Extending your Hadoop Implementation to the Cloud
•
1 like•1,367 views
This document discusses strategies for extending Hadoop implementations to the cloud. It describes using infrastructure as a service (IaaS) to run Hadoop clusters in the cloud, leveraging cloud storage for flexibility, and using Hadoop as a service offerings. Hybrid scenarios involving both on-premises and cloud deployments are also covered. Demonstrations show deploying Hadoop clusters to Microsoft Azure and using Azure HDInsight.
Report
Share
Report
Share
Recommended
Cloud computing and Hadoop introduction
This document introduces BioCloud, a tool for using cloud computing platforms like Hadoop to process large biological datasets in parallel. It discusses how biology applications are becoming more resource-intensive and how cloud platforms can provide scalable computing resources at a lower cost than local hardware. It provides an overview of Hadoop and MapReduce as an framework for processing vast amounts of data across clusters of machines. Examples of companies using Hadoop include Google, Yahoo, and Facebook for applications involving terabytes of data.
Cloud Computing: Hadoop
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop automatically manages data replication and platform failure to ensure very large data sets can be processed efficiently in a reliable, fault-tolerant manner. Common uses of Hadoop include log analysis, data warehousing, web indexing, machine learning, financial analysis, and scientific applications.
Hadoop in the Cloud: Common Architectural Patterns
The document discusses how companies are using Microsoft Azure services like HDInsight, Data Factory, Machine Learning, and others to gain insights from large volumes of data. Specifically, it provides examples of:
1) A large computer manufacturer/retailer analyzing clickstream data with HDInsight to understand customer behavior and provide real-time recommendations to increase online conversions.
2) An industrial automation company partnering with an oil company to use IoT sensors and analytics to monitor LNG fueling stations for proactive maintenance based on sensor data analyzed with HDInsight, Data Factory, and Machine Learning.
3) How data from various industries like retail, oil and gas, manufacturing, and others can be analyzed
Accelerating analytics in the cloud with the Starburst Presto + Alluxio stack
Alluxio Tech Talk
January 21, 2020
Speakers:
Matt Fuller, Starburst
Dipti Borkar, Alluxio
With the advent of the public clouds and data increasingly siloed across many locations -- on premises and in the public cloud -- enterprises are looking for more flexibility and higher performance approaches to analyze their structured data.
Join us for this tech talk where we’ll introduce the Starburst Presto, Alluxio, and cloud object store stack for building a highly-concurrent and low-latency analytics platform. This stack provides a strong solution to run fast SQL across multiple storage systems including HDFS, S3, and others in public cloud, hybrid cloud, and multi-cloud environments. You’ll learn more about:
- The architecture of Presto, an open source distributed SQL engine
- How the Presto + Alluxio stack queries data from cloud object storage like S3 for faster and more cost-effective analytics
- Achieving data locality and cross-job caching with Alluxio regardless of where data is persisted
Case study on big data
Hadoop is an open source framework that allows for the distributed processing of large datasets across clusters of computers. It has two main components: a processing layer called MapReduce that allows for parallel processing, and a storage layer called HDFS that provides fault tolerance. Hadoop can be used to analyze large, diverse datasets including structured, semi-structured, and unstructured data for applications such as recommendations, fraud detection, and risk modeling. Tools like Hive, HBase, HDFS and Sqoop work with Hadoop to process and transfer both structured and unstructured big data.
Databricks for Dummies
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
Tropos.io - Hadoop in the Cloud - BA4ALL 2016
A quick talk on accellerating big data analytics using public cloud technology I gave at the BA4ALL conference.
Accelerate Analytics and ML in the Hybrid Cloud Era
Alluxio Webinar
April 6, 2021
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Alex Ma, Alluxio
Peter Behrakis, Alluxio
Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.
In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.
In this tech talk, we'll go over:
- What is Alluxio Data Orchestration?
- How does it work?
- Alluxio customer results
Recommended
Cloud computing and Hadoop introduction
This document introduces BioCloud, a tool for using cloud computing platforms like Hadoop to process large biological datasets in parallel. It discusses how biology applications are becoming more resource-intensive and how cloud platforms can provide scalable computing resources at a lower cost than local hardware. It provides an overview of Hadoop and MapReduce as an framework for processing vast amounts of data across clusters of machines. Examples of companies using Hadoop include Google, Yahoo, and Facebook for applications involving terabytes of data.
Cloud Computing: Hadoop
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Hadoop automatically manages data replication and platform failure to ensure very large data sets can be processed efficiently in a reliable, fault-tolerant manner. Common uses of Hadoop include log analysis, data warehousing, web indexing, machine learning, financial analysis, and scientific applications.
Hadoop in the Cloud: Common Architectural Patterns
The document discusses how companies are using Microsoft Azure services like HDInsight, Data Factory, Machine Learning, and others to gain insights from large volumes of data. Specifically, it provides examples of:
1) A large computer manufacturer/retailer analyzing clickstream data with HDInsight to understand customer behavior and provide real-time recommendations to increase online conversions.
2) An industrial automation company partnering with an oil company to use IoT sensors and analytics to monitor LNG fueling stations for proactive maintenance based on sensor data analyzed with HDInsight, Data Factory, and Machine Learning.
3) How data from various industries like retail, oil and gas, manufacturing, and others can be analyzed
Accelerating analytics in the cloud with the Starburst Presto + Alluxio stack
Alluxio Tech Talk
January 21, 2020
Speakers:
Matt Fuller, Starburst
Dipti Borkar, Alluxio
With the advent of the public clouds and data increasingly siloed across many locations -- on premises and in the public cloud -- enterprises are looking for more flexibility and higher performance approaches to analyze their structured data.
Join us for this tech talk where we’ll introduce the Starburst Presto, Alluxio, and cloud object store stack for building a highly-concurrent and low-latency analytics platform. This stack provides a strong solution to run fast SQL across multiple storage systems including HDFS, S3, and others in public cloud, hybrid cloud, and multi-cloud environments. You’ll learn more about:
- The architecture of Presto, an open source distributed SQL engine
- How the Presto + Alluxio stack queries data from cloud object storage like S3 for faster and more cost-effective analytics
- Achieving data locality and cross-job caching with Alluxio regardless of where data is persisted
Case study on big data
Hadoop is an open source framework that allows for the distributed processing of large datasets across clusters of computers. It has two main components: a processing layer called MapReduce that allows for parallel processing, and a storage layer called HDFS that provides fault tolerance. Hadoop can be used to analyze large, diverse datasets including structured, semi-structured, and unstructured data for applications such as recommendations, fraud detection, and risk modeling. Tools like Hive, HBase, HDFS and Sqoop work with Hadoop to process and transfer both structured and unstructured big data.
Databricks for Dummies
Tech talk on what Azure Databricks is, why you should learn it and how to get started. We'll use PySpark and talk about some real live examples from the trenches, including the pitfalls of leaving your clusters running accidentally and receiving a huge bill ;)
After this you will hopefully switch to Spark-as-a-service and get rid of your HDInsight/Hadoop clusters.
This is part 1 of an 8 part Data Science for Dummies series:
Databricks for dummies
Titanic survival prediction with Databricks + Python + Spark ML
Titanic with Azure Machine Learning Studio
Titanic with Databricks + Azure Machine Learning Service
Titanic with Databricks + MLS + AutoML
Titanic with Databricks + MLFlow
Titanic with DataRobot
Deployment, DevOps/MLops and Operationalization
Tropos.io - Hadoop in the Cloud - BA4ALL 2016
A quick talk on accellerating big data analytics using public cloud technology I gave at the BA4ALL conference.
Accelerate Analytics and ML in the Hybrid Cloud Era
Alluxio Webinar
April 6, 2021
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Alex Ma, Alluxio
Peter Behrakis, Alluxio
Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.
In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.
In this tech talk, we'll go over:
- What is Alluxio Data Orchestration?
- How does it work?
- Alluxio customer results
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
Data Orchestration for AI, Big Data, and Cloud
This document discusses the need for data orchestration across fragmented data environments. As more data is generated and stored across different storage systems and clouds, data silos have become inevitable. A data orchestration solution like Alluxio can abstract and orchestrate data across these silos, making data locally accessible to compute frameworks regardless of where the data is stored. Alluxio provides a unified view of data locations, enables data access from any application, and allows data to be burst elastically across clouds for compute. Many large companies are adopting data orchestration to improve data access, reduce costs, and gain more flexibility in their cloud environments.
Hadoop Tutorial For Beginners
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Hd insight essentials quick view
These slides provide highlights of my book HDInsight Essentials. Book link is here: http://www.packtpub.com/establish-a-big-data-solution-using-hdinsight/book
Big data architecture on cloud computing infrastructure
This document provides an overview of using OpenStack and Sahara to implement a big data architecture on cloud infrastructure. It discusses:
- The characteristics and service models of cloud computing
- An introduction to OpenStack, why it is used, and some of its key statistics
- What Sahara is and its role in provisioning and managing Hadoop, Spark, and Storm clusters on OpenStack
- Sahara's architecture, how it integrates with OpenStack, and examples of how it can be used to quickly provision data processing clusters and execute analytic jobs on cloud infrastructure.
Big data vahidamiri-datastack.ir
This document provides an overview of big data processing tools and NoSQL databases. It discusses how Hadoop uses MapReduce and HDFS to distribute processing across large clusters. Spark is presented as an alternative to Hadoop. The CAP theorem is explained as relating to consistency, availability, and network partitions. Different types of NoSQL databases are described including key-value, column, document and graph databases. Examples are provided for each type.
The Fundamentals Guide to HDP and HDInsight
This session will give you the architectural overview and introduction in to inner workings of HDP 2.0 (http://hortonworks.com/products/hdp-windows/) and HDInsight. The world has embraced the Hadoop toolkit to solve their data problems from ETL, data warehouses to event processing pipelines. As Hadoop consists of many components, services and interfaces, understanding its architecture is crucial, before you can successfully integrate it in to your own environment.
BigData- On - AWS Cloud -1
This document discusses how companies like Yelp, Unilever, and Expedia use Amazon Web Services (AWS) for big data analytics. It provides background on cloud computing and defines AWS as an infrastructure as a service provider. The key benefits of using AWS for big data are the ability to automatically scale infrastructure, pay for only what is used, and focus on core business solutions rather than managing hardware. AWS Elastic MapReduce allows running Hadoop and Spark clusters on AWS, making it easy to process vast amounts of data in parallel across cloud resources in a cost effective and automated way. The clusters integrate with other AWS services for computing, networking, storage, and data management.
The Pandemic Changes Everything, the Need for Speed and Resiliency
This document discusses how the COVID-19 pandemic has accelerated the need for cloud computing and digital transformation. Some key points:
- By 2021, over 90% of organizations will rely on a mix of on-premises, private clouds, public clouds, and legacy systems to meet infrastructure needs.
- By 2023, an emerging cloud ecosystem for extending resource control and analytics will underlie all IT and business automation initiatives anywhere.
- Resilient business models and superior customer experience will be critical as organizations shift more operations and services to the cloud.
عصر کلان داده، چرا و چگونه؟
ارائه در زمینه کلان داده،
کارگاه آموزشی "عصر کلان داده، چرا و چگونه؟" در بیست و دومین کنفرانس انجمن کامپیوتر ایران csicc2017.ir
وحید امیری
vahidamiry.ir
datastack.ir
Enabling big data & AI workloads on the object store at DBS
DBS Bank is headquartered in Singapore and has evolved its data platforms over three generations from proprietary systems to a hybrid cloud-native platform using open source technologies. It is using Alluxio to unify access to data stored in its on-premises object store and HDFS as well as enable analytics workloads to burst into AWS. Alluxio provides data caching to speed up analytics jobs, intelligent data tiering for efficiency, and policy-driven data migration to the cloud over time. DBS is exploring further using cloud services for speech processing and moving more workloads to the cloud while keeping data on-premises for compliance.
Hadoop distributions - ecosystem
An overview of hadoop distributions and its ecosystem particularly focused on hortonworks distribution.
Hadoop
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers. It reliably stores and processes gobs of information across many commodity computers. Key components of Hadoop include the HDFS distributed file system for high-bandwidth storage, and MapReduce for parallel data processing. Hadoop can deliver data and run large-scale jobs reliably in spite of system changes or failures by detecting and compensating for hardware problems in the cluster.
Alluxio Use Cases and Future Directions
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Data Orchestration for Analytics and AI in the Cloud Era
Calvin Jia, Founding Engineer (Alluxio)
Bin Fan, Founding Engineer, VP of Open Source (Alluxio)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Hd insight overview
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.
Hw09 Rethinking The Data Warehouse With Hadoop And Hive
The document discusses Hive, a system for managing and querying large datasets stored in Hadoop. It describes how Hive provides a familiar SQL-like interface, simplifying Hadoop programming. The document also outlines how Facebook uses Hive and Hadoop for analytics, with over 4TB of new data added daily across a large cluster.
Orchestrate a Data Symphony
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Orchestrate a Data Symphony
Speaker:
Haoyuan Li, Alluxio
For more Alluxio events: https://www.alluxio.io/events/
Enabling Apache Spark for Hybrid Cloud
1) Alluxio provides a solution for accessing data across hybrid cloud environments by serving as an abstraction layer between applications and underlying storage systems.
2) It allows compute resources and data storage to be separated and scaled independently through its unified namespace and ability to access data locally through intelligent data tiering even when stored remotely.
3) Use cases include bursting big data workloads between on-premise and cloud environments, accelerating popular big data frameworks on object storage, and enabling data orchestration for agility across multiple clouds and storage systems.
Hadoop
This document discusses Hadoop technology. It was developed by Doug Cutting and Michael J. Cafarella to support large-scale data processing for the Nutch search engine project. Hadoop features include distributed storage via HDFS and distributed processing via MapReduce. It allows for scalable and fault-tolerant processing of large data sets across commodity hardware. While Hadoop provides low-cost processing of big data, its administration and integration with other systems can be challenging.
Accelerate Analytics and ML in the Hybrid Cloud Era
Alluxio Webinar
September 22, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Alex Ma, Alluxio
Peter Behrakis, Alluxio
Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.
In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.
In this tech talk, we'll go over:
- What is Alluxio Data Orchestration?
- How does it work?
- Alluxio customer results
Azure Machine Learning using R
The document discusses building a machine learning model to predict flight delays using data from the Bureau of Transportation Statistics and National Oceanic and Atmospheric Administration. It describes preparing the data, which includes reading flight and weather data from various sources, inspecting and transforming the data. The goal is to predict delays by building models and evaluating their performance.
Azure Machine Learning 101
This document provides an overview of Azure Machine Learning including: an agenda covering what machine learning is, what Azure Machine Learning is, how to apply machine learning, and a tutorial. It discusses machine learning concepts like algorithms, datasets, features, models and training. It describes Azure Machine Learning Studio, workspaces, experiments, datasets, algorithms, models and exposing models as web services. Finally, it provides resources for machine learning on Azure and leaves time for questions.
More Related Content
What's hot
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
Data Orchestration for AI, Big Data, and Cloud
This document discusses the need for data orchestration across fragmented data environments. As more data is generated and stored across different storage systems and clouds, data silos have become inevitable. A data orchestration solution like Alluxio can abstract and orchestrate data across these silos, making data locally accessible to compute frameworks regardless of where the data is stored. Alluxio provides a unified view of data locations, enables data access from any application, and allows data to be burst elastically across clouds for compute. Many large companies are adopting data orchestration to improve data access, reduce costs, and gain more flexibility in their cloud environments.
Hadoop Tutorial For Beginners
The presentation covers following topics: 1) Hadoop Introduction 2) Hadoop nodes and daemons 3) Architecture 4) Hadoop best features 5) Hadoop characteristics. For more further knowledge of Hadoop refer the link: http://data-flair.training/blogs/hadoop-tutorial-for-beginners/
Hd insight essentials quick view
These slides provide highlights of my book HDInsight Essentials. Book link is here: http://www.packtpub.com/establish-a-big-data-solution-using-hdinsight/book
Big data architecture on cloud computing infrastructure
This document provides an overview of using OpenStack and Sahara to implement a big data architecture on cloud infrastructure. It discusses:
- The characteristics and service models of cloud computing
- An introduction to OpenStack, why it is used, and some of its key statistics
- What Sahara is and its role in provisioning and managing Hadoop, Spark, and Storm clusters on OpenStack
- Sahara's architecture, how it integrates with OpenStack, and examples of how it can be used to quickly provision data processing clusters and execute analytic jobs on cloud infrastructure.
Big data vahidamiri-datastack.ir
This document provides an overview of big data processing tools and NoSQL databases. It discusses how Hadoop uses MapReduce and HDFS to distribute processing across large clusters. Spark is presented as an alternative to Hadoop. The CAP theorem is explained as relating to consistency, availability, and network partitions. Different types of NoSQL databases are described including key-value, column, document and graph databases. Examples are provided for each type.
The Fundamentals Guide to HDP and HDInsight
This session will give you the architectural overview and introduction in to inner workings of HDP 2.0 (http://hortonworks.com/products/hdp-windows/) and HDInsight. The world has embraced the Hadoop toolkit to solve their data problems from ETL, data warehouses to event processing pipelines. As Hadoop consists of many components, services and interfaces, understanding its architecture is crucial, before you can successfully integrate it in to your own environment.
BigData- On - AWS Cloud -1
This document discusses how companies like Yelp, Unilever, and Expedia use Amazon Web Services (AWS) for big data analytics. It provides background on cloud computing and defines AWS as an infrastructure as a service provider. The key benefits of using AWS for big data are the ability to automatically scale infrastructure, pay for only what is used, and focus on core business solutions rather than managing hardware. AWS Elastic MapReduce allows running Hadoop and Spark clusters on AWS, making it easy to process vast amounts of data in parallel across cloud resources in a cost effective and automated way. The clusters integrate with other AWS services for computing, networking, storage, and data management.
The Pandemic Changes Everything, the Need for Speed and Resiliency
This document discusses how the COVID-19 pandemic has accelerated the need for cloud computing and digital transformation. Some key points:
- By 2021, over 90% of organizations will rely on a mix of on-premises, private clouds, public clouds, and legacy systems to meet infrastructure needs.
- By 2023, an emerging cloud ecosystem for extending resource control and analytics will underlie all IT and business automation initiatives anywhere.
- Resilient business models and superior customer experience will be critical as organizations shift more operations and services to the cloud.
عصر کلان داده، چرا و چگونه؟
ارائه در زمینه کلان داده،
کارگاه آموزشی "عصر کلان داده، چرا و چگونه؟" در بیست و دومین کنفرانس انجمن کامپیوتر ایران csicc2017.ir
وحید امیری
vahidamiry.ir
datastack.ir
Enabling big data & AI workloads on the object store at DBS
DBS Bank is headquartered in Singapore and has evolved its data platforms over three generations from proprietary systems to a hybrid cloud-native platform using open source technologies. It is using Alluxio to unify access to data stored in its on-premises object store and HDFS as well as enable analytics workloads to burst into AWS. Alluxio provides data caching to speed up analytics jobs, intelligent data tiering for efficiency, and policy-driven data migration to the cloud over time. DBS is exploring further using cloud services for speech processing and moving more workloads to the cloud while keeping data on-premises for compliance.
Hadoop distributions - ecosystem
An overview of hadoop distributions and its ecosystem particularly focused on hortonworks distribution.
Hadoop
Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers. It reliably stores and processes gobs of information across many commodity computers. Key components of Hadoop include the HDFS distributed file system for high-bandwidth storage, and MapReduce for parallel data processing. Hadoop can deliver data and run large-scale jobs reliably in spite of system changes or failures by detecting and compensating for hardware problems in the cluster.
Alluxio Use Cases and Future Directions
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Data Orchestration for Analytics and AI in the Cloud Era
Calvin Jia, Founding Engineer (Alluxio)
Bin Fan, Founding Engineer, VP of Open Source (Alluxio)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
Hd insight overview
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.
Hw09 Rethinking The Data Warehouse With Hadoop And Hive
The document discusses Hive, a system for managing and querying large datasets stored in Hadoop. It describes how Hive provides a familiar SQL-like interface, simplifying Hadoop programming. The document also outlines how Facebook uses Hive and Hadoop for analytics, with over 4TB of new data added daily across a large cluster.
Orchestrate a Data Symphony
Data Orchestration Summit
www.alluxio.io/data-orchestration-summit-2019
November 7, 2019
Orchestrate a Data Symphony
Speaker:
Haoyuan Li, Alluxio
For more Alluxio events: https://www.alluxio.io/events/
Enabling Apache Spark for Hybrid Cloud
1) Alluxio provides a solution for accessing data across hybrid cloud environments by serving as an abstraction layer between applications and underlying storage systems.
2) It allows compute resources and data storage to be separated and scaled independently through its unified namespace and ability to access data locally through intelligent data tiering even when stored remotely.
3) Use cases include bursting big data workloads between on-premise and cloud environments, accelerating popular big data frameworks on object storage, and enabling data orchestration for agility across multiple clouds and storage systems.
Hadoop
This document discusses Hadoop technology. It was developed by Doug Cutting and Michael J. Cafarella to support large-scale data processing for the Nutch search engine project. Hadoop features include distributed storage via HDFS and distributed processing via MapReduce. It allows for scalable and fault-tolerant processing of large data sets across commodity hardware. While Hadoop provides low-cost processing of big data, its administration and integration with other systems can be challenging.
Accelerate Analytics and ML in the Hybrid Cloud Era
Alluxio Webinar
September 22, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Alex Ma, Alluxio
Peter Behrakis, Alluxio
Many companies we talk to have on premises data lakes and use the cloud(s) to burst compute. Many are now establishing new object data lakes as well. As a result, running analytics such as Hive, Spark, Presto and machine learning are experiencing sluggish response times with data and compute in multiple locations. We also know there is an immense and growing data management burden to support these workflows.
In this talk, we will walk through what Alluxio’s Data Orchestration for the hybrid cloud era is and how it solves the performance and data management challenges we see.
In this tech talk, we'll go over:
- What is Alluxio Data Orchestration?
- How does it work?
- Alluxio customer results
What's hot (20)
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & Alluxio
Big data architecture on cloud computing infrastructure
Big data architecture on cloud computing infrastructure
The Pandemic Changes Everything, the Need for Speed and Resiliency
The Pandemic Changes Everything, the Need for Speed and Resiliency
Enabling big data & AI workloads on the object store at DBS
Enabling big data & AI workloads on the object store at DBS
Hw09 Rethinking The Data Warehouse With Hadoop And Hive
Hw09 Rethinking The Data Warehouse With Hadoop And Hive
Accelerate Analytics and ML in the Hybrid Cloud Era
Accelerate Analytics and ML in the Hybrid Cloud Era
Viewers also liked
Azure Machine Learning using R
The document discusses building a machine learning model to predict flight delays using data from the Bureau of Transportation Statistics and National Oceanic and Atmospheric Administration. It describes preparing the data, which includes reading flight and weather data from various sources, inspecting and transforming the data. The goal is to predict delays by building models and evaluating their performance.
Azure Machine Learning 101
This document provides an overview of Azure Machine Learning including: an agenda covering what machine learning is, what Azure Machine Learning is, how to apply machine learning, and a tutorial. It discusses machine learning concepts like algorithms, datasets, features, models and training. It describes Azure Machine Learning Studio, workspaces, experiments, datasets, algorithms, models and exposing models as web services. Finally, it provides resources for machine learning on Azure and leaves time for questions.
Data lake – On Premise VS Cloud
The document discusses Ido Friedman and his background working with various data technologies. It then discusses the concept of a data lake and how it serves as a single store for raw and transformed data used for reporting, analytics, and machine learning. The rest of the document discusses how traditional tools like SQL have changed with the rise of Hadoop and cloud storage. It provides examples of performance and cost differences between running data workloads on Hadoop clusters versus cloud-based data processing services like BigQuery and Dataproc. The document concludes that a large data lake is now possible in the cloud and discusses various deployment options to consider.
Forrester whos hot in bsm
Enterprises continue investing in business service management (BSM) projects despite economic recession. The BSM vendor landscape has changed significantly since 2007, with only seven of 20 vendors from a 2007 Forrester Wave still offering BSM solutions. Vendors are categorized as offering strategic IT transformation programs, BSM reporting solutions, service catalog management solutions, or transaction management solutions. Leading vendors for strategic programs include BMC Software, CA, HP Software, and IBM. Vendors focusing on reporting include ASG, CA, Compuware, Efecte, FireScope, IBM, Nimsoft, Novell, and USU.
Big Data Platform Processes Daily Healthcare Data for Clinic Use at Mayo Clinic
The document summarizes Mayo Clinic's implementation of a big data platform to process and analyze large volumes of daily healthcare data, including HL7 messages, for enterprise-wide clinical and non-clinical usage. The platform, built on Hadoop and using technologies like Storm and Elasticsearch, reliably handles 20-50 times more data than their current daily volumes. It provides ultra-fast free text search capabilities. The system supports applications like processing data for colorectal surgery, exceeding requirements and outperforming previous RDBMS-only systems. Ongoing work involves further enhancing capabilities and integrating with additional components as part of a unified data platform.
Spark crash course workshop at Hadoop Summit
This document provides an overview of installing and programming with Apache Spark on Hortonworks Data Platform (HDP). It introduces Spark and its components, benefits over other frameworks, and Hortonworks' commitment to Spark. The document outlines an example Spark programming workflow using Resilient Distributed Datasets (RDDs) in Scala, and covers common RDD transformations, actions, and persistence methods. It also discusses Spark deployment modes like standalone and on YARN, and reference HDP architectures using Spark.
Cortana Analytics Workshop: Azure Data Lake
Rajesh Dadhia. This session introduces the newest services in the Cortana Analytics family. Azure Data Lake is a hyper-scale data repository designed for big data analytics workloads. It provides a single place to store any type of data in its native format. In this session, we will show how the HDFS compatibility of Azure Data Lake as a Hadoop File System enables all Hadoop workloads including Azure HDInsight, Hortonworks and Cloudera. Further, we will focus on the key capabilities of the Azure Data Lake that make it an ideal choice for storing, accessing and sharing data for a wide range of analytics applications. Go to https://channel9.msdn.com/ to find the recording of this session.
Hands-On with U-SQL and Azure Data Lake Analytics (ADLA)
U-SQL is the query language for big data analytics on the Azure Data Lake platform. This session will explore the unification of SQL and C# in this new query language, examples of combining data from external sources such as Azure SQL Database and Blob storage with Azure Data Lake store, creating and referencing assemblies, job submission and tools. The ADL platform will also be compared and contrasted to the HDInsight/Hadoop platform.
The How and Why of Feature Engineering
Feature engineering--the underdog of machine learning. This deck provides an overview of feature generation methods for text, image, audio, feature cleaning and transformation methods, how well they work and why.
Azure Data Lake Intro (SQLBits 2016)
Azure Data Lake Intro (SQLBits 2016 ADL/USQL Pre-Conference)
Data Lake concept, Azure Data Lake, HDINSIGHT, Azure Data Lake Storage and Analytics
Evolution of Big Data at Intel - Crawl, Walk and Run Approach
Intel's big data journey began in 2011 with an evaluation of Hadoop. Since then, Intel has expanded its use of Hadoop and Cloudera across multiple environments. Intel's 3-year roadmap focuses on evolving its Hadoop platform to support more advanced analytics, real-time capabilities, and integrating with traditional BI tools. Key strategies include designing for scalability, following an iterative approach to understand data, and leveraging open source technologies.
Microsoft Azure vs Amazon Web Services (AWS) Services & Feature Mapping
If you are a Cloud Architect, Developer, IT Manager, Director or whoever may be, if you are associated with Azure or AWS cloud in some form, I’m sure you must have come across a common question.
“What is the alternate service available in Azure or AWS vice versa and it’s pricing?” I’m sure you will say yes!
Agreed, it’s hard to remember all the services offered by public clouds, i.e. Azure and AWS. Remembering existing services and their benefits itself is a big task, on top of that updating ourselves with the new feature releases and enhancements is another major task.
So I put together a Service & Feature Mappings between Microsoft Azure & AWS for my and colleagues quick reference.
I hope you also find this piece informative.
Azure Data Lake Analytics Deep Dive
Azure Data Lake Analytics provides a big data analytics service for processing large amounts of data stored in Azure Data Lake Store. It allows users to run analytics jobs using U-SQL, a language that unifies SQL with C# for querying structured, semi-structured and unstructured data. Jobs are compiled, scheduled and run in parallel across multiple Azure Data Lake Analytics Units (ADLAUs). The key components include storage, a job queue, parallelization, and a U-SQL runtime. Partitioning input data improves performance by enabling partition elimination and parallel aggregation of query results.
Improve the Development Process with DevOps Practices by Fedorov Vadim
The document discusses improving development processes with DevOps practices. It describes current issues like a lack of communication between development and operations teams and differences between development and production environments. It evaluates project maturity levels and suggests organizational changes like shared success metrics for teams. The document recommends implementing infrastructure as code, automating testing, and using a delivery pipeline to improve quality, repeatability and reduce risks. Adopting DevOps practices and tools can help align development and production environments.
Overview of Machine Learning and Feature Engineering
Machine Learning 101 Tutorial at Strata NYC, Sep 2015
Overview of machine learning models and features. Visualization of feature space and feature engineering methods.
Viewers also liked (15)
Big Data Platform Processes Daily Healthcare Data for Clinic Use at Mayo Clinic
Big Data Platform Processes Daily Healthcare Data for Clinic Use at Mayo Clinic
Hands-On with U-SQL and Azure Data Lake Analytics (ADLA)
Hands-On with U-SQL and Azure Data Lake Analytics (ADLA)
Evolution of Big Data at Intel - Crawl, Walk and Run Approach
Evolution of Big Data at Intel - Crawl, Walk and Run Approach
Microsoft Azure vs Amazon Web Services (AWS) Services & Feature Mapping
Microsoft Azure vs Amazon Web Services (AWS) Services & Feature Mapping
Improve the Development Process with DevOps Practices by Fedorov Vadim
Improve the Development Process with DevOps Practices by Fedorov Vadim
Overview of Machine Learning and Feature Engineering
Overview of Machine Learning and Feature Engineering
Similar to Extending your Hadoop Implementation to the Cloud
Cloud computing
This document provides an overview of cloud computing including popular definitions, deployment models, delivery models, NoSQL databases, vendor lock-in, and security concerns. It defines cloud computing based on an on-demand, self-service model with scalable, measurable resources. The document outlines infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) delivery models and discusses public, private, hybrid, and community cloud deployment models.
Introduction to Big Data Analytics on Apache Hadoop
The document discusses Hadoop and big data. It defines Hadoop as an open source, scalable, and fault tolerant platform for storing and processing large amounts of unstructured data distributed across machines. It describes Hadoop's core components like HDFS for data storage and MapReduce/YARN for data processing. It also discusses how Hadoop fits into big data scenarios and landscapes, applying Hadoop to save money, the concept of data lakes, Hadoop in the cloud, and big data analytics with Hadoop.
Integrating Google Cloud Dataproc with Alluxio for faster performance in the ...
Google Dataproc is Google Cloud's fully managed Apache Spark and Apache Hadoop service. Alluxio is an open source data orchestration platform that can be used with Dataproc to accelerate analytics workloads. With a single initialization action, Alluxio can be installed on a Dataproc cluster to cache data from Cloud Storage for faster queries. Alluxio also enables "zero-copy bursting" of workloads to the cloud by allowing frameworks to access data directly from remote HDFS without needing to copy it. This provides elastic compute capacity while avoiding high network latency and bandwidth costs of copying large datasets.
Introduction to SQL Azure
This document provides a 15-minute overview of Microsoft's cloud database SQL Azure:
- SQL Azure is a cloud database that moves data management to centralized data centers, allowing for automatic high availability, fault tolerance, and scaling of databases without local management.
- It is a robust, relational database in the cloud that supports T-SQL and is priced based on usage like electricity, avoiding upfront costs.
- The overview demonstrates basic SQL Azure features and use, and how it provides bi-directional data sync between cloud and on-premises databases.
Agile Infrastructure with Windows Azure
Over 60 CIOs and Tech Leaders attended the #GoCloudWebinar on “AGILE INFRASTRUCTURE WITH WINDOWS AZURE” hosted by Aditi Technologies and Microsoft. Our CTO, Wade Wegner and Microsoft Azure solution specialist, Dina Frandsen discussed how Windows Azure Infrastructure Services (WAIS) can help organizations stay agile and what Windows Azure technology environment looks like and what it means to your organization.
We Explored
1. How IT teams can execute fast and stay lean with WAIS – A case study
2. Which enterprise workloads are best suited of WAIS migration
3. What are the best practices on how to plan, execute, deploy WAIS
Download this slidedeck and Sign up with the below link for viewing the Webinar - http://www.aditi.com/webevent/Agile_Infrastructure_with_WAIS/
Cloud1 Computing 01
Cloud computing provides on-demand access to computing resources like servers, storage, databases, networking, software, analytics and more over the internet. It offers advantages like flexibility, scalability, fault tolerance and low upfront costs. There are different cloud deployment models like public cloud, private cloud and hybrid cloud. Popular cloud computing services include Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). Cloud-native applications are designed to take advantage of the cloud environment and scale horizontally.
VMworld 2013: Big Data Platform Building Blocks: Serengeti, Resource Manageme...
VMworld 2013
Abhishek Kashyap, Pivotal
Kevin Leong, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
From limited Hadoop compute capacity to increased data scientist efficiency
Alluxio Tech Talk
Oct 17, 2019
Speaker:
Alex Ma, Alluxio
Want to leverage your existing investments in Hadoop with your data on-premise and still benefit from the elasticity of the cloud?
Like other Hadoop users, you most likely experience very large and busy Hadoop clusters, particularly when it comes to compute capacity. Bursting HDFS data to the cloud can bring challenges – network latency impacts performance, copying data via DistCP means maintaining duplicate data, and you may have to make application changes to accomodate the use of S3.
“Zero-copy” hybrid bursting with Alluxio keeps your data on-prem and syncs data to compute in the cloud so you can expand compute capacity, particularly for ephemeral Spark jobs.
Data Orchestration Platform for the Cloud
This document discusses using a hybrid cloud approach with data orchestration to enable analytics workloads on data stored both on-premises and in the cloud. It outlines reasons for a hybrid approach including reducing time to production and leveraging cloud flexibility. It then describes alternatives like lift-and-shift or compute-driven approaches and their issues. Finally, it introduces a data orchestration platform that can cache and tier data intelligently while enabling analytics frameworks to access both on-premises and cloud-based data with low latency.
OpenStack and CloudForms Do's and Dont's
Considerations for Your Next Cloud Project – CloudForms & OpenStack Do’s and Don’ts
In this Session we will discuss Organizational and Operational Considerations on how to move into Infrastructure as a Service Environments and showcase how Enterprises today address different aspects of Cloud Management.
Focus of this session is on Design and Operational Aspects of running an Open Hybrid Cloud. The session will also touch on Process and Organizational Aspects.
VMworld 2013: Beyond Mission Critical: Virtualizing Big-Data, Hadoop, HPC, Cl...
VMworld 2013
Chris Greer, FedEx
Richard McDougall, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
Dallas Breakfast Seminar
During the “Architecting for the Cloud” breakfast seminar where we discussed the requirements of modern cloud-based applications and how to overcome the confinement of traditional on-premises infrastructure.
We heard from data management practitioners and cloud strategists from Amazon Web Services and NuoDB about how organizations are meeting the challenges associated with building new or migrating existing applications to the cloud.
Finally, we discussed how the right cloud-based architecture can:
- Handle rapid user growth by adding new servers on demand
- Provide high performance even in the face of heavy application usage
- Offer around-the-clock resiliency and uptime
- Provide easy and fast access across multiple geographies
- Deliver cloud-enabled apps in public, private, or hybrid cloud environments
Intro to Windows Azure
Windows Azure is an operating system for building and hosting scalable cloud applications and services. It handles the deployment, availability, patching and hardware configuration of applications in the cloud so developers can focus on writing code. Windows Azure provides flexible and scalable computing and storage resources that can easily expand or contract based on demand. It also allows applications to be built using a variety of languages and tools and supports both new cloud services and existing applications migrated to the cloud.
Vmware Serengeti - Based on Infochimps Ironfan
This document discusses virtualizing Hadoop for the enterprise. It begins with discussing trends driving changes in enterprise IT like cloud, mobile apps, and big data. It then discusses how Hadoop can address big, fast, and flexible data needs. The rest of the document discusses how virtualizing Hadoop through solutions like Project Serengeti can provide enterprises with elasticity, high availability, and operational simplicity for their Hadoop implementations. It also discusses how virtualization allows enterprises to integrate Hadoop with other workloads and data platforms.
Microsoft-Azure-Overvi2222222222222ew.pptx
This document provides an introduction to Microsoft Azure and its services. It outlines 7 modules that cover: 1) an introduction to Azure, 2) virtual machines, 3) networking, 4) Active Directory, 5) cloud services and web sites, 6) SQL Server and SharePoint, and 7) management and monitoring. The instructor is then introduced as Michael Washam, the original developer of the Azure PowerShell cmdlets and a globally recognized speaker on Azure.
ICS-Azure Migrations & Application Modernization_V2.pptx
This document discusses migrations and application modernization. It provides an overview of migration strategies and approaches, including assessing applications, planning a migration, executing the migration, and optimizing in the cloud. It also discusses modernizing applications to be cloud-native through re-architecting or re-platforming approaches. Key benefits of modernization include making applications more cost-efficient, scalable, and automated. The document also highlights archive storage as a solution for low-cost, secure storage of infrequently accessed data.
Azure Data Factory ETL Patterns in the Cloud
This document discusses ETL patterns in the cloud using Azure Data Factory. It covers topics like ETL vs ELT, the importance of scale and flexible schemas in cloud ETL, and how Azure Data Factory supports workflows, templates, and integration with on-premises and cloud data. It also provides examples of nightly ETL data flows, handling schema drift, loading dimensional models, and data science scenarios using Azure data services.
Alluxio 2.0 Deep Dive – Simplifying data access for cloud workloads
Alluxio provides a data orchestration platform that allows applications to access data closer to compute across different storage systems through a unified namespace. Key features include intelligent multi-tier caching that provides local performance for remote data, API translation that enables popular frameworks to access different storages without changes, and data elasticity through a global namespace. Alluxio powers analytics and AI workloads in hybrid cloud environments.
Modernizing Your Data Platform for Analytics and AI in the Hybrid Cloud Era
This document discusses modernizing a data platform for analytics and AI across single, hybrid, or multi-cloud environments using Alluxio. It describes Alluxio's key features like data locality, metadata locality, asynchronous data operations, and policy-driven data management that enable consistent performance, portability, and cost savings. Examples are provided of how Alluxio can be used to transition from on-premises HDFS to object storage to hybrid cloud and multi-cloud configurations.
Introducing Azure SQL Data Warehouse
The new Microsoft Azure SQL Data Warehouse (SQL DW) is an elastic data warehouse-as-a-service and is a Massively Parallel Processing (MPP) solution for "big data" with true enterprise class features. The SQL DW service is built for data warehouse workloads from a few hundred gigabytes to petabytes of data with truly unique features like disaggregated compute and storage allowing for customers to be able to utilize the service to match their needs. In this presentation, we take an in-depth look at implementing a SQL DW, elastic scale (grow, shrink, and pause), and hybrid data clouds with Hadoop integration via Polybase allowing for a true SQL experience across structured and unstructured data.
Similar to Extending your Hadoop Implementation to the Cloud (20)
Introduction to Big Data Analytics on Apache Hadoop
Introduction to Big Data Analytics on Apache Hadoop
Integrating Google Cloud Dataproc with Alluxio for faster performance in the ...
Integrating Google Cloud Dataproc with Alluxio for faster performance in the ...
VMworld 2013: Big Data Platform Building Blocks: Serengeti, Resource Manageme...
VMworld 2013: Big Data Platform Building Blocks: Serengeti, Resource Manageme...
From limited Hadoop compute capacity to increased data scientist efficiency
From limited Hadoop compute capacity to increased data scientist efficiency
VMworld 2013: Beyond Mission Critical: Virtualizing Big-Data, Hadoop, HPC, Cl...
VMworld 2013: Beyond Mission Critical: Virtualizing Big-Data, Hadoop, HPC, Cl...
ICS-Azure Migrations & Application Modernization_V2.pptx
ICS-Azure Migrations & Application Modernization_V2.pptx
Alluxio 2.0 Deep Dive – Simplifying data access for cloud workloads
Alluxio 2.0 Deep Dive – Simplifying data access for cloud workloads
Modernizing Your Data Platform for Analytics and AI in the Hybrid Cloud Era
Modernizing Your Data Platform for Analytics and AI in the Hybrid Cloud Era
More from DataWorks Summit
Data Science Crash Course
Introduction: This workshop will provide a hands-on introduction to Machine Learning (ML) with an overview of Deep Learning (DL).
Format: An introductory lecture on several supervised and unsupervised ML techniques followed by light introduction to DL and short discussion what is current state-of-the-art. Several python code samples using the scikit-learn library will be introduced that users will be able to run in the Cloudera Data Science Workbench (CDSW).
Objective: To provide a quick and short hands-on introduction to ML with python’s scikit-learn library. The environment in CDSW is interactive and the step-by-step guide will walk you through setting up your environment, to exploring datasets, training and evaluating models on popular datasets. By the end of the crash course, attendees will have a high-level understanding of popular ML algorithms and the current state of DL, what problems they can solve, and walk away with basic hands-on experience training and evaluating ML models.
Prerequisites: For the hands-on portion, registrants must bring a laptop with a Chrome or Firefox web browser. These labs will be done in the cloud, no installation needed. Everyone will be able to register and start using CDSW after the introductory lecture concludes (about 1hr in). Basic knowledge of python highly recommended.
Floating on a RAFT: HBase Durability with Apache Ratis
In a world with a myriad of distributed storage systems to choose from, the majority of Apache HBase clusters still rely on Apache HDFS. Theoretically, any distributed file system could be used by HBase. One major reason HDFS is predominantly used are the specific durability requirements of HBase's write-ahead log (WAL) and HDFS providing that guarantee correctly. However, HBase's use of HDFS for WALs can be replaced with sufficient effort.
This talk will cover the design of a "Log Service" which can be embedded inside of HBase that provides a sufficient level of durability that HBase requires for WALs. Apache Ratis (incubating) is a library-implementation of the RAFT consensus protocol in Java and is used to build this Log Service. We will cover the design choices of the Ratis Log Service, comparing and contrasting it to other log-based systems that exist today. Next, we'll cover how the Log Service "fits" into HBase and the necessary changes to HBase which enable this. Finally, we'll discuss how the Log Service can simplify the operational burden of HBase.
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Utilizing Apache NiFi we read various open data REST APIs and camera feeds to ingest crime and related data real-time streaming it into HBase and Phoenix tables. HBase makes an excellent storage option for our real-time time series data sources. We can immediately query our data utilizing Apache Zeppelin against Phoenix tables as well as Hive external tables to HBase.
Apache Phoenix tables also make a great option since we can easily put microservices on top of them for application usage. I have an example Spring Boot application that reads from our Philadelphia crime table for front-end web applications as well as RESTful APIs.
Apache NiFi makes it easy to push records with schemas to HBase and insert into Phoenix SQL tables.
Resources:
https://community.hortonworks.com/articles/54947/reading-opendata-json-and-storing-into-phoenix-tab.html
https://community.hortonworks.com/articles/56642/creating-a-spring-boot-java-8-microservice-to-read.html
https://community.hortonworks.com/articles/64122/incrementally-streaming-rdbms-data-to-your-hadoop.html
HBase Tales From the Trenches - Short stories about most common HBase operati...
Whilst HBase is the most logical answer for use cases requiring random, realtime read/write access to Big Data, it may not be so trivial to design applications that make most of its use, neither the most simple to operate. As it depends/integrates with other components from Hadoop ecosystem (Zookeeper, HDFS, Spark, Hive, etc) or external systems ( Kerberos, LDAP), and its distributed nature requires a "Swiss clockwork" infrastructure, many variables are to be considered when observing anomalies or even outages. Adding to the equation there's also the fact that HBase is still an evolving product, with different release versions being used currently, some of those can carry genuine software bugs. On this presentation, we'll go through the most common HBase issues faced by different organisations, describing identified cause and resolution action over my last 5 years supporting HBase to our heterogeneous customer base.
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
LocationTech GeoMesa enables spatial and spatiotemporal indexing and queries for HBase and Accumulo. In this talk, after an overview of GeoMesa’s capabilities in the Cloudera ecosystem, we will dive into how GeoMesa leverages Accumulo’s Iterator interface and HBase’s Filter and Coprocessor interfaces. The goal will be to discuss both what spatial operations can be pushed down into the distributed database and also how the GeoMesa codebase is organized to allow for consistent use across the two database systems.
Managing the Dewey Decimal System
OCLC has been using HBase since 2012 to enable single-search-box access to over a billion items from your library and the world’s library collection. This talk will provide an overview of how HBase is structured to provide this information and some of the challenges they have encountered to scale to support the world catalog and how they have overcome them.
Practical NoSQL: Accumulo's dirlist Example
Many individuals/organizations have a desire to utilize NoSQL technology, but often lack an understanding of how the underlying functional bits can be utilized to enable their use case. This situation can result in drastic increases in the desire to put the SQL back in NoSQL.
Since the initial commit, Apache Accumulo has provided a number of examples to help jumpstart comprehension of how some of these bits function as well as potentially help tease out an understanding of how they might be applied to a NoSQL friendly use case. One very relatable example demonstrates how Accumulo could be used to emulate a filesystem (dirlist).
In this session we will walk through the dirlist implementation. Attendees should come away with an understanding of the supporting table designs, a simple text search supporting a single wildcard (on file/directory names), and how the dirlist elements work together to accomplish its feature set. Attendees should (hopefully) also come away with a justification for sometimes keeping the SQL out of NoSQL.
HBase Global Indexing to support large-scale data ingestion at Uber
Danny Chen presented on Uber's use of HBase for global indexing to support large-scale data ingestion. Uber uses HBase to provide a global view of datasets ingested from Kafka and other data sources. To generate indexes, Spark jobs are used to transform data into HFiles, which are loaded into HBase tables. Given the large volumes of data, techniques like throttling HBase access and explicit serialization are used. The global indexing solution supports requirements for high throughput, strong consistency and horizontal scalability across Uber's data lake.
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Recently, Apache Phoenix has been integrated with Apache (incubator) Omid transaction processing service, to provide ultra-high system throughput with ultra-low latency overhead. Phoenix has been shown to scale beyond 0.5M transactions per second with sub-5ms latency for short transactions on industry-standard hardware. On the other hand, Omid has been extended to support secondary indexes, multi-snapshot SQL queries, and massive-write transactions.
These innovative features make Phoenix an excellent choice for translytics applications, which allow converged transaction processing and analytics. We share the story of building the next-gen data tier for advertising platforms at Verizon Media that exploits Phoenix and Omid to support multi-feed real-time ingestion and AI pipelines in one place, and discuss the lessons learned.
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
This document discusses using Apache NiFi to build a high-speed cyber security data pipeline. It outlines the challenges of ingesting, transforming, and routing large volumes of security data from various sources to stakeholders like security operations centers, data scientists, and executives. It proposes using NiFi as a centralized data gateway to ingest data from multiple sources using a single entry point, transform the data according to destination needs, and reliably deliver the data while avoiding issues like network traffic and data duplication. The document provides an example NiFi flow and discusses metrics from processing over 20 billion events through 100+ production flows and 1000+ transformations.
Supporting Apache HBase : Troubleshooting and Supportability Improvements
This document discusses supporting Apache HBase and improving troubleshooting and supportability. It introduces two Cloudera employees who work on HBase support and provides an overview of typical troubleshooting scenarios for HBase like performance degradation, process crashes, and inconsistencies. The agenda covers using existing tools like logs and metrics to troubleshoot HBase performance issues with a general approach, and introduces htop as a real-time monitoring tool for HBase.
Security Framework for Multitenant Architecture
In the healthcare sector, data security, governance, and quality are crucial for maintaining patient privacy and ensuring the highest standards of care. At Florida Blue, the leading health insurer of Florida serving over five million members, there is a multifaceted network of care providers, business users, sales agents, and other divisions relying on the same datasets to derive critical information for multiple applications across the enterprise. However, maintaining consistent data governance and security for protected health information and other extended data attributes has always been a complex challenge that did not easily accommodate the wide range of needs for Florida Blue’s many business units. Using Apache Ranger, we developed a federated Identity & Access Management (IAM) approach that allows each tenant to have their own IAM mechanism. All user groups and roles are propagated across the federation in order to determine users’ data entitlement and access authorization; this applies to all stages of the system, from the broadest tenant levels down to specific data rows and columns. We also enabled audit attributes to ensure data quality by documenting data sources, reasons for data collection, date and time of data collection, and more. In this discussion, we will outline our implementation approach, review the results, and highlight our “lessons learned.”
Presto: Optimizing Performance of SQL-on-Anything Engine
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Airbnb, Bloomberg, Comcast, Facebook, FINRA, LinkedIn, Lyft, Netflix, Twitter, and Uber, in the last few years Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments over Object Stores, HDFS, NoSQL and RDBMS data stores.
With the ever-growing list of connectors to new data sources such as Azure Blob Storage, Elasticsearch, Netflix Iceberg, Apache Kudu, and Apache Pulsar, recently introduced Cost-Based Optimizer in Presto must account for heterogeneous inputs with differing and often incomplete data statistics. This talk will explore this topic in detail as well as discuss best use cases for Presto across several industries. In addition, we will present recent Presto advancements such as Geospatial analytics at scale and the project roadmap going forward.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
Extending Twitter's Data Platform to Google Cloud
Twitter's Data Platform is built using multiple complex open source and in house projects to support Data Analytics on hundreds of petabytes of data. Our platform support storage, compute, data ingestion, discovery and management and various tools and libraries to help users for both batch and realtime analytics. Our DataPlatform operates on multiple clusters across different data centers to help thousands of users discover valuable insights. As we were scaling our Data Platform to multiple clusters, we also evaluated various cloud vendors to support use cases outside of our data centers. In this talk we share our architecture and how we extend our data platform to use cloud as another datacenter. We walk through our evaluation process, challenges we faced supporting data analytics at Twitter scale on cloud and present our current solution. Extending Twitter's Data platform to cloud was complex task which we deep dive in this presentation.
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
At Comcast, our team has been architecting a customer experience platform which is able to react to near-real-time events and interactions and deliver appropriate and timely communications to customers. By combining the low latency capabilities of Apache Flink and the dataflow capabilities of Apache NiFi we are able to process events at high volume to trigger, enrich, filter, and act/communicate to enhance customer experiences. Apache Flink and Apache NiFi complement each other with their strengths in event streaming and correlation, state management, command-and-control, parallelism, development methodology, and interoperability with surrounding technologies. We will trace our journey from starting with Apache NiFi over three years ago and our more recent introduction of Apache Flink into our platform stack to handle more complex scenarios. In this presentation we will compare and contrast which business and technical use cases are best suited to which platform and explore different ways to integrate the two platforms into a single solution.
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Companies are increasingly moving to the cloud to store and process data. One of the challenges companies have is in securing data across hybrid environments with easy way to centrally manage policies. In this session, we will talk through how companies can use Apache Ranger to protect access to data both in on-premise as well as in cloud environments. We will go into details into the challenges of hybrid environment and how Ranger can solve it. We will also talk through how companies can further enhance the security by leveraging Ranger to anonymize or tokenize data while moving into the cloud and de-anonymize dynamically using Apache Hive, Apache Spark or when accessing data from cloud storage systems. We will also deep dive into the Ranger’s integration with AWS S3, AWS Redshift and other cloud native systems. We will wrap it up with an end to end demo showing how policies can be created in Ranger and used to manage access to data in different systems, anonymize or de-anonymize data and track where data is flowing.
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Advanced Big Data Processing frameworks have been proposed to harness the fast data transmission capability of Remote Direct Memory Access (RDMA) over high-speed networks such as InfiniBand, RoCEv1, RoCEv2, iWARP, and OmniPath. However, with the introduction of the Non-Volatile Memory (NVM) and NVM express (NVMe) based SSD, these designs along with the default Big Data processing models need to be re-assessed to discover the possibilities of further enhanced performance. In this talk, we will present, NRCIO, a high-performance communication runtime for non-volatile memory over modern network interconnects that can be leveraged by existing Big Data processing middleware. We will show the performance of non-volatile memory-aware RDMA communication protocols using our proposed runtime and demonstrate its benefits by incorporating it into a high-performance in-memory key-value store, Apache Hadoop, Tez, Spark, and TensorFlow. Evaluation results illustrate that NRCIO can achieve up to 3.65x performance improvement for representative Big Data processing workloads on modern data centers.
Computer Vision: Coming to a Store Near You
Background: Some early applications of Computer Vision in Retail arose from e-commerce use cases - but increasingly, it is being used in physical stores in a variety of new and exciting ways, such as:
● Optimizing merchandising execution, in-stocks and sell-thru
● Enhancing operational efficiencies, enable real-time customer engagement
● Enhancing loss prevention capabilities, response time
● Creating frictionless experiences for shoppers
Abstract: This talk will cover the use of Computer Vision in Retail, the implications to the broader Consumer Goods industry and share business drivers, use cases and benefits that are unfolding as an integral component in the remaking of an age-old industry.
We will also take a ‘peek under the hood’ of Computer Vision and Deep Learning, sharing technology design principles and skill set profiles to consider before starting your CV journey.
Deep learning has matured considerably in the past few years to produce human or superhuman abilities in a variety of computer vision paradigms. We will discuss ways to recognize these paradigms in retail settings, collect and organize data to create actionable outcomes with the new insights and applications that deep learning enables.
We will cover the basics of object detection, then move into the advanced processing of images describing the possible ways that a retail store of the near future could operate. Identifying various storefront situations by having a deep learning system attached to a camera stream. Such things as; identifying item stocks on shelves, a shelf in need of organization, or perhaps a wandering customer in need of assistance.
We will also cover how to use a computer vision system to automatically track customer purchases to enable a streamlined checkout process, and how deep learning can power plausible wardrobe suggestions based on what a customer is currently wearing or purchasing.
Finally, we will cover the various technologies that are powering these applications today. Deep learning tools for research and development. Production tools to distribute that intelligence to an entire inventory of all the cameras situation around a retail location. Tools for exploring and understanding the new data streams produced by the computer vision systems.
By the end of this talk, attendees should understand the impact Computer Vision and Deep Learning are having in the Consumer Goods industry, key use cases, techniques and key considerations leaders are exploring and implementing today.
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Whole genome shotgun based next generation transcriptomics and metagenomics studies often generate 100 to 1000 gigabytes (GB) sequence data derived from tens of thousands of different genes or microbial species. De novo assembling these data requires an ideal solution that both scales with data size and optimizes for individual gene or genomes. Here we developed an Apache Spark-based scalable sequence clustering application, SparkReadClust (SpaRC), that partitions the reads based on their molecule of origin to enable downstream assembly optimization. SpaRC produces high clustering performance on transcriptomics and metagenomics test datasets from both short read and long read sequencing technologies. It achieved a near linear scalability with respect to input data size and number of compute nodes. SpaRC can run on different cloud computing environments without modifications while delivering similar performance. In summary, our results suggest SpaRC provides a scalable solution for clustering billions of reads from the next-generation sequencing experiments, and Apache Spark represents a cost-effective solution with rapid development/deployment cycles for similar big data genomics problems.
More from DataWorks Summit (20)
Floating on a RAFT: HBase Durability with Apache Ratis
Floating on a RAFT: HBase Durability with Apache Ratis
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
Tracking Crime as It Occurs with Apache Phoenix, Apache HBase and Apache NiFi
HBase Tales From the Trenches - Short stories about most common HBase operati...
HBase Tales From the Trenches - Short stories about most common HBase operati...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
Optimizing Geospatial Operations with Server-side Programming in HBase and Ac...
HBase Global Indexing to support large-scale data ingestion at Uber
HBase Global Indexing to support large-scale data ingestion at Uber
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Scaling Cloud-Scale Translytics Workloads with Omid and Phoenix
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Supporting Apache HBase : Troubleshooting and Supportability Improvements
Supporting Apache HBase : Troubleshooting and Supportability Improvements
Presto: Optimizing Performance of SQL-on-Anything Engine
Presto: Optimizing Performance of SQL-on-Anything Engine
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
Event-Driven Messaging and Actions using Apache Flink and Apache NiFi
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Securing Data in Hybrid on-premise and Cloud Environments using Apache Ranger
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Meets NVM: Accelerating Big Data Processing with Non-Volatile Memory...
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Big Data Genomics: Clustering Billions of DNA Sequences with Apache Spark
Recently uploaded
HCL Notes and Domino License Cost Reduction in the World of DLAU
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Introduction of Cybersecurity with OSS at Code Europe 2024
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Azure API Management to expose backend services securely
How to use Azure API Management to expose backend service securely
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
June Patch Tuesday
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
5th LF Energy Power Grid Model Meet-up Slides
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
This case study explores designing a scalable e-commerce platform, covering key requirements, system components, and best practices.
Columbus Data & Analytics Wednesdays - June 2024
Columbus Data & Analytics Wednesdays, June 2024 with Maria Copot 20
Trusted Execution Environment for Decentralized Process Mining
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Main news related to the CCS TSI 2023 (2023/1695)
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Recently uploaded (20)
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
HCL Notes and Domino License Cost Reduction in the World of DLAU
HCL Notes and Domino License Cost Reduction in the World of DLAU
GraphRAG for Life Science to increase LLM accuracy
GraphRAG for Life Science to increase LLM accuracy
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Monitoring and Managing Anomaly Detection on OpenShift.pdf
Introduction of Cybersecurity with OSS at Code Europe 2024
Introduction of Cybersecurity with OSS at Code Europe 2024
Azure API Management to expose backend services securely
Azure API Management to expose backend services securely
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...
WeTestAthens: Postman's AI & Automation Techniques
WeTestAthens: Postman's AI & Automation Techniques
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Ocean lotus Threat actors project by John Sitima 2024 (1).pptx
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Your One-Stop Shop for Python Success: Top 10 US Python Development Providers
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
Energy Efficient Video Encoding for Cloud and Edge Computing Instances
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
System Design Case Study: Building a Scalable E-Commerce Platform - Hiike
Trusted Execution Environment for Decentralized Process Mining
Trusted Execution Environment for Decentralized Process Mining
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
Deep Dive: Getting Funded with Jason Jason Lemkin Founder & CEO @ SaaStr
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdf
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Overcoming the PLG Trap: Lessons from Canva's Head of Sales & Head of EMEA Da...
Extending your Hadoop Implementation to the Cloud
- 1. EXTENDING YOUR HADOOP IMPLEMENTATION TO THE CLOUD Matt Winkler Principal Lead Program Manager Big Data @ Microsoft We’re Hiring @mwinkle
- 2. AGENDA Decisions IaaS Cloud Storage Hadoop as a Service Hybrid Scenarios Next Steps
- 3. WHY CLOUD? Elasticity Cost Optimization Economic flexibility Support for bursting workloads Global footprint
- 4. WHY ON-PREMISES? Compliance requirements Specific control over hardware/networking Integration requirements for additional apps to be close to cluster
- 5. WHEN CLOUD? Data born in the cloud Global apps Satisfy geopolitical or compliance constraints Dev/Test Backup Geo-Redundancy Bursting to cloud
- 6. IAAS – RUN YOUR HADOOP IN THE CLOUD IaaS offerings across the cloud providers offer: OS choice Node configuration Customized networking topology Repeatable, scriptable deployments You still have to: Set up the cluster Manage data movement into the cluster Integrate with your other applications Manage patching and updates of OS and apps Obtain support and/or licenses
- 7. DEMO Deploying a Hadoop Cluster to Azure
- 8. LEVERAGE CLOUD STORAGE FOR FLEXIBILITY Cloud storage enables economic flexibility, scale and rich features Size clusters independent of storage needs Clusters become stateless to operate across the data Price continues decreasing Geo-Redundancy allows for business continuity/disaster recover planning
- 9. CLOUD STORAGE USAGE PATTERNS HDFS within the cluster Move data in from cloud storage on boot (optional) backup/age data to cloud storage (optional) move data out to cloud storage to rebuild cluster Default file system using cloud storage connectors To Hadoop apps, they just see a path to data and most things “just work” Apps which rely specifically on HDFS may encounter compat issues The physics change in exchange for flexibility
- 10. LEVERAGING HADOOP AS A SERVICE Hadoop Services Cluster creation on demand Default integration with cloud storage Integration across services and apps Higher level abstractions API set for integrating into apps Azure HDInsight Clusters provisioned on top of Azure Blob storage Deploy clusters of any size Entire stack supported by Microsoft Azure Active Directory Service Bus Scheduler Multi-Factor Authentication Express Route Azure SQL Database Azure Web Site Some example services
- 11. DEMO Getting Started with HDInsight
- 12. HYBRID SCENARIOS Key scenarios Offsite backup Dev/Test Burst to Cloud The decision is not an XOR, it’s on-premises AND cloud
- 13. Microsoft Azure Azure Storage HDInsight (Hadoop) Hadoop cluster deployed to IaaS DEMO On-Premises Hadoop Cluster (HDP 2.1) Running on CentOS HDFS YARN Tez Hive MR Falcon
- 14. GETTING STARTED Get started in the cloud (getting started cards available @ the Microsoft booth and up here at the stage) Create an HDInsight cluster, or try out deploying a Hadoop cluster to Azure http://aka.ms/howtohdinsight
- 19. Data being landed into HDFS, on-prem
- 20. Syncing to blob store
- 21. New file @ 1:38pm
- 22. @ 2:01 pm
- 23. From HDI Cluster