This document summarizes updates from the AWS Users' Group meeting. It provides information about getting connected to the Slack channel and user group site. The meeting will describe Amazon Redshift, how it differs from SQL databases, storage options like disk-based and S3 storage, and ways to load data from S3, EMR, DynamoDB or remote hosts. It will also compare Redshift to Athena and include a demo of loading data and executing queries. The next month's topic is still to be determined and speakers are needed.
Cost and Performance Optimisation in Amazon RDS - AWS Summit Sydney 2018Amazon Web Services
Cost and Performance Optimisation in Amazon RDS
This session is for database administrators and other technical users looking to learn the top techniques for optimising the performance and cost of operating Amazon RDS. You will leave with a toolkit of best-practices that can be applied to your deployments for achieving optimal performance, flexibility, and cost-savings.
Brad Staszcuk, Solutions Architect, Amazon Web Services
If you are interested to know more about AWS Chicago Summit, please use the following to register: http://amzn.to/1RooPPL
Many AWS customers store vast amounts of data in Amazon S3, a low cost, scalable, and durable object store; Amazon DynamoDB, a NoSQL database; or Amazon Kinesis, a real time data stream processing service. With large datasets in various AWS services, how do you derive value from this information in a cost-effective way? Using Amazon Elastic MapReduce (Amazon EMR) with applications in the Apache Hadoop ecosystem, you can directly interact with data in each of these storage services for scalable analytics workloads or ad hoc queries. You can quickly and easily launch an Amazon EMR cluster from the AWS Management Console, and scale your cluster to match the compute and memory resources needed for your workflow, independent from the storage capacity used in your AWS storage services. The webinar will accelerate your use of Amazon EMR by showing you how to create and monitor Amazon EMR clusters, and provide several use cases and architectures for using Amazon EMR with different AWS data stores.
Learning Objectives: • Recognize when to use Amazon EMR • Understand the steps required to set up and monitor an Amazon EMR cluster • Architect applications that effectively use Amazon EMR • Understand how to use HUE for ad hoc query of data in Amazon S3
Who Should Attend: • Developers, LOB owners, Continuous Integration & Continuous Delivery (CICD) practitioners
Cost and Performance Optimisation in Amazon RDS - AWS Summit Sydney 2018Amazon Web Services
Cost and Performance Optimisation in Amazon RDS
This session is for database administrators and other technical users looking to learn the top techniques for optimising the performance and cost of operating Amazon RDS. You will leave with a toolkit of best-practices that can be applied to your deployments for achieving optimal performance, flexibility, and cost-savings.
Brad Staszcuk, Solutions Architect, Amazon Web Services
If you are interested to know more about AWS Chicago Summit, please use the following to register: http://amzn.to/1RooPPL
Many AWS customers store vast amounts of data in Amazon S3, a low cost, scalable, and durable object store; Amazon DynamoDB, a NoSQL database; or Amazon Kinesis, a real time data stream processing service. With large datasets in various AWS services, how do you derive value from this information in a cost-effective way? Using Amazon Elastic MapReduce (Amazon EMR) with applications in the Apache Hadoop ecosystem, you can directly interact with data in each of these storage services for scalable analytics workloads or ad hoc queries. You can quickly and easily launch an Amazon EMR cluster from the AWS Management Console, and scale your cluster to match the compute and memory resources needed for your workflow, independent from the storage capacity used in your AWS storage services. The webinar will accelerate your use of Amazon EMR by showing you how to create and monitor Amazon EMR clusters, and provide several use cases and architectures for using Amazon EMR with different AWS data stores.
Learning Objectives: • Recognize when to use Amazon EMR • Understand the steps required to set up and monitor an Amazon EMR cluster • Architect applications that effectively use Amazon EMR • Understand how to use HUE for ad hoc query of data in Amazon S3
Who Should Attend: • Developers, LOB owners, Continuous Integration & Continuous Delivery (CICD) practitioners
Amazon Web Services: Lessons for Architecting Data in the CloudSafe Software
Learn tips for transitioning your data architecture into the cloud. We’ll explore storing data, automating data processing, and delivering data with AWS, with lessons from our experience helping clients deploy their data into Amazon. You’ll discover the standard design patterns we live by, and learn best practices for Amazon services including S3, RDS, Lambda, SNS, and SQS. Plus, get a peek at the future of data delivery as we take a look at AWS API Gateway.
AWS SSA Webinar 21 - Getting Started with Data lakes on AWSCobus Bernard
In this session, we will take you through getting started with a Data Lake by looking at how you can ingest data to Amazon S3, query it with Amazon Athena and perform ETL operations on it using AWS Glue. We will be using the Redshift cluster from the previous session to export data to S3 to query.
Overview on Amazon EMR and its benefits for a wide variety of use cases and how to get started alongside Apache Zeppelin for interactive data analytics and document collaboration.
How Amazon.com is Leveraging Amazon Redshift (DAT306) | AWS re:Invent 2013Amazon Web Services
Learn how Amazon’s enterprise data warehouse, one of the world's largest data warehouses managing petabytes of data, is leveraging Amazon Redshift. Learn about Amazon's enterprise data warehouse best practices and solutions, and how they’re using Amazon Redshift technology to handle design and scale challenges.
AWS SSA Webinar 20 - Getting Started with Data Warehouses on AWSCobus Bernard
In this session, we will take you through setting up an Amazon Redshift cluster and at the ways you can populate it with data. We will start by using AWS DMS to replicate the data as-is as well as doing some ETL on it. This will be followed by AWS Glue where you can do more advanced ETL operations. Lastly, we will look at how you can use Amazon Kinesis Firehose to stream event directly to the Redshift cluster.
Organizations often need to quickly analyze large amounts of data, such as logs generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes. In this session you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using standard ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR.
Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. It provides cost-efficient, re-sizable capacity for an industry-standard relational database and manages common database administration tasks
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process.
2nd video of AWS Solution Architect Associate Exam series by SaMtheCloudGuy.
https://aws.amazon.com/certification/certified-solutions-architect-associate/
https://www.facebook.com/samthecloudguy/ https://www.slideshare.net/samthecloudguy/
https://www.youtube.com/c/SaMtheCloudGuy
More videos coming soon.
Amazon Athena is a new serverless query service that makes it easy to analyze data in Amazon S3, using standard SQL. With Athena, there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3.
Announcing Amazon Athena - Instantly Analyze Your Data in S3 Using SQLAmazon Web Services
Amazon Athena is a new serverless query service that makes it easy to analyze data in Amazon S3, using standard SQL. With Athena, there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3.
In this webinar, we will show you how easy it is to start querying your data stored in Amazon S3, with Amazon Athena. First we will use Athena to create the schema for data already in S3. Then, we will demonstrate how you can run interactive queries through the built-in query editor. We will provide best practices and use cases for Athena. Then, we will talk about supported queries, data formats, and strategies to save costs when querying data with Athena.
Learning Objectives:
• Learn about the capabilities and features of Amazon Athena
• Understand the different use cases
• Describe how to run queries and options to store and visualize results
• Understand integration with other AWS big data services such as Amazon QuickSight
Scaling your Analytics with Amazon Elastic MapReduce (BDT301) | AWS re:Invent...Amazon Web Services
Big data technologies let you work with any velocity, volume, or variety of data in a highly productive environment. Join the General Manager of Amazon EMR, Peter Sirota, to learn how to scale your analytics, use Hadoop with Amazon EMR, write queries with Hive, develop real world data flows with Pig, and understand the operational needs of a production data platform.
Amazon Elastic MapReduce (Amazon EMR) is a web service that allows you to easily and securely provision and manage your Hadoop clusters. In this talk, we will introduce you to Amazon EMR design patterns, such as using various data stores like Amazon S3, how to take advantage of both transient and active clusters, and how to work with other Amazon EMR architectural patterns. We will dive deep on how to dynamically scale your cluster and address the ways you can fine-tune your cluster. We will discuss bootstrapping Hadoop applications from our partner ecosystem that you can use natively with Amazon EMR. Lastly, we will share best practices on how to keep your Amazon EMR cluster cost-effective.
Migrating minimal databases with minimal downtime to AWS RDS, Amazon Redshift and Amazon Aurora
Migration of databases to same and different engines and from on premise to cloud
Schema conversion from Oracle and SQL Server to MySQL and Aurora
Amazon Aurora is a MySQL and PostgreSQL compatible relational database built for the cloud, that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. AWS Database Migration Service helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database. In this session, we explore features of Amazon Aurora and demonstrate database migration using the AWS Database Migration Service.
AWS Certified Solutions Architect Professional Course S15-S18Neal Davis
This deck contains the slides from our AWS Certified Solutions Architect Professional video course. It covers:
Section 15 Analytics Services
Section 16 Monitoring, Logging and Auditing
Section 17 Security: Defense in Depth
Section 18 Cost Management
Full course can be found here: https://digitalcloud.training/courses/aws-certified-solutions-architect-professional-video-course/
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting (in its original format) and extract value. In this session, learn how to architect and implement a data lake in the AWS Cloud. Learn about best practices as we walk through architectural blueprints.
Amazon Web Services: Lessons for Architecting Data in the CloudSafe Software
Learn tips for transitioning your data architecture into the cloud. We’ll explore storing data, automating data processing, and delivering data with AWS, with lessons from our experience helping clients deploy their data into Amazon. You’ll discover the standard design patterns we live by, and learn best practices for Amazon services including S3, RDS, Lambda, SNS, and SQS. Plus, get a peek at the future of data delivery as we take a look at AWS API Gateway.
AWS SSA Webinar 21 - Getting Started with Data lakes on AWSCobus Bernard
In this session, we will take you through getting started with a Data Lake by looking at how you can ingest data to Amazon S3, query it with Amazon Athena and perform ETL operations on it using AWS Glue. We will be using the Redshift cluster from the previous session to export data to S3 to query.
Overview on Amazon EMR and its benefits for a wide variety of use cases and how to get started alongside Apache Zeppelin for interactive data analytics and document collaboration.
How Amazon.com is Leveraging Amazon Redshift (DAT306) | AWS re:Invent 2013Amazon Web Services
Learn how Amazon’s enterprise data warehouse, one of the world's largest data warehouses managing petabytes of data, is leveraging Amazon Redshift. Learn about Amazon's enterprise data warehouse best practices and solutions, and how they’re using Amazon Redshift technology to handle design and scale challenges.
AWS SSA Webinar 20 - Getting Started with Data Warehouses on AWSCobus Bernard
In this session, we will take you through setting up an Amazon Redshift cluster and at the ways you can populate it with data. We will start by using AWS DMS to replicate the data as-is as well as doing some ETL on it. This will be followed by AWS Glue where you can do more advanced ETL operations. Lastly, we will look at how you can use Amazon Kinesis Firehose to stream event directly to the Redshift cluster.
Organizations often need to quickly analyze large amounts of data, such as logs generated from a wide variety of sources and formats. However, traditional approaches require a lot of time and effort designing complex data transformation and loading processes; and configuring data warehouses. Using AWS, you can start querying your datasets within minutes. In this session you will learn how you can deploy a managed Presto environment in minutes to interactively query log data using standard ANSI SQL. Presto is a popular open source SQL engine for running interactive analytic queries against data sources of all sizes. We will talk about common use cases and best practices for running Presto on Amazon EMR.
Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. It provides cost-efficient, re-sizable capacity for an industry-standard relational database and manages common database administration tasks
Best Practices for Migrating your Data Warehouse to Amazon RedshiftAmazon Web Services
You can gain substantially more business insights and save costs by migrating your existing data warehouse to Amazon Redshift. This session will cover the key benefits of migrating to Amazon Redshift, migration strategies, and tools and resources that can help you in the process.
2nd video of AWS Solution Architect Associate Exam series by SaMtheCloudGuy.
https://aws.amazon.com/certification/certified-solutions-architect-associate/
https://www.facebook.com/samthecloudguy/ https://www.slideshare.net/samthecloudguy/
https://www.youtube.com/c/SaMtheCloudGuy
More videos coming soon.
Amazon Athena is a new serverless query service that makes it easy to analyze data in Amazon S3, using standard SQL. With Athena, there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3.
Announcing Amazon Athena - Instantly Analyze Your Data in S3 Using SQLAmazon Web Services
Amazon Athena is a new serverless query service that makes it easy to analyze data in Amazon S3, using standard SQL. With Athena, there is no infrastructure to setup or manage, and you can start analyzing your data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3.
In this webinar, we will show you how easy it is to start querying your data stored in Amazon S3, with Amazon Athena. First we will use Athena to create the schema for data already in S3. Then, we will demonstrate how you can run interactive queries through the built-in query editor. We will provide best practices and use cases for Athena. Then, we will talk about supported queries, data formats, and strategies to save costs when querying data with Athena.
Learning Objectives:
• Learn about the capabilities and features of Amazon Athena
• Understand the different use cases
• Describe how to run queries and options to store and visualize results
• Understand integration with other AWS big data services such as Amazon QuickSight
Scaling your Analytics with Amazon Elastic MapReduce (BDT301) | AWS re:Invent...Amazon Web Services
Big data technologies let you work with any velocity, volume, or variety of data in a highly productive environment. Join the General Manager of Amazon EMR, Peter Sirota, to learn how to scale your analytics, use Hadoop with Amazon EMR, write queries with Hive, develop real world data flows with Pig, and understand the operational needs of a production data platform.
Amazon Elastic MapReduce (Amazon EMR) is a web service that allows you to easily and securely provision and manage your Hadoop clusters. In this talk, we will introduce you to Amazon EMR design patterns, such as using various data stores like Amazon S3, how to take advantage of both transient and active clusters, and how to work with other Amazon EMR architectural patterns. We will dive deep on how to dynamically scale your cluster and address the ways you can fine-tune your cluster. We will discuss bootstrapping Hadoop applications from our partner ecosystem that you can use natively with Amazon EMR. Lastly, we will share best practices on how to keep your Amazon EMR cluster cost-effective.
Migrating minimal databases with minimal downtime to AWS RDS, Amazon Redshift and Amazon Aurora
Migration of databases to same and different engines and from on premise to cloud
Schema conversion from Oracle and SQL Server to MySQL and Aurora
Amazon Aurora is a MySQL and PostgreSQL compatible relational database built for the cloud, that combines the performance and availability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases. AWS Database Migration Service helps you migrate databases to AWS quickly and securely. The source database remains fully operational during the migration, minimizing downtime to applications that rely on the database. In this session, we explore features of Amazon Aurora and demonstrate database migration using the AWS Database Migration Service.
AWS Certified Solutions Architect Professional Course S15-S18Neal Davis
This deck contains the slides from our AWS Certified Solutions Architect Professional video course. It covers:
Section 15 Analytics Services
Section 16 Monitoring, Logging and Auditing
Section 17 Security: Defense in Depth
Section 18 Cost Management
Full course can be found here: https://digitalcloud.training/courses/aws-certified-solutions-architect-professional-video-course/
Today organizations find themselves in a data rich world with a growing need for increased agility and accessibility of all this data for analysis and deriving keen insights to drive strategic decisions. Creating a data lake helps you to manage all the disparate sources of data you are collecting (in its original format) and extract value. In this session, learn how to architect and implement a data lake in the AWS Cloud. Learn about best practices as we walk through architectural blueprints.
Data warehousing in the era of Big Data: Deep Dive into Amazon RedshiftAmazon Web Services
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze all of your data for a fraction of the cost of traditional data warehouses. In this session, we take an in-depth look at data warehousing with Amazon Redshift for big data analytics. We cover best practices to take advantage of Amazon Redshift's columnar technology and parallel processing capabilities to deliver high throughput and query performance. We also discuss how to design optimal schemas, load data efficiently, and use work load management.
Data Con LA 2020
Description
In this session, I introduce the Amazon Redshift lake house architecture which enables you to query data across your data warehouse, data lake, and operational databases to gain faster and deeper insights. With a lake house architecture, you can store data in open file formats in your Amazon S3 data lake.
Speaker
Antje Barth, Amazon Web Services, Sr. Developer Advocate, AI and Machine Learning
(BDT322) How Redfin & Twitter Leverage Amazon S3 For Big DataAmazon Web Services
Analyzing large data sets requires significant compute and storage capacity that can vary in size based on the amount of input data and the analysis required. This characteristic of big data workloads is ideally suited to the pay-as-you-go cloud model, where applications can easily scale up and down based on demand. Learn how Amazon S3 can help scale your big data platform. Hear from Redfin and Twitter about how they build their big data platforms on AWS and how they use S3 as an integral piece of their big data platforms.
Migrating Your Oracle Database to PostgreSQL - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about the capabilities of the PostgreSQL database
- Learn about PostgreSQL offerings on AWS
- Learn how to migrate from Oracle to PostgreSQL with minimal disruption
Migrating Your Oracle Database to PostgreSQL - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Learn about the capabilities of the PostgreSQL database
- Learn about PostgreSQL offerings on AWS
- Learn how to migrate from Oracle to PostgreSQL with minimal disruption
Aplicaciones a gran escala: Cómo servir a millones de usuariosAmazon Web Services
(Diapositivas de presentación son en inglés.)
¿Cómo podemos hacer escalar nuestras aplicaciones? Escalar aplicaciones no es un tarea sencilla ya que existen múltiples variables a analizar (red, servidores, almacenamiento, aplicación, arquitectura, cdn, etc.), así como diferentes alternativas para construir y operar plataformas a gran escala. En esta sesión se cubrirá el recorrido de una plataforma que pueda dar cobertura desde un usuario hasta millones de usuarios.
2 years ago if someone had claimed they could stand up a petabyte scale data warehouse in under an hour and then have a non-technical business user querying it live 30 minutes later without knowing any SQL or coding language, they would have been laughed out of the room. These days, that’s called taking advantage of disruptive technology. Amazon Web Services and Tableau Software have shifted the entire paradigm by which organizations not only store and access their data, but ultimately how they innovate with it. The fast, scalable, and inexpensive services that AWS provides for housing data combined with Tableau’s unbelievably flexible and user friendly visual analytic solution means that within hours an organization can securely put the power of their massive data assets into the hands of their domain experts without expensive overhead or lengthy ramp-up time. Attend this webinar to learn how Amazon Web Services and Tableau Software are leveraged together everyday to: • Empower visual ad-hoc data discovery against big data • Revolutionize corporate reporting and dashboards • Promote data driven decision making at every level The presentation will include: • A live demonstration of AWS and Tableau working together • A real customer case study focused on fraud detection and online video metrics • Live Q&A and an opportunity to trial both solutions
Serverless Analytics with Amazon Redshift Spectrum, AWS Glue, and Amazon Quic...Amazon Web Services
Learning Objectives:
- Understand how to build a serverless big data solution quickly and easily
- Learn how to discover and prepare all your data for analytics
- Learn how to query and visualize analytics on all your data to create actionable insights
Scaling on AWS for the First 10 Million Users at Websummit DublinAmazon Web Services
In this talk from the Dublin Websummit 2014 AWS Technical Evangelist Ian Massingham discusses the techniques that AWS customers can use to create highly scalable infrastructure to support the operation of large scale applications on the AWS cloud.
Includes a walk-through of how you can evolve your architecture as your application becomes more popular and you need to scale up your infrastructure to support increased demand.
Scaling on AWS for the First 10 Million Users at Websummit DublinIan Massingham
In this talk from the Dublin Websummit 2014 AWS Technical Evangelist Ian Massingham discusses the techniques that AWS customers can use to create highly scalable infrastructure to support the operation of large scale applications on the AWS cloud.
Includes a walk-through of how you can evolve your architecture as your application becomes more popular and you need to scale up your infrastructure to support increased demand.
January 2017 - Deep dive on AWS Lambda and DevOpsDavid McDaniel
A deep dive on AWS Lambda using Java, how it's different from traditional architecture and designs, and how to alter thinking about DevOps and the value of your code.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
2. Getting Connected
Slack Channel: https://DenverAWSUsersGroup.slack.com
You will need an invitation to join, please email me: david@mobile-360.com.
We are now listed on AWS UG site:
https://aws.amazon.com/usergroups/americas/
We are sponsored by CloudAcademy! They have a free portal for our members at:
https://cloudacademy.com/aws-usergroup/?code=newawsugs
We are also sponsored and a member of the official Global AWS Communities!
See them at https://awsug.support
3. What we’re going to do tonight
1. Describe Amazon Redshift
2. Talk about how it’s different from regular SQL Databases
3. Talk about storage options for Redshift
a. Standard Disk-based storage
b. Spectrum and S3 (CSV & Parquet) storage
4. Describe ways to load data
a. S3, EMR, DynamoDB or Remote Hosts
5. Compare to Athena
4. What is Redshift?
Amazon Redshift is a fast, fully managed data warehouse that makes it simple and cost-effective to analyze
all your data using standard SQL and your existing Business Intelligence (BI) tools. It allows you to run
complex analytic queries against petabytes of structured data, using sophisticated query optimization,
columnar storage on high-performance local disks, and massively parallel query execution. Most results come
back in seconds. With Amazon Redshift, you can start small for just $0.25 per hour with no commitments and
scale out to petabytes of data for $1,000 per terabyte per year, less than a tenth the cost of traditional
solutions.
Amazon Redshift also includes Redshift Spectrum, allowing you to directly run SQL queries against exabytes
of unstructured data in Amazon S3. No loading or transformation is required, and you can use open data
formats, including CSV, TSV, Parquet, Sequence, and RCFile. Redshift Spectrum automatically scales query
compute capacity based on the data being retrieved, so queries against Amazon S3 run fast, regardless of
dataset size.
Recently announced 4x compression improvement in Redshift.
5. How Redshift is Different
Redshift is a column-oriented database whereas regular SQL databases are row-oriented in nature. This
means that Redshift stores groups of columns together rather than groups of rows. This can be hugely
beneficial when processing many rows, but only a few columns, which is typical in BI and Analytical
processing. Many data warehouse databases will be denormalized to reduce joins and therefore tables
will be very wide (many columns) to provide the most value, even though individual queries will only use a
small number of columns.
7. Storage Options
1. Local Disk Storage
a. Traditional, SSD-based, ties storage to compute.
b. Ties compute to storage.
c. Must make FULL read-only copies to scale.
2. S3 - Used with Redshift Spectrum
a. Uses Amazon Athena Meta-data to understand files in S3.
b. Decouples storage from compute.
c. Still must make read-only copies, but of meta-data only, so smaller & faster to scale.
8. How do we load data?
Multiple ways:
1. Preferred way: Use COPY command to load data from files in one of many
formats from:
a. S3
b. EMR
c. Remote EC2 Hosts
d. DynamoDB Tables
2. Use DML:
9. How is it different from Athena?
Athena Redshift
Storage on S3 Storage on attached SSD disks
Automatically scales Must add more instances/change instance
size
Massive parallelism Only as parallel as you configure
Data can be stored in multiple formats per
table
Data can be loaded from files in multiple
formats
10. Demo!
1. Create Schemas for Redshift tables
2. Load data in multiple formats from S3
3. Create Redshift Spectrum Schemas
4. Load data (really, meta-data)
5. Execute queries
6. Tableau visualization