An overview of how Datadog has built and scaled our Postgres clusters to support the ingestion of trillions of metric data points per day, by Seth Rosenblum, Lead Data Reliability Engineer.
Building highly reliable data pipeline @datadog par Quentin FrançoisParis Data Engineers !
Certaines fonctionnalités au cœur du produit de Datadog reposent sur des pipelines de données construits avec Spark qui traitent des milliers de milliards de points chaque jour. Dans cette présentation, nous verrons les grands principes que nous appliquons chez Datadog pour assurer que nos pipelines restent fiables malgré la croissance exponentielle du volume de données, les pannes matérielles, les données corrompues et les erreurs humaines.
Paris Data Eng' Meetup du 26 février 2019 @Datadog
Datadog: a Real-Time Metrics Database for One Quadrillion Points/DayC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2mAKgJi.
Ian Nowland and Joel Barciauskas talk about the challenges Datadog faces as the company has grown its real-time metrics systems that collect, process, and visualize data to the point they now handle trillions of points per day. They also talk about how the architecture has evolved, and what they are looking to in the future as they architect for a quadrillion points per day. Filmed at qconnewyork.com.
Ian Nowland is the VP Engineering Metrics and Alerting at Datadog. Joel Barciauskas currently leads Datadog's distribution metrics team, providing accurate, low latency percentile measures for customers across their infrastructure.
Monitoring, Hold the Infrastructure - Getting the Most out of AWS Lambda – Da...Amazon Web Services
Just as we got a hang of monitoring our server-based applications, they take away the server. How do you monitor something that doesn’t exist? What metrics matter most in a serverless world? In this session, we will look at how applications are different in a AWS Lambda-based world and how to monitor them. Join us as we work our way through the stack and demonstrate how to capture the health and performance of your services.
The focus of this session is not tool specific. Attendees will learn production tested lessons and leave with frameworks they can implement with their serverless workloads regardless of the platforms and tools they use.
Speaker: Matt Williams, Evangelist, Datadog
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
Building highly reliable data pipeline @datadog par Quentin FrançoisParis Data Engineers !
Certaines fonctionnalités au cœur du produit de Datadog reposent sur des pipelines de données construits avec Spark qui traitent des milliers de milliards de points chaque jour. Dans cette présentation, nous verrons les grands principes que nous appliquons chez Datadog pour assurer que nos pipelines restent fiables malgré la croissance exponentielle du volume de données, les pannes matérielles, les données corrompues et les erreurs humaines.
Paris Data Eng' Meetup du 26 février 2019 @Datadog
Datadog: a Real-Time Metrics Database for One Quadrillion Points/DayC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2mAKgJi.
Ian Nowland and Joel Barciauskas talk about the challenges Datadog faces as the company has grown its real-time metrics systems that collect, process, and visualize data to the point they now handle trillions of points per day. They also talk about how the architecture has evolved, and what they are looking to in the future as they architect for a quadrillion points per day. Filmed at qconnewyork.com.
Ian Nowland is the VP Engineering Metrics and Alerting at Datadog. Joel Barciauskas currently leads Datadog's distribution metrics team, providing accurate, low latency percentile measures for customers across their infrastructure.
Monitoring, Hold the Infrastructure - Getting the Most out of AWS Lambda – Da...Amazon Web Services
Just as we got a hang of monitoring our server-based applications, they take away the server. How do you monitor something that doesn’t exist? What metrics matter most in a serverless world? In this session, we will look at how applications are different in a AWS Lambda-based world and how to monitor them. Join us as we work our way through the stack and demonstrate how to capture the health and performance of your services.
The focus of this session is not tool specific. Attendees will learn production tested lessons and leave with frameworks they can implement with their serverless workloads regardless of the platforms and tools they use.
Speaker: Matt Williams, Evangelist, Datadog
Hoodie: How (And Why) We built an analytical datastore on SparkVinoth Chandar
Exploring a specific problem of ingesting petabytes of data in Uber and why they ended up building an analytical datastore from scratch using Spark. Then, discuss design choices and implementation approaches in building Hoodie to provide near-real-time data ingestion and querying using Spark and HDFS.
https://spark-summit.org/2017/events/incremental-processing-on-large-analytical-datasets/
Building a system for machine and event-oriented data with RocanaTreasure Data, Inc.
In this session, we’ll follow the flow of data through an end-to-end system built to handle tens of terabytes an hour of event-oriented data, providing real-time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive can be stitched together to form the base platform; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality. Finally, a brief demo of Rocana Ops, an application for large scale data center operations, will be given, along with an explanation about how it uses the underlying platform.
Rental Cars and Industrialized Learning to Rank with Sean DownesDatabricks
Data can be viewed as the exhaust of online activity. With the rise of cloud-based data platforms, barriers to data storage and transfer have crumbled. The demand for creative applications and learning from those datasets has accelerated. Rapid acceleration can quickly accrue disorder, and disorderly data design can turn the deepest data lake into an impenetrable swamp.
In this talk, I will discuss the evolution of the data science workflow at Expedia with a special emphasis on Learning to Rank problems. From the heroic early days of ad-hoc Spark exploration to our first production sort model on the cloud, we will explore the process of industrializing the workflow. Layered over our story, I will share some best practices and suggestions on how to keep your data productive, or even pull your organization out of the data swamp.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2l2Rr6L.
Doug Daniels discusses the cloud-based platform they have built at DataDog and how it differs from a traditional datacenter-based analytics stack. He walks through the decisions they have made at each layer, covers the pros and cons of these decisions and discusses the tooling they have built. Filmed at qconsf.com.
Doug Daniels is a Director of Engineering at Datadog, where he works on high-scale data systems for monitoring, data science, and analytics. Prior to joining Datadog, he was CTO at Mortar Data and an architect and developer at Wireless Generation, where he designed data systems to serve more than 4 million students in 49 states.
This presentation recounts the story of Macys.com and Bloomingdales.com's migration from legacy RDBMS to NoSQL Cassandra in partnership with DataStax.
One thing that differentiates this talk from others on Cassandra is Macy's philosophy of "doing more with less." You will see why we emphasize the performance tuning aspects of iterative development when you see how much processing we can support on relatively small configurations.
This session will cover:
1) The process that led to our decision to use Cassandra
2) The approach we used for migrating from DB2 & Coherence to Cassandra without disrupting the production environment
3) The various schema options that we tried and how we settled on the current one. We'll show you a selection of some of our extensive performance tuning benchmarks, as well as how these performance results figured into our final schema designs.
4) Our lessons learned and next steps
Symantec: Cassandra Data Modelling techniques in actionDataStax Academy
Our product presents an aggregated view of metadata collected for billions of objects (files, emails, sharepoint objects etc.). We used Cassandra to store those billions of objects along with aggregated view of that metadata. Customers can analyse the corpus of data in real time by searching in completely flexible way i.e. be able to get summary aggregates for many billions of objects, and then be able to further drill down to items by filtering using various facets of the metadata. We achieve this using a combination of Cassandra and ElasticSearch. This presentation will talk about various data modelling techniques we use to aggregate and then further summarise all that metadata and be able to search the summary in real t
Using Pluggable Apache Spark SQL Filters to Help GridPocket Users Keep Up wit...Spark Summit
Analyzing and comparing your energy consumption with that of other consumers provides healthy peer pressure and useful insight leading to energy conservation and impacting the bottom line. We helped GridPocket (http://www.gridpocket.com/), a smart grid company developing energy management applications for electricity water and gas utilities, implement high scale anonymized energy comparison queries with an order of magnitude lower cost and higher performance than was previously possible. IoT use cases like that of GridPocket are swamping our planet with data, and drive demand for analytics on extremely scalable and low cost storage. Enter Spark SQL over Object Storage: highly scalable and low cost storage which provides RESTful APIs to store and retrieve objects and their metadata. Key performance indicators (KPIs) of query performance and cost are the number of bytes shipped from Object Storage to Spark and the number of incurred REST requests. We propose Pluggable Spark SQL Filters, which extend the existing Spark SQL partitioning mechanism with an ability to dynamically filter irrelevant objects during query execution. Our approach handles any data format supported by Spark SQL (Parquet, JSON, csv etc.), and unlike pushdown compatible formats such as Parquet which require touching each object to determine its relevance, it avoids accessing irrelevant objects altogether. We developed a pluggable interface for developing and deploying Filters, and implemented GridPocket’s filter which screens objects according to their metadata, for example geo-spatial bounding boxes which describe the area covered by an object’s data points. This leads to drastically lower KPIs since there is no need to ship the entire dataset from Object Storage to Spark if you are only comparing yourself with your neighborhood. We demonstrate GridPocket analytics notebooks, report on our implementation and resulting 10-20x speedups, explain how to implement a Pluggable File Filter, and how we applied this to other use cases.
La collecte de données au sein d'un DataLake sans impacter les systèmes opérationnels est un challenge pour de nombreuses entreprises.
Lors du meetup Paris Data Engineers du 26 mars 2019, Dimitri Capitaine nous a présenté Data Collector qui est un outil de Change Data Capture (CDC) développé en interne chez OVH. Data Collector est capable d'assurer une réplication fiable et performante des bases de données jusqu'au DataLake.
Hugo Larcher nous a alors présenté un cas d'utilisation autour de l'exploitation de données aéronautiques avec une touche d'IoT et de DataViz.
Learn about features with demos and announcements, from cross-cluster replication and frozen indices in Elasticsearch to Kibana Spaces and the ever-growing set of data integrations in Beats and Logstash.
Capital One: Using Cassandra In Building A Reporting PlatformDataStax Academy
As a leader in the financial industry, Capital One applications generate huge amounts of data that require fast and accurate handling, storage and analysis. We are transforming how we report operational data to our internal users so that they can make quick and precise business decisions to serve our customers. As part of this transformation, we are building a new Go-based data processing framework that will enable us to transfer data from multiple data stores (RDBMS, files, etc.) to a single NoSQL database - Cassandra. This new NoSQL store will act as a reporting database that will receive data on a near real-time basis and serve the data through scorecards and reports. We would like to share our experience in defining this fast data platform and the methodologies used to model financial data in Cassandra.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. You¹ll also hear from Dan Wagner, CEO at Civis Analytics, as he discusses why the Civis data science platform was designed on top of Amazon Redshift and the AWS platform in order to help smart organizations bridge their data silos, build 360 degree view of their customer relationships, and identify opportunities for driving their companies forward by leveraging enormous datasets, the power of analytics, and economies of scale on the AWS platform.
Building a system for machine and event-oriented data with RocanaTreasure Data, Inc.
In this session, we’ll follow the flow of data through an end-to-end system built to handle tens of terabytes an hour of event-oriented data, providing real-time streaming, in-memory, SQL, and batch access to this data. We’ll go into detail on how open source systems such as Hadoop, Kafka, Solr, and Impala/Hive can be stitched together to form the base platform; describe how and where to perform data transformation and aggregation; provide a simple and pragmatic way of managing event metadata; and talk about how applications built on top of this platform get access to data and extend its functionality. Finally, a brief demo of Rocana Ops, an application for large scale data center operations, will be given, along with an explanation about how it uses the underlying platform.
Rental Cars and Industrialized Learning to Rank with Sean DownesDatabricks
Data can be viewed as the exhaust of online activity. With the rise of cloud-based data platforms, barriers to data storage and transfer have crumbled. The demand for creative applications and learning from those datasets has accelerated. Rapid acceleration can quickly accrue disorder, and disorderly data design can turn the deepest data lake into an impenetrable swamp.
In this talk, I will discuss the evolution of the data science workflow at Expedia with a special emphasis on Learning to Rank problems. From the heroic early days of ad-hoc Spark exploration to our first production sort model on the cloud, we will explore the process of industrializing the workflow. Layered over our story, I will share some best practices and suggestions on how to keep your data productive, or even pull your organization out of the data swamp.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2l2Rr6L.
Doug Daniels discusses the cloud-based platform they have built at DataDog and how it differs from a traditional datacenter-based analytics stack. He walks through the decisions they have made at each layer, covers the pros and cons of these decisions and discusses the tooling they have built. Filmed at qconsf.com.
Doug Daniels is a Director of Engineering at Datadog, where he works on high-scale data systems for monitoring, data science, and analytics. Prior to joining Datadog, he was CTO at Mortar Data and an architect and developer at Wireless Generation, where he designed data systems to serve more than 4 million students in 49 states.
This presentation recounts the story of Macys.com and Bloomingdales.com's migration from legacy RDBMS to NoSQL Cassandra in partnership with DataStax.
One thing that differentiates this talk from others on Cassandra is Macy's philosophy of "doing more with less." You will see why we emphasize the performance tuning aspects of iterative development when you see how much processing we can support on relatively small configurations.
This session will cover:
1) The process that led to our decision to use Cassandra
2) The approach we used for migrating from DB2 & Coherence to Cassandra without disrupting the production environment
3) The various schema options that we tried and how we settled on the current one. We'll show you a selection of some of our extensive performance tuning benchmarks, as well as how these performance results figured into our final schema designs.
4) Our lessons learned and next steps
Symantec: Cassandra Data Modelling techniques in actionDataStax Academy
Our product presents an aggregated view of metadata collected for billions of objects (files, emails, sharepoint objects etc.). We used Cassandra to store those billions of objects along with aggregated view of that metadata. Customers can analyse the corpus of data in real time by searching in completely flexible way i.e. be able to get summary aggregates for many billions of objects, and then be able to further drill down to items by filtering using various facets of the metadata. We achieve this using a combination of Cassandra and ElasticSearch. This presentation will talk about various data modelling techniques we use to aggregate and then further summarise all that metadata and be able to search the summary in real t
Using Pluggable Apache Spark SQL Filters to Help GridPocket Users Keep Up wit...Spark Summit
Analyzing and comparing your energy consumption with that of other consumers provides healthy peer pressure and useful insight leading to energy conservation and impacting the bottom line. We helped GridPocket (http://www.gridpocket.com/), a smart grid company developing energy management applications for electricity water and gas utilities, implement high scale anonymized energy comparison queries with an order of magnitude lower cost and higher performance than was previously possible. IoT use cases like that of GridPocket are swamping our planet with data, and drive demand for analytics on extremely scalable and low cost storage. Enter Spark SQL over Object Storage: highly scalable and low cost storage which provides RESTful APIs to store and retrieve objects and their metadata. Key performance indicators (KPIs) of query performance and cost are the number of bytes shipped from Object Storage to Spark and the number of incurred REST requests. We propose Pluggable Spark SQL Filters, which extend the existing Spark SQL partitioning mechanism with an ability to dynamically filter irrelevant objects during query execution. Our approach handles any data format supported by Spark SQL (Parquet, JSON, csv etc.), and unlike pushdown compatible formats such as Parquet which require touching each object to determine its relevance, it avoids accessing irrelevant objects altogether. We developed a pluggable interface for developing and deploying Filters, and implemented GridPocket’s filter which screens objects according to their metadata, for example geo-spatial bounding boxes which describe the area covered by an object’s data points. This leads to drastically lower KPIs since there is no need to ship the entire dataset from Object Storage to Spark if you are only comparing yourself with your neighborhood. We demonstrate GridPocket analytics notebooks, report on our implementation and resulting 10-20x speedups, explain how to implement a Pluggable File Filter, and how we applied this to other use cases.
La collecte de données au sein d'un DataLake sans impacter les systèmes opérationnels est un challenge pour de nombreuses entreprises.
Lors du meetup Paris Data Engineers du 26 mars 2019, Dimitri Capitaine nous a présenté Data Collector qui est un outil de Change Data Capture (CDC) développé en interne chez OVH. Data Collector est capable d'assurer une réplication fiable et performante des bases de données jusqu'au DataLake.
Hugo Larcher nous a alors présenté un cas d'utilisation autour de l'exploitation de données aéronautiques avec une touche d'IoT et de DataViz.
Learn about features with demos and announcements, from cross-cluster replication and frozen indices in Elasticsearch to Kibana Spaces and the ever-growing set of data integrations in Beats and Logstash.
Capital One: Using Cassandra In Building A Reporting PlatformDataStax Academy
As a leader in the financial industry, Capital One applications generate huge amounts of data that require fast and accurate handling, storage and analysis. We are transforming how we report operational data to our internal users so that they can make quick and precise business decisions to serve our customers. As part of this transformation, we are building a new Go-based data processing framework that will enable us to transfer data from multiple data stores (RDBMS, files, etc.) to a single NoSQL database - Cassandra. This new NoSQL store will act as a reporting database that will receive data on a near real-time basis and serve the data through scorecards and reports. We would like to share our experience in defining this fast data platform and the methodologies used to model financial data in Cassandra.
In this presentation, you will get a look under the covers of Amazon Redshift, a fast, fully-managed, petabyte-scale data warehouse service for less than $1,000 per TB per year. Learn how Amazon Redshift uses columnar technology, optimized hardware, and massively parallel processing to deliver fast query performance on data sets ranging in size from hundreds of gigabytes to a petabyte or more. You¹ll also hear from Dan Wagner, CEO at Civis Analytics, as he discusses why the Civis data science platform was designed on top of Amazon Redshift and the AWS platform in order to help smart organizations bridge their data silos, build 360 degree view of their customer relationships, and identify opportunities for driving their companies forward by leveraging enormous datasets, the power of analytics, and economies of scale on the AWS platform.
Visually Transform Data in Azure Data Factory or Azure Synapse Analytics (PAS...Cathrine Wilhelmsen
Visually Transform Data in Azure Data Factory or Azure Synapse Analytics (Presented as part of the "Batte of the Data Transformation Tools" Learning Path at PASS Data Community Summit on November 15th, 2023)
by Peter Dalton, Principal Consultant AWS and Taz Sayed, Sr Technical Account Manager AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
PostgreSQL Performance Problems: Monitoring and AlertingGrant Fritchey
PostgreSQL can be difficult to troubleshoot when the pressure is on without the right knowledge and tools. Knowing where to find the information you need to improve performance is central to your ability to act quickly and solve problems. In this training, we'll discuss the various query statistic views and log information that's available in PostgreSQL so that you can solve problems quickly. Along the way, we'll highlight a handful of open-source and paid tools that can help you track data over time and provide better alerting capabilities so that you know about problems before they become critical.
AWS Summit 2013 | India - Petabyte Scale Data Warehousing at Low Cost, Abhish...Amazon Web Services
Amazon Redshift is a fast and powerful, fully managed, petabyte-scale data warehouse service in the cloud. In this session we'll give an introduction to the service and its pricing before diving into how it delivers fast query performance on data sets ranging from hundreds of gigabytes to a petabyte or more.
Day 4 - Big Data on AWS - RedShift, EMR & the Internet of ThingsAmazon Web Services
Big Data is everywhere these days. But what is it and how can you use it to fuel your business? Data is as important to organizations as labour and capital, and if organizations can effectively capture, analyze, visualize and apply big data insights to their business goals, they can differentiate themselves from their competitors and outperform them in terms of operational efficiency and the bottom line.
Join this session to understand the different AWS Big Data and Analytics services such as Amazon Elastic MapReduce (Hadoop), Amazon Redshift (Data Warehouse) and Amazon Kinesis (Streaming), when to use them and how they work together.
Reasons to attend:
- Learn how AWS can help you process and make better use of your data with meaningful insights.
- Learn about Amazon Elastic MapReduce and Amazon Redshift, fully managed petabyte-scale data warehouse solutions.
- Learn about real time data processing with Amazon Kinesis.
The Practice of Presto & Alluxio in E-Commerce Big Data PlatformAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Practice of Presto & Alluxio in E-Commerce Big Data Platform
Wenjun Tao, Sr. Software Engineer, JD.com
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Level: Intermediate
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Ingesting Over Four Million Rows Per Second With QuestDB Timeseries Database ...javier ramirez
How would you build a database to support sustained ingestion of several hundreds of thousands rows per second while running near real-time queries on top?
In this session I will go over some of the technical decisions and trade-offs we applied when building QuestDB, an open source time-series database developed mainly in JAVA, and how we can achieve over four million row writes per second on a single instance without blocking or slowing down the reads. There will be code and demos, of course.
We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
by Ben Willett, Solutions Architect, AWS
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Migrate your Data Warehouse to Amazon Redshift - September Webinar SeriesAmazon Web Services
You can gain substantially more business insights and save costs by migrating your on-premise data warehouse to Amazon Redshift, a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. This webinar will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process.
Learning Objectives:
• Understand how Amazon Redshift can deliver a richer, faster analytics at much lower costs.
• Learn key factors to consider before migrating and how to put together a migration plan.
• Learn best practices and tools for migrating schema, data, ETL and SQL queries.
How do you get data from your sources into your Redshift data warehouse? We'll show how to use AWS Glue and Amazon Kinesis Firehose to make it easy to automate the work to get data loaded.
Speakers:
Natalie Rabinovich- Solutions Architect, AWS
Gareth Eagar - Solutions Architect, AWS
Traditional data warehouses become expensive and slow down as the volume of your data grows. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it easy to analyze all of your data using existing business intelligence tools for 1/10th the traditional cost. This session will provide an introduction to Amazon Redshift and cover the essentials you need to deploy your data warehouse in the cloud so that you can achieve faster analytics and save costs.
Description of some of the elements that go in to creating a PostgreSQL-as-a-Service for organizations with many teams and a diverse ecosystem of applications and teams.
Similar to Monitoring and scaling postgres at datadog (20)
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.
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.
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.
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Designing for Privacy in Amazon Web ServicesKrzysztofKkol1
Data privacy is one of the most critical issues that businesses face. This presentation shares insights on the principles and best practices for ensuring the resilience and security of your workload.
Drawing on a real-life project from the HR industry, the various challenges will be demonstrated: data protection, self-healing, business continuity, security, and transparency of data processing. This systematized approach allowed to create a secure AWS cloud infrastructure that not only met strict compliance rules but also exceeded the client's expectations.
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.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
We describe the deployment and use of Globus Compute for remote computation. This content is aimed at researchers who wish to compute on remote resources using a unified programming interface, as well as system administrators who will deploy and operate Globus Compute services on their research computing infrastructure.
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
Experience our free, in-depth three-part Tendenci Platform Corporate Membership Management workshop series! In Session 1 on May 14th, 2024, we began with an Introduction and Setup, mastering the configuration of your Corporate Membership Module settings to establish membership types, applications, and more. Then, on May 16th, 2024, in Session 2, we focused on binding individual members to a Corporate Membership and Corporate Reps, teaching you how to add individual members and assign Corporate Representatives to manage dues, renewals, and associated members. Finally, on May 28th, 2024, in Session 3, we covered questions and concerns, addressing any queries or issues you may have.
For more Tendenci AMS events, check out www.tendenci.com/events
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
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.
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.
Multiple Your Crypto Portfolio with the Innovative Features of Advanced Crypt...Hivelance Technology
Cryptocurrency trading bots are computer programs designed to automate buying, selling, and managing cryptocurrency transactions. These bots utilize advanced algorithms and machine learning techniques to analyze market data, identify trading opportunities, and execute trades on behalf of their users. By automating the decision-making process, crypto trading bots can react to market changes faster than human traders
Hivelance, a leading provider of cryptocurrency trading bot development services, stands out as the premier choice for crypto traders and developers. Hivelance boasts a team of seasoned cryptocurrency experts and software engineers who deeply understand the crypto market and the latest trends in automated trading, Hivelance leverages the latest technologies and tools in the industry, including advanced AI and machine learning algorithms, to create highly efficient and adaptable crypto trading bots
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.
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).
14. How we do it
Requirements
▸Write master is writeable, read replicas are
readable!
15. How we do it
Requirements
▸Write master is writeable, read replicas are
readable!
▸Read replicas are up to date and don’t lag
16. How we do it
Requirements
▸Write master is writeable, read replicas are
readable!
▸Read replicas are up to date and don’t lag
▸Additional read replicas can be provisioned
quickly
17. How we do it
Solutions
▸PostgreSQL!
▸http://bit.ly/pg-repl-docs
▸WAL-E
▸https://github.com/wal-e/wal-e
18. How we do it
Solutions
▸PostgreSQL!
▸http://bit.ly/pg-repl-docs
▸WAL-E WAL-G
▸https://github.com/wal-g/wal-g
19. How we do it
Requirements
▸Write master is writeable, read replicas are
readable!
▸Read replicas are up to date and don’t lag
▸Additional read replicas can be provisioned
quickly
20. What are we alerting on?
▸Write master is writeable, read replicas are
readable!
▸Up/Down checks
▸Latency
21. What are we alerting on?
▸Read replicas are up to date and don’t lag
▸Write master standby availability
▸Write master standby replication lag
▸Read replica lag
22.
23. What are we alerting on?
▸Additional read replicas can be provisioned
quickly
▸Base backups are functioning properly
32. “Aside from shared_buffers, the most
important memory-allocation parameter
is work_mem… Raising this value can
dramatically improve the performance of certain
queries…”
Robert Haas
36. Latency vs Potential
EXPLAIN ANALYZE
http://bit.ly/pg-explain
‣ Explain displays the execution plan
‣ Analyze runs it and gathers stats
37. Latency vs Potential
EXPLAIN ANALYZE
Merge Right Join (cost=25870.55..31017.51 rows=229367 width=92) (actual time=2884.501..5147.047 rows=354834 loops=1)
Merge Cond: (a.uid = b.uid)
-> Index Scan using foo on bar a (cost=0.00..537.29 rows=9246 width=27) (actual time=0.049..41.782 rows=9246 loops=1)
-> Materialize (cost=25870.49..27204.80 rows=106745 width=81) (actual time=2884.413..3804.537 rows=354834 loops=1)
-> Sort (cost=25870.49..26137.35 rows=106745 width=81) (actual time=2884.406..3099.732 rows=111878 loops=1)
Sort Key: b.uid
Sort Method: external merge Disk: 8928kB
…
Total runtime: 5588.105 ms
(14 rows)
http://bit.ly/pg-auto-explain
38. “Aside from shared_buffers, the most
important memory-allocation parameter
is work_mem… Raising this value can
dramatically improve the performance of certain
queries, but it's important not to overdo it.”
Robert Haas
41. Summary
1. Collect as many metrics as you can, before
you need them
2. If the metrics that you have aren’t providing
the right value, build ones that do
3. Be aggressive in monitoring slow queries,
catch them while they’re easy to find
