Highly configurable and extensible data processing framework at PubMaticDataWorks Summit
PubMatic is a leading advertisement technology company that processes 500 billion transactions (50 terabytes of data) per day in real-time and batch processing pipeline on a 900-node cluster to power highly efficient machine learning algorithms, provide real time feedback to ad-server for optimization and provide in depth insights on customer inventory and audience.
At PubMatic, scaling with ever growing volume has always been the biggest challenge; we have been optimizing our technology stack for performance and costs. Another challenge is to support the demand for variety reports and analytics by customers and internal stakeholders. Writing custom jobs to provide analytics leads to repetitive efforts and redundancy of business logic in many different jobs.
To solve the above problems, we built a platform that allows creating configuration driven data processing pipeline with high re-usability of business functions. It is also extensible to utilize cutting-edge technologies in the ever-changing big data ecosystem. This platform enables our development teams to build a robust batch data processing pipeline to power analytics dashboards. It also empowers novice users to provide a configuration with fact and dimensions to generate ad-hoc reports in a single data processing job. Framework intelligently identifies and re-uses existing business functions based on user inputs. It also provides an abstraction layer that keeps core business logic un-affected by the any technology changes. This framework is currently powered by Spark, but it can be easily configured with other technologies.
Framework significantly improved time to develop data processing jobs from weeks to few days, it simplified unit testing and QA automation, as well as provided simpler interfaces to the customers and internal stakeholders to generate custom reports.
Speaker
Kunal Umrigar, Sr. Director Engineering Big Data & Analytics, PubMatic
Democratizing data science Using spark, hive and druidDataWorks Summit
MZ is re-inventing how the entire world experiences data via our mobile games division MZ Games Studios, our digital marketing division Cognant, and our live data platform division Satori.
Growing need of data science capabilities across the organization requires an architecture that can democratize building these applications and disseminating insight from the outcome of data science applications to the wider organization.
Attend this session to learn about how we built a platform for data science using spark, hive, and druid specifically for our performance marketing division cognant.This platform powers several data science application like fraud detection and bid optimization at large scale.
We will be sharing lessons learned over past 3 years in building this platform by also walking through some of the actual data science applications built on top of this platform.
Attendees from ML engineering and data science background can gain deep insight from our experience of building this platform.
Speakers
Pushkar Priyadarshi, Director of Engineer, Michaine Zone Inc
Igor Yurinok, Staff Software Engineer, MZ
Learn why 451 Research believes Infochimps is well-positioned with an easy-to-consume managed service for those without Hadoop expertise, as well as a stack of technologically interesting projects for the 'devops' crowd.
Opening with a market positioning statement and ending with a competitive and SWOT analysis, Matt Aslett provides a comprehensive impact report.
DataWorks Summit 2017 - Sydney Keynote
Madhu Kochar, Vice President, Analytics Product Development and Client Success, IBM
Data science holds the promise of transforming businesses and disrupting entire industries. However, many organizations struggle to deploy and scale key technologies such as machine learning and deep learning. IBM will share how it is making data science accessible to all by simplifying the use of a range of open source technologies and data sources, including high performing and open architectures geared for cognitive workloads.
The document discusses managing a multi-tenant data lake at Comcast over time. It began as an experiment in 2013 with 10 nodes and has grown significantly to over 1500 nodes currently. Governance was instituted to manage the diverse user community and workloads. Tools like the Command Center were developed to provide monitoring, alerting and visualization of the large Hadoop environment. SLA management, support processes, and ongoing training are needed to effectively operate the multi-tenant data lake at scale.
Unlocking Operational Intelligence from the Data LakeMongoDB
The document discusses operationalizing data lakes by integrating MongoDB with Hadoop to enable both real-time and batch processing capabilities. It describes how MongoDB can be used to power operational applications with low-latency access to analytics models generated from raw data stored in Hadoop, while Hadoop is still used for its batch processing and analytics capabilities on large datasets. By combining both technologies, companies can unlock insights from their data lakes and avoid being part of the 70% of Hadoop projects that fail to meet objectives due to skills and integration challenges.
In this slidedeck, Infochimps Director of Product, Tim Gasper, discusses how Infochimps tackles business problems for customers by deploying a comprehensive Big Data infrastructure in days; sometimes in just hours. Tim unlocks how Infochimps is now taking that same aggressive approach to deliver faster time to value by helping customers develop analytic applications with impeccable speed.
How Market Intelligence From Hadoop on Azure Shows Trucking Companies a Clear...DataWorks Summit
TMW Systems (a Trimble Company) has been in the business of long-haul trucking, logistics operations and fleet management for more than thirty years, but we wanted more data, so we turned to our customer community. Now, we turn that data into market intelligence, which we then provide back to our customers. To do this, we invested heavily in Hortonworks Data Platform running on Microsoft Azure in the cloud. In our talk, we’ll share our strategy for capturing operational, maintenance, financial and mobile communications information and how we provide that back to our customer base. Our approach enables advanced analytics by leveraging Big Data technologies to find new relationships in data that may have been previously overlooked. Survey responses capture business performance metrics, strategy and emerging trends from 150 businesses, representing more than 31 billion dollars in freight movement. Learn how we combine that survey data with other sources like machine and sensor data to help guide our customers to profitability.
Highly configurable and extensible data processing framework at PubMaticDataWorks Summit
PubMatic is a leading advertisement technology company that processes 500 billion transactions (50 terabytes of data) per day in real-time and batch processing pipeline on a 900-node cluster to power highly efficient machine learning algorithms, provide real time feedback to ad-server for optimization and provide in depth insights on customer inventory and audience.
At PubMatic, scaling with ever growing volume has always been the biggest challenge; we have been optimizing our technology stack for performance and costs. Another challenge is to support the demand for variety reports and analytics by customers and internal stakeholders. Writing custom jobs to provide analytics leads to repetitive efforts and redundancy of business logic in many different jobs.
To solve the above problems, we built a platform that allows creating configuration driven data processing pipeline with high re-usability of business functions. It is also extensible to utilize cutting-edge technologies in the ever-changing big data ecosystem. This platform enables our development teams to build a robust batch data processing pipeline to power analytics dashboards. It also empowers novice users to provide a configuration with fact and dimensions to generate ad-hoc reports in a single data processing job. Framework intelligently identifies and re-uses existing business functions based on user inputs. It also provides an abstraction layer that keeps core business logic un-affected by the any technology changes. This framework is currently powered by Spark, but it can be easily configured with other technologies.
Framework significantly improved time to develop data processing jobs from weeks to few days, it simplified unit testing and QA automation, as well as provided simpler interfaces to the customers and internal stakeholders to generate custom reports.
Speaker
Kunal Umrigar, Sr. Director Engineering Big Data & Analytics, PubMatic
Democratizing data science Using spark, hive and druidDataWorks Summit
MZ is re-inventing how the entire world experiences data via our mobile games division MZ Games Studios, our digital marketing division Cognant, and our live data platform division Satori.
Growing need of data science capabilities across the organization requires an architecture that can democratize building these applications and disseminating insight from the outcome of data science applications to the wider organization.
Attend this session to learn about how we built a platform for data science using spark, hive, and druid specifically for our performance marketing division cognant.This platform powers several data science application like fraud detection and bid optimization at large scale.
We will be sharing lessons learned over past 3 years in building this platform by also walking through some of the actual data science applications built on top of this platform.
Attendees from ML engineering and data science background can gain deep insight from our experience of building this platform.
Speakers
Pushkar Priyadarshi, Director of Engineer, Michaine Zone Inc
Igor Yurinok, Staff Software Engineer, MZ
Learn why 451 Research believes Infochimps is well-positioned with an easy-to-consume managed service for those without Hadoop expertise, as well as a stack of technologically interesting projects for the 'devops' crowd.
Opening with a market positioning statement and ending with a competitive and SWOT analysis, Matt Aslett provides a comprehensive impact report.
DataWorks Summit 2017 - Sydney Keynote
Madhu Kochar, Vice President, Analytics Product Development and Client Success, IBM
Data science holds the promise of transforming businesses and disrupting entire industries. However, many organizations struggle to deploy and scale key technologies such as machine learning and deep learning. IBM will share how it is making data science accessible to all by simplifying the use of a range of open source technologies and data sources, including high performing and open architectures geared for cognitive workloads.
The document discusses managing a multi-tenant data lake at Comcast over time. It began as an experiment in 2013 with 10 nodes and has grown significantly to over 1500 nodes currently. Governance was instituted to manage the diverse user community and workloads. Tools like the Command Center were developed to provide monitoring, alerting and visualization of the large Hadoop environment. SLA management, support processes, and ongoing training are needed to effectively operate the multi-tenant data lake at scale.
Unlocking Operational Intelligence from the Data LakeMongoDB
The document discusses operationalizing data lakes by integrating MongoDB with Hadoop to enable both real-time and batch processing capabilities. It describes how MongoDB can be used to power operational applications with low-latency access to analytics models generated from raw data stored in Hadoop, while Hadoop is still used for its batch processing and analytics capabilities on large datasets. By combining both technologies, companies can unlock insights from their data lakes and avoid being part of the 70% of Hadoop projects that fail to meet objectives due to skills and integration challenges.
In this slidedeck, Infochimps Director of Product, Tim Gasper, discusses how Infochimps tackles business problems for customers by deploying a comprehensive Big Data infrastructure in days; sometimes in just hours. Tim unlocks how Infochimps is now taking that same aggressive approach to deliver faster time to value by helping customers develop analytic applications with impeccable speed.
How Market Intelligence From Hadoop on Azure Shows Trucking Companies a Clear...DataWorks Summit
TMW Systems (a Trimble Company) has been in the business of long-haul trucking, logistics operations and fleet management for more than thirty years, but we wanted more data, so we turned to our customer community. Now, we turn that data into market intelligence, which we then provide back to our customers. To do this, we invested heavily in Hortonworks Data Platform running on Microsoft Azure in the cloud. In our talk, we’ll share our strategy for capturing operational, maintenance, financial and mobile communications information and how we provide that back to our customer base. Our approach enables advanced analytics by leveraging Big Data technologies to find new relationships in data that may have been previously overlooked. Survey responses capture business performance metrics, strategy and emerging trends from 150 businesses, representing more than 31 billion dollars in freight movement. Learn how we combine that survey data with other sources like machine and sensor data to help guide our customers to profitability.
"You don't need a bigger boat": serverless MLOps for reasonable companiesData Science Milan
It is indeed a wonderful time to build machine learning systems, as the growing ecosystems of tools and shared best practices make even small teams incredibly productive at scale. In this talk, we present our philosophy for modern, no-nonsense data pipelines, highlighting the advantages of a (almost) pure serverless and open-source approach, and showing how the entire toolchain works - from raw data to model serving - on a real-world dataset.
Finally, we argue that the crucial component for analyzing data pipelines is not the model per se, but the surrounding DAG, and present our proposal for producing automated "DAG cards" from Metaflow classes.
Bio:
Jacopo Tagliabue was co-founder and CTO of Tooso, an A.I. company in San Francisco acquired by Coveo in 2019. Jacopo is currently the Lead A.I. Scientist at Coveo. When not busy building A.I. products, he is exploring research topics at the intersection of language, reasoning and learning, with several publications at major conferences (e.g. WWW, SIGIR, RecSys, NAACL). In previous lives, he managed to get a Ph.D., do scienc-y things for a pro basketball team, and simulate a pre-Columbian civilization.
Topics: MLOps, Metaflow, model cards.
Moustafa Soliman "HP Vertica- Solving Facebook Big Data challenges" Dataconomy Media
Moustafa Soliman, Business Intelligence Developer from Hewlett Packard presented "HP Vertica - Solving Facebook Big Data Challenges" as part of "Big Data Stockholm" meetup on April 1st at SUP46.
1) The document discusses Distributed R, an open source platform for scalable predictive analytics using the R programming language. It allows building and evaluating predictive models on large datasets using distributed computing across multiple nodes.
2) As an example, it describes how Distributed R could be used to predict the outcomes of March Madness basketball games by training a random forest model on team statistics to learn what factors are most important and use that to predict winners.
3) The models trained using Distributed R can then be deployed back to the HP Vertica database for scoring and predictions or exposed as web services to power business intelligence and applications.
Real-time Analytics for Data-Driven ApplicationsVMware Tanzu
Real-time analytics is important for data-driven applications. Ampool provides an active data store (ADS) that can ingest data in real-time, analyze it using various engines, and serve the results concurrently. This eliminates "data blackout periods" and enables applications to use up-to-date information. Ampool's ADS is powered by Apache Geode and has connectors for ingesting and processing data. It supports both transactional and analytical workloads in memory for low-latency.
My other computer is a datacentre - 2012 editionSteve Loughran
An updated version of the "my other computer is a datacentre" talk, presented at the Bristol University HPC talk.
Because it is targeted at universities, it emphasises some of the interesting problems -the classic CS ones of scheduling, new ones of availability and failure handling within what is now a single computer, and emergent problems of power and heterogeneity. It also includes references, all of which are worth reading, and, being mostly Google and Microsoft papers, are free to download without needing ACM or IEEE library access.
Comments welcome.
Using Hadoop for Cognitive Analytics discusses using Hadoop and external data sources for cognitive analytics. The document outlines solution architectures that integrate external and customer-specific metrics to improve decision making. Microservices are used for data ingestion and curation from various sources into Hadoop for storage and analytics. This allows combining business metrics with hyperlocal data at precise locations to provide insights.
How Apache Spark and Apache Hadoop are being used to keep banking regulators ...DataWorks Summit
The global financial crisis showed that traditional IT systems at banks were ill equiped to monitor and manage the daily-changing risk landscape during the global financial crisis. The sheer amount of data that needed to be crunched meant that many of the banks were day(s) behind in calculating, understanding and reporting their risk positions. Post crisis, a review by banking regulator, led the regulators to introduce a new legislation BCBS 239: Principles for effective risk data aggregation and reporting, that requires banks to meet more stringent (timeliness) requirement, in their ability to aggregate and report on their quickly-changing risk positions or risk fines to the tune of $millions. To meet these new requirements, banks have been forced to re-think their traditional IT architectures, which are unable to cope with sheer volume of risk data, and are instead turning to Apache Hadoop and Apache Spark to build out next generation of risk systems. In this talk you will discover, how some of the leading banks in the world are leveraging Apache Hadoop and Apache Spark to meet BCBS 239 regulation.
Speaker
Kunal Taneja
This document provides an overview of HDInsight and Hadoop. It defines big data and Hadoop, describing HDInsight as Microsoft's implementation of Hadoop in the cloud. It outlines the Hadoop ecosystem including HDFS, MapReduce, YARN, Hive, Pig and Sqoop. It discusses advantages of using HDInsight in the cloud and provides information on working with HDInsight clusters, loading and querying data, and different approaches to big data solutions.
Optimizing industrial operations using the big data ecosystemDataWorks Summit
GE Digital is undertaking a journey to optimize the reliability, availability, and efficiency of assets in the industrial sector and converge IT and OT. To do so, GE Digital is building cloud-based products that enable customers to analyze the asset data, detect anomalies, and provide recommendations for operating plants efficiently while increasing productivity. In a energy sector such as oil and gas, power, or renewables, a single plant comprises multiple complex assets, such as steam turbines, gas turbines, and compressors, to generate power. Each system contains various sensors to detect the operating conditions of the assets, generating large volumes of variety of data. A highly scalable distributed environment is required to analyze such a large volume of data and provide operating insights in near real time.
In this session I will share the challenges encountered when analyzing the large volumes of data, in-stream data analysis and how we standardized the industrial data based on data frames, and performance tuning.
This document discusses Pivotal's vision and products for big data and Hadoop. It introduces Hadoop as an open source framework for distributed storage and processing of large datasets. Pivotal Hadoop is presented as providing an enterprise-grade Hadoop distribution with additional capabilities like SQL query processing, data management tools, and stream processing. Key components of Pivotal Hadoop include the HAWQ database for interactive SQL queries on Hadoop data and tools for data loading, analytics, and administration. Real-world use cases and benchmarks are shown to demonstrate how Pivotal Hadoop can enable both interactive analysis and massive-scale data processing.
Indexing 3-dimensional trajectories: Apache Spark and Cassandra integrationCesare Cugnasco
Data visualization can be a tricky problem, even more if the dataset is made of several billions of 3-dimensional particles moving along the time. The talk will focus on some simple indexing and data thinning techniques and how (and how do not) implement them with Cassandra and Spark.
This document discusses Symantec's journey towards enabling self-service analytics clusters using Cloudbreak and Ambari. It describes how Symantec built a self-service analytics platform using Ambari to automate the deployment of Hadoop clusters on their private OpenStack cloud. However, they later needed a solution that could deploy clusters across different cloud providers. They adopted Cloudbreak to deploy clusters on AWS and contributed extensions like Keystone v3 support to enable Cloudbreak to work with their OpenStack cloud as well. This allows them to deploy analytics clusters across different clouds through a single tool and interface.
The document discusses how machine data from various sources such as IoT devices, industrial systems, mobile devices, and other systems can be collected and analyzed using Splunk software. Splunk provides capabilities for data ingestion, indexing, searching, analyzing, and visualizing large amounts of machine data. It also discusses how Splunk has been used by companies in various industries to gain insights from their machine data to improve operations, security, customer experience, and business outcomes. Specific use cases highlighted include predictive maintenance, anomaly detection, supply chain optimization, and understanding customer behavior.
InfoSphere BigInsights - Analytics power for Hadoop - field experienceWilfried Hoge
This document provides an overview and summary of InfoSphere BigInsights, an analytics platform for Hadoop. It discusses key features such as real-time analytics, storage integration, search, data exploration, predictive modeling, and application tooling. Case studies are presented on analyzing binary data and developing applications for transformation and analysis. Partnerships and certifications with other vendors are also mentioned. The document aims to demonstrate how BigInsights brings enterprise-grade features to Apache Hadoop and provides analytics capabilities for business users.
2014 10 09 Top reasons to use IBM BigInsights as your Big Data Hadoop systemToby Woolfe
The document discusses why manufacturers should use IBM BigInsights as their Hadoop platform. It outlines 10 key reasons, including IBM's experience in the automotive industry, the capabilities BigInsights adds to open source Hadoop like performance and security features, IBM's commitment and track record of large Hadoop deployments, and case studies of manufacturers like General Motors that have successfully used BigInsights.
This document discusses building a new generation of intelligent data platforms. It emphasizes that most big data projects spend 80% of time on data integration and quality. It also notes that Informatica developers are 5 times more productive than those coding by hand for Hadoop. The document promotes Informatica's tools for enabling existing developers to work with big data platforms like Hadoop through visual interfaces and pre-built connectors and transformations.
Applying Machine learning to IOT: End to End Distributed Distributed Pipeline...Carol McDonald
This discusses the architecture of an end-to-end application that combines streaming data with machine learning to do real-time analysis and visualization of where and when Uber cars are clustered, so as to analyze and visualize the most popular Uber locations.
Functional programming for optimization problems in Big DataPaco Nathan
Enterprise Data Workflows with Cascading.
Silicon Valley Cloud Computing Meetup talk at Cloud Tech IV, 4/20 2013
http://www.meetup.com/cloudcomputing/events/111082032/
We (Concurrent) conducted a survey of Cascading users. The Cascading community is one of the most mature Hadoop development communities, with the majority having over 3 years experience. See what they are using, why they are using it and what future challenges they anticipate.
"You don't need a bigger boat": serverless MLOps for reasonable companiesData Science Milan
It is indeed a wonderful time to build machine learning systems, as the growing ecosystems of tools and shared best practices make even small teams incredibly productive at scale. In this talk, we present our philosophy for modern, no-nonsense data pipelines, highlighting the advantages of a (almost) pure serverless and open-source approach, and showing how the entire toolchain works - from raw data to model serving - on a real-world dataset.
Finally, we argue that the crucial component for analyzing data pipelines is not the model per se, but the surrounding DAG, and present our proposal for producing automated "DAG cards" from Metaflow classes.
Bio:
Jacopo Tagliabue was co-founder and CTO of Tooso, an A.I. company in San Francisco acquired by Coveo in 2019. Jacopo is currently the Lead A.I. Scientist at Coveo. When not busy building A.I. products, he is exploring research topics at the intersection of language, reasoning and learning, with several publications at major conferences (e.g. WWW, SIGIR, RecSys, NAACL). In previous lives, he managed to get a Ph.D., do scienc-y things for a pro basketball team, and simulate a pre-Columbian civilization.
Topics: MLOps, Metaflow, model cards.
Moustafa Soliman "HP Vertica- Solving Facebook Big Data challenges" Dataconomy Media
Moustafa Soliman, Business Intelligence Developer from Hewlett Packard presented "HP Vertica - Solving Facebook Big Data Challenges" as part of "Big Data Stockholm" meetup on April 1st at SUP46.
1) The document discusses Distributed R, an open source platform for scalable predictive analytics using the R programming language. It allows building and evaluating predictive models on large datasets using distributed computing across multiple nodes.
2) As an example, it describes how Distributed R could be used to predict the outcomes of March Madness basketball games by training a random forest model on team statistics to learn what factors are most important and use that to predict winners.
3) The models trained using Distributed R can then be deployed back to the HP Vertica database for scoring and predictions or exposed as web services to power business intelligence and applications.
Real-time Analytics for Data-Driven ApplicationsVMware Tanzu
Real-time analytics is important for data-driven applications. Ampool provides an active data store (ADS) that can ingest data in real-time, analyze it using various engines, and serve the results concurrently. This eliminates "data blackout periods" and enables applications to use up-to-date information. Ampool's ADS is powered by Apache Geode and has connectors for ingesting and processing data. It supports both transactional and analytical workloads in memory for low-latency.
My other computer is a datacentre - 2012 editionSteve Loughran
An updated version of the "my other computer is a datacentre" talk, presented at the Bristol University HPC talk.
Because it is targeted at universities, it emphasises some of the interesting problems -the classic CS ones of scheduling, new ones of availability and failure handling within what is now a single computer, and emergent problems of power and heterogeneity. It also includes references, all of which are worth reading, and, being mostly Google and Microsoft papers, are free to download without needing ACM or IEEE library access.
Comments welcome.
Using Hadoop for Cognitive Analytics discusses using Hadoop and external data sources for cognitive analytics. The document outlines solution architectures that integrate external and customer-specific metrics to improve decision making. Microservices are used for data ingestion and curation from various sources into Hadoop for storage and analytics. This allows combining business metrics with hyperlocal data at precise locations to provide insights.
How Apache Spark and Apache Hadoop are being used to keep banking regulators ...DataWorks Summit
The global financial crisis showed that traditional IT systems at banks were ill equiped to monitor and manage the daily-changing risk landscape during the global financial crisis. The sheer amount of data that needed to be crunched meant that many of the banks were day(s) behind in calculating, understanding and reporting their risk positions. Post crisis, a review by banking regulator, led the regulators to introduce a new legislation BCBS 239: Principles for effective risk data aggregation and reporting, that requires banks to meet more stringent (timeliness) requirement, in their ability to aggregate and report on their quickly-changing risk positions or risk fines to the tune of $millions. To meet these new requirements, banks have been forced to re-think their traditional IT architectures, which are unable to cope with sheer volume of risk data, and are instead turning to Apache Hadoop and Apache Spark to build out next generation of risk systems. In this talk you will discover, how some of the leading banks in the world are leveraging Apache Hadoop and Apache Spark to meet BCBS 239 regulation.
Speaker
Kunal Taneja
This document provides an overview of HDInsight and Hadoop. It defines big data and Hadoop, describing HDInsight as Microsoft's implementation of Hadoop in the cloud. It outlines the Hadoop ecosystem including HDFS, MapReduce, YARN, Hive, Pig and Sqoop. It discusses advantages of using HDInsight in the cloud and provides information on working with HDInsight clusters, loading and querying data, and different approaches to big data solutions.
Optimizing industrial operations using the big data ecosystemDataWorks Summit
GE Digital is undertaking a journey to optimize the reliability, availability, and efficiency of assets in the industrial sector and converge IT and OT. To do so, GE Digital is building cloud-based products that enable customers to analyze the asset data, detect anomalies, and provide recommendations for operating plants efficiently while increasing productivity. In a energy sector such as oil and gas, power, or renewables, a single plant comprises multiple complex assets, such as steam turbines, gas turbines, and compressors, to generate power. Each system contains various sensors to detect the operating conditions of the assets, generating large volumes of variety of data. A highly scalable distributed environment is required to analyze such a large volume of data and provide operating insights in near real time.
In this session I will share the challenges encountered when analyzing the large volumes of data, in-stream data analysis and how we standardized the industrial data based on data frames, and performance tuning.
This document discusses Pivotal's vision and products for big data and Hadoop. It introduces Hadoop as an open source framework for distributed storage and processing of large datasets. Pivotal Hadoop is presented as providing an enterprise-grade Hadoop distribution with additional capabilities like SQL query processing, data management tools, and stream processing. Key components of Pivotal Hadoop include the HAWQ database for interactive SQL queries on Hadoop data and tools for data loading, analytics, and administration. Real-world use cases and benchmarks are shown to demonstrate how Pivotal Hadoop can enable both interactive analysis and massive-scale data processing.
Indexing 3-dimensional trajectories: Apache Spark and Cassandra integrationCesare Cugnasco
Data visualization can be a tricky problem, even more if the dataset is made of several billions of 3-dimensional particles moving along the time. The talk will focus on some simple indexing and data thinning techniques and how (and how do not) implement them with Cassandra and Spark.
This document discusses Symantec's journey towards enabling self-service analytics clusters using Cloudbreak and Ambari. It describes how Symantec built a self-service analytics platform using Ambari to automate the deployment of Hadoop clusters on their private OpenStack cloud. However, they later needed a solution that could deploy clusters across different cloud providers. They adopted Cloudbreak to deploy clusters on AWS and contributed extensions like Keystone v3 support to enable Cloudbreak to work with their OpenStack cloud as well. This allows them to deploy analytics clusters across different clouds through a single tool and interface.
The document discusses how machine data from various sources such as IoT devices, industrial systems, mobile devices, and other systems can be collected and analyzed using Splunk software. Splunk provides capabilities for data ingestion, indexing, searching, analyzing, and visualizing large amounts of machine data. It also discusses how Splunk has been used by companies in various industries to gain insights from their machine data to improve operations, security, customer experience, and business outcomes. Specific use cases highlighted include predictive maintenance, anomaly detection, supply chain optimization, and understanding customer behavior.
InfoSphere BigInsights - Analytics power for Hadoop - field experienceWilfried Hoge
This document provides an overview and summary of InfoSphere BigInsights, an analytics platform for Hadoop. It discusses key features such as real-time analytics, storage integration, search, data exploration, predictive modeling, and application tooling. Case studies are presented on analyzing binary data and developing applications for transformation and analysis. Partnerships and certifications with other vendors are also mentioned. The document aims to demonstrate how BigInsights brings enterprise-grade features to Apache Hadoop and provides analytics capabilities for business users.
2014 10 09 Top reasons to use IBM BigInsights as your Big Data Hadoop systemToby Woolfe
The document discusses why manufacturers should use IBM BigInsights as their Hadoop platform. It outlines 10 key reasons, including IBM's experience in the automotive industry, the capabilities BigInsights adds to open source Hadoop like performance and security features, IBM's commitment and track record of large Hadoop deployments, and case studies of manufacturers like General Motors that have successfully used BigInsights.
This document discusses building a new generation of intelligent data platforms. It emphasizes that most big data projects spend 80% of time on data integration and quality. It also notes that Informatica developers are 5 times more productive than those coding by hand for Hadoop. The document promotes Informatica's tools for enabling existing developers to work with big data platforms like Hadoop through visual interfaces and pre-built connectors and transformations.
Applying Machine learning to IOT: End to End Distributed Distributed Pipeline...Carol McDonald
This discusses the architecture of an end-to-end application that combines streaming data with machine learning to do real-time analysis and visualization of where and when Uber cars are clustered, so as to analyze and visualize the most popular Uber locations.
Functional programming for optimization problems in Big DataPaco Nathan
Enterprise Data Workflows with Cascading.
Silicon Valley Cloud Computing Meetup talk at Cloud Tech IV, 4/20 2013
http://www.meetup.com/cloudcomputing/events/111082032/
We (Concurrent) conducted a survey of Cascading users. The Cascading community is one of the most mature Hadoop development communities, with the majority having over 3 years experience. See what they are using, why they are using it and what future challenges they anticipate.
This document summarizes the results of a survey of Cascading users. It finds that Cascading is most popular among those building and managing big data applications. Many users explored alternatives like Hive and Pig before adopting Cascading due to its scalability and portability across compute frameworks. The survey also shows that Cascading users value reliability and performance at scale and are interested in new frameworks like Spark.
The document discusses several big data frameworks: Spark, Presto, Cloudera Impala, and Apache Hadoop. Spark aims to make data analytics faster by loading data into memory for iterative querying. Presto extends R with distributed parallelism for scalable machine learning and graph algorithms. Hadoop uses MapReduce to distribute computations across large hardware clusters and handles failures automatically. While useful for batch processing, Hadoop has disadvantages for small files and online transactions.
AWS Partner Webcast - Hadoop in the Cloud: Unlocking the Potential of Big Dat...Amazon Web Services
Amazon Elastic MapReduce (Amazon EMR) makes it easy to provision and manage Hadoop in the AWS Cloud. Hadoop is available in multiple distributions and Amazon EMR gives you the option of using the Amazon Distribution or the MapR Distribution for Hadoop.
This webinar will show you examples of how to use Amazon EMR to with the MapR Distribution for Hadoop. You will learn how you can free yourself from the heavy lifting required to run Hadoop on-premises, and gain the advantages of using the cloud to increase flexibility and accelerate projects while lowering costs.
What we'll learn:
• See a live demonstration of how you can quickly and easily launch your first Hadoop cluster in a few steps.
• Examples of real world applications and customer successes in production
• Best practices for maximizing the benefits of using MapR with AWS.
The document discusses the development of an internal data pipeline platform at Indix to democratize access to data. It describes the scale of data at Indix, including over 2.1 billion product URLs and 8 TB of HTML data crawled daily. Previously, the data was not discoverable, schemas changed and were hard to track, and using code limited who could access the data. The goals of the new platform were to enable easy discovery of data, transparent schemas, minimal coding needs, UI-based workflows for anyone to use, and optimized costs. The platform developed was called MDA (Marketplace of Datasets and Algorithms) and enabled SQL-based workflows using Spark. It has continued improving since its first release in 2016
Cisco Big Data Warehouse Expansion Featuring MapR DistributionAppfluent Technology
The document discusses Cisco's Big Data Warehouse Expansion solution featuring MapR Distribution including Apache Hadoop. The solution reduces data warehouse management costs by enabling organizations to store and analyze more data at lower costs. It does this by offloading infrequently used data from the existing data warehouse to low-cost big data stores running on Cisco UCS hardware optimized for MapR Distribution. This provides benefits like enhanced analytics, improved performance, reduced costs and risks, and competitive advantages from being able to utilize more company data assets.
Developing Enterprise Consciousness: Building Modern Open Data PlatformsScyllaDB
ScyllaDB, along side some of the other major distributed real-time technologies gives businesses a unique opportunity to achieve enterprise consciousness - a business platform that delivers data to the people that need when they need it any time, anywhere.
This talk covers how modern tools in the open data platform can help companies synchronize data across their applications using open source tools and technologies and more modern low-code ETL/ReverseETL tools.
Topics:
- Business Platform Challenges
- What Enterprise Consciousness Solves
- How ScyllaDB Empowers Enterprise Consciousness
- What can ScyllaDB do for Big Companies
- What can ScyllaDB do for smaller companies.
Business Growth Is Fueled By Your Event-Centric Digital Strategyzitipoff
The document discusses how event-driven architecture (EDA) can fuel business growth through an event-centric digital strategy. It covers:
1) EDA's role in digital business strategies and how it enables organizations to respond rapidly to events.
2) Key components of an EDA system including Kafka, Spark and Cassandra, and how technologies like these provide benefits such as scalability, fault tolerance and real-time processing.
3) Examples of Netflix and Amazon successfully leveraging EDA for hyper-personalization to retain customers and increase sales.
The document provides an overview of Hadoop, including:
- What Hadoop is and its core modules like HDFS, YARN, and MapReduce.
- Reasons for using Hadoop like its ability to process large datasets faster across clusters and provide predictive analytics.
- When Hadoop should and should not be used, such as for real-time analytics versus large, diverse datasets.
- Options for deploying Hadoop including as a service on cloud platforms, on infrastructure as a service providers, or on-premise with different distributions.
- Components that make up the Hadoop ecosystem like Pig, Hive, HBase, and Mahout.
Big Data with hadoop, Spark and BigQuery (Google cloud next Extended 2017 Kar...Imam Raza
Google Next Extended (https://cloudnext.withgoogle.com/) is an annual Google event focusing on Google cloud technologies. This presentation is from tech talk held in Google Next Extended 2017 Karachi event
This document contains Anil Kumar's resume. It summarizes his contact information, professional experience working with Hadoop and related technologies like MapReduce, Pig, and Hive. It also lists his technical skills and qualifications, including being a MapR certified Hadoop Professional. His work experience includes developing MapReduce algorithms, installing and configuring MapR Hadoop clusters, and working on projects for clients like Pfizer and American Express involving data analytics using Hadoop, Spark, and Hive.
Hadoop performance modeling for job estimation and resource provisioningLeMeniz Infotech
Hadoop performance modeling for job estimation and resource provisioning
Do Your Projects With Technology Experts
To Get this projects Call : 9566355386 / 99625 88976
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Blog : http://www.ieeeprojectsinpondicherry.blogspot.in/
Youtube:https://www.youtube.com/watch?v=eesBNUnKvws
End to End Machine Learning Open Source Solution Presented in Cisco Developer...Manish Harsh
The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level compute optimization, and exposes GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.
The business analytics marketplace is experiencing a challenge as classic BI tools meet up with evolving big data technologies, in particular Hadoop. We explore how IBM works to meet this challenge, providing a big picture perspective of their big data offerings around Hadoop, its open data platform and BigInsights.
1. Hadoop adoption has matured from initial small deployments to scaling up across enterprises, but configuring and managing large Hadoop environments can be difficult and expensive.
2. Hadoop as a Service (HaaS) provides an alternative where enterprises can deploy Hadoop in the cloud to avoid the challenges of managing large on-premise clusters.
3. HaaS allows enterprises to focus on data analysis rather than infrastructure while reducing costs and providing scalability, high availability, and self-configuration capabilities not easily achieved on-premise.
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Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
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### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
2. 2What is Hadoop?
“Apache Hadoop is an open-source software framework for storage
and large-scale processing of data-sets on clusters of commodity
hardware.“ (Wikipedia)
Designed for
Possible to:
Works on:
• Batch
Processing
• Horizontal Scaling
• Bringing
Computation to
Data
Principles of Hadoop:
3. 3Main Features
Reliable and Redundant
• No performance or data loss even on failure
Powerful
• Possible to have huge clusters (largest 40,000 nodes)
• Supports “Best of Breed Analytics“
Scalable
• Linearly scalable with increase in data volume
Cost Efficient
• No need for expensive hardware. Supports commodity hardware
Simple and flexible APIs
• Great ecosystem with multitude of solutions to support
4. 4Traditional vs. Hadoop
Traditional Hadoop
More and larger server necessary to
accomplish tasks:
• computing capacity
• data capacity
Instead of upgrading the server, the cluster
size is increased with more machines
5. 5
MapReduce are programming model to run applications
mostly on Hadoop
What is MapReduce?
Mapper
• Converts
input
(K,V) to
new (K,V)
Shuffle
• Sorts and
Groups
similar
keys with
all its
values
Reducer
• Translates
the Value
each
unique
Key to
new (K,V)
9. 9Challenges with Map Reduce
Complex jobs which requires multiple mappers and
reducers
Chaining multiple MR jobs and scheduling them together
Wrong level of granularity of MR
Transforming business rules into Map Reduce paradigm
Testing and maintaining the code
10. 10Growing opportunities in Hadoop
With the growing job trends in Hadoop, there is a huge gap
in the skillset required to meet the demand
Huge investment already made by enterprises in existing
business processes and training
13. 13What is Cascading ?
Cascading is a open source Java framework that
provides application development platform for building
data applications on Hadoop.
Developed by Chris Wensel in 2007
Underlying motivation for developing the Cascading Java
framework
Difficulty for Java developers
to write MapReduce Code
MapReduce is based on
functional programming
element
14. 14Enterprise Data Flow - Challenge
Business Goals Data Sources
Using existing Skillset,
business process and tools
16. 16Cascading in Short
Functional programming way to Hadoop
Alternative and Easy API for MapReduce
Reusable Java components
Possibility for Test driven development
Can be used with any JVM- based languages
Java, JRuby, Clojure, etc
33. 33
Cascading Pattern is a machine learning project within the Cascading
development framework used to build enterprise data workflows
Pattern uses the industrial standard Predictive Model Markup Language
(PMML), an XML-based file format developed by Data Mining group
PMML is supported by most of the popular analytical tools such as R,
SaS, TeraData, Weka, Knime, Microsoft etc
Cascading Pattern
http://www.dmg.org/
34. 34
Track trips
Maintain Logbook
Get Notified about best gas stations
Manage and compare vehicle cost
Fleet management
Social platform connecting drivers
Cascading Pattern on CarbookPlus
www.carbookplus.com
35. 35CarbookPlus Fuel Cost Predicition
“MDM: Mobilitäts Daten
Marktplatz”, is a German federal
government organization that
provides open data about the
fuel prices across Germany on
real time.
http://www.mdm-portal.de/
Our Objective :
• Store the data from MDM into
HDFS
• Process and clean the data with
Cascading
• Build a model with R, predicting
the fuel price trend for the next 7
days & 24 hours
• Export the model as PMML
• Scale-out on the hadoop cluster,
with Cascading Pattern
• Store the results in Mongodb
39. 39Algorithms Supported by Cascading Pattern
Random Forest
Linear Regression
Logistical Regression
K-Means Clustering
Hierarchical Clustering
Multinominal Model
https://github.com/cascading/pattern
40. 40
Cascading Pattern to Support more predictive models
Neural Network
Support Vector Machine
More new features in Cascading 3.0
Future of Cascading
YARN
Cluster Resource Management
HDFS
Distributed Storage
Cascading 3.0
Spark
Tez
Execution Engine
Storm
42. 42Questions?
Q & A
Thank you !!
Vinoth Kannan
Credits
www.soundcloud.com
www.concurrentinc.co
m
www.cascading.org
Big Data Engineer
WidasConcepts Gmbh
www.widas.de
@WidasConcepts@vinoth4v
/WidasConcepts
vinoth.kannan@widas.de
Editor's Notes
Pipe -
Each – Defines Filter or Function each tuple has to pass through
GroupBy – groups the filed on selected tuple stream by field name. Allows merging
CoGroup – joins on common set of values. Joins can be Inner, outer, Left or Right
Every – applies aggregtor to every group of tuples
Subassembly - nesting reusable pipe assemblies into a Pipe class for inclusion in a larger pipe assembly.
A Scheme defines what is stored in a Tap instance by declaring the Tuple field names, and alternately parsing or rendering the incoming or outgoing Tuple stream, respectively.
A Scheme defines the type of resource data will be sourced from or sinked to.