoop. It integrates with Ambari to simplify monitoring Hadoop. Viewpoint uses Ambari's REST APIs to collect metrics and alerts from Hadoop and store them in a database for trend analysis and visualization. This allows Viewpoint to deliver comprehensive Hadoop monitoring without having to understand its various monitoring technologies.
Starting Small and Scaling Big with Hadoop (Talend and Hortonworks webinar)) ...Hortonworks
This document discusses using Hadoop and the Hortonworks Data Platform (HDP) for big data applications. It outlines how HDP can help organizations optimize their existing data warehouse, lower storage costs, unlock new applications from new data sources, and achieve an enterprise data lake architecture. The document also discusses how Talend's data integration platform can be used with HDP to easily develop batch, real-time, and interactive data integration jobs on Hadoop. Case studies show how companies have used Talend and HDP together to modernize their data architecture and product inventory and pricing forecasting.
The document discusses building an information analytics platform by integrating Hadoop and SAP HANA. It describes VUPICO's profile as a consulting firm focused on analytics using Hadoop and SAP HANA. It outlines the benefits of integrating these technologies, such as scalability, real-time access to large data, and a common view of business data. It also discusses using SAP HANA VORA to bridge Hadoop and SAP HANA and generate further value from predictive analytics.
Discover HDP 2.1: Apache Solr for Hadoop SearchHortonworks
This document appears to be a presentation about Apache Solr for Hadoop search using the Hortonworks Data Platform (HDP). The agenda includes an overview of Apache Solr and Hadoop search, a demo of Hadoop search, and a question and answer section. The presentation discusses how Solr provides scalable indexing of data stored in HDFS and powerful search capabilities. It also includes a reference architecture showing how Solr integrates with Hadoop for search and indexing.
YARN Ready: Integrating to YARN with Tez Hortonworks
YARN Ready webinar series helps developers integrate their applications to YARN. Tez is one vehicle to do that. We take a deep dive including code review to help you get started.
This document discusses Oracle Data Integration solutions for tapping into big data reservoirs. It begins with an overview of Oracle Data Integration and how it can improve agility, reduce risk and costs. It then discusses Oracle's approach to comprehensive data integration and governance capabilities including real-time data movement, data transformation, data federation, and more. The document also provides examples of how Oracle Data Integration has been used by customers for big data use cases involving petabytes of data.
Ted Dunning is the Chief Applications Architect at MapR Technologies and a committer for Apache Drill, Zookeeper, and other projects. The document discusses goals around real-time or near-time processing and microservices. It describes how to design microservices for isolation using self-describing data, private databases, and shared storage only where necessary. Various scenarios involving fraud detection, IoT data aggregation, and global data recovery are presented. Lessons focus on decoupling services, propagating events rather than table updates, and how data architecture should reflect business structure.
Modern data management using Kappa and streaming architectures, including discussion by EBay's Connie Yang about the Rheos platform and the use of Oracle GoldenGate, Kafka, Flink, etc.
This document provides an overview of real-time processing capabilities on Hortonworks Data Platform (HDP). It discusses how a trucking company uses HDP to analyze sensor data from trucks in real-time to monitor for violations and integrate predictive analytics. The company collects data using Kafka and analyzes it using Storm, HBase and Hive on Tez. This provides real-time dashboards as well as querying of historical data to identify issues with routes, trucks or drivers. The document explains components like Kafka, Storm and HBase and how they enable a unified YARN-based architecture for multiple workloads on a single HDP cluster.
This document provides an overview of Apache Atlas and how it addresses big data governance issues for enterprises. It discusses how Atlas provides a centralized metadata repository that allows users to understand data across Hadoop components. It also describes how Atlas integrates with Apache Ranger to enable dynamic security policies based on metadata tags. Finally, it outlines new capabilities in upcoming Atlas releases, including cross-component data lineage tracking and a business taxonomy/catalog.
SAS Modernization architectures - Big Data AnalyticsDeepak Ramanathan
The document discusses strategies and technologies for scalable analytics using modern data architectures like Hadoop. It describes how declining storage costs and increasing CPU speeds have enabled organizations to leverage huge amounts of data through platforms like Hadoop. The document also summarizes SAS's big data strategy, how its technologies integrate with Hadoop, and how organizations can use SAS solutions to extract insights from data through the entire analytics lifecycle including data preparation, modeling, visualization and more.
Administrative Reporting of SAS Visual Analytics 7.1 and Integration with E...Francesco Marelli
SAS Visual Analytics 7.1 release introduces a new feature for audit and monitoring in near real-time dedicated to administrators using predefined reports; the new 2.4 release of SAS Environment Manager contains many new features including the ability to create a datamart that can be integrated with Visual Analytics to provide detailed information about the environment. Keinavo will explain how the deployment, integrated in its Cloud offering, of a "BI on BI" system with these features helps maintain efficient systems with an uptime of 99.9%.
Partners 2013 LinkedIn Use Cases for Teradata Connectors for HadoopEric Sun
Teradata Connectors for Hadoop enable high-volume data movement between Teradata and Hadoop platforms. LinkedIn conducted a proof-of-concept using the connectors for use cases like copying clickstream data from Hadoop to Teradata for analytics and publishing dimension tables from Teradata to Hadoop for machine learning. The connectors help address challenges of scalability and tight processing windows for these large-scale data transfers.
The strategic relationship between Hortonworks and SAP enables SAP to resell Hortonworks Data Platform (HDP) and provide enterprise support for their global customer base. This means SAP customers can incorporate enterprise Hadoop as a complement within a data architecture that includes SAP HANA, Sybase and SAP BusinessObjects enabling a broad range of new analytic applications.
This document discusses architecting Hadoop for adoption and data applications. It begins by explaining how traditional systems struggle as data volumes increase and how Hadoop can help address this issue. Potential Hadoop use cases are presented such as file archiving, data analytics, and ETL offloading. Total cost of ownership (TCO) is discussed for each use case. The document then covers important considerations for deploying Hadoop such as hardware selection, team structure, and impact across the organization. Lastly, it discusses lessons learned and the need for self-service tools going forward.
Webinar - Accelerating Hadoop Success with Rapid Data Integration for the Mod...Hortonworks
Many enterprises are turning to Apache Hadoop to enable Big Data Analytics and reduce the costs of traditional data warehousing. Yet, it is hard to succeed when 80% of the time is spent on moving data and only 20% on using it. It’s time to swap the 80/20! The Big Data experts at Attunity and Hortonworks have a solution for accelerating data movement into and out of Hadoop that enables faster time-to-value for Big Data projects and a more complete and trusted view of your business. Join us to learn how this solution can work for you.
This document discusses harnessing big data in real-time. It outlines how business requirements are increasingly demanding real-time insights from data. Traditional systems struggle with high latency, complexity, and costs when dealing with big data. The document proposes using SAP HANA and Hadoop together to enable instant analytics on vast amounts of data. It provides examples of using this approach for cancer genome analysis and other use cases to generate personalized and timely results.
This document discusses Tableau's role in big data architectures and its integration with Hadoop. It outlines different workload categories for business intelligence and their considerations for Tableau. Three integration models are described: isolated exploration, live interactive query, and integrated advanced analytics. Capability models are presented for each integration approach regarding suitability for Hadoop. Finally, architecture patterns are shown for isolated exploration, live interactive querying, and an integrated advanced analytics platform using Tableau and Hadoop.
Lowering the entry point to getting going with Hadoop and obtaining business ...DataWorks Summit
SAS is a leader in advanced analytics with over 40 years of experience. They provide tools to manage, explore, develop models, and deploy analytics from, with, and within Hadoop. This allows customers to realize value from Hadoop throughout the entire analytics lifecycle. SAS helps address challenges like Hadoop skills shortages and tools not being optimized for big data. They demonstrated identifying reasons for abandoned shopping carts using Hadoop and SAS analytics tools.
Data Integration for Big Data (OOW 2016, Co-Presented With Oracle)Rittman Analytics
Oracle Data Integration Platform is a cornerstone for big data solutions that provides five core capabilities: business continuity, data movement, data transformation, data governance, and streaming data handling. It includes eight core products that can operate in the cloud or on-premise, and is considered the most innovative in areas like real-time/streaming integration and extract-load-transform capabilities with big data technologies. The platform offers a comprehensive architecture covering key areas like data ingestion, preparation, streaming integration, parallel connectivity, and governance.
Bring Your SAP and Enterprise Data to Hadoop, Kafka, and the CloudDataWorks Summit
This document discusses how organizations can leverage data and analytics to power their business models. It provides examples of Fortune 100 companies that are using Attunity products to build data lakes and ingest data from SAP and other sources into Hadoop, Apache Kafka, and the cloud in order to perform real-time analytics. The document outlines the benefits of Attunity's data replication tools for extracting, transforming, and loading SAP and other enterprise data into data lakes and data warehouses.
Hortonworks Data Platform 2.2 includes Apache HBase for fast NoSQL data access. In this 30-minute webinar, we discussed HBase innovations that are included in HDP 2.2, including: support for Apache Slider; Apache HBase high availability (HA); block ache compression; and wire-level encryption.
Ambari Meetup: 2nd April 2013: Teradata Viewpoint Hadoop Integration with AmbariHortonworks
Teradata Viewpoint provides a unified monitoring solution for Teradata Database, Aster, and Hadoop. It integrates with Ambari to simplify monitoring Hadoop. Viewpoint uses Ambari's REST APIs to collect metrics and alerts from Hadoop and store them in a database for trend analysis and visualization. This allows Viewpoint to deliver comprehensive Hadoop monitoring without having to understand its various monitoring technologies.
Starting Small and Scaling Big with Hadoop (Talend and Hortonworks webinar)) ...Hortonworks
This document discusses using Hadoop and the Hortonworks Data Platform (HDP) for big data applications. It outlines how HDP can help organizations optimize their existing data warehouse, lower storage costs, unlock new applications from new data sources, and achieve an enterprise data lake architecture. The document also discusses how Talend's data integration platform can be used with HDP to easily develop batch, real-time, and interactive data integration jobs on Hadoop. Case studies show how companies have used Talend and HDP together to modernize their data architecture and product inventory and pricing forecasting.
The document discusses building an information analytics platform by integrating Hadoop and SAP HANA. It describes VUPICO's profile as a consulting firm focused on analytics using Hadoop and SAP HANA. It outlines the benefits of integrating these technologies, such as scalability, real-time access to large data, and a common view of business data. It also discusses using SAP HANA VORA to bridge Hadoop and SAP HANA and generate further value from predictive analytics.
Discover HDP 2.1: Apache Solr for Hadoop SearchHortonworks
This document appears to be a presentation about Apache Solr for Hadoop search using the Hortonworks Data Platform (HDP). The agenda includes an overview of Apache Solr and Hadoop search, a demo of Hadoop search, and a question and answer section. The presentation discusses how Solr provides scalable indexing of data stored in HDFS and powerful search capabilities. It also includes a reference architecture showing how Solr integrates with Hadoop for search and indexing.
YARN Ready: Integrating to YARN with Tez Hortonworks
YARN Ready webinar series helps developers integrate their applications to YARN. Tez is one vehicle to do that. We take a deep dive including code review to help you get started.
This document discusses Oracle Data Integration solutions for tapping into big data reservoirs. It begins with an overview of Oracle Data Integration and how it can improve agility, reduce risk and costs. It then discusses Oracle's approach to comprehensive data integration and governance capabilities including real-time data movement, data transformation, data federation, and more. The document also provides examples of how Oracle Data Integration has been used by customers for big data use cases involving petabytes of data.
Ted Dunning is the Chief Applications Architect at MapR Technologies and a committer for Apache Drill, Zookeeper, and other projects. The document discusses goals around real-time or near-time processing and microservices. It describes how to design microservices for isolation using self-describing data, private databases, and shared storage only where necessary. Various scenarios involving fraud detection, IoT data aggregation, and global data recovery are presented. Lessons focus on decoupling services, propagating events rather than table updates, and how data architecture should reflect business structure.
Modern data management using Kappa and streaming architectures, including discussion by EBay's Connie Yang about the Rheos platform and the use of Oracle GoldenGate, Kafka, Flink, etc.
This document provides an overview of real-time processing capabilities on Hortonworks Data Platform (HDP). It discusses how a trucking company uses HDP to analyze sensor data from trucks in real-time to monitor for violations and integrate predictive analytics. The company collects data using Kafka and analyzes it using Storm, HBase and Hive on Tez. This provides real-time dashboards as well as querying of historical data to identify issues with routes, trucks or drivers. The document explains components like Kafka, Storm and HBase and how they enable a unified YARN-based architecture for multiple workloads on a single HDP cluster.
This document provides an overview of Apache Atlas and how it addresses big data governance issues for enterprises. It discusses how Atlas provides a centralized metadata repository that allows users to understand data across Hadoop components. It also describes how Atlas integrates with Apache Ranger to enable dynamic security policies based on metadata tags. Finally, it outlines new capabilities in upcoming Atlas releases, including cross-component data lineage tracking and a business taxonomy/catalog.
SAS Modernization architectures - Big Data AnalyticsDeepak Ramanathan
The document discusses strategies and technologies for scalable analytics using modern data architectures like Hadoop. It describes how declining storage costs and increasing CPU speeds have enabled organizations to leverage huge amounts of data through platforms like Hadoop. The document also summarizes SAS's big data strategy, how its technologies integrate with Hadoop, and how organizations can use SAS solutions to extract insights from data through the entire analytics lifecycle including data preparation, modeling, visualization and more.
Administrative Reporting of SAS Visual Analytics 7.1 and Integration with E...Francesco Marelli
SAS Visual Analytics 7.1 release introduces a new feature for audit and monitoring in near real-time dedicated to administrators using predefined reports; the new 2.4 release of SAS Environment Manager contains many new features including the ability to create a datamart that can be integrated with Visual Analytics to provide detailed information about the environment. Keinavo will explain how the deployment, integrated in its Cloud offering, of a "BI on BI" system with these features helps maintain efficient systems with an uptime of 99.9%.
SAS Visual Analytics is a business intelligence tool that provides a single solution for faster, smarter decisions. It delivers self-service analytics capabilities across the enterprise through a central entry point and integrated components for data preparation, exploration, report design, and mobile BI. The tool allows users to gain immediate insights from big data analysis.
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
SAS on Your (Apache) Cluster, Serving your Data (Analysts)DataWorks Summit
SAS is a both a Language for processing data and an Application for doing Analytics. SAS has adapted to the Hadoop eco-system and intends to be a good citizen amongst the choices for processing large volumes of data on your cluster. As more people inside an organization want to access and process the accumulated data, the “schema on read” approach can degenerate into “redo work someone else might have done already”.
This talk begins comparing and contrasting different data storage strategies, and describes the flexibility provided by SAS to accommodate different approaches. These different storage techniques are ranked according to convenience, performance, interoperabilty – both practicality and cost of the translation. Techniques considered include:
· Storing the rawdata (weblogs, CSVs)
· Storing Hadoop metadata, then using Hive/Impala/Hawk
· Storing in Hadoop optimized formats (avro, protobufs, RCfile, parquet)
· Storing in Proprietary formats
The talk finishes up discussing the array of analytical techniques that SAS has converted to run on your cluster, with particular mention of situations where HDFS is just plain better than the RDBMS that came before it.
The document provides an overview of training on SAS Enterprise Guide and Enterprise Miner for analytical capabilities. It discusses the process flow involving data compilation in EG, analysis, and presentation. Advanced analytical techniques in EM like cluster analysis, decision trees, and regressions are also covered. Practical exercises on credit scoring using EG and EM are demonstrated involving steps of data acquisition, understanding data, selecting important variables, and modeling.
This presentation provides an overview of Hadoop, including:
- A brief history of data and the rise of big data from various sources.
- An introduction to Hadoop as an open source framework used for distributed processing and storage of large datasets across clusters of computers.
- Descriptions of the key components of Hadoop - HDFS for storage, and MapReduce for processing - and how they work together in the Hadoop architecture.
- An explanation of how Hadoop can be installed and configured in standalone, pseudo-distributed and fully distributed modes.
- Examples of major companies that use Hadoop like Amazon, Facebook, Google and Yahoo to handle their large-scale data and analytics needs.
This document discusses how data visualization and visual analytics tools from SAS help the Australian Institute of Health and Welfare analyze large amounts of health and welfare data to support evidence-based policymaking. The Institute collects and links complex data sets to produce information for research and policy discussions. Warren Richter of the Institute explains how SAS visual analytics allows them to explore integrated data from various sectors to gain insights and address questions from policymakers. Visualization simplifies vast data for non-technical users and helps unite teams around common analyses and decisions.
- The document outlines a presentation about the dendextend R package for extending and exploring dendrograms.
- It begins with a motivating example using the Iris dataset and different clustering algorithms.
- The presentation then covers how to get and use dendextend functions to modify and compare dendrograms, connect dendrograms to other packages like ggplot2, and shows examples of dendextend used in research.
This document discusses high performance analytics and summarizes key capabilities of SAS Visual Analytics including easy analytics, visualizations for any skill level, calculated measures, automatic forecasting, and saved report packages. It also provides examples of public data sources that can be analyzed in SAS Visual Analytics including agricultural production and pricing data from India.
Recognized as the industry leader in analytics and with more than 36 years of experi¬ence, SAS provides a framework of capabilities to help insurers significantly improve their fraud management processes. With SAS, you get:
• A hybrid approach to fraud detection, including link analysis
• Streamlined case management. Systematically facilitate investigations, and cap¬ture and display all pertinent information without corrupting the system with duplicate data entry.
• Advanced text analytics and data mining.
Hadoop World 2011: Big Data Analytics – Data Professionals: The New Enterpris...Cloudera, Inc.
This presentation will explore how Hadoop and Big Data are re-inventing enterprise workflows, and the pivotal role of the Data Analyst. It will examine the changing face of analytics and the streamlining of iterative queries through evolved user interfaces. The speaker will cut through hype around “shorter time to insight” and explain how combining Hadoop and SQL-based analytics help companies discover emergent trends hidden in unstructured data, without having to retrain data miners or restaff. In particular, it will highlight changes to Big Data analysis from this paradigm and illustrate stepwise how analysts can now connect to Big Data platforms, assemble working data sets from disparate sources, analyze and mine that data for actionable insight, publish the results as visualizations and for feeding reporting tools, and operationalize Map-Reduce and Big Data outcomes into company workflows – all without touching the command line.
Using Big Data to create a data drive organizationEdward Chenard
This document discusses how companies can become data-driven by leveraging big data. It provides information on how companies are seeing returns from big data initiatives, with most seeing improved business decisions and revenue streams. It emphasizes that a data-driven organization requires both data and people to make evidence-based decisions. Additional sections discuss how to obtain a competitive advantage through fast data integration, where to start with big data problem solving, and how to create a data-driven culture by moving from data to insights to actions.
HBase es un sistema de almacenamiento de datos NoSQL de código abierto distribuido y basado en columnas inspirado en Bigtable de Google. Usa HDFS para el almacenamiento y se divide en regiones que se distribuyen entre servidores. Grandes empresas como Facebook, Salesforce y Explorys usan HBase para almacenar decenas de petabytes de datos y miles de millones de filas.
Hadoop is a distributed processing framework for large datasets. It stores data across clusters of commodity hardware in a Hadoop Distributed File System (HDFS) and provides tools for distributed processing using MapReduce. HDFS uses a master-slave architecture with a namenode managing metadata and datanodes storing data blocks. Data is replicated across nodes for reliability. MapReduce allows distributed processing of large datasets in parallel across clusters.
This document appears to be a slide presentation about the dendextend R package. It begins with an outline of the talk including an example using the Iris dataset and overview of useful functions in the package. It then goes through various functions in more detail like labels, cutree, set and sending dendrograms to other packages. It emphasizes exploring and modifying dendrograms using dendextend and comparing dendrograms visually.
Moving Health Care Analytics to Hadoop to Build a Better Predictive ModelDataWorks Summit
This document discusses Dignity Health's move to using Hadoop for healthcare analytics to build better predictive models. It outlines their goals of saving costs and lives by leveraging over 30 TB of clinical data using Hadoop and SAS technologies on their Dignity Health Insights platform. The presentation agenda covers Dignity Health, healthcare analytics challenges, their big data ecosystem architecture featuring Hadoop, and how they are using this infrastructure for applications like sepsis surveillance analytics.
Best Practices for Big Data Analytics with Machine Learning by DatameerDatameer
Don't forget! You can watch the full Datameer recording here:
http://info.datameer.com/Online-Slideshare-Big-Data-Analytics-Machine-Learning-OnDemand.html
Learn through industry use cases, how to empower users to identify patterns & relationships for recommendations using big data analytics.
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
Big data a possible game changer for e-governanceSomenath Nag
Big data is an IT trend on the fast track. It is one of the most disruptive IT trends that will change the way business is done today. It will make the organizations a proactive one from the current reactive state through the insights generated from the vast volume of data that is getting generated across different medium. There is a huge potential of using Big Data in e-governance projects for improving efficiency, transparency, and resource utilization of the system.
3 Things to Learn:
-How data is driving digital transformation to help businesses innovate rapidly
-How Choice Hotels (one of largest hoteliers) is using Cloudera Enterprise to gain meaningful insights that drive their business
-How Choice Hotels has transformed business through innovative use of Apache Hadoop, Cloudera Enterprise, and deployment in the cloud — from developing customer experiences to meeting IT compliance requirements
SharePoint 2013 on Azure: Your Dedicated Farm in the CloudJamie McAllister
With the Virtual Machine and Virtual Networking services of Windows Azure, it is now possible to deploy and operate a Microsoft SharePoint 2013 Server farm on Windows Azure. In this session we will discuss the key considerations, architecture and operations required to do this successfully. At the end you be able to build your own SharePoint Farm on the Cloud!
Revving Tableau Server Performance: Performance Degradation Causes and Cures Senturus
Learn the causes of Tableau Server performance loss and how you can turn things around. Topics discussed include the most common causes of performance degradation, how to get ahead of your performance issues, when to scale out and when to scale up, and cloud-based options. View the video recording and download this deck at: https://www.senturus.com/resources/revving-tableau-server-performance/.
Senturus, a business analytics consulting firm, has a resource library with hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
Using Kafka and Kudu for fast, low-latency SQL analytics on streaming dataMike Percy
The document discusses using Kafka and Kudu for low-latency SQL analytics on streaming data. It describes the challenges of supporting both streaming and batch workloads simultaneously using traditional solutions. The authors propose using Kafka to ingest data and Kudu for structured storage and querying. They demonstrate how this allows for stream processing, batch processing, and querying of up-to-second data with low complexity. Case studies from Xiaomi and TPC-H benchmarks show the advantages of this approach over alternatives.
Architecting application with Hadoop - using clickstream analytics as an examplehadooparchbook
Delivered by Mark Grover at Northern CO Hadoop User Group:
http://www.meetup.com/Northern-Colorado-Big-Data-Meetup/events/224717963/
Scaling Tableau to the Enterprise: The Perks and Pitfalls of Tableau Server W...Senturus
This document discusses the benefits and pitfalls of implementing Tableau Server. Tableau Server allows users to share workbooks, access dashboards via mobile devices, and reduces licensing costs. Common pitfalls include underpowered hardware, lack of governance and security planning, and outdated Tableau versions. Proper installation, data preparation, training and end user considerations are emphasized.
Hadoop Essentials -- The What, Why and How to Meet Agency ObjectivesCloudera, Inc.
This session will provide an executive overview of the Apache Hadoop ecosystem, its basic concepts, and its real-world applications. Attendees will learn how organizations worldwide are using the latest tools and strategies to harness their enterprise information to solve business problems and the types of data analysis commonly powered by Hadoop. Learn how various projects make up the Apache Hadoop ecosystem and the role each plays to improve data storage, management, interaction, and analysis. This is a valuable opportunity to gain insights into Hadoop functionality and how it can be applied to address compelling business challenges in your agency.
Application Architectures with Hadoop | Data Day Texas 2015Cloudera, Inc.
This document discusses application architectures using Hadoop. It begins with an introduction to the speaker and his book on Hadoop architectures. It then presents a case study on clickstream analysis, describing how web logs could be analyzed in Hadoop. The document discusses challenges of Hadoop implementation and various architectural considerations for data storage, modeling, ingestion, processing and more. It focuses on choices for storage layers, file formats, schema design and processing engines like MapReduce, Spark and Impala.
This document discusses application architectures using Hadoop. It provides an example case study of clickstream analysis. It covers challenges of Hadoop implementation and various architectural considerations for data storage and modeling, data ingestion, and data processing. For data processing, it discusses different processing engines like MapReduce, Pig, Hive, Spark and Impala. It also discusses what specific processing needs to be done for the clickstream data like sessionization and filtering.
Data Science Languages and Industry AnalyticsWes McKinney
September 19, 2015 talk at Berkeley Institute for Data Science. On how comparatively poor JSON / structured data tools pose a challenge for the data science languages (Python, R, Julia, etc.).
Hadoop Application Architectures tutorial at Big DataService 2015hadooparchbook
This document outlines a presentation on architectural considerations for Hadoop applications. It introduces the presenters who are experts from Cloudera and contributors to Apache Hadoop projects. It then discusses a case study on clickstream analysis, how this was challenging before Hadoop due to data storage limitations, and how Hadoop provides a better solution by enabling active archiving of large volumes and varieties of data at scale. Finally, it covers some of the challenges in implementing Hadoop, such as choices around storage managers, data modeling and file formats, data movement workflows, metadata management, and data access and processing frameworks.
The document discusses application architectures using Hadoop. It provides an example case study of clickstream analysis of web logs. It discusses challenges of Hadoop implementation and various architectural considerations for data storage, modeling, ingestion, processing and what specific processing needs to happen for the case study. These include sessionization, filtering, and business intelligence/discovery. Storage options, file formats, schema design, and processing engines like MapReduce, Spark and Impala are also covered.
Architectural considerations for Hadoop Applicationshadooparchbook
The document discusses architectural considerations for Hadoop applications using a case study on clickstream analysis. It covers requirements for data ingestion, storage, processing, and orchestration. For data storage, it considers HDFS vs HBase, file formats, and compression formats. SequenceFiles are identified as a good choice for raw data storage as they allow for splittable compression.
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1) StumbleUpon uses open source tools like Kafka, HBase, Hive and Pig to build a scalable big data infrastructure to process large amounts of data from its services in real-time and batch.
2) Data is collected from various services using Kafka and stored in HBase for real-time analytics. Batch processing is done using Pig and data is loaded into Hive for ad-hoc querying.
3) The infrastructure powers various applications like recommendations, ads and business intelligence dashboards.
"Analyzing Twitter Data with Hadoop - Live Demo", presented at Oracle Open World 2014. The repository for the slides is in https://github.com/cloudera/cdh-twitter-example
Should I move my database to the cloud?James Serra
So you have been running on-prem SQL Server for a while now. Maybe you have taken the step to move it from bare metal to a VM, and have seen some nice benefits. Ready to see a TON more benefits? If you said “YES!”, then this is the session for you as I will go over the many benefits gained by moving your on-prem SQL Server to an Azure VM (IaaS). Then I will really blow your mind by showing you even more benefits by moving to Azure SQL Database (PaaS/DBaaS). And for those of you with a large data warehouse, I also got you covered with Azure SQL Data Warehouse. Along the way I will talk about the many hybrid approaches so you can take a gradual approve to moving to the cloud. If you are interested in cost savings, additional features, ease of use, quick scaling, improved reliability and ending the days of upgrading hardware, this is the session for you!
The document discusses architectural considerations for Hadoop applications based on a case study of clickstream analysis. It covers requirements for data ingestion, storage, processing, and orchestration. For data storage, it recommends storing raw clickstream data in HDFS using the Avro file format with Snappy compression. For processed data, it recommends using the Parquet columnar storage format to enable efficient analytical queries. The document also discusses partitioning strategies and HDFS directory layout design.
You’ve successfully deployed Hadoop, but are you taking advantage of all of Hadoop’s features to operate a stable and effective cluster? In the first part of the talk, we will cover issues that have been seen over the last two years on hundreds of production clusters with detailed breakdown covering the number of occurrences, severity, and root cause. We will cover best practices and many new tools and features in Hadoop added over the last year to help system administrators monitor, diagnose and address such incidents.
The second part of our talk discusses new features for making daily operations easier. This includes features such as ACLs for simplified permission control, snapshots for data protection and more. We will also cover tuning configuration and features that improve cluster utilization, such as short-circuit reads and datanode caching.
The document discusses using Cloudera DataFlow to address challenges with collecting, processing, and analyzing log data across many systems and devices. It provides an example use case of logging modernization to reduce costs and enable security solutions by filtering noise from logs. The presentation shows how DataFlow can extract relevant events from large volumes of raw log data and normalize the data to make security threats and anomalies easier to detect across many machines.
Cloudera Data Impact Awards 2021 - Finalists Cloudera, Inc.
The document outlines the 2021 finalists for the annual Data Impact Awards program, which recognizes organizations using Cloudera's platform and the impactful applications they have developed. It provides details on the challenges, solutions, and outcomes for each finalist project in the categories of Data Lifecycle Connection, Cloud Innovation, Data for Enterprise AI, Security & Governance Leadership, Industry Transformation, People First, and Data for Good. There are multiple finalists highlighted in each category demonstrating innovative uses of data and analytics.
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.
The document outlines the agenda for Cloudera's Enterprise Data Cloud event in Vienna. It includes welcome remarks, keynotes on Cloudera's vision and customer success stories. There will be presentations on the new Cloudera Data Platform and customer case studies, followed by closing remarks. The schedule includes sessions on Cloudera's approach to data warehousing, machine learning, streaming and multi-cloud capabilities.
Machine Learning with Limited Labeled Data 4/3/19Cloudera, Inc.
Cloudera Fast Forward Labs’ latest research report and prototype explore learning with limited labeled data. This capability relaxes the stringent labeled data requirement in supervised machine learning and opens up new product possibilities. It is industry invariant, addresses the labeling pain point and enables applications to be built faster and more efficiently.
Data Driven With the Cloudera Modern Data Warehouse 3.19.19Cloudera, Inc.
In this session, we will cover how to move beyond structured, curated reports based on known questions on known data, to an ad-hoc exploration of all data to optimize business processes and into the unknown questions on unknown data, where machine learning and statistically motivated predictive analytics are shaping business strategy.
Introducing Cloudera DataFlow (CDF) 2.13.19Cloudera, Inc.
Watch this webinar to understand how Hortonworks DataFlow (HDF) has evolved into the new Cloudera DataFlow (CDF). Learn about key capabilities that CDF delivers such as -
-Powerful data ingestion powered by Apache NiFi
-Edge data collection by Apache MiNiFi
-IoT-scale streaming data processing with Apache Kafka
-Enterprise services to offer unified security and governance from edge-to-enterprise
Introducing Cloudera Data Science Workbench for HDP 2.12.19Cloudera, Inc.
Cloudera’s Data Science Workbench (CDSW) is available for Hortonworks Data Platform (HDP) clusters for secure, collaborative data science at scale. During this webinar, we provide an introductory tour of CDSW and a demonstration of a machine learning workflow using CDSW on HDP.
Shortening the Sales Cycle with a Modern Data Warehouse 1.30.19Cloudera, Inc.
Join Cloudera as we outline how we use Cloudera technology to strengthen sales engagement, minimize marketing waste, and empower line of business leaders to drive successful outcomes.
Leveraging the cloud for analytics and machine learning 1.29.19Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on Azure. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Modernizing the Legacy Data Warehouse – What, Why, and How 1.23.19Cloudera, Inc.
Join us to learn about the challenges of legacy data warehousing, the goals of modern data warehousing, and the design patterns and frameworks that help to accelerate modernization efforts.
Leveraging the Cloud for Big Data Analytics 12.11.18Cloudera, Inc.
Learn how organizations are deriving unique customer insights, improving product and services efficiency, and reducing business risk with a modern big data architecture powered by Cloudera on AWS. In this webinar, you see how fast and easy it is to deploy a modern data management platform—in your cloud, on your terms.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
Explore new trends and use cases in data warehousing including exploration and discovery, self-service ad-hoc analysis, predictive analytics and more ways to get deeper business insight. Modern Data Warehousing Fundamentals will show how to modernize your data warehouse architecture and infrastructure for benefits to both traditional analytics practitioners and data scientists and engineers.
The document discusses the benefits and trends of modernizing a data warehouse. It outlines how a modern data warehouse can provide deeper business insights at extreme speed and scale while controlling resources and costs. Examples are provided of companies that have improved fraud detection, customer retention, and machine performance by implementing a modern data warehouse that can handle large volumes and varieties of data from many sources.
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.
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Designing AI-Powered APIs on Azure: Best Practices& ConsiderationsDinusha Kumarasiri
AI is transforming APIs, enabling smarter automation, enhanced decision-making, and seamless integrations. This presentation explores key design principles for AI-infused APIs on Azure, covering performance optimization, security best practices, scalability strategies, and responsible AI governance. Learn how to leverage Azure API Management, machine learning models, and cloud-native architectures to build robust, efficient, and intelligent API solutions
What Do Contribution Guidelines Say About Software Testing? (MSR 2025)Andre Hora
Software testing plays a crucial role in the contribution process of open-source projects. For example, contributions introducing new features are expected to include tests, and contributions with tests are more likely to be accepted. Although most real-world projects require contributors to write tests, the specific testing practices communicated to contributors remain unclear. In this paper, we present an empirical study to understand better how software testing is approached in contribution guidelines. We analyze the guidelines of 200 Python and JavaScript open-source software projects. We find that 78% of the projects include some form of test documentation for contributors. Test documentation is located in multiple sources, including CONTRIBUTING files (58%), external documentation (24%), and README files (8%). Furthermore, test documentation commonly explains how to run tests (83.5%), but less often provides guidance on how to write tests (37%). It frequently covers unit tests (71%), but rarely addresses integration (20.5%) and end-to-end tests (15.5%). Other key testing aspects are also less frequently discussed: test coverage (25.5%) and mocking (9.5%). We conclude by discussing implications and future research.
Secure Test Infrastructure: The Backbone of Trustworthy Software DevelopmentShubham Joshi
A secure test infrastructure ensures that the testing process doesn’t become a gateway for vulnerabilities. By protecting test environments, data, and access points, organizations can confidently develop and deploy software without compromising user privacy or system integrity.
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GitHub Actions (GA) has become the de facto tool that developers use to automate software workflows, seamlessly building, testing, and deploying code. Yet when GA fails, it disrupts development, causing delays and driving up costs. Diagnosing failures becomes especially challenging because error logs are often long, complex and unstructured. Given these difficulties, this study explores the potential of large language models (LLMs) to generate correct, clear, concise, and actionable contextual descriptions (or summaries) for GA failures, focusing on developers’ perceptions of their feasibility and usefulness. Our results show that over 80% of developers rated LLM explanations positively in terms of correctness for simpler/small logs. Overall, our findings suggest that LLMs can feasibly assist developers in understanding common GA errors, thus, potentially reducing manual analysis. However, we also found that improved reasoning abilities are needed to support more complex CI/CD scenarios. For instance, less experienced developers tend to be more positive on the described context, while seasoned developers prefer concise summaries. Overall, our work offers key insights for researchers enhancing LLM reasoning, particularly in adapting explanations to user expertise.
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Societal challenges of AI: biases, multilinguism and sustainabilityJordi Cabot
Towards a fairer, inclusive and sustainable AI that works for everybody.
Reviewing the state of the art on these challenges and what we're doing at LIST to test current LLMs and help you select the one that works best for you
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This document presents specific use cases for Agentic AI (Artificial Intelligence), featuring Large Language Models (LLMs), Generative AI, and snippets of Python code alongside each use case.
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3. 3
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Cloudera and SAS Online Webinar
Wednesday, May 7, 2014 - 10am PST/1pm PST
Mike Ames, SAS
Eli Collins, Cloudera
Scott Armstrong, Cloudera