Big Data Taiwan 2014 Track2-2: Informatica Big Data SolutionEtu Solution
講者:Informatica 資深產品顧問 | 尹寒柏
議題簡介:Big Data 時代,比的不是數據數量,而是了解數據的深度。現在,因為 Big Data 技術的成熟,讓非資訊背景的 CXO 們,可以讓過去像是專有名詞的 CI (Customer Intelligence) 變成動詞,從 BI 進入 CI,更連結消費者經濟的脈動,洞悉顧客的意圖。不過,有個 Big Data 時代要 注意的思維,那就是競爭到最後,不單只是看數據量的增長,還要比誰能更了解數據的深度。而 Informatica 正是這個最佳解決的答案。我們透過 Informatica 解決在企業及時提供可信賴數據的巨大壓力;同時隨著日益增高的數據量和複雜程度,Informatica 也有能力提供更快速彙集數據技術,從而讓數據變的有意義並可供企業用來促進效率提升、完善品質、保證確定性和發揮優勢的功能。Inforamtica 提供了更為快速有效地實現此目標的方案,是精誠集團在 Big Data 時代的最佳工具。
Hybrid Data Lake Architecture with Presto & Spark in the cloud accessing on-p...Alluxio, Inc.
Alluxio Community Office Hour
September 29, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speaker(s):
Adit Madan, Alluxio
In this talk, we describe the architecture to migrate analytics workloads incrementally to any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) directly on on-prem data without copying the data to cloud storage.
In this Office Hour:
- We will go over an architecture for running elastic compute clusters in the cloud using on-prem HDFS.
- Have a casual online video chat with Alluxio Open Source core maintainers to address any Alluxio related questions from our community members
20100806 cloudera 10 hadoopable problems webinarCloudera, Inc.
Jeff Hammerbacher introduced 10 common problems that are suitable for solving with Hadoop. These include modeling true risk, customer churn analysis, recommendation engines, ad targeting, point of sale transaction analysis, analyzing network data to predict failures, threat analysis, trade surveillance, search quality, and using Hadoop as a data sandbox. Many of these problems involve analyzing large and complex datasets from multiple sources to discover patterns and relationships.
Dev Lakhani, Data Scientist at Batch Insights "Real Time Big Data Applicatio...Dataconomy Media
Dev Lakhani, Data Scientist at Batch Insights talks on "Real Time Big Data Applications for Investment Banks and Financial Institutions" at the first Big Data Frankfurt event that took place at Die Zentrale, organised by Dataconomy Media
Hadoop Administrator Online training course by (Knowledgebee Trainings) with mastering Hadoop Cluster: Planning & Deployment, Monitoring, Performance tuning, Security using Kerberos, HDFS High Availability using Quorum Journal Manager (QJM) and Oozie, Hcatalog/Hive Administration.
Contact : knowledgebee@beenovo.com
2015 nov 27_thug_paytm_rt_ingest_brief_finalAdam Muise
The document discusses Paytm Labs' transition from batch data ingestion to real-time data ingestion using Apache Kafka and Confluent. It outlines their current batch-driven pipeline and some of its limitations. Their new approach, called DFAI (Direct-From-App-Ingest), will have applications directly write data to Kafka using provided SDKs. This data will then be streamed and aggregated in real-time using their Fabrica framework to generate views for different use cases. The benefits of real-time ingestion include having fresher data available and a more flexible schema.
Debunking Common Myths of Hadoop Backup & Test Data ManagementImanis Data
These slides are from a webinar where Hari Mankude, CTO at Talena, discussed key concepts associated with Hadoop data management processes around scalable backup, recovery and test data management.
The document provides an overview of big data analytics using Hadoop. It discusses how Hadoop allows for distributed processing of large datasets across computer clusters. The key components of Hadoop discussed are HDFS for storage, and MapReduce for parallel processing. HDFS provides a distributed, fault-tolerant file system where data is replicated across multiple nodes. MapReduce allows users to write parallel jobs that process large amounts of data in parallel on a Hadoop cluster. Examples of how companies use Hadoop for applications like customer analytics and log file analysis are also provided.
Big Data Taiwan 2014 Track2-2: Informatica Big Data SolutionEtu Solution
講者:Informatica 資深產品顧問 | 尹寒柏
議題簡介:Big Data 時代,比的不是數據數量,而是了解數據的深度。現在,因為 Big Data 技術的成熟,讓非資訊背景的 CXO 們,可以讓過去像是專有名詞的 CI (Customer Intelligence) 變成動詞,從 BI 進入 CI,更連結消費者經濟的脈動,洞悉顧客的意圖。不過,有個 Big Data 時代要 注意的思維,那就是競爭到最後,不單只是看數據量的增長,還要比誰能更了解數據的深度。而 Informatica 正是這個最佳解決的答案。我們透過 Informatica 解決在企業及時提供可信賴數據的巨大壓力;同時隨著日益增高的數據量和複雜程度,Informatica 也有能力提供更快速彙集數據技術,從而讓數據變的有意義並可供企業用來促進效率提升、完善品質、保證確定性和發揮優勢的功能。Inforamtica 提供了更為快速有效地實現此目標的方案,是精誠集團在 Big Data 時代的最佳工具。
Hybrid Data Lake Architecture with Presto & Spark in the cloud accessing on-p...Alluxio, Inc.
Alluxio Community Office Hour
September 29, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speaker(s):
Adit Madan, Alluxio
In this talk, we describe the architecture to migrate analytics workloads incrementally to any public cloud (AWS, Google Cloud Platform, or Microsoft Azure) directly on on-prem data without copying the data to cloud storage.
In this Office Hour:
- We will go over an architecture for running elastic compute clusters in the cloud using on-prem HDFS.
- Have a casual online video chat with Alluxio Open Source core maintainers to address any Alluxio related questions from our community members
20100806 cloudera 10 hadoopable problems webinarCloudera, Inc.
Jeff Hammerbacher introduced 10 common problems that are suitable for solving with Hadoop. These include modeling true risk, customer churn analysis, recommendation engines, ad targeting, point of sale transaction analysis, analyzing network data to predict failures, threat analysis, trade surveillance, search quality, and using Hadoop as a data sandbox. Many of these problems involve analyzing large and complex datasets from multiple sources to discover patterns and relationships.
Dev Lakhani, Data Scientist at Batch Insights "Real Time Big Data Applicatio...Dataconomy Media
Dev Lakhani, Data Scientist at Batch Insights talks on "Real Time Big Data Applications for Investment Banks and Financial Institutions" at the first Big Data Frankfurt event that took place at Die Zentrale, organised by Dataconomy Media
Hadoop Administrator Online training course by (Knowledgebee Trainings) with mastering Hadoop Cluster: Planning & Deployment, Monitoring, Performance tuning, Security using Kerberos, HDFS High Availability using Quorum Journal Manager (QJM) and Oozie, Hcatalog/Hive Administration.
Contact : knowledgebee@beenovo.com
2015 nov 27_thug_paytm_rt_ingest_brief_finalAdam Muise
The document discusses Paytm Labs' transition from batch data ingestion to real-time data ingestion using Apache Kafka and Confluent. It outlines their current batch-driven pipeline and some of its limitations. Their new approach, called DFAI (Direct-From-App-Ingest), will have applications directly write data to Kafka using provided SDKs. This data will then be streamed and aggregated in real-time using their Fabrica framework to generate views for different use cases. The benefits of real-time ingestion include having fresher data available and a more flexible schema.
Debunking Common Myths of Hadoop Backup & Test Data ManagementImanis Data
These slides are from a webinar where Hari Mankude, CTO at Talena, discussed key concepts associated with Hadoop data management processes around scalable backup, recovery and test data management.
The document provides an overview of big data analytics using Hadoop. It discusses how Hadoop allows for distributed processing of large datasets across computer clusters. The key components of Hadoop discussed are HDFS for storage, and MapReduce for parallel processing. HDFS provides a distributed, fault-tolerant file system where data is replicated across multiple nodes. MapReduce allows users to write parallel jobs that process large amounts of data in parallel on a Hadoop cluster. Examples of how companies use Hadoop for applications like customer analytics and log file analysis are also provided.
This document provides an introduction to big data, including what it is, sources of big data, and how it is used. It discusses key concepts like volume, velocity, variety, and veracity of big data. It also describes the Hadoop ecosystem for distributed storage and processing of large datasets, including components like HDFS, MapReduce, Hive, HBase and ecosystem players like Cloudera and Hortonworks. The document outlines common big data use cases and how organizations are deploying Hadoop solutions in both on-premise and cloud environments.
IoT devices generate high volume, continuous streams of data that must be analyzed in-memory – before they land on disk – to identify potential outliers/failures or business opportunities. Companies need to build robust yet flexible applications that can instantly act on the information derived from analyzing their IoT data. Attend this session to learn how you can easily handle real-time data acquisition across structured and semi-structured data, as well as windowing, fast in-memory streaming analytics, event correlation, visualization, alerts, workflows and smart data storage.
Introduction to Kudu - StampedeCon 2016StampedeCon
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets.
Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem that fills the gap described above, complementing HDFS and HBase to provide a new option to achieve fast scans and fast random access from a single API.
The document discusses business intelligence for big data using Hadoop. It describes how 90% of companies are using or plan to use Hadoop to transform structured or semi-structured data for analysis and reporting. While Hadoop provides scalability through distributed processing and storage, its MapReduce programming model makes data transformation difficult for developers accustomed to graphical tools. The document traces how Google and Yahoo developed MapReduce for specific use cases of indexing the internet at massive scales, and how it has since been generalized beyond those specific needs.
The document discusses the challenges of managing large volumes of data from various sources in a traditional divided approach. It argues that Hadoop provides a solution by allowing all data to be stored together in a single system and processed as needed. This addresses the problems caused by keeping data isolated in different silos and enables new types of analysis across all available data.
The document discusses optimizing a data warehouse by offloading some workloads and data to Hadoop. It identifies common challenges with data warehouses like slow transformations and queries. Hadoop can help by handling large-scale data processing, analytics, and long-term storage more cost effectively. The document provides examples of how customers benefited from offloading workloads to Hadoop. It then outlines a process for assessing an organization's data warehouse ecosystem, prioritizing workloads for migration, and developing an optimization plan.
This document discusses transforming a traditional data center to a software-defined data center by starting with software-defined storage. It recommends implementing a scale-out software-defined storage solution like SUSE Enterprise Storage powered by Ceph to address growing storage needs that outpace budgets. SUSE is well-suited as a partner because of its expertise in storage, reference architectures, and ability to support current infrastructure while enabling future transformation to a software-defined model. The presentation provides guidance on evaluating requirements, architecting a solution, and implementing storage-first to overcome objections typically associated with traditional storage.
Empowering you with Democratized Data Access, Data Science and Machine LearningDataWorks Summit
Data science with its specialized tools and knowledge has been a forte of data scientists. However, it is not easy even for data scientists to get access to data that could be in different data stores in the organization. To unleash the power of data and gain valuable insights, machine learning needs to be made easily consumable by various stake holders and access to data made simpler. As an organization's data volumes continue to grow, delivering these insights real time is a complex challenge to solve.
This session will provide on overview of an approach to building a scalable solution where machine and deep learning and access to data is made much more consumable and simpler by the fastest SQL on Hadoop engine on the planet, a rich data scientist toolset and an infrastructure that can deliver the responsiveness needed for production environments.
Speakers:
Pandit Prasad, Program Director, IBM
Ashutosh Mate, Global Senior Solutions Architect, IBM
1) Hadoop is well-suited for data science tasks like exploring large datasets directly, mining larger datasets to achieve better machine learning outcomes, and performing large-scale data preparation efficiently.
2) Traditional data architectures present barriers to speeding data-driven innovation due to the high cost of schema changes, whereas Hadoop's "schema on read" model has a lower barrier.
3) A Hortonworks Sandbox provides a free virtual environment to learn Hadoop and accelerate validating its use for an organization's unique data architecture and use cases.
DataStax Training – Everything you need to become a Cassandra RockstarDataStax
Looking to strengthen your expertise of Cassandra and DataStax Enterprise? This DataStax Training Webinar provides an overview of what you need to get the most out of Cassandra and your DataStax Enterprise environment. Whether you’re a developer or administrator, novice or a Cassandra expert, there is a class that will meet your experience level and needs.
Hadoop Reporting and Analysis - JaspersoftHortonworks
Hadoop is deployed for a variety of uses, including web analytics, fraud detection, security monitoring, healthcare, environmental analysis, social media monitoring, and other purposes.
Standing Up an Effective Enterprise Data Hub -- Technology and BeyondCloudera, Inc.
Federal organizations increasingly are focused on creating environments that enable more data-driven decisions. Yet ensuring that all data is considered and is current, complete, and accurate is a tall order for most. To make data analytics meaningful to support real-world transformation, agency staff need business tools that provide user-friendly dashboards, on-demand reporting, and methods to manage efficiently the rise of voluminous and varied data sets and types commonly associated with big data. In most cases, existing systems are insufficient to support these requirements. Enter the enterprise data hub (EDH), a software architecture specifically designed to be a unified platform that can economically store unlimited data and enable diverse access to it at scale. Plan to attend this discussion to understand the key considerations to making an EDH the architectural center of your agency’s modern data strategy.
Webinar | From Zero to 1 Million with Google Cloud Platform and DataStaxDataStax
Google Cloud Platform delivers the industry’s leading cloud-based services to create anything from simple websites to complex applications. DataStax delivers Apache Cassandra™, the leading distributed database technology, to the enterprise. Together, DataStax Enterprise on Google Cloud Platform delivers the performance, agility, infinite elasticity and innovation organizations need to build high-performance, highly-available online applications.
Join Allan Naim, Global Product Lead at Google Cloud Platform and Darshan Rawal, Sr. Director of Product Management at DataStax as they share their expertise on why DataStax and Google Cloud Platform deliver the industry’s most robust Infrastructure-as-a Service (IaaS) platform and how your organization find success with NoSQL and Cloud services.
View to learn how to:
- Handle more than 1 Million requests per second for data-intensive online applications with Apache Cassandra on Google Cloud Platform
- Leverage the technology infrastructure and global network powering Google’s search engine with DataStax to deploy blazing-fast and always-on applications
- Transform your business into a data-driven company, a change that is critical as future success and discoveries hinge on the ability to quickly take action on data
Key trends in Big Data and new reference architecture from Hewlett Packard En...Ontico
Динамичное развитие инструментов для обработки Больших Данных порождает новые подходы к повышению производительности. Ключевые новые технологии в Hadoop 2.0, такие как Yarn labeling и Storage Tiering, уже используются компаниями Yahoo и Ebay. Эти новые технологии открывают путь для серьезного повышения эффективности ИТ-инфраструктуры для Hadoop, достигая прироста производительности в несколько десятков процентов при одновременном снижении потребления памяти и электроэнергии.
Эталонная архитектура для Hadoop от HP — HP Big Data Reference Architecture — предлагает использование специализированных "микросерверов" HP Moonshot вкупе с высокоплотными узлами хранения HP Apollo для достижения лучших на сегодня показателей полезной отдачи от железа в Hadoop.
Cloudera Breakfast Series, Analytics Part 1: Use All Your DataCloudera, Inc.
The document discusses how traditional analytics processes involve siloed data and platforms, long timelines for data discovery, and difficulties accessing and sharing data. It proposes that an Enterprise Data Hub (EDH) using Cloudera can help address these issues by providing unified storage for all types of data, shorter analytics lifecycles, and the ability to do more with data by using 100x more data and more types of data. The EDH allows organizations to use all of their data and gain insights sooner.
This document discusses big data concepts like volume, velocity, and variety of data. It introduces NoSQL databases as an alternative to relational databases for big data that does not require data cleansing or schema definition. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key Hadoop components like HDFS, MapReduce, Hive, Pig and YARN are described at a high level. The document also discusses using Azure services like Azure Storage, HDInsight and Stream Analytics with Hadoop.
Rob peglar introduction_analytics _big data_hadoopGhassan Al-Yafie
This document provides an introduction to analytics and big data using Hadoop. It discusses the growth of digital data and challenges of big data. Hadoop is presented as a solution for storing and processing large, unstructured datasets across commodity servers. The key components of Hadoop - HDFS for distributed storage and MapReduce for distributed processing - are described at a high level. Examples of industries using big data analytics are also listed.
Innovation in the Data Warehouse - StampedeCon 2016StampedeCon
Enterprise Holding’s first started with Hadoop as a POC in 2013. Today, we have clusters on premises and in the cloud. This talk will explore our experience with Big Data and outline three common big data architectures (batch, lambda, and kappa). Then, we’ll dive into the decision points to necessary for your own cluster, for example: cloud vs on premises, physical vs virtual, workload, and security. These decisions will help you understand what direction to take. Finally, we’ll share some lessons learned with the pieces of our architecture worked well and rant about those which didn’t. No deep Hadoop knowledge is necessary, architect or executive level.
1) Big data is growing exponentially and new frameworks like Hadoop are needed to analyze large, unstructured datasets.
2) Hadoop uses distributed computing and storage across commodity servers to provide scalable and cost-effective analytics. It leverages local disks on each node for temporary data to improve performance.
3) Virtualizing Hadoop simplifies operations, enables mixed workloads, and provides high availability through features like vMotion and HA. It also allows for elastic scaling of compute and storage resources.
This document discusses how traditional tech giants face slowing growth rates and are looking to customer relationship intelligence to drive ongoing success and transformation. It notes that 80% of next year's revenue will come from existing customers, so these companies must understand their complex customer relationships to meet changing demands and capture opportunities. The document advocates that by using customer relationship intelligence to gain insights from customer contract data, companies can solve revenue leakage today and position themselves for future growth.
This document provides an introduction to big data, including what it is, sources of big data, and how it is used. It discusses key concepts like volume, velocity, variety, and veracity of big data. It also describes the Hadoop ecosystem for distributed storage and processing of large datasets, including components like HDFS, MapReduce, Hive, HBase and ecosystem players like Cloudera and Hortonworks. The document outlines common big data use cases and how organizations are deploying Hadoop solutions in both on-premise and cloud environments.
IoT devices generate high volume, continuous streams of data that must be analyzed in-memory – before they land on disk – to identify potential outliers/failures or business opportunities. Companies need to build robust yet flexible applications that can instantly act on the information derived from analyzing their IoT data. Attend this session to learn how you can easily handle real-time data acquisition across structured and semi-structured data, as well as windowing, fast in-memory streaming analytics, event correlation, visualization, alerts, workflows and smart data storage.
Introduction to Kudu - StampedeCon 2016StampedeCon
Over the past several years, the Hadoop ecosystem has made great strides in its real-time access capabilities, narrowing the gap compared to traditional database technologies. With systems such as Impala and Spark, analysts can now run complex queries or jobs over large datasets within a matter of seconds. With systems such as Apache HBase and Apache Phoenix, applications can achieve millisecond-scale random access to arbitrarily-sized datasets.
Despite these advances, some important gaps remain that prevent many applications from transitioning to Hadoop-based architectures. Users are often caught between a rock and a hard place: columnar formats such as Apache Parquet offer extremely fast scan rates for analytics, but little to no ability for real-time modification or row-by-row indexed access. Online systems such as HBase offer very fast random access, but scan rates that are too slow for large scale data warehousing workloads.
This talk will investigate the trade-offs between real-time transactional access and fast analytic performance from the perspective of storage engine internals. It will also describe Kudu, the new addition to the open source Hadoop ecosystem that fills the gap described above, complementing HDFS and HBase to provide a new option to achieve fast scans and fast random access from a single API.
The document discusses business intelligence for big data using Hadoop. It describes how 90% of companies are using or plan to use Hadoop to transform structured or semi-structured data for analysis and reporting. While Hadoop provides scalability through distributed processing and storage, its MapReduce programming model makes data transformation difficult for developers accustomed to graphical tools. The document traces how Google and Yahoo developed MapReduce for specific use cases of indexing the internet at massive scales, and how it has since been generalized beyond those specific needs.
The document discusses the challenges of managing large volumes of data from various sources in a traditional divided approach. It argues that Hadoop provides a solution by allowing all data to be stored together in a single system and processed as needed. This addresses the problems caused by keeping data isolated in different silos and enables new types of analysis across all available data.
The document discusses optimizing a data warehouse by offloading some workloads and data to Hadoop. It identifies common challenges with data warehouses like slow transformations and queries. Hadoop can help by handling large-scale data processing, analytics, and long-term storage more cost effectively. The document provides examples of how customers benefited from offloading workloads to Hadoop. It then outlines a process for assessing an organization's data warehouse ecosystem, prioritizing workloads for migration, and developing an optimization plan.
This document discusses transforming a traditional data center to a software-defined data center by starting with software-defined storage. It recommends implementing a scale-out software-defined storage solution like SUSE Enterprise Storage powered by Ceph to address growing storage needs that outpace budgets. SUSE is well-suited as a partner because of its expertise in storage, reference architectures, and ability to support current infrastructure while enabling future transformation to a software-defined model. The presentation provides guidance on evaluating requirements, architecting a solution, and implementing storage-first to overcome objections typically associated with traditional storage.
Empowering you with Democratized Data Access, Data Science and Machine LearningDataWorks Summit
Data science with its specialized tools and knowledge has been a forte of data scientists. However, it is not easy even for data scientists to get access to data that could be in different data stores in the organization. To unleash the power of data and gain valuable insights, machine learning needs to be made easily consumable by various stake holders and access to data made simpler. As an organization's data volumes continue to grow, delivering these insights real time is a complex challenge to solve.
This session will provide on overview of an approach to building a scalable solution where machine and deep learning and access to data is made much more consumable and simpler by the fastest SQL on Hadoop engine on the planet, a rich data scientist toolset and an infrastructure that can deliver the responsiveness needed for production environments.
Speakers:
Pandit Prasad, Program Director, IBM
Ashutosh Mate, Global Senior Solutions Architect, IBM
1) Hadoop is well-suited for data science tasks like exploring large datasets directly, mining larger datasets to achieve better machine learning outcomes, and performing large-scale data preparation efficiently.
2) Traditional data architectures present barriers to speeding data-driven innovation due to the high cost of schema changes, whereas Hadoop's "schema on read" model has a lower barrier.
3) A Hortonworks Sandbox provides a free virtual environment to learn Hadoop and accelerate validating its use for an organization's unique data architecture and use cases.
DataStax Training – Everything you need to become a Cassandra RockstarDataStax
Looking to strengthen your expertise of Cassandra and DataStax Enterprise? This DataStax Training Webinar provides an overview of what you need to get the most out of Cassandra and your DataStax Enterprise environment. Whether you’re a developer or administrator, novice or a Cassandra expert, there is a class that will meet your experience level and needs.
Hadoop Reporting and Analysis - JaspersoftHortonworks
Hadoop is deployed for a variety of uses, including web analytics, fraud detection, security monitoring, healthcare, environmental analysis, social media monitoring, and other purposes.
Standing Up an Effective Enterprise Data Hub -- Technology and BeyondCloudera, Inc.
Federal organizations increasingly are focused on creating environments that enable more data-driven decisions. Yet ensuring that all data is considered and is current, complete, and accurate is a tall order for most. To make data analytics meaningful to support real-world transformation, agency staff need business tools that provide user-friendly dashboards, on-demand reporting, and methods to manage efficiently the rise of voluminous and varied data sets and types commonly associated with big data. In most cases, existing systems are insufficient to support these requirements. Enter the enterprise data hub (EDH), a software architecture specifically designed to be a unified platform that can economically store unlimited data and enable diverse access to it at scale. Plan to attend this discussion to understand the key considerations to making an EDH the architectural center of your agency’s modern data strategy.
Webinar | From Zero to 1 Million with Google Cloud Platform and DataStaxDataStax
Google Cloud Platform delivers the industry’s leading cloud-based services to create anything from simple websites to complex applications. DataStax delivers Apache Cassandra™, the leading distributed database technology, to the enterprise. Together, DataStax Enterprise on Google Cloud Platform delivers the performance, agility, infinite elasticity and innovation organizations need to build high-performance, highly-available online applications.
Join Allan Naim, Global Product Lead at Google Cloud Platform and Darshan Rawal, Sr. Director of Product Management at DataStax as they share their expertise on why DataStax and Google Cloud Platform deliver the industry’s most robust Infrastructure-as-a Service (IaaS) platform and how your organization find success with NoSQL and Cloud services.
View to learn how to:
- Handle more than 1 Million requests per second for data-intensive online applications with Apache Cassandra on Google Cloud Platform
- Leverage the technology infrastructure and global network powering Google’s search engine with DataStax to deploy blazing-fast and always-on applications
- Transform your business into a data-driven company, a change that is critical as future success and discoveries hinge on the ability to quickly take action on data
Key trends in Big Data and new reference architecture from Hewlett Packard En...Ontico
Динамичное развитие инструментов для обработки Больших Данных порождает новые подходы к повышению производительности. Ключевые новые технологии в Hadoop 2.0, такие как Yarn labeling и Storage Tiering, уже используются компаниями Yahoo и Ebay. Эти новые технологии открывают путь для серьезного повышения эффективности ИТ-инфраструктуры для Hadoop, достигая прироста производительности в несколько десятков процентов при одновременном снижении потребления памяти и электроэнергии.
Эталонная архитектура для Hadoop от HP — HP Big Data Reference Architecture — предлагает использование специализированных "микросерверов" HP Moonshot вкупе с высокоплотными узлами хранения HP Apollo для достижения лучших на сегодня показателей полезной отдачи от железа в Hadoop.
Cloudera Breakfast Series, Analytics Part 1: Use All Your DataCloudera, Inc.
The document discusses how traditional analytics processes involve siloed data and platforms, long timelines for data discovery, and difficulties accessing and sharing data. It proposes that an Enterprise Data Hub (EDH) using Cloudera can help address these issues by providing unified storage for all types of data, shorter analytics lifecycles, and the ability to do more with data by using 100x more data and more types of data. The EDH allows organizations to use all of their data and gain insights sooner.
This document discusses big data concepts like volume, velocity, and variety of data. It introduces NoSQL databases as an alternative to relational databases for big data that does not require data cleansing or schema definition. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key Hadoop components like HDFS, MapReduce, Hive, Pig and YARN are described at a high level. The document also discusses using Azure services like Azure Storage, HDInsight and Stream Analytics with Hadoop.
Rob peglar introduction_analytics _big data_hadoopGhassan Al-Yafie
This document provides an introduction to analytics and big data using Hadoop. It discusses the growth of digital data and challenges of big data. Hadoop is presented as a solution for storing and processing large, unstructured datasets across commodity servers. The key components of Hadoop - HDFS for distributed storage and MapReduce for distributed processing - are described at a high level. Examples of industries using big data analytics are also listed.
Innovation in the Data Warehouse - StampedeCon 2016StampedeCon
Enterprise Holding’s first started with Hadoop as a POC in 2013. Today, we have clusters on premises and in the cloud. This talk will explore our experience with Big Data and outline three common big data architectures (batch, lambda, and kappa). Then, we’ll dive into the decision points to necessary for your own cluster, for example: cloud vs on premises, physical vs virtual, workload, and security. These decisions will help you understand what direction to take. Finally, we’ll share some lessons learned with the pieces of our architecture worked well and rant about those which didn’t. No deep Hadoop knowledge is necessary, architect or executive level.
1) Big data is growing exponentially and new frameworks like Hadoop are needed to analyze large, unstructured datasets.
2) Hadoop uses distributed computing and storage across commodity servers to provide scalable and cost-effective analytics. It leverages local disks on each node for temporary data to improve performance.
3) Virtualizing Hadoop simplifies operations, enables mixed workloads, and provides high availability through features like vMotion and HA. It also allows for elastic scaling of compute and storage resources.
This document discusses how traditional tech giants face slowing growth rates and are looking to customer relationship intelligence to drive ongoing success and transformation. It notes that 80% of next year's revenue will come from existing customers, so these companies must understand their complex customer relationships to meet changing demands and capture opportunities. The document advocates that by using customer relationship intelligence to gain insights from customer contract data, companies can solve revenue leakage today and position themselves for future growth.
Technical specialist Tom Miseur conducted a webinar discussing the basics of getting started with performance and load testing. Learn how to create a PTP (performance test plan), define requirements and objectives, define test scope and approach, and then finally how to create, execute, and analyze test results.
The document is a presentation about running SagePFW in a private cloud called vCloud by Vertical Solutions. It defines cloud computing, discusses the benefits of vCloud such as scalability, security and cost savings over an in-house IT system. It provides an example showing the vCloud solution can save a SagePFW client over $30,000 in costs over 3 years compared to an in-house system. The presentation concludes by taking questions.
O documento descreve as principais funcionalidades da IDE Visual Studio 2013, incluindo pilares como simplicidade e garantia da qualidade de software, as distribuições disponíveis e novas funcionalidades como importação/exportação de breakpoints, snippets em HTML e C#, pesquisa rápida e recursos de refatoração e depuração aprimorados.
New Research: Cloud, Cost & Complexity Impact IAM & ITSymplified
This document discusses the challenges that organizations face with identity and access management (IAM) in the current landscape of cloud computing, mobile users, and diverse applications. It finds that most enterprises now manage identities for external users like contractors and customers. Additionally, organizations grapple with legacy IAM solutions that do not meet the needs of managing users across cloud, mobile, and diverse applications. As a result, many organizations use multiple disparate tools to handle IAM, which increases complexity, cost, and security risks. The top priorities for IAM solutions are security, total cost of ownership, and ease of implementation.
Elets Technomedia is a technology media and research company focused on ICT in government, education and healthcare. It provides information on ICT developments through print publications, web portals, and events. It publishes magazines on eGovernment, ICT in education, and ICT in healthcare. It also hosts flagship ICT events in India and Asia and other events on topics like secure IT, cloud computing, education, and healthcare.
Learn more about electronics recycling in the Washington D.C. Metro Area which is home to a booming technology sector and two states with current electronics recycling laws. Electronics recycling protects the environment and is good for the economy.
TXT Next is a software solutions and services company with over 20 years of experience working with market leaders. It has strong domain expertise in aerospace & defense, banking & finance, and high tech manufacturing. TXT Next delivers leading edge software platforms and services to help customers succeed and offers specialized capabilities like focused methodologies, tight partnerships with major IT companies, proprietary products, and quality assurance certifications.
Outlines how the scope can be widened through the use of Preceptive Software for optimisation of processes and reduced costs in conjunction with our proffesional services
Presence Agent y Presence Scripting para personas con limitaciones visualesPresence Technology
Presence es el primer proveedor de Tecnología para Contact Centers en Colombia en integrarse con JAWS, lector de pantalla para personas con limitaciones Visuales.
Enriched Insights in Finance: Blending Data, Boosting PerformanceProphix Software
Join Prophix and Aberdeen Group’s VP and Principal Analyst Michael Lock for a webinar. We will cover:
• Top data challenges companies face today
• Best-in-Class strategies for data management and integration
• The strategic and operational impact of analytics in the finance department
From limited Hadoop compute capacity to increased data scientist efficiencyAlluxio, Inc.
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 CloudAlluxio, Inc.
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.
Weet u nog waar uw bedrijfsdata zich bevindt? Uw data bevindt zich (straks) overal. In samenwerking met Commvault laten we zien, hoe uw organisatie ‘in control’ kan blijven over én meerwaarde kan geven aan uw data ongeacht of deze zich on-premise, in de cloud of op een end-user device bevindt.
Presentatie 9 juni 2016
Accelerate Analytics and ML in the Hybrid Cloud EraAlluxio, Inc.
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
Alluxio 2.0 Deep Dive – Simplifying data access for cloud workloadsAlluxio, Inc.
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.
The Future of Data Management: The Enterprise Data HubCloudera, Inc.
The document discusses the future of data management through the use of an enterprise data hub (EDH). It notes that an EDH provides a centralized platform for ingesting, storing, exploring, processing, analyzing and serving diverse data from across an organization on a large scale in a cost effective manner. This approach overcomes limitations of traditional data silos and enables new analytic capabilities.
In this session, we will describe the key elements of a Dell EMC Isilon Data Lake and its key advantages including reduced IT costs, simplified management, increased operational flexibility and in-place data analytics. Dell EMC products to be featured include Isilon, ECS and Virtustream.
In this session, you will learn about the Dell EMC Isilon Data Lake and its advantages including:
• Data consolidation and increased efficiency to lower capital costs
• Streamlined management to reduce operating costs
• Improved operational flexibility and scalability to meet growing storage requirements
• Simple integration with a choice of public or private cloud storage providers
• Powerful in-place data analytics that accelerate time to insight while eliminating the need for a separate analytics storage infrastructure.
You will also hear how this solution can be easily extended to include data from remote and branch office locations with an efficient software defined storage solution.
10 Reasons Snowflake Is Great for AnalyticsSenturus
Learn why Snowflake analytic data warehouse makes sense for BI including data loading flexibility and scalability, consumption-based storage and compute costs, Time Travel and data sharing features, support across a range of BI tools like Power BI and Tableau and ability to allocate compute costs. View this on-demand webinar: https://senturus.com/resources/10-reasons-snowflake-is-great-for-analytics/.
Senturus offers a full spectrum of services in business intelligence and training on Cognos, Tableau and Power BI. Our resource library has hundreds of free live and recorded webinars, blog posts, demos and unbiased product reviews available on our website at: http://www.senturus.com/senturus-resources/.
This document discusses using Azure HDInsight for big data applications. It provides an overview of HDInsight and describes how it can be used for various big data scenarios like modern data warehousing, advanced analytics, and IoT. It also discusses the architecture and components of HDInsight, how to create and manage HDInsight clusters, and how HDInsight integrates with other Azure services for big data and analytics workloads.
Alluxio Data Orchestration Platform for the CloudShubham Tagra
Alluxio originated as an open source project at UC Berkeley to orchestrate data for cloud applications by providing a unified namespace and intelligent data caching across multiple data sources. It provides consistent high performance for analytics and AI workloads running on object stores by caching frequently accessed data in memory and tiering data to flash/disk based on policies. Alluxio can also enable hybrid cloud environments by allowing on-premises workloads to burst to public clouds without data movement through "zero-copy" access to remote data.
Turning Data into Business Value with a Modern Data PlatformCloudera, Inc.
The document discusses how data has become a strategic asset for businesses and how a modern data platform can help organizations drive customer insights, improve products and services, lower business risks, and modernize IT. It provides examples of companies using analytics to personalize customer solutions, detect sepsis early to save lives, and protect the global finance system. The document also outlines the evolution of Hadoop platforms and how Cloudera Enterprise provides a common workload pattern to store, process, and analyze data across different workloads and databases in a fast, easy, and secure manner.
Achieving Separation of Compute and Storage in a Cloud WorldAlluxio, Inc.
Alluxio Tech Talk
Feb 12, 2019
Speaker:
Dipti Borkar, Alluxio
The rise of compute intensive workloads and the adoption of the cloud has driven organizations to adopt a decoupled architecture for modern workloads – one in which compute scales independently from storage. While this enables scaling elasticity, it introduces new problems – how do you co-locate data with compute, how do you unify data across multiple remote clouds, how do you keep storage and I/O service costs down and many more.
Enter Alluxio, a virtual unified file system, which sits between compute and storage that allows you to realize the benefits of a hybrid cloud architecture with the same performance and lower costs.
In this webinar, we will discuss:
- Why leading enterprises are adopting hybrid cloud architectures with compute and storage disaggregated
- The new challenges that this new paradigm introduces
- An introduction to Alluxio and the unified data solution it provides for hybrid environments
How to Build Multi-disciplinary Analytics Applications on a Shared Data PlatformCloudera, Inc.
The document discusses building multi-disciplinary analytics applications on a shared data platform. It describes challenges with traditional fragmented approaches using multiple data silos and tools. A shared data platform with Cloudera SDX provides a common data experience across workloads through shared metadata, security, and governance services. This approach optimizes key design goals and provides business benefits like increased insights, agility, and decreased costs compared to siloed environments. An example application of predictive maintenance is given to improve fleet performance.
Data Orchestration for the Hybrid Cloud EraAlluxio, Inc.
Alluxio Community Office Hour
October 20, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speaker(s):
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
Accelerate Analytics and ML in the Hybrid Cloud EraAlluxio, Inc.
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
This document provides an overview and agenda for a presentation on Dell storage solutions for mid-market organizations. It discusses Dell Storage and Fluid Data Architecture, provides a deep dive on the Dell PowerVault MD3 and Dell EqualLogic storage arrays, and covers storage tools. Key points include Dell's vision for making data fluid by optimizing storage across primary, offsite, backup and cloud storage. It also summarizes features and benefits of the Dell PowerVault MD3 such as scalability, performance, availability, manageability and reliable data protection capabilities like dynamic disk pools and remote replication.
ADV Slides: Platforming Your Data for Success – Databases, Hadoop, Managed Ha...DATAVERSITY
Thirty years is a long time for a technology foundation to be as active as relational databases. Are their replacements here? In this webinar, we say no.
Databases have not sat around while Hadoop emerged. The Hadoop era generated a ton of interest and confusion, but is it still relevant as organizations are deploying cloud storage like a kid in a candy store? We’ll discuss what platforms to use for what data. This is a critical decision that can dictate two to five times additional work effort if it’s a bad fit.
Drop the herd mentality. In reality, there is no “one size fits all” right now. We need to make our platform decisions amidst this backdrop.
This webinar will distinguish these analytic deployment options and help you platform 2020 and beyond for success.
Unstructured data is growing at a staggering rate. It is breaking traditional storage and IT budgets and burying IT professionals under a mountain of operational challenges. Listen as Cloudian and Storage Switzerland discuss panel-style discussion the seven key reasons why organizations can dramatically lower storage infrastructure costs by deploying a hardware-agnostic object storage solution instead of sticking with legacy NAS.
This document discusses different options for deploying a Hadoop cluster, including using an appliance like Oracle's Big Data Appliance, deploying on cloud infrastructure through Amazon EMR, or building your own "do-it-yourself" cluster. It provides details on the hardware, software, and costs associated with each option. The conclusion compares the pros and cons of each approach, noting that appliances provide high performance and integration but may be less flexible, while cloud deployments offer scalability and pay-per-use but require consideration of data privacy. Building your own cluster gives more control but requires more work to set up and manage.
Similar to Archiving is a No-brainer - Bloor Analyst and RainStor Executive Discuss (20)
Big Data Analytics on Hadoop RainStor InfographicRainStor
A look at how RainStor's compression helps solve the Cost, Complexity and Compliance Risk challenges of managing big data on Hadoop. RainStor runs natively on Hadoop, integrates with YARN and Hue. Can be accessed through Hive, Pig or MapReduce.
TDWI Checklist Report: Active Data ArchivingRainStor
The document discusses best practices for active data archiving. It recommends embracing modern archiving platforms and practices to address problems with traditional archives. A modern archive should serve compliance needs through immutable, auditable data storage, while also enabling analytics through online access. It should scale to large volumes of structured and unstructured data from various sources and support roles-based security and multi-tenancy. The archive's primary tier should be a robust database for online querying and exploration of archived data.
Rain stor isilon_emc_real_Examine the Real Cost of Storing & Analyzing Your M...RainStor
This document discusses the real costs of storing and analyzing big data. It summarizes that unstructured data is growing rapidly, with over 70% growth expected between 2013 and 2017. Hadoop has become a popular platform for big data analytics. The document examines the total cost of ownership for big data storage and finds that costs can be significantly reduced by using scale-out NAS solutions like EMC Isilon combined with RainStor's analytical archive software. Case studies show banks and financial institutions saving over 90% on storage costs and getting faster query performance using this approach.
This document discusses the changing landscape of data management as the volume of data grows exponentially. It introduces the concept of "Total Data" which advocates a flexible approach to data management that processes all applicable data across operational databases, data warehouses, Hadoop, and archives. The trends driving more data include greater understanding of data's value, improved processing capabilities, and the rise of machine-generated data. New approaches are needed to virtually access and analyze large datasets at lower costs. RainStor provides a specialized database that can reduce, retain, and retrieve large volumes of historical structured data at 10x lower costs than alternatives.
RainStor provides data retention software that uses deduplication to reduce storage needs and costs for retaining large volumes of structured data. It has over 50 customers across industries and geographies. RainStor recently opened a new US office and signed partnerships with Informatica and Group2000 to expand its presence in the US market and support growing demand for its data archiving solutions.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
E-commerce Development Services- Hornet DynamicsHornet Dynamics
For any business hoping to succeed in the digital age, having a strong online presence is crucial. We offer Ecommerce Development Services that are customized according to your business requirements and client preferences, enabling you to create a dynamic, safe, and user-friendly online store.
SOCRadar's Aviation Industry Q1 Incident Report is out now!
The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
SOCRadar’s Aviation Industry, Quarterly Incident Report, provides an in-depth analysis of these threats, detected and examined through our extensive monitoring of hacker forums, Telegram channels, and dark web platforms.
Hand Rolled Applicative User ValidationCode KataPhilip Schwarz
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
DDS Security Version 1.2 was adopted in 2024. This revision strengthens support for long runnings systems adding new cryptographic algorithms, certificate revocation, and hardness against DoS attacks.
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Measures in SQL (SIGMOD 2024, Santiago, Chile)Julian Hyde
SQL has attained widespread adoption, but Business Intelligence tools still use their own higher level languages based upon a multidimensional paradigm. Composable calculations are what is missing from SQL, and we propose a new kind of column, called a measure, that attaches a calculation to a table. Like regular tables, tables with measures are composable and closed when used in queries.
SQL-with-measures has the power, conciseness and reusability of multidimensional languages but retains SQL semantics. Measure invocations can be expanded in place to simple, clear SQL.
To define the evaluation semantics for measures, we introduce context-sensitive expressions (a way to evaluate multidimensional expressions that is consistent with existing SQL semantics), a concept called evaluation context, and several operations for setting and modifying the evaluation context.
A talk at SIGMOD, June 9–15, 2024, Santiago, Chile
Authors: Julian Hyde (Google) and John Fremlin (Google)
https://doi.org/10.1145/3626246.3653374
Revolutionizing Visual Effects Mastering AI Face Swaps.pdfUndress Baby
The quest for the best AI face swap solution is marked by an amalgamation of technological prowess and artistic finesse, where cutting-edge algorithms seamlessly replace faces in images or videos with striking realism. Leveraging advanced deep learning techniques, the best AI face swap tools meticulously analyze facial features, lighting conditions, and expressions to execute flawless transformations, ensuring natural-looking results that blur the line between reality and illusion, captivating users with their ingenuity and sophistication.
Web:- https://undressbaby.com/
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
SMS API Integration in Saudi Arabia| Best SMS API ServiceYara Milbes
Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.
2. Speakers
&
Topics
2
Philip Howard,
Research Director
Bloor
Deirdre Mahon,
VP Marketing
RainStor
§
What
is
an
Archive
§
Industry
Trends
§
Key
Requirements
§
Best
Prac>ces
§
Customer
Examples
22. Analy>cal
Archive:
End-‐to-‐end
QUERY/
ANALYZE
SQL
BI
Tools;
Hive,
MapReduce
SCALE
-‐
Any
PlaWorm
COMPRESS
LOAD/
VALIDATE
Billions
Records/Day
10-‐40X
(90%+)
AVAILABILITY
ReplicaGon
DW
Source
Move
RETAIN
/
DISPOSE
Rules
Based
IN
STORE
QUERY
GOVERN
SECURE
-‐
Enterprise-‐grade
23. Analy>cal
Archive
-‐
Key
Capabili>es
23
QUERY
GOVERN
§ Standard
SQL
92,
99
§ MapReduce,
Pig,
Hive
§ BI
Tools
§ Rules
based
Data
Disposi>on
§ WORM
(17a-‐4)
§ Legal
Hold
§ Record-‐level
Reten>on
§ Audit
Trail
DATA
IN
§ Works
with
standard
ETL
§ ODBC/JDBC
Compa>ble
§ FastConnect™
for
Teradata
§ Validate
-‐
Fingerprint
STORE
§ Compression
-‐
20-‐40X
§ Immutable
Data
Model
§ Encryp>on
&
Masking
§ Authen>ca>on
§ Schema
Evolu>on
CAPACITY
BUY-‐BACK
INSTANT
ACCESS
NO
FINES
SAVE
YOU
MONEY
24. Fits
with
Your
Enterprise
24
EDW’S
Deployment
PlaWorms
Scale-‐out
NAS
HDFS
Cluster
/
DAS
Cloud
Worm
25. Teradata
-‐
Analy>cal
Archive
Solu>on
QUERY/
ANALYZE
SQL
BI
Tools;
Hive,
MapReduce
SCALE
-‐
Any
PlaWorm
COMPRESS
LOAD/
VALIDATE
Billions
Records/Day
10-‐40X
(90%+)
AVAILABILITY
ReplicaGon
TERADATA
FastConnect™
RETAIN
/
DISPOSE
Rules
Based
GOVERN
SECURE
-‐
Enterprise-‐grade
FastForward™
26. SEC
17a-‐4(f)
Compliance
Archive
Requirements
26
Records
stored
in
non-‐eraseable
media
(WORM)
Recording
process
must
be
verifiable
Fully
Accessible
to
AuthoriGes
&
Backed-‐up
Records
should
be
Recognizable
&
IdenGfiable
Downloadable
to
any
acceptable
medium
27. 27
Global
Financial
Services
Lower
Compliant
Data
Reten>on
Costs
by
a
Decimal
Point!
Challenges
§ Cost:
Data
volumes
in
disparate
trading
applica>ons
growing
at
70-‐100%
/
Year
-‐
Storage
costs
rising
@
60%
/
Year
§ Compliance:
Must
provide
performant
EBS
and
other
queries
for
SEC
SoluGon
§ A
RainStor
Archive
for
storing
and
repor>ng
against
historical
trade
data
§ 13
years
of
history
loaded
from
Sybase
IQ
§ Daily
feed
from
trading
applica>on
to
RainStor
§ Runs
on
low-‐cost
NAS
Tier
3
storage
and
VMs
§ RainStor
completely
replaced
Sybase
IQ
ü 90%
cost
savings
-‐
$5M
ROI
ü 6
Projects
live
-‐
13
more
in
progress.
90%
Storage
Cost
Reduc>on
“ It’s like shrink-wrapping your
data…forever!”
–
VP,
Technology
Benefits
ü 30X
Data
Compression
ü 3X
Faster
Query
Compared
to
Sybase
28. 28
Retain
Cri>cal
Analy>cs
Data
at
Lowest
Cost
§ Ini>ally
60TB
Offload
Business
&
Compliance
Queries
§ Run
Na>vely
on
Hadoop
SQL
+
MapReduce
40X
Compression
RAINSTOR
SOLUTION:
Offload
Historical
Data
from
Teradata
to
RainStor
Meets
SLAs
for
Business
Query
Savings
on
TD
Licenses
Capacity
Buy-‐Back
-‐
$Millions
Saved
on
Sopware
&
Hardware
Enterprise
DW
FastConnect™
Dell
Servers
R720s
Cloudera
Hadoop
400TB
ü ü ü
29. § Gone
through
the
pain
so
you
don't
have
to.
– Crea>ng
an
archive
is
not
easy!
– RainStor
Makes
it
simple,
cost
effec>ve,
compliant-‐ready
&
fast
to
deploy
§ Solving
archiving
problems
for
world
leaders
-‐
10
years
of
proven
experience.
– Enabled
customers
to
deploy
cost
efficiently
without
compromise
– Support
and
service
rated
as
world
class
by
our
customers
§ Technology
soluGons
that
create
business
value.
– Transforms
archiving
to
be
a
strategic
asset.
Why
RainStor®