Analytics in Search
Many companies including Lucidworks have embraced the Kibana open source code to add visualization and analytics to enhance search management. Ravi Krishnamurthy , VP of Professional Services at Lucidworks, will show Silk, Lucid's implementation of Kibana, which provides all the capabilities of the open source code but adds enterprise-critical capabilities like authentication and security to protect restricted content.
Scalable Monitoring Using Apache Spark and Friends with Utkarsh BhatnagarDatabricks
This session will give a new dimension to Apache Spark’s usage. See how Apache Spark and other open source projects can be used together in providing a scalable, real-time monitoring system. Apache Spark plays the central role in providing this scalable solution, since without Spark Streaming we would not be able to process millions of events in real time. This approach can provide a lot of learning to the DevOps/Infrastructure domain on how to build a scalable and automated logging and monitoring solution using Apache Spark, Apache Kafka, Grafana and some other open-source technologies.
Sony PlayStation’s monitoring pipeline processes about 40 billion events every day, and generates metrics in near real-time (within 30 seconds). All the components, used along with Apache Spark, are horizontally scalable using any auto-scaling techniques, which enhances the reliability of this efficient and highly available monitoring solution. Sony Interactive Entertainment has been using Apache Spark, and specifically Spark Streaming, for the last three years. Hear about some important lessons they have learned. For example, they still use Spark Streaming’s receiver-based method in certain use cases instead of Direct Streaming, and will share the application of both the methods, giving the knowledge back to the community.
DataStax & O'Reilly Media: Large Scale Data Analytics with Spark and Cassandr...DataStax Academy
In this in-depth workshop you will gain hands on experience with using Spark and Cassandra inside the DataStax Enterprise Platform. The focus of the workshop will be working through data analytics exercises to understand the major developer developer considerations. You will also gain an understanding of the internals behind the integration that allow for large scale data loading and analysis. It will also review some of the major machine learning libraries in Spark as an example of data analysis.
The workshop will start with a review the basics of how Spark and Cassandra are integrated. Then we will work through a series of exercises that will show how to perform large scale Data Analytics with Spark and Cassandra. A major part of the workshop will be to understand effective data modeling techniques in Cassandra that allow for fast parallel loading of the data into Spark to perform large scale analytics on that data. The exercises will also look at how to how to use the open source Spark Notebook to run interactive data analytics with the DataStax Enterprise Platform.
Virtualizing Analytics with Apache Spark: Keynote by Arsalan Tavakoli Spark Summit
In the race to invent multi-million dollar business opportunities with exclusive insights, data scientists and engineers are hampered by a multitude of challenges just to make one use case a reality – the need to ingest data from multiple sources, apply real-time analytics, build machine learning algorithms, and intermix different data processing models, all while navigating around their legacy data infrastructure that is just not up to the task. This need has created the demand for Virtual Analytics, where the complexities of disparate data and technology silos have been abstracted away, coupled with a powerful range of analytics and processing horsepower, all in one unified data platform. This talk describes how Databricks is powering this revolutionary new trend with Apache Spark.
Analytics in Search
Many companies including Lucidworks have embraced the Kibana open source code to add visualization and analytics to enhance search management. Ravi Krishnamurthy , VP of Professional Services at Lucidworks, will show Silk, Lucid's implementation of Kibana, which provides all the capabilities of the open source code but adds enterprise-critical capabilities like authentication and security to protect restricted content.
Scalable Monitoring Using Apache Spark and Friends with Utkarsh BhatnagarDatabricks
This session will give a new dimension to Apache Spark’s usage. See how Apache Spark and other open source projects can be used together in providing a scalable, real-time monitoring system. Apache Spark plays the central role in providing this scalable solution, since without Spark Streaming we would not be able to process millions of events in real time. This approach can provide a lot of learning to the DevOps/Infrastructure domain on how to build a scalable and automated logging and monitoring solution using Apache Spark, Apache Kafka, Grafana and some other open-source technologies.
Sony PlayStation’s monitoring pipeline processes about 40 billion events every day, and generates metrics in near real-time (within 30 seconds). All the components, used along with Apache Spark, are horizontally scalable using any auto-scaling techniques, which enhances the reliability of this efficient and highly available monitoring solution. Sony Interactive Entertainment has been using Apache Spark, and specifically Spark Streaming, for the last three years. Hear about some important lessons they have learned. For example, they still use Spark Streaming’s receiver-based method in certain use cases instead of Direct Streaming, and will share the application of both the methods, giving the knowledge back to the community.
DataStax & O'Reilly Media: Large Scale Data Analytics with Spark and Cassandr...DataStax Academy
In this in-depth workshop you will gain hands on experience with using Spark and Cassandra inside the DataStax Enterprise Platform. The focus of the workshop will be working through data analytics exercises to understand the major developer developer considerations. You will also gain an understanding of the internals behind the integration that allow for large scale data loading and analysis. It will also review some of the major machine learning libraries in Spark as an example of data analysis.
The workshop will start with a review the basics of how Spark and Cassandra are integrated. Then we will work through a series of exercises that will show how to perform large scale Data Analytics with Spark and Cassandra. A major part of the workshop will be to understand effective data modeling techniques in Cassandra that allow for fast parallel loading of the data into Spark to perform large scale analytics on that data. The exercises will also look at how to how to use the open source Spark Notebook to run interactive data analytics with the DataStax Enterprise Platform.
Virtualizing Analytics with Apache Spark: Keynote by Arsalan Tavakoli Spark Summit
In the race to invent multi-million dollar business opportunities with exclusive insights, data scientists and engineers are hampered by a multitude of challenges just to make one use case a reality – the need to ingest data from multiple sources, apply real-time analytics, build machine learning algorithms, and intermix different data processing models, all while navigating around their legacy data infrastructure that is just not up to the task. This need has created the demand for Virtual Analytics, where the complexities of disparate data and technology silos have been abstracted away, coupled with a powerful range of analytics and processing horsepower, all in one unified data platform. This talk describes how Databricks is powering this revolutionary new trend with Apache Spark.
Slidedeck related to the talk presented at the Manila Data Day event March 2020. The demo covers Azure services like Data Lake Storage (Gen 2), Azure Data Factory, Azure Databricks, Azure Synapse, Key Vault and Active directory to build a modern data warehouse.
Part 3 - Modern Data Warehouse with Azure SynapseNilesh Gule
Slide deck of the third part of building Modern Data Warehouse using Azure. This session covered Azure Synapse, formerly SQL Data Warehouse. We look at the Azure Synapse Architecture, external files, integration with Azuer Data Factory.
The recording of the session is available on YouTube
https://www.youtube.com/watch?v=LZlu6_rFzm8&WT.mc_id=DP-MVP-5003170
Hello All,
It is time for the second Tokyo Azure Meetup!
As a natural continuation of our first topic, we will proceed with Big Data.
Until recently you needed to learn new language or master new concepts in order get started with Big Data.
Moreover, you needed to spend a lot of time setting up infrastructure that will meet the business demands for Big Data processing.
Not any more!
If you know C# and T-SQL you are ready to become Big Data master!
Public cloud and especially Microsoft Azure are very well suited for working with Big Data.
Join us for our next event and and I can assure you that after the session you will be ready to start working with Big Data.
And maybe you are asking why this is important.
I believe that we don't have choice but build smart applications and get as much possible insights from the data we collect from various sources in order to take the best business decisions and please our customers.
Today we have so much data available publicly or coming from our customers and it is very challenging to process it and turn it into valuable business asset.
Not any more!
Join for our next meetup and you will see how Microsoft create amazing opportunity for each .Net developer to become Big Data expert and every company to start using Big Data to accelerate its growth.
I have been working closely with the product team developing U-SQL language that empower Azure Data Lake Analytics, which is one of the processing engines for Azure Data Lake and I will be very happy to share my experience with you!
See you very soon!
Kanio
Time Series Analysis Using an Event Streaming PlatformDr. Mirko Kämpf
Advanced time series analysis (TSA) requires very special data preparation procedures to convert raw data into useful and compatible formats.
In this presentation you will see some typical processing patterns for time series based research, from simple statistics to reconstruction of correlation networks.
The first case is relevant for anomaly detection and to protect safety.
Reconstruction of graphs from time series data is a very useful technique to better understand complex systems like supply chains, material flows in factories, information flows within organizations, and especially in medical research.
With this motivation we will look at typical data aggregation patterns. We investigate how to apply analysis algorithms in the cloud. Finally we discuss a simple reference architecture for TSA on top of the Confluent Platform or Confluent cloud.
Michal Malohlava's presentation on Building Your Own Recommendation Engine 03.17.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Using Spark-Solr at Scale: Productionizing Spark for Search with Apache Solr...Databricks
This talk is a case-study on how Apache Spark and the Spark-Solr library is being used at Flipp for driving search relevancy. Flipp is a Toronto based digital flyer and ecommerce company which helps shoppers save money on weekly shopping. Our customers have the option of browsing through our 5+ million products from the brick-and-mortar retailers in North America. This makes Search a very challenging function in our app. How to show the most relevant and personalized search results to users on a query?
The talk will focus on using user signals such as Click Through Rate (CTR) and Impressions to increase search relevancy. I will also talk about how PySpark is used to create the Flipp Search ETL platform for collecting user signals and reading product data from Solr. The problem scenario will be explained in which keyword search and basic relevancy algorithms become ineffective when dealing with a large product database. The solutions will cover the following implementations being used at Flipp to drive relevancy: – Utilizing user clicks and popularity data to derive and index normalized item weights to implement the Search Crowd Curation models in Apache Solr
– How around 5+ million items are classified into Google Categories in real time using Keras and Apache Spark to power product category curation in Solr.
– How to create a crowd sourced query intent categorizer in Solr using the Spark-Solr library.
– The use of offline and online metrics at Flipp for evaluating changes in search relevancy.
– Future plans for incorporating Kafka-connect in Apache Solr with structured streaming to perform real-time product indexing with Spark-Solr library.
Snowplow and Kinesis - Presentation to the inaugural Amazon Kinesis London Us...Alexander Dean
This is my presentation to the inaugural meetup of the Amazon Kinesis London User Group.
In it I briefly introduced Snowplow, explained why we were excited about Kinesis (drawing on my "three eras" blog post) and then set out how we are updating Snowplow to run on Kinesis. I concluded with a live demo of what we have running on Kinesis so far.
Spark and Online Analytics: Spark Summit East talky by Shubham ChopraSpark Summit
Apache Spark was designed as a batch analytics system. By caching RDDs, Spark speeds up jobs that iteratively process the same data. This pattern is also applicable to online analytics. We use Bloomberg’s Spark Server as a server runtime for online analytics. Our framework implements certain useful patterns applicable to online query processing and is centered on the idea of “Managed” DataFrames that can be refreshed and updated as per user requirements, without violating the immutability of RDDs/DataFrames. However, Spark presents significant challenges with respect to availability and resilience in an online setting where Spark is required to respond to queries with high SLAs. In this talk, we try to identify specific areas where slow-down or failures can result in the largest hits on online-query performance and potential solutions to address these.
Realtime streaming architecture in INFINARIOJozo Kovac
About our experience with realtime analyses on never-ending stream of user events. Discuss Lambda architecture, Kappa, Apache Kafka and our own approach.
When you are what you own: do physically attractive people benefit more from ...Almer Postma
Although desirable brands may positively affect impressions of its owner, brand ownership may also evoke negative reactions if a brand’s image is seen as incongruent with the brand owner. An experimental study tests the influence of physical attractiveness of a brand owner and observers’ level of materialism on the transference of brand sophistication onto a brand owner. Brand sophistication and physical attractiveness are manipulated and levels of materialism are measured. Results suggest that attractive brand owners are generally perceived as sophisticated, regardless of brand sophistication or observers’ materialism. For less attractive brand owners, owning a sophisticated brand may backfire, and decrease perceptions of sophistication, particularly when the observer is materialistic. Implications are that desirable brands are most likely to increase liking of brand owners when the brand fits the owner, and that owning desirable brands may backfire for lower levels of fit.
Slidedeck related to the talk presented at the Manila Data Day event March 2020. The demo covers Azure services like Data Lake Storage (Gen 2), Azure Data Factory, Azure Databricks, Azure Synapse, Key Vault and Active directory to build a modern data warehouse.
Part 3 - Modern Data Warehouse with Azure SynapseNilesh Gule
Slide deck of the third part of building Modern Data Warehouse using Azure. This session covered Azure Synapse, formerly SQL Data Warehouse. We look at the Azure Synapse Architecture, external files, integration with Azuer Data Factory.
The recording of the session is available on YouTube
https://www.youtube.com/watch?v=LZlu6_rFzm8&WT.mc_id=DP-MVP-5003170
Hello All,
It is time for the second Tokyo Azure Meetup!
As a natural continuation of our first topic, we will proceed with Big Data.
Until recently you needed to learn new language or master new concepts in order get started with Big Data.
Moreover, you needed to spend a lot of time setting up infrastructure that will meet the business demands for Big Data processing.
Not any more!
If you know C# and T-SQL you are ready to become Big Data master!
Public cloud and especially Microsoft Azure are very well suited for working with Big Data.
Join us for our next event and and I can assure you that after the session you will be ready to start working with Big Data.
And maybe you are asking why this is important.
I believe that we don't have choice but build smart applications and get as much possible insights from the data we collect from various sources in order to take the best business decisions and please our customers.
Today we have so much data available publicly or coming from our customers and it is very challenging to process it and turn it into valuable business asset.
Not any more!
Join for our next meetup and you will see how Microsoft create amazing opportunity for each .Net developer to become Big Data expert and every company to start using Big Data to accelerate its growth.
I have been working closely with the product team developing U-SQL language that empower Azure Data Lake Analytics, which is one of the processing engines for Azure Data Lake and I will be very happy to share my experience with you!
See you very soon!
Kanio
Time Series Analysis Using an Event Streaming PlatformDr. Mirko Kämpf
Advanced time series analysis (TSA) requires very special data preparation procedures to convert raw data into useful and compatible formats.
In this presentation you will see some typical processing patterns for time series based research, from simple statistics to reconstruction of correlation networks.
The first case is relevant for anomaly detection and to protect safety.
Reconstruction of graphs from time series data is a very useful technique to better understand complex systems like supply chains, material flows in factories, information flows within organizations, and especially in medical research.
With this motivation we will look at typical data aggregation patterns. We investigate how to apply analysis algorithms in the cloud. Finally we discuss a simple reference architecture for TSA on top of the Confluent Platform or Confluent cloud.
Michal Malohlava's presentation on Building Your Own Recommendation Engine 03.17.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Using Spark-Solr at Scale: Productionizing Spark for Search with Apache Solr...Databricks
This talk is a case-study on how Apache Spark and the Spark-Solr library is being used at Flipp for driving search relevancy. Flipp is a Toronto based digital flyer and ecommerce company which helps shoppers save money on weekly shopping. Our customers have the option of browsing through our 5+ million products from the brick-and-mortar retailers in North America. This makes Search a very challenging function in our app. How to show the most relevant and personalized search results to users on a query?
The talk will focus on using user signals such as Click Through Rate (CTR) and Impressions to increase search relevancy. I will also talk about how PySpark is used to create the Flipp Search ETL platform for collecting user signals and reading product data from Solr. The problem scenario will be explained in which keyword search and basic relevancy algorithms become ineffective when dealing with a large product database. The solutions will cover the following implementations being used at Flipp to drive relevancy: – Utilizing user clicks and popularity data to derive and index normalized item weights to implement the Search Crowd Curation models in Apache Solr
– How around 5+ million items are classified into Google Categories in real time using Keras and Apache Spark to power product category curation in Solr.
– How to create a crowd sourced query intent categorizer in Solr using the Spark-Solr library.
– The use of offline and online metrics at Flipp for evaluating changes in search relevancy.
– Future plans for incorporating Kafka-connect in Apache Solr with structured streaming to perform real-time product indexing with Spark-Solr library.
Snowplow and Kinesis - Presentation to the inaugural Amazon Kinesis London Us...Alexander Dean
This is my presentation to the inaugural meetup of the Amazon Kinesis London User Group.
In it I briefly introduced Snowplow, explained why we were excited about Kinesis (drawing on my "three eras" blog post) and then set out how we are updating Snowplow to run on Kinesis. I concluded with a live demo of what we have running on Kinesis so far.
Spark and Online Analytics: Spark Summit East talky by Shubham ChopraSpark Summit
Apache Spark was designed as a batch analytics system. By caching RDDs, Spark speeds up jobs that iteratively process the same data. This pattern is also applicable to online analytics. We use Bloomberg’s Spark Server as a server runtime for online analytics. Our framework implements certain useful patterns applicable to online query processing and is centered on the idea of “Managed” DataFrames that can be refreshed and updated as per user requirements, without violating the immutability of RDDs/DataFrames. However, Spark presents significant challenges with respect to availability and resilience in an online setting where Spark is required to respond to queries with high SLAs. In this talk, we try to identify specific areas where slow-down or failures can result in the largest hits on online-query performance and potential solutions to address these.
Realtime streaming architecture in INFINARIOJozo Kovac
About our experience with realtime analyses on never-ending stream of user events. Discuss Lambda architecture, Kappa, Apache Kafka and our own approach.
When you are what you own: do physically attractive people benefit more from ...Almer Postma
Although desirable brands may positively affect impressions of its owner, brand ownership may also evoke negative reactions if a brand’s image is seen as incongruent with the brand owner. An experimental study tests the influence of physical attractiveness of a brand owner and observers’ level of materialism on the transference of brand sophistication onto a brand owner. Brand sophistication and physical attractiveness are manipulated and levels of materialism are measured. Results suggest that attractive brand owners are generally perceived as sophisticated, regardless of brand sophistication or observers’ materialism. For less attractive brand owners, owning a sophisticated brand may backfire, and decrease perceptions of sophistication, particularly when the observer is materialistic. Implications are that desirable brands are most likely to increase liking of brand owners when the brand fits the owner, and that owning desirable brands may backfire for lower levels of fit.
Sebastian keckert status of the hzb quadrupole resonatorthinfilmsworkshop
The systematic research on superconducting thin films requires dedicated testing equipment. The Quadrupole Resonator (QPR) is a specialized tool to characterize the superconducting properties of circular samples. A calorimetric measurement of the RF surface losses allows the surface resistance to be measured with sub nano-ohm resolution. This measurement can be performed over a wide temperature and magnetic field range, at frequencies of 433, 866 and 1300 MHz. The system at Helmholtz-Zentrum Berlin (HZB) is based on a resonator built at CERN and has been optimized to lower peak electric fields and an improved resolution. An alternative calorimetry chamber has been designed in order to provide flat samples for coating and to ease changing of samples. In this talk the current status of the project at HZB will be presented.
Transitioning from Java to Scala for Spark - March 13, 2019Gravy Analytics
Gravy Analytics ingests ~17 billion records daily of data and improve and refine that data into many data products at various levels of aggregation. To meet the challenges of our product requirements and scale we constantly evaluate new technologies. Spark has become central to our ability to process ever increasing amounts of data through our data factory. In late 2017 and throughout 2018, we have improved our ability to work with Spark by migrating all Spark jobs to Scala. In this discussion, we’ll cover areas which were more difficult from a Spark perspective to develop in Java than Scala as well as some of the challenges we met along the way.
Databricks Meetup @ Los Angeles Apache Spark User GroupPaco Nathan
Los Angeles Apache Spark Users Group 2014-12-11 http://meetup.com/Los-Angeles-Apache-Spark-Users-Group/events/218748643/
A look ahead at Spark Streaming in Spark 1.2 and beyond, with case studies, demos, plus an overview of approximation algorithms that are useful for real-time analytics.
Bringing Streaming Data To The Masses: Lowering The “Cost Of Admission” For Y...confluent
(Bob Lehmann, Bayer) Kafka Summit SF 2018
You’ve built your streaming data platform. The early adopters are “all in” and have developed producers, consumers and stream processing apps for a number of use cases. A large percentage of the enterprise, however, has expressed interest but hasn’t made the leap. Why?
In 2014, Bayer Crop Science (formerly Monsanto) adopted a cloud first strategy and started a multi-year transition to the cloud. A Kafka-based cross-datacenter DataHub was created to facilitate this migration and to drive the shift to real-time stream processing. The DataHub has seen strong enterprise adoption and supports a myriad of use cases. Data is ingested from a wide variety of sources and the data can move effortlessly between an on premise datacenter, AWS and Google Cloud. The DataHub has evolved continuously over time to meet the current and anticipated needs of our internal customers. The “cost of admission” for the platform has been lowered dramatically over time via our DataHub Portal and technologies such as Kafka Connect, Kubernetes and Presto. Most operations are now self-service, onboarding of new data sources is relatively painless and stream processing via KSQL and other technologies is being incorporated into the core DataHub platform.
In this talk, Bob Lehmann will describe the origins and evolution of the Enterprise DataHub with an emphasis on steps that were taken to drive user adoption. Bob will also talk about integrations between the DataHub and other key data platforms at Bayer, lessons learned and the future direction for streaming data and stream processing at Bayer.
Kafka: Journey from Just Another Software to Being a Critical Part of PayPal ...confluent
PayPal currently processes tens of billions of signals per day from different sources in batch and streaming mode. The data processing platform is the one powering these different analytical needs and use cases, not just at PayPal but our adjacencies like Venmo, Hyperwallet and iZettle. End users of this platform demand access to data insights with as much flexibility as possible to explore it with low processing latency.
One such use case is where our Switchboard(data de-multiplexer) platform where we process approximately 20 billion events daily and provide data to different teams and platforms with-in PayPal and also to platform outside PayPal for more insights. When we started building this platform Kafka was just another asynchronous message processing platform for us but we have seen it evolving to a place where its adds value not just in terms of event processing but also for platform resiliency and scalability.
Takeaway for the audience: Most people work with and have knowledge about data. With this talk I want to present information which is relevant and meaningful to the audience. Information and examples which will make it easier for attendees to understand our complex system and hopefully have some practical takeaways to use Kafka for similar problems on their hand.
Mike Spicer is the lead architect for the IBM Streams team. In his presentation, Mike provides an overview of the many key new features available in IBM Streams V4.1. Simpler development, simpler management, and Spark integration are a few of the capabilities included in IBM Streams V4.1.
Intro to Machine Learning with H2O and AWSSri Ambati
Navdeep Gill @ Galvanize Seattle- May 2016
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
From http://www.csdn.net/article/2015-12-17/2826501
《Databricks公司联合创始人、Spark首席架构师辛湜:Spark发展:回顾2015,展望2016 》
辛湜介绍了Spark的目标是“Unified engine across data workloads and platforms”。在谈到Spark在2015年最大的改变时,他感觉应该是增加了DataFrames API。对于Spark的生态圈,他表示主要侧重三个不同的方向,一个是上层的应用,二是下层的环境,还有最重要的是连接到的数据源。
End-to-End Data Pipelines with Apache SparkBurak Yavuz
This presentation is about building a data product backed by Apache Spark. The source code for the demo can be found at http://brkyvz.github.io/spark-pipeline
Apache Spark At Apple with Sam Maclennan and Vishwanath LakkundiDatabricks
At Apple we rely on processing large datasets to power key components of Apple’s largest production services. Spark is continuing to replace and augment traditional MR workloads with its speed and low barrier to entry. Our current analytics infrastructure consists of over an exabyte of storage and close to a million cores. Our footprint is also growing further with the addition of new elastic services for streaming, adhoc and interactive analytics.
In this talk we will cover the challenges of working at scale with tricks and lessons learned managing large multi-tenant clusters. We will also discuss designing and building a self-service elastic analytics platform on Mesos.
Big Data 2.0 - How Spark technologies are reshaping the world of big data ana...Lillian Pierson
In this one-hour webinar, you will be introduced to Spark, the data engineering that supports it, and the data science advances that it has spurned. You’ll discover the interesting story of its academic origins and then get an overview of the organizations who are using the technology. After being briefed on some impressive Spark case studies, you’ll come to know of the next-generation Spark 2.0 (to be released in just a few months). We will also tell you about the tremendous impact that learning Spark can have upon your current salary, and the best ways to get trained in this ground-breaking new technology.
Architecting an Open Source AI Platform 2018 editionDavid Talby
How to build a scalable AI platform using open source software. The end-to-end architecture covers data integration, interactive queries & visualization, machine learning & deep learning, deploying models to production, and a full 24x7 operations toolset in a high-compliance environment.
Doug Cutting discusses:
- A brief history of Spark and its rise in popularity across developers and enterprises
- Spark's advantages over MapReduce
- The One Platform Initiative and the roadmap for Spark
- The future of data processing in Hadoop
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
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.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
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https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
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✅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…
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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
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A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Top 7 Unique WhatsApp API Benefits | Saudi ArabiaYara Milbes
Discover the transformative power of the WhatsApp API in our latest SlideShare presentation, "Top 7 Unique WhatsApp API Benefits." In today's fast-paced digital era, effective communication is crucial for both personal and professional success. Whether you're a small business looking to enhance customer interactions or an individual seeking seamless communication with loved ones, the WhatsApp API offers robust capabilities that can significantly elevate your experience.
In this presentation, we delve into the top 7 distinctive benefits of the WhatsApp API, provided by the leading WhatsApp API service provider in Saudi Arabia. Learn how to streamline customer support, automate notifications, leverage rich media messaging, run scalable marketing campaigns, integrate secure payments, synchronize with CRM systems, and ensure enhanced security and privacy.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
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.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
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https://sumonreview.com/ai-pilot-review/
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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
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
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
Scala Jday 2014
1. SCALA in CY 2014:
Gaining Business Momentum
by
Russ Hertzberg
2. Agenda
• Rapid Growth Phase for the Platform Ecosystem &
Developer Community
• Spawning A Popular Early Stage Hadoop Sub-system
• Spark, Databricks, and Spark-ups (Spark Based Startups)
• A Successful Relationship: Scala in a Java World
• Developments to Monitor:
• The Future of Typesafe and Databricks
• Scala Platforms & ‘New Data Management’ Ecosystems
• Game Changing Applications
• Opportunities and Actions in Scala for Ukraine Software
Developers
3. Growth Indicators: Conferences and User
Groups
• Spark Summit December
2013…450 Attendees, 2
Days
• Spark Summit July
2014…1200 Attendees, 3
Days
• Scala By the Bay (San
Francisco): 1983
Members
• New York Scala: 1935
Members
5. Growth Indicators: Application
Diversification
• Genomic Analysis @ UC
Berkely AMPLab (Spark
Based)
• Real Time Online Advertising
Auctions (Sharethrough)
• Middleware Platform (J9)
• Integrated Online Ticketing
and Event Mgmt. (Ticketfly)
• Next Generation Email Anti-
Spam Service (Heluna)
6. Growth Indicators: Professional Investors
• Typesafe: Greylock Partners, Juniper
Networks, Shasta Ventures
• Databricks: New Enterprise
Associates, Andreeson Horowitz
• Other Startup Investors: a16z, Sierra
Ventures, Mission Ventures, UMC
Capital, Bosch Venture Capital,
Artiman Ventures, Sofinnova
Ventures, Intel Capital, SingTel
Innov8, Investor Growth Capital,
QuestMark Partners, TransLink
Capital, Goldman Sachs
• Early Stage Big $$ Example: $107M @
http://www.guavus.com/
Big Data Investors
Have Big Bucks for Scala Related
Software Ventures
7. Apache Spark
• Launched in January of 2010
• New Hadoop Component up
to 100X Faster than Map
Reduce, with Stream
Processing
• Written in Scala with Scala,
Java, and Python APIs
• Latest Release on May 30,
2014 with 110 Contributors
8. Spark and Databricks
• ‘Creators’ of Spark
• Spin Out Company from
Berkeley AMPLab
• Just raised $33M B Round
• Dozens of Production
Deployments as of July 2014
• Moving the New Platform
Quickly:
• More SQL Connectors
• MLLIB R Integration
• Flume Streaming
10. Spark Ups…Startups Creating a ‘Spark’
Ecosystem, and More!
• Adatao
• Alpine
• Graphflow
• Guavus
• Sharethrough
• Plus the Hadoop Disti
Integrations:
• Cloudera
• Hortonworks
• MapR
• IBM
• SAP Hanna Integration
and SAP Certified Spark
Distribution
11. Scala in a Java World
Cooperation
• Run on JVMs
• Use with Existing Java
Libraries
• Support for Java Generics
in Scala
• Use Java Data Structures
As Is in Scala
Competition
• The Full Typesafe
Platform
• Akka and Actors
• Play Framework
• Big Momentum for Scala
in Next Gen Data
Management
• Java 8 Oracle Reaction
13. The Future (or end game) of Typesafe
• Gamify It!
• Acquisition in 1, 3, 5, or
n years?
• If Acquired, By Whom?
• Private Forever?
• Initial Public Offering and
a Long Term Platform
ISV?
14. The Future (or end game) of Databricks
• Gamify It!
• Acquisition in 1, 3, 5, or
n years?
• If Acquired, By Whom?
• Private Forever?
• Initial Public Offering and
a Long Term Platform
ISV?
15. Scala Platforms and Evolving Data
Management Ecosystems
• Processing Enough Data
to Create Radical New
Knowledge and Value is
the Goal
• Scala and Scala Platforms
are a Strategic Driver in
this High Growth Software
Segment Attracting Big
Bucks
• Harness and Harvest More
Data for Useful Purposes!
16. Game Changing Applications
• Healthcare Genome
Processing…New
Frontiers in Mind/Body
Knowledge & Disease
Treatment
• Mobile Device
Behavioral Insight…to
Create Autonomic Life
Improvement
• Mining Social Network
Data…a billion API
requests per day!
17. Opportunities and Potential Action Items in
Scala for Ukraine Software Developers
1. In Projects with Architecture Control, Evaluate Scala
Fairly! (do not default to Java because you have body
count)
2. Leap into Spark or Others Will Beat You
3. Think About Platform Technologies You Can Build
Yourself to Make Your Own Services More Valuable and
Efficient. Invest Intellectual Energy in Your Own
Intellectual Property
4. Educate and Certify More Scala Developers
18. Some Link References
• http://www.typesafe.com/company/casestudies
• http://databricks.com/
• http://blog.mikiobraun.de/2014/01/apache-spark.html
• http://radar.oreilly.com/2013/11/how-companies-are-using-spark.
html
• https://amplab.cs.berkeley.edu/benchmark/
• https://github.com/twitter/scalding
• http://www.theserverside.com/feature/The-Scala-debate-demystified-
Balancing-the-rants-with-the-raves
• http://boldradius.com/blog-post/
UwO3nQEAAA3xKXiL/five-tips-for-onboarding-scala-developers