Hongbin Ma and Luke Han (Kyligence)
Apache Kylin is an open source distributed analytics engine that provides a SQL interface and multi-dimensional analysis on Hadoop supporting extremely large datasets. In the forthcoming Kylin release, we optimized query performance by exploring the potentials of parallel storage on top of HBase. This talk explains how that work was done.
Apache kylin 2.0: from classic olap to real-time data warehouseYang Li
Apache Kylin, which started as a big data OLAP engine, is reaching its v2.0. Yang Li explains how, armed with snowflake schema support, a full SQL interface, spark cubing, and the ability to consume real-time streaming data, Apache Kylin is closing the gap to becoming a real-time data warehouse.
Apache Kylin is an open source Distributed Analytics Engine from eBay Inc. that provides SQL interface and multi-dimensional analysis (OLAP) on Hadoop supporting extremely large datasets and subsecond query latency.
Talks about best practices and patterns on how to design an efficient cube in Kylin. Covers concepts like mandatory dimension, hierarchy dimension, derived dimension, incremental build, aggregation group etc.
Kylin is an open source Distributed Analytics Engine from eBay Inc. that provides SQL interface and multi-dimensional analysis (OLAP) on Hadoop supporting extremely large datasets
Kylin Open Source Web Site: http://kylin.io
Apache kylin 2.0: from classic olap to real-time data warehouseYang Li
Apache Kylin, which started as a big data OLAP engine, is reaching its v2.0. Yang Li explains how, armed with snowflake schema support, a full SQL interface, spark cubing, and the ability to consume real-time streaming data, Apache Kylin is closing the gap to becoming a real-time data warehouse.
Apache Kylin is an open source Distributed Analytics Engine from eBay Inc. that provides SQL interface and multi-dimensional analysis (OLAP) on Hadoop supporting extremely large datasets and subsecond query latency.
Talks about best practices and patterns on how to design an efficient cube in Kylin. Covers concepts like mandatory dimension, hierarchy dimension, derived dimension, incremental build, aggregation group etc.
Kylin is an open source Distributed Analytics Engine from eBay Inc. that provides SQL interface and multi-dimensional analysis (OLAP) on Hadoop supporting extremely large datasets
Kylin Open Source Web Site: http://kylin.io
Apache Kylin on HBase: Extreme OLAP engine for big dataShi Shao Feng
Apache Kylin is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets.
Apache Kylin general introduction, including background, business needs and technical challenges, theory and architecture, features and some tech detail. Following with performance and benchmark, finally, ecosystem and roadmap.
More detail, please visit http://kylin.io or follow @ApacheKylin.
During Kylin OLAP development, we setup many engineering principles in the team. These principles are very important to delivery Kylin with high quality and on schedule.
Apache Kylin: OLAP Engine on Hadoop - Tech Deep DiveXu Jiang
Kylin is an open source Distributed Analytics Engine from eBay Inc. that provides SQL interface and multi-dimensional analysis (OLAP) on Hadoop supporting extremely large datasets.
If you want to do multi-dimension analysis on large data sets (billion+ rows) with low query latency (sub-seconds), Kylin is a good option. Kylin also provides seamless integration with existing BI tools (e.g Tableau).
This presentation contains following slides,
Introduction To OLAP
Data Warehousing Architecture
The OLAP Cube
OLTP Vs. OLAP
Types Of OLAP
ROLAP V/s MOLAP
Benefits Of OLAP
Introduction - Apache Kylin
Kylin - Architecture
Kylin - Advantages and Limitations
Introduction - Druid
Druid - Architecture
Druid vs Apache Kylin
References
For any queries
Contact Us:- argonauts007@gmail.com
Experimentation plays a vital role in business growth at eBay by providing valuable insights and prediction on how users will reach to changes made to the eBay website and applications. On a given day, eBay has several hundred experiments running at the same time. Our experimentation data processing pipeline handles billions of rows user behavioral and transactional data per day to generate detailed reports covering 100+ metrics over 50 dimensions.
In this session, we will share our journey of how we moved this complex process from Data warehouse to Hadoop. We will give an overview of the experimentation platform and data processing pipeline. We will highlight the challenges and learnings we faced implementing this platform in Hadoop and how this transformation led us to build a scalable, flexible and reliable data processing workflow in Hadoop. We will cover our work done on performance optimizations, methods to establish resilience and configurability, efficient storage formats and choices of different frameworks used in the pipeline.
AWS Partner Webcast - Analyze Big Data for Consumer Applications with Looker ...Amazon Web Services
Analyze Big Data for Consumer Applications with Looker BI and Amazon Redshift Customizing the customer experience based on user behavior is a constant challenge for today’s consumer apps. Business intelligence helps analyze and model large amounts of data. Looker offers a modern approach to BI leveraging AWS that’s fast, agile, and easy to manage. Join this webinar to learn how MessageMe, which provides emotionally engaging messaging apps to consumers, leverages Looker business intelligence software and the Amazon Redshift data warehouse service to analyze billions of rows of customer data in seconds.
Webinar topics include:
• How MessageMe turns billions of rows of customer data stored in Amazon Redshift into actionable insights
• How Looker connects directly to Amazon Redshift in just a few clicks, enabling MessageMe to build a modern, big data analytics in the cloud. Who should attend
• Information or Solution Architects, Data Analysts, BI Directors, DBAs, Development Leads, Developers, or Technical IT Leaders.
Presenters:
• Justin Rosenthal, CTO, MessageMe
• Keenan Rice, VP, Marketing & Alliances, Looker
• Tina Adams, Senior Product Manager, AWS
Apache Kylin on HBase: Extreme OLAP engine for big dataShi Shao Feng
Apache Kylin is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets.
Apache Kylin general introduction, including background, business needs and technical challenges, theory and architecture, features and some tech detail. Following with performance and benchmark, finally, ecosystem and roadmap.
More detail, please visit http://kylin.io or follow @ApacheKylin.
During Kylin OLAP development, we setup many engineering principles in the team. These principles are very important to delivery Kylin with high quality and on schedule.
Apache Kylin: OLAP Engine on Hadoop - Tech Deep DiveXu Jiang
Kylin is an open source Distributed Analytics Engine from eBay Inc. that provides SQL interface and multi-dimensional analysis (OLAP) on Hadoop supporting extremely large datasets.
If you want to do multi-dimension analysis on large data sets (billion+ rows) with low query latency (sub-seconds), Kylin is a good option. Kylin also provides seamless integration with existing BI tools (e.g Tableau).
This presentation contains following slides,
Introduction To OLAP
Data Warehousing Architecture
The OLAP Cube
OLTP Vs. OLAP
Types Of OLAP
ROLAP V/s MOLAP
Benefits Of OLAP
Introduction - Apache Kylin
Kylin - Architecture
Kylin - Advantages and Limitations
Introduction - Druid
Druid - Architecture
Druid vs Apache Kylin
References
For any queries
Contact Us:- argonauts007@gmail.com
Experimentation plays a vital role in business growth at eBay by providing valuable insights and prediction on how users will reach to changes made to the eBay website and applications. On a given day, eBay has several hundred experiments running at the same time. Our experimentation data processing pipeline handles billions of rows user behavioral and transactional data per day to generate detailed reports covering 100+ metrics over 50 dimensions.
In this session, we will share our journey of how we moved this complex process from Data warehouse to Hadoop. We will give an overview of the experimentation platform and data processing pipeline. We will highlight the challenges and learnings we faced implementing this platform in Hadoop and how this transformation led us to build a scalable, flexible and reliable data processing workflow in Hadoop. We will cover our work done on performance optimizations, methods to establish resilience and configurability, efficient storage formats and choices of different frameworks used in the pipeline.
AWS Partner Webcast - Analyze Big Data for Consumer Applications with Looker ...Amazon Web Services
Analyze Big Data for Consumer Applications with Looker BI and Amazon Redshift Customizing the customer experience based on user behavior is a constant challenge for today’s consumer apps. Business intelligence helps analyze and model large amounts of data. Looker offers a modern approach to BI leveraging AWS that’s fast, agile, and easy to manage. Join this webinar to learn how MessageMe, which provides emotionally engaging messaging apps to consumers, leverages Looker business intelligence software and the Amazon Redshift data warehouse service to analyze billions of rows of customer data in seconds.
Webinar topics include:
• How MessageMe turns billions of rows of customer data stored in Amazon Redshift into actionable insights
• How Looker connects directly to Amazon Redshift in just a few clicks, enabling MessageMe to build a modern, big data analytics in the cloud. Who should attend
• Information or Solution Architects, Data Analysts, BI Directors, DBAs, Development Leads, Developers, or Technical IT Leaders.
Presenters:
• Justin Rosenthal, CTO, MessageMe
• Keenan Rice, VP, Marketing & Alliances, Looker
• Tina Adams, Senior Product Manager, AWS
Creating a Modern Data Architecture for Digital TransformationMongoDB
By managing Data in Motion, Data at Rest, and Data in Use differently, modern Information Management Solutions are enabling a whole range of architecture and design patterns that allow enterprises to fully harness the value in data flowing through their systems. In this session we explored some of the patterns (e.g. operational data lakes, CQRS, microservices and containerisation) that enable CIOs, CDOs and senior architects to tame the data challenge, and start to use data as a cross-enterprise asset.
Cloud-native Semantic Layer on Data LakeDatabricks
With larger volume and more real-time data stored in data lake, it becomes more complex to manage these data and serve analytics and applications. With different service interfaces, data caliber, performance bias on different scenarios, the business users begin to suffer low confidence on quality and efficiency to get insight from data.
L'architettura di classe enterprise di nuova generazione - Massimo BrignoliData Driven Innovation
La nascita dei data lake - La aziende, ormai, sono sommerse dai dati e il classico datawarehouse fa fatica a macinare questi dati per numerosità e varietà. In molti hanno iniziato a guardare a delle architetture chiamate Data Lakes con Hadoop come tecnologia di riferimento. Ma questa soluzione va bene per tutto? Vieni a capire come operazionalizzare i data lakes per creare delle moderne architetture di gestione dati.
This was presented by the MongoDB team at the Singapore VIP event on 24th Jan 2019.
The presentation covers-
What is MongoDB
Why MongoDB
MongoDB As a Service, Serverless Platform and Mobile
MongoDB Atlas: Database as a Service (Available on AWS, Azure and Google Cloud)
Usecases
Key Methodologies for Migrating from Oracle to PostgresEDB
This presentation reviews the key methodologies that all members of your team should consider, before planning a migration from Oracle to Postgres including:
• Prioritizing the right application or project for your first Oracle migration
• Planning a well-defined, phased migration process to minimize risk and increase time to value
• Handling common concerns and pitfalls related to a migration project
• Leveraging resources before, during, and after your migration
• Becoming independent from an Oracle database – without sacrificing performance
With EDB Postgres’ database compatibility for Oracle, it is easy to migrate from your existing Oracle databases. The compatibility feature set includes compatibility for PL/SQL, Oracle’s SQL syntax, and built in SQL functions. This means that many applications can be easily migrated over to EDB Postgres. It also allows you to continue using your existing Oracle skills.
For more information please contact us at sales@enterprisedb.com
We live in a world of unprecedented change. To be successful in this world of change, you will need to develop a data culture, creating an environment where every team and every individual is empowered to do great things because of the data at their fingertips. In this event you will learn how to create a culture of data and how the Microsoft Modern BI platform and tools can help you to can harness the power of data once only reserved for data scientists. Learn about how to tap into the power of natural language, self-service business insights and visualization capabilities – and make insights available to anyone, anywhere, at any time.
AWS Summit 2013 | India - Big Data Analytics, Abhishek SinhaAmazon Web Services
The volume, velocity and variety of data has changed drastically in the last decade. Everything generates data today, from your customers on social networks, to the instances running your web applications. The tools to support collecting, storing, organizing, analyzing and sharing of data are all available in a couple of clicks, with Amazon Web Services. Attend this session to learn how Big Data in the cloud can help you easily unlock business opportunities hidden in your data today.
Unlocking Operational Intelligence from the Data LakeMongoDB
Hadoop-based data lakes are enabling enterprises and governments to efficiently capture and analyze unprecedented volumes of data. Join this webinar to learn how digital transformation is driving the rise of the data lake, the role Hadoop plays in generating new classes of analytics and insight, the critical capabilities you need to evaluate in an operational database for your data lake, and more.
DataMass Summit - Machine Learning for Big Data in SQL ServerŁukasz Grala
Sesja pokazująca zarówno Machine Learning Server (czyli algorytmy uczenia maszynowego w językach R i Python), ale także możliwość korzystania z danych JSON w SQL Server, czy też łączenia się do danych znajdujących się na HDFS, HADOOP, czy Spark poprzez Polybase w SQL Server, by te dane wykorzystywać do analizy, predykcji poprzez modele w językach R lub Python.
Partner Enablement: Key Differentiators of Denodo Platform 6.0 for the FieldDenodo
If you’re a Denodo Partner, this presentation is for you. Learn how to gain a competitive edge in the marketplace with Denodo Platform 6.0, and leverage off the new features and functionality.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/Qh8MeX.
How to Build Modern Data Architectures Both On Premises and in the CloudVMware Tanzu
Enterprises are beginning to consider the deployment of data science and data warehouse platforms on hybrid (public cloud, private cloud, and on premises) infrastructure. This delivers the flexibility and freedom of choice to deploy your analytics anywhere you need it and to create an adaptable and agile analytics platform.
But the market is conspiring against customer desire for innovation...
Leading public cloud vendors are interested in pushing their new, but proprietary, analytic stacks, locking customers into subpar Analytics as a Service (AaaS) for years to come.
In tandem, Legacy Data Warehouse vendors are trying to extend the lifecycle of their costly and aging appliances with new features of marginal value, simply imitating the same limiting models of public cloud vendors.
New vendors are coming up with interesting ideas, but these ideas are often lacking critical features that don’t provide support for hybrid solutions, limiting the immediate value to users.
It is 2017—you can, in fact, have your analytics cake and eat it too! Solve your short term costs and capabilities challenges, and establish a long term hybrid data strategy by running the same open source analytics platform on your infrastructure as it exists today.
In this webinar you will learn how Pivotal can help you build a modern analytical architecture able to run on your public, private cloud, or on-premises platform of your choice, while fully leveraging proven open source technologies and supporting the needs of diverse analytical users.
Let’s have a productive discussion about how to deploy a solid cloud analytics strategy.
Presenter : Jacque Istok, Head of Data Technical Field for Pivotal
https://content.pivotal.io/webinars/jul-20-how-to-build-modern-data-architectures-both-on-premises-and-in-the-cloud
Building a Pluggable Analytics Stack with Cassandra (Jim Peregord, Element Co...DataStax
Element Fleet has the largest benchmark database in our industry and we needed a robust and linearly scalable platform to turn this data into actionable insights for our customers. The platform needed to support advanced analytics, streaming data sets, and traditional business intelligence use cases.
In this presentation, we will discuss how we built a single, unified platform for both Advanced Analytics and traditional Business Intelligence using Cassandra on DSE. With Cassandra as our foundation, we are able to plug in the appropriate technology to meet varied use cases. The platform we’ve built supports real-time streaming (Spark Streaming/Kafka), batch and streaming analytics (PySpark, Spark Streaming), and traditional BI/data warehousing (C*/FiloDB). In this talk, we are going to explore the entire tech stack and the challenges we faced trying support the above use cases. We will specifically discuss how we ingest and analyze IoT (vehicle telematics data) in real-time and batch, combine data from multiple data sources into to single data model, and support standardized and ah-hoc reporting requirements.
About the Speaker
Jim Peregord Vice President - Analytics, Business Intelligence, Data Management, Element Corp.
Similar to Apache Kylin’s Performance Boost from Apache HBase (20)
hbaseconasia2017: Building online HBase cluster of Zhihu based on KubernetesHBaseCon
Zhiyong Bai
As a high performance and scalable key value database, Zhihu use HBase to provide online data store system along with Mysql and Redis. Zhihu’s platform team had accumulated some experience in technology of container, and this time, based on Kubernetes, we build flexible platform of online HBase system, create multiple logic isolated HBase clusters on the shared physical cluster with fast rapid,and provide customized service for different business needs. Combined with Consul and DNS server, we implement high available access of HBase using client mainly written with Python. This presentation is mainly shared the architecture of online HBase platform in Zhihu and some practical experience in production environment.
hbaseconasia2017 hbasecon hbase
Jingcheng Du
Apache Beam is an open source and unified programming model for defining batch and streaming jobs that run on many execution engines, HBase on Beam is a connector that allows Beam to use HBase as a bounded data source and target data store for both batch and streaming data sets. With this connector HBase can work with many batch and streaming engines directly, for example Spark, Flink, Google Cloud Dataflow, etc. In this session, I will introduce Apache Beam, and the current implementation of HBase on Beam and the future plan on this.
hbaseconasia2017 hbasecon hbase
https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
hbaseconasia2017: HBase Disaster Recovery Solution at HuaweiHBaseCon
Ashish Singhi
HBase Disaster recovery solution aims to maintain high availability of HBase service in case of disaster of one HBase cluster with very minimal user intervention. This session will introduce the HBase disaster recovery use cases and the various solutions adopted at Huawei like.
a) Cluster Read-Write mode
b) DDL operations synchronization with standby cluster
c) Mutation and bulk loaded data replication
d) Further challenges and pending work
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
hbaseconasia2017: Removable singularity: a story of HBase upgrade in PinterestHBaseCon
Tianying Chang
HBase is used to serve online facing traffic in Pinterest. It means no downtime is allowed. However, we were on HBase 94. To upgrade to latest version, we need to figure out a way to live upgrade while keeping Pinterest site live. Recently, we successfully upgrade 94 HBase cluster to 1.2 with no downtime. We made change to both Asynchbase and HBase server side. We will talk about what we did and how we did it. We will also talk about the finding in config and performance tuning we did to achieve low latency.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
Xinxin Fan and Hongxiang Jiang
First, we will give a brief introduction about the HBase service at Netease,include the basic cluster info and the key HBase service. And then we will talk same tips about the tuning practices for HBase. Last, we will introduce some improvements at the internal HBase version.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
hbaseconasia2017: Large scale data near-line loading method and architectureHBaseCon
Shuaifeng Zhou
When we do real-time data loading to HBase, we use put/putlist interface. After receiving put request, regionserver will write WAL, write data into memory store, flush memory store to disk-store, then compact files again and again. That precedure occupies too much resource and causing read/write performance decrease. To solve the problem, we provide a kind of near-line loading method and architecture, greatly increase the loading bandwidth, and decrease the influence to read operations.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
hbaseconasia2017: Ecosystems with HBase and CloudTable service at HuaweiHBaseCon
Jieshan Bi and Yanhui Zhong
1. CTBase: A light-weight HBase client for structured data.
1). Schematized table, more friendly for structured data storage.
2). Global secondary index for HBase.
3). HBase Query DSL. JSON based light-weight API.
4) Cluster table. Pre-joining with keys, a better solution for cross-table join queries from HBase.
2. Tagram: Distributed bitmap index implementation with HBase.
1). Distributed bitmap index for accelerating AD-HOC queries with low cardinality columns.
2). Powerful and flexible query API.
3). Tagram offers millisecond-level query latency.
3. CloudTable Service Introduction: HBase on Huawei cloud.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
hbaseconasia2017: HBase Practice At XiaoMiHBaseCon
Zheng Hu
We'll share some HBase experience at XiaoMi:
1. How did we tuning G1GC for HBase Clusters.
2. Development and performance of Async HBase Client.
hbaseconasia2017 hbasecon hbase xiaomi https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
HBase-2.0.0 has been a couple of years in the making. It is chock-a-block full of a long list of new features and fixes. In this session, the 2.0.0 release manager will perform the impossible, describing the release content inside the session time bounds.
hbaseconasia2017 hbasecon hbase https://www.eventbrite.com/e/hbasecon-asia-2017-tickets-34935546159#
As HBase and Hadoop continue to become routine across enterprises, these enterprises inevitably shift priorities from effective deployments to cost-efficient operations. Consolidation of infrastructure, the sum of hardware, software, and system-administrator effort, is the most common strategy to reduce costs. As a company grows, the number of business organizations, development teams, and individuals accessing HBase grows commensurately, creating a not-so-simple requirement: HBase must effectively service many users, each with a variety of use-cases. This is problem is known as multi-tenancy. While multi-tenancy isn’t a new problem, it also isn’t a solved one, in HBase or otherwise. This talk will present a high-level view of the common issues organizations face when multiple users and teams share a single HBase instance and how certain HBase features were designed specifically to mitigate the issues created by the sharing of finite resources.
HBaseCon2017 Removable singularity: a story of HBase upgrade in PinterestHBaseCon
HBase is used to serve online facing traffic in Pinterest. It means no downtime is allowed. However, we were on HBase 94. To upgrade to latest version, we need to figure out a way to live upgrade while keeping Pinterest site live. Recently, we successfully upgrade 94 HBase cluster to 1.2 with no downtime. We made change to both Asynchbase and HBase server side. We will talk about what we did and how we did it. We will also talk about the finding in config and performance tuning we did to achieve low latency.
HBaseCon2017 Quanta: Quora's hierarchical counting system on HBaseHBaseCon
Hundreds of millions of people use Quora to find accurate, informative, and trustworthy answers to their questions. As it so happens, counting things at scale is both an important and a difficult problem to solve.
In this talk, we will be talking about Quanta, Quora's counting system built on top of HBase that powers our high-volume near-realtime analytics that serves many applications like ads, content views, and many dashboards. In addition to regular counting, Quanta supports count propagation along the edges of an arbitrary DAG. HBase is the underlying data store for both the counting data and the graph data.
We will describe the high-level architecture of Quanta and share our design goals, constraints, and choices that enabled us to build Quanta very quickly on top of our existing infrastructure systems.
In the age of NoSQL, big data storage engines such as HBase have given up ACID semantics of traditional relational databases, in exchange for high scalability and availability. However, it turns out that in practice, many applications require consistency guarantees to protect data from concurrent modification in a massively parallel environment. In the past few years, several transaction engines have been proposed as add-ons to HBase; three different engines, namely Omid, Tephra, and Trafodion were open-sourced in Apache alone. In this talk, we will introduce and compare the different approaches from various perspectives including scalability, efficiency, operability and portability, and make recommendations pertaining to different use cases.
In order to effectively predict and prevent online fraud in real time, Sift Science stores hundreds of terabytes of data in HBase—and needs it to be always available. This talk will cover how we used circuit-breaking, cluster failover, monitoring, and automated recovery procedures to improve our HBase uptime from 99.7% to 99.99% on top of unreliable cloud hardware and networks.
In DiDi Chuxing Company, which is China’s most popular ride-sharing company. we use HBase to serve when we have a bigdata problem.
We run three clusters which serve different business needs. We backported the Region Grouping feature back to our internal HBase version so we could isolate the different use cases.
We built the Didi HBase Service platform which is popular amongst engineers at our company. It includes a workflow and project management function as well as a user monitoring view.
Internally we recommend users use Phoenix to simplify access.even more,we used row timestamp;multidimensional table schema to slove muti dimension query problems
C++, Go, Python, and PHP clients get to HBase via thrift2 proxies and QueryServer.
We run many important buisness applications out of our HBase cluster such as ETA/GPS/History Order/API metrics monitoring/ and Traffic in the Cloud. If you are interested in any aspects listed above, please come to our talk. We would like to share our experiences with you.
HBaseCon2017 gohbase: Pure Go HBase ClientHBaseCon
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Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
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Venez le découvrir lors de cette session ignite
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
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AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-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
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Unleash Unlimited Potential with One-Time Purchase
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How Recreation Management Software Can Streamline Your Operations.pptxwottaspaceseo
Recreation management software streamlines operations by automating key tasks such as scheduling, registration, and payment processing, reducing manual workload and errors. It provides centralized management of facilities, classes, and events, ensuring efficient resource allocation and facility usage. The software offers user-friendly online portals for easy access to bookings and program information, enhancing customer experience. Real-time reporting and data analytics deliver insights into attendance and preferences, aiding in strategic decision-making. Additionally, effective communication tools keep participants and staff informed with timely updates. Overall, recreation management software enhances efficiency, improves service delivery, and boosts customer satisfaction.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
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Prosigns: Transforming Business with Tailored Technology SolutionsProsigns
Unlocking Business Potential: Tailored Technology Solutions by Prosigns
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Custom Software Development: Prosigns specializes in creating bespoke software solutions that cater to your unique business needs. Our team of experts works closely with you to understand your requirements and deliver tailor-made software that enhances efficiency and drives growth.
Web and Mobile App Development: From responsive websites to intuitive mobile applications, Prosigns develops cutting-edge solutions that engage users and deliver seamless experiences across devices.
AI & ML Solutions: Harnessing the power of Artificial Intelligence and Machine Learning, Prosigns provides smart solutions that automate processes, provide valuable insights, and drive informed decision-making.
Blockchain Integration: Prosigns offers comprehensive blockchain solutions, including development, integration, and consulting services, enabling businesses to leverage blockchain technology for enhanced security, transparency, and efficiency.
DevOps Services: Prosigns' DevOps services streamline development and operations processes, ensuring faster and more reliable software delivery through automation and continuous integration.
Microsoft Dynamics 365 Support: Prosigns provides comprehensive support and maintenance services for Microsoft Dynamics 365, ensuring your system is always up-to-date, secure, and running smoothly.
Learn how our collaborative approach and dedication to excellence help businesses achieve their goals and stay ahead in today's digital landscape. From concept to deployment, Prosigns is your trusted partner for transforming ideas into reality and unlocking the full potential of your business.
Join us on a journey of innovation and growth. Let's partner for success with Prosigns.
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
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First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
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In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoam
Apache Kylin’s Performance Boost from Apache HBase
1. Hongbin Ma, Luke Han
Kyligence Inc.
Apache Kylin’s
Performance Boost from
Apache HBase
2. About us
Hongbin Ma| 马洪宾
PMC member of Apache Kylin
Technical partner of Kyligence Inc.
mahongbin@apache.org
Kyligence Inc.
Kyligence is a leading data intelligence company focusing on Big Data technologies and
innovation, offering intelligent platform and product powered by Apache Kylin™ for
enterprise ready business analytics solutions.
Luke Han | 韩卿
Co-creator & VP of Apache Kylin
ASF Member
Co-founder & CEO at Kyligence Inc.
lukehan@apache.org
4. What is Apache Kylin
Apache Kylin is an open source distributed analytics engine that
provides a SQL interface for multi-dimensional analysis on Hadoop
Works well with extremely large datasets
Provides REST API, ODBC and JDBC as user interface
Widely adopted by many companies like eBay, JD, Baidu, NetEase, VIP.com,
etc.
6. What is Apache Kylin
Apache Kylin is an open source distributed analytics engine that
provides a SQL interface for multi-dimensional analysis on Hadoop
Works well with extremely large datasets
Provides REST API, ODBC and JDBC as user interface
Widely adopted by many companies like eBay, JD, Baidu, NetEase, VIP.com,
etc.
Apache Kylin pre-calculates OLAP cubes with a horizontal scalable
computation framework(MapReduce, Spark, etc.) and store the cubes
into a reliable & scalable data store(HBase, Casscandra, etc.)
7. Architecture Design
Cube Builder
(MapReduce, Spark, etc…)
SQL
Low Latency -
SecondsRouting
3rd Party App
(Web App, Mobile…)
Metadata
SQL-Based Tool
(BI Tools: Tableau…)
Query Engine
Hadoop
Hive
REST API JDBC/ODBC
Online Analysis Data Flow
Offline Data Flow
Clients/Users interactive with
Kylin via SQL
OLAP Cube is transparent to
users
Star Schema Data Key Value Data
Data
Cube
OLAP
Cubes
(HBase)
SQL
REST ServerDataSource
Abstraction
Engine
Abstraction
Storage
Abstraction
9. Cubes stored in HBase
Let’s take a looks at
cuboid (D1,D3,D5)
where all dimensions are:
(D1,D2,D3,D4,D5)
This cuboid is donated as “cuboid 00010101”
10. Why HBase as the first choice?
Well integrated with Hadoop
Block encoding to reduce storage footprint
Good at both seeking and scanning
Coprocessors to move computation to data
Scalable and flexible as a data store
11. Region server
How Kylin queries HBase
Kylin Query
Server
region
coprocessor
Country Metrics…DateSellerIDCuboidID
2. Scan with Fuzzy Key Filter
1. Filter/Aggregation push down
3. Half baked results
12. May still be slow when
The cuboid is large because there’s really lots of combinations in it
Cuboid layout is not friendly to query, e.g. filter on suffix dimensions while
group by prefix dimensions.
The filter in query is huge and complex
Regions are returning too many half-baked results
14. Novelty
Compared with “pure” MPP solutions
Cube data is more query-friendly because it is pre-aggregated and sorted.
Faster speed
Less CPU consumption
Less storage read
Able to leverage column storage and inverted index just like typical MPP
Compared with “pure” Cubing technologies
Overcome the bottleneck in cube size
Overcome the bottleneck in cube visiting speed
15. Problem
The sizes of different cuboids in the same cube may vary
Too many parallelism for small cuboids is harmful
A RPC is required for each shard, we don’t want to abuse network/CPU
resource
16. Solution: Shard Circle
0
1
2
3
4
5
6
7
8
9
Given estimated size for each cuboid 𝑆𝑖,
and expected size for each region 𝑆𝑟 (specified by modeler)
𝑟𝑒𝑔𝑖𝑜𝑛𝑁𝑢𝑚 =
𝑆𝑖
𝑆𝑟
𝑐𝑢𝑏𝑜𝑖𝑑𝑅𝑒𝑔𝑖𝑜𝑛𝑁𝑢𝑚 =
𝑆𝑖 ∗ 𝑓𝑎𝑐𝑡𝑜𝑟
𝑆𝑟
𝑐𝑢𝑏𝑜𝑖𝑑𝐶𝑖𝑟𝑐𝑙𝑒𝑆𝑡𝑎𝑟𝑡 = ℎ𝑎𝑠ℎ 𝑖 𝑀𝑂𝐷 𝑟𝑒𝑔𝑖𝑜𝑛𝑁𝑢𝑚
17. Salted Cuboid Rows
ShardID at the beginning of row key
Configurable policies for computing ShardID
From hash result of remaining row key – facilitate randomize
From specific dimension values – facilitate runtime performance
Country Metrics…DateSellerIDCuboidIDShardID
18. Compute ShardID from SellerID
For queries those group by SellerID
Each shard aggregating non-joint subset of SellerIDs
No further aggregation at merge side
For queries those filter by SellerID
The push down SellerID filter can be trimmed to contain only interested
SellerIDs
20. Small cuboids getting less shards
1.005586592
0.625 0.625
0.678571429
0.794117647
0
0.2
0.4
0.6
0.8
1
1.2
SQL 1 SQL 2 SQL 3 SQL 4 SQL 5
13 regions 23 regions
21. Q & A
To get more information about Apache Kylin:
Apache Kylin Website: http://kylin.apache.org
Kyligence Website: http://kyligence.io
Twitter: @ApacheKylin
Mail list: dev@kylin.apache.org