This document discusses event processing and big data. It begins with an introduction to complex event processing (CEP) including key concepts like event streams and windows. It then covers big data scenarios where CEP can be used to analyze large volumes and varieties of event data in real-time. The document also provides an overview of the Oracle CEP architecture and how it can integrate with big data technologies like Hadoop.
Learn how graph technologies can be applied to real-world use cases, using medical, network security, and financial data. By combining graph models and machine learning techniques, we can discover relationships, classify information, and identify patterns and anomalies in data. We can answer questions such as “How did other investigators approach similar cases?” and “Do these symptoms seem similar to ones we’ve seen in other diseases?” Presented by Sungpack Hong, Research Director, Oracle Labs.
Oracle Spatial Studio: Fast and Easy Spatial Analytics and MapsJean Ihm
Learn about a new tool, Spatial Studio, that lets you quickly and easily do spatial analytics and create maps, even if you don't have GIS or Spatial knowledge. Now business users and non-GIS developers have a simple user interface to access the spatial features in Oracle Database.
Spatial Studio lets you prepare your data for spatial analysis, perform spatial analysis operations, publish, and share the results – as well access spatial analyses results via REST and incorporate in applications and workflows. Presented by Carol Palmer, Sr. Principal Product Manager, and David Lapp, Sr. Principal Product Manager, Oracle Spatial and Graph.
Presentation video including demo and resources available here: https://devgym.oracle.com/pls/apex/dg/office_hours/3084 .
Powerful Spatial Features You Never Knew Existed in Oracle Spatial and Graph ...Jean Ihm
Dan Geringer - BIWA Summit 2018 presentation. Even expert users may not know some of the powerful functions available in Oracle Spatial and Graph, or how to optimize common spatial requirements. I often find myself working with customers that implement spatial requirements the way they had to with other spatial solutions, instead of the best way they can by leveraging powerful unique capabilities available in Oracle Spatial and Graph. Many times the reason is "I didn't know that existed". This session will cover how Oracle Spatial and Graph natively integrates with key Oracle Database features such as transparent data encryption (TDE), redaction, partitioning (all types), and also powerful nearest neighbor strategies, new spatial functions introduced in 12c, as well as an overview of spatial functions you never knew existed. Customer use cases and code examples will be included. This session is intended for a technical audience, but others will also gain useful insights on the powerful capabilities of Oracle Spatial and Graph.
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEASandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning with examples, AutoML for training models and this ends with an example of how to predict fraud , to determining shopping patterns to Wine picking and different algorithms as an example and also how to predict workload for your databases. We will also use OML in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automatically
3rd in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
See the magic of graphs in this session. Graph analysis can answer questions like detecting patterns of fraud or identifying influential customers - and do it quickly and efficiently. We’ll show you the APIs for accessing graphs and running analytics such as finding influencers, communities, anomalies, and how to use them from various languages including Groovy, Python, and Javascript, with Jupiter and Zeppelin notebooks.
Albert Godfrind (EMEA Solutions Architect), Zhe Wu (Architect), and Jean Ihm (Product Manager) walk you through, and take your questions.
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019 Sandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. We take a view on our current state of ML in the Autonomous Database Cloud and how do we process this data in ADW/ATP with zeppelin notebooks to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. We will cover some sample notebooks to some use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. Some of the other use cases is to use convolution filters to determine maintenance windows within the database workloads , determine best times to do database backups , security anomaly timelines and many others. This presentation will accompany several examples with how to apply these techniques , machine learning knowledge is preferred but not a prerequisite
Learn how graph technologies can be applied to real-world use cases, using medical, network security, and financial data. By combining graph models and machine learning techniques, we can discover relationships, classify information, and identify patterns and anomalies in data. We can answer questions such as “How did other investigators approach similar cases?” and “Do these symptoms seem similar to ones we’ve seen in other diseases?” Presented by Sungpack Hong, Research Director, Oracle Labs.
Oracle Spatial Studio: Fast and Easy Spatial Analytics and MapsJean Ihm
Learn about a new tool, Spatial Studio, that lets you quickly and easily do spatial analytics and create maps, even if you don't have GIS or Spatial knowledge. Now business users and non-GIS developers have a simple user interface to access the spatial features in Oracle Database.
Spatial Studio lets you prepare your data for spatial analysis, perform spatial analysis operations, publish, and share the results – as well access spatial analyses results via REST and incorporate in applications and workflows. Presented by Carol Palmer, Sr. Principal Product Manager, and David Lapp, Sr. Principal Product Manager, Oracle Spatial and Graph.
Presentation video including demo and resources available here: https://devgym.oracle.com/pls/apex/dg/office_hours/3084 .
Powerful Spatial Features You Never Knew Existed in Oracle Spatial and Graph ...Jean Ihm
Dan Geringer - BIWA Summit 2018 presentation. Even expert users may not know some of the powerful functions available in Oracle Spatial and Graph, or how to optimize common spatial requirements. I often find myself working with customers that implement spatial requirements the way they had to with other spatial solutions, instead of the best way they can by leveraging powerful unique capabilities available in Oracle Spatial and Graph. Many times the reason is "I didn't know that existed". This session will cover how Oracle Spatial and Graph natively integrates with key Oracle Database features such as transparent data encryption (TDE), redaction, partitioning (all types), and also powerful nearest neighbor strategies, new spatial functions introduced in 12c, as well as an overview of spatial functions you never knew existed. Customer use cases and code examples will be included. This session is intended for a technical audience, but others will also gain useful insights on the powerful capabilities of Oracle Spatial and Graph.
Introduction to Machine Learning - From DBA's to Data Scientists - OGBEMEASandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning with examples, AutoML for training models and this ends with an example of how to predict fraud , to determining shopping patterns to Wine picking and different algorithms as an example and also how to predict workload for your databases. We will also use OML in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automatically
3rd in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
See the magic of graphs in this session. Graph analysis can answer questions like detecting patterns of fraud or identifying influential customers - and do it quickly and efficiently. We’ll show you the APIs for accessing graphs and running analytics such as finding influencers, communities, anomalies, and how to use them from various languages including Groovy, Python, and Javascript, with Jupiter and Zeppelin notebooks.
Albert Godfrind (EMEA Solutions Architect), Zhe Wu (Architect), and Jean Ihm (Product Manager) walk you through, and take your questions.
The Machine Learning behind the Autonomous Database- EMEA Tour Oct 2019 Sandesh Rao
Autonomous Database is one of the hottest Oracle products where we have attempted to use Machine Learning for several aspects of the service. We take a view on our current state of ML in the Autonomous Database Cloud and how do we process this data in ADW/ATP with zeppelin notebooks to find anomalies in them to troubleshoot them at a scale of several petabytes a year and conduct AIOps. We will cover some sample notebooks to some use cases we will cover are a Log Anomaly timeline which we reduce significant amounts of logs using semi-supervised machine learning techniques to reduce logs and match them in near real time. Some of the other use cases is to use convolution filters to determine maintenance windows within the database workloads , determine best times to do database backups , security anomaly timelines and many others. This presentation will accompany several examples with how to apply these techniques , machine learning knowledge is preferred but not a prerequisite
Introduction to Machine Learning and Data Science using Autonomous Database ...Sandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning , autoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use different open source frameworks as well as some of the tools in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automaticall
Gimel is a data abstraction framework built on Apache Spark - providing unified Data Access via API & SQL to different technologies such as kafka, elastic, HBASE, Rest API, File, Object stores, Relational , etc.
We spoke about this recently in the "cloud track" in the "Scale By The Bay" Conference.
https://www.scale.bythebay.io/schedule
https://sched.co/e55D
Youtube - https://www.youtube.com/watch?v=cy8g2WZbEBI&ab_channel=FunctionalTV
https://youtu.be/m6_0iI4XDpU
Elastic como solución de analítica avanzada en los procesos del sector petrolero. Analítica de datos de sensores en tiempo real para adicionar valor a las decisiones estratégicas de las organizaciones
Site | https://www.infoq.com/qconai2018/
Youtube | https://www.youtube.com/watch?v=2h0biIli2F4&t=19s
At PayPal, data engineers, analysts and data scientists work with a variety of datasources (Messaging, NoSQL, RDBMS, Documents, TSDB), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive).
Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM). To solve this problem and to make product development more effective, PayPal Data Platform developed "Gimel", a unified analytics data platform which provides access to any storage through a single unified data API and SQL, that are powered by a centralized data catalog.
In this session, we will introduce you to the various components of Gimel - Compute Platform, Data API, PCatalog, GSQL and Notebooks. We will provide a demo depicting how Gimel reduces TTM by helping our engineers write a single line of code to access any storage without knowing the complexity behind the scenes.
Delicious : EDQ, OGG and ODI over Exadata for PerfectionGurcan Orhan
Oracle Golden Gate (OGG) handles extraction phase for operational reporting, Enterprise Data Quality (EDQ) leverage source system based data quality problems (misspells/duplications in defined or undefined conditions) and Oracle Data Integrator (ODI) handles and controls both of those tools and moreover loading your data into data warehouse.
These 3 tools are integrated to work together while you have control of OGG and EDQ via ODI over Exadata machine in a faster manner with minimum effort of development and tools already exist in ODI.
At the same time use Exadata's extensive features to decrease ETL jobs' duration and obtain high availibility.
In this presentation, see how this 3 tools are merged to be used together.
Splunk Ninjas: New features, pivot, and search dojoSplunk
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Unified Data Catalog - Recommendations powered by Apache Spark & Neo4jDeepak Chandramouli
Youtube | https://youtu.be/zGX0fRLdd6s?list=PLPaGQXwz_-RaoHicnGhL5SyOAp3_lUTQ2&t=1
This is a talk from PayPal at Nodes Online Summit, organized by Neo4j.
For more session details and video - please visit this link.
https://neo4j.com/online-summit/session/recommendations-unified-data-catalog-spark-neo4j
Anna Vergeles, Nataliia Manakova "Unsupervised Real-Time Stream-Based Novelty...Fwdays
High-load systems produce lots of telemetry information in every time slot. That is quite a challenge to say if the working load has changed significantly right now or everything runs as expected. This presentation covers the novelty detection technique used for cloud systems that combine non-real-time learning with real-time estimation ensemble.
20 tips and tricks with the Autonomous DatabaseSandesh Rao
This covers the top 20 questions most DBA’s , Developers will have on the Autonomous Database from provisioning to backups , troubleshooting , tips and tricks , security and HA . This is a good introduction for on-prem DBA’s who want to learn how this works quickly without spending too much time on it . Questions like what does the free tier cover , how do I do backup or if its automated how do I manage it , how to scale up and down , how to use tools like SQLDeveloper and SQLModeler , endpoints , terraform all in a quick 45 minute session which might take weeks to pickup reading documentation or spanning several presentations
Power of Splunk Search Processing Language (SPL) ...Splunk
This session will unveil the power of the Splunk Search Processing Language (SPL). See how to use Splunk's simple search language for searching and filtering through data, charting statistics and predicting values, converging data sources and grouping transactions, and finally data science and exploration. We'll begin with basic search commands and build up to more powerful advanced tactics to help you harness your Splunk Fu!
implementation of a big data architecture for real-time analytics with data s...Joseph Arriola
My topic presented in DataStax Accelerate 2019 was "Implementation of a Big Data architecture for real-time analytics with DataStax Enterprise Graph, Analytics and Search". To show some of the most widely used open source technologies in the market. and how to integrate them with an Enterprise tool, looking for do real-time analytics.
Horizon: Deep Reinforcement Learning at ScaleDatabricks
To build a decision-making system, we must provide answers to two sets of questions: (1) ""What will happen if I make decision X?"" and (2) ""How should I pick which decision to make?"".
Typically, the first set of questions are answered with supervised learning: we build models to forecast whether someone will click on an ad, or visit a post. The second set of questions are more open-ended. In this talk, we will dive into how we can answer ""how"" questions, starting with heuristics and search. This will lead us to bandits, reinforcement learning, and Horizon: an open-source platform for training and deploying reinforcement learning models at massive scale. At Facebook, we are using Horizon, built using PyTorch 1.0 and Apache Spark, in a variety of AI-related and control tasks, spanning recommender systems, marketing & promotion distribution, and bandwidth optimization.
The talk will cover the key components of Horizon and the lessons we learned along the way that influenced the development of the platform.
Author: Jason Gauci
Introduction to Machine Learning and Data Science using Autonomous Database ...Sandesh Rao
This session will focus on basics of what Machine Learning is , different types of Machine Learning and Neural Networks , supervised and unsupervised machine learning , autoML for training models and this ends with an example of how to predict workloads using Average Active sessions and different algorithms as an example and also how to predict maintenance windows for your databases. We will also use different open source frameworks as well as some of the tools in the Autonomous Database cloud to do this. If you are a DBA and want to learn something about machine learning and use the tools to perform your tasks more efficiently and automaticall
Gimel is a data abstraction framework built on Apache Spark - providing unified Data Access via API & SQL to different technologies such as kafka, elastic, HBASE, Rest API, File, Object stores, Relational , etc.
We spoke about this recently in the "cloud track" in the "Scale By The Bay" Conference.
https://www.scale.bythebay.io/schedule
https://sched.co/e55D
Youtube - https://www.youtube.com/watch?v=cy8g2WZbEBI&ab_channel=FunctionalTV
https://youtu.be/m6_0iI4XDpU
Elastic como solución de analítica avanzada en los procesos del sector petrolero. Analítica de datos de sensores en tiempo real para adicionar valor a las decisiones estratégicas de las organizaciones
Site | https://www.infoq.com/qconai2018/
Youtube | https://www.youtube.com/watch?v=2h0biIli2F4&t=19s
At PayPal, data engineers, analysts and data scientists work with a variety of datasources (Messaging, NoSQL, RDBMS, Documents, TSDB), compute engines (Spark, Flink, Beam, Hive), languages (Scala, Python, SQL) and execution models (stream, batch, interactive).
Due to this complex matrix of technologies and thousands of datasets, engineers spend considerable time learning about different data sources, formats, programming models, APIs, optimizations, etc. which impacts time-to-market (TTM). To solve this problem and to make product development more effective, PayPal Data Platform developed "Gimel", a unified analytics data platform which provides access to any storage through a single unified data API and SQL, that are powered by a centralized data catalog.
In this session, we will introduce you to the various components of Gimel - Compute Platform, Data API, PCatalog, GSQL and Notebooks. We will provide a demo depicting how Gimel reduces TTM by helping our engineers write a single line of code to access any storage without knowing the complexity behind the scenes.
Delicious : EDQ, OGG and ODI over Exadata for PerfectionGurcan Orhan
Oracle Golden Gate (OGG) handles extraction phase for operational reporting, Enterprise Data Quality (EDQ) leverage source system based data quality problems (misspells/duplications in defined or undefined conditions) and Oracle Data Integrator (ODI) handles and controls both of those tools and moreover loading your data into data warehouse.
These 3 tools are integrated to work together while you have control of OGG and EDQ via ODI over Exadata machine in a faster manner with minimum effort of development and tools already exist in ODI.
At the same time use Exadata's extensive features to decrease ETL jobs' duration and obtain high availibility.
In this presentation, see how this 3 tools are merged to be used together.
Splunk Ninjas: New features, pivot, and search dojoSplunk
Besides seeing the newest features in Splunk Enterprise and learning the best practices for data models and pivot, we will show you how to use a handful of search commands that will solve most search needs. Learn these well and become a ninja.
Unified Data Catalog - Recommendations powered by Apache Spark & Neo4jDeepak Chandramouli
Youtube | https://youtu.be/zGX0fRLdd6s?list=PLPaGQXwz_-RaoHicnGhL5SyOAp3_lUTQ2&t=1
This is a talk from PayPal at Nodes Online Summit, organized by Neo4j.
For more session details and video - please visit this link.
https://neo4j.com/online-summit/session/recommendations-unified-data-catalog-spark-neo4j
Anna Vergeles, Nataliia Manakova "Unsupervised Real-Time Stream-Based Novelty...Fwdays
High-load systems produce lots of telemetry information in every time slot. That is quite a challenge to say if the working load has changed significantly right now or everything runs as expected. This presentation covers the novelty detection technique used for cloud systems that combine non-real-time learning with real-time estimation ensemble.
20 tips and tricks with the Autonomous DatabaseSandesh Rao
This covers the top 20 questions most DBA’s , Developers will have on the Autonomous Database from provisioning to backups , troubleshooting , tips and tricks , security and HA . This is a good introduction for on-prem DBA’s who want to learn how this works quickly without spending too much time on it . Questions like what does the free tier cover , how do I do backup or if its automated how do I manage it , how to scale up and down , how to use tools like SQLDeveloper and SQLModeler , endpoints , terraform all in a quick 45 minute session which might take weeks to pickup reading documentation or spanning several presentations
Power of Splunk Search Processing Language (SPL) ...Splunk
This session will unveil the power of the Splunk Search Processing Language (SPL). See how to use Splunk's simple search language for searching and filtering through data, charting statistics and predicting values, converging data sources and grouping transactions, and finally data science and exploration. We'll begin with basic search commands and build up to more powerful advanced tactics to help you harness your Splunk Fu!
implementation of a big data architecture for real-time analytics with data s...Joseph Arriola
My topic presented in DataStax Accelerate 2019 was "Implementation of a Big Data architecture for real-time analytics with DataStax Enterprise Graph, Analytics and Search". To show some of the most widely used open source technologies in the market. and how to integrate them with an Enterprise tool, looking for do real-time analytics.
Horizon: Deep Reinforcement Learning at ScaleDatabricks
To build a decision-making system, we must provide answers to two sets of questions: (1) ""What will happen if I make decision X?"" and (2) ""How should I pick which decision to make?"".
Typically, the first set of questions are answered with supervised learning: we build models to forecast whether someone will click on an ad, or visit a post. The second set of questions are more open-ended. In this talk, we will dive into how we can answer ""how"" questions, starting with heuristics and search. This will lead us to bandits, reinforcement learning, and Horizon: an open-source platform for training and deploying reinforcement learning models at massive scale. At Facebook, we are using Horizon, built using PyTorch 1.0 and Apache Spark, in a variety of AI-related and control tasks, spanning recommender systems, marketing & promotion distribution, and bandwidth optimization.
The talk will cover the key components of Horizon and the lessons we learned along the way that influenced the development of the platform.
Author: Jason Gauci
Распространенные ошибки применения баз данныхSergey Xek
Доклад не про БД в чистом виде а про архитектуру веб-приложений, использующих БД.
Выбор хранилища данных — сложная задача, с которой часто сталкиваются разработчики. Чаще всего результат этого выбора — это компромисс. Я расскажу о собственном опыте, набитых «шишках», рассмотрю важные, на мой взгляд, связанные с этой задачей проблемы.
Подробно:
• Зачастую в стартапе изначально проектируется архитектура вокруг БД, рассчитанная на огромные нагрузки, на большое масштабирование, которые потом в реальной жизни никогда не понадобятся.
• Или проектируется архитектура, которая якобы дает отказоустойчивость, но при этом проблемы нижних уровней абстракции во внимание не принимаются.
• При выборе основной БД для проекта выбирается БД, которая не дает большого запаса фич в будущем, появляется дороговизна и сложность изменения.
• Используйте инструменты, которые вы хорошо изучили. «Психологическая» популярность NoSQL. Достоинства и недостатки SQL и NoSQL БД.
• Проблемы использования БД как хранилища/движка обработки событий зачастую не оправдано. Альтернативы.
• Использование БД для поиска, плюсы и минусы.
• Eventual consistency рулит, и как из этого можно извлечь пользу.
Целевая аудитория:
Доклад будет интересен веб-разработчикам, особенно из стартапов и небольших команд, техническим руководителям.
Регулирует все аспекты сна:
засыпание,
качество сна и релаксации во время сна,
восполнение энергии и легкость пробуждения.
Помогает нормализации сна при смене часовых поясов, частых перелётах и нарушении режима отдыха
Уменьшает негативное воздействие стресса, полезен при тревожности и беспокойстве
Способствует регуляции выработки мелатонина и серотонина
SplunkLive! Frankfurt 2018 - Data Onboarding OverviewSplunk
Presented at SplunkLive! Frankfurt 2018:
Splunk Data Collection Architecture
Apps and Technology Add-ons
Demos / Examples
Best Practices
Resources and Q&A
Fast Data Mining: Real Time Knowledge Discovery for Predictive Decision MakingCodemotion
Fast Data as a different approach to Big Data for managing large quantities of “in-flight” data that help organizations get a jump on those business-critical decisions. Difference between Big Data and Fast Data is comparable to the amount of time you wait downloading a movie from an online store and playing the dvd instantly.
Data Mining as a process to extract info from a data set and transform it into an understandable structure in order to deliver predictive, advanced analytics to enterprises and operational environments.
The combination of Fast Data and Data Mining are changing the “Rules”
5th in the AskTOM Office Hours series on graph database technologies. https://devgym.oracle.com/pls/apex/dg/office_hours/3084
PGQL: A Query Language for Graphs
Learn how to query graphs using PGQL, an expressive and intuitive graph query language that's a lot like SQL. With PGQL, it's easy to get going writing graph analysis queries to the database in a very short time. Albert and Oskar show what you can do with PGQL, and how to write and execute PGQL code.
IDERA Live | The Ever Growing Science of Database MigrationsIDERA Software
You can watch the replay for this webcast in the IDERA Resource Center: http://ow.ly/QHaG50A58ZB
Many information technology professionals may not recognize it, but the bulk of their work has been and continues to be nothing more than database migrations. In the old days to share files across systems, then to move files into relational databases, then to load into data warehouses, and finally now we're moving to NoSQL and the cloud. In the presentation we'll delve into the ever growing and increasingly complex world of database migrations. Some of these considerations include what issues must be planned for and overcome, what problems are likely to occur, and what types of tools exist.
Database expert Bert Scalzo will cover these and many other database migration concerns.
About Bert: Bert Scalzo is an Oracle ACE, author, speaker, consultant, and a major contributor for many popular database tools used by millions of people worldwide. He has 30+ years of database experience and has worked for several major database vendors. He has BS, MS and Ph.D. in computer science plus an MBA. He has presented at numerous events and webcasts. His areas of key interest include data modeling, database benchmarking, database tuning, SQL optimization, "star schema" data warehousing, running databases on Linux or VMware, and using NVMe flash based technology to speed up database performance.
The Four Hats of Load and Performance Testing with special guest MentoraSOASTA
Performance testing may be the most critical function to assuring business success and continuity under unexpected application stress conditions. Professionals in this domain develop several key skills to model realistic workloads, develop robust scripts, monitor complex environments, and deliver actionable results.
In this webinar hear how good teams effectively utilize the skills associated with the four hats of performance testing:
- Business Analyst, for effective test planning
- Developer, for creating maintainable scripts
- Systems Engineer, to identify and configure resource monitors
- Data Analyst, to interpret and report results
Dan Downing, Managing Principal at Mentora, is a veteran performance tester, teacher, author, and presenter, with 30 years of enterprise testing expertise. Join Dan and fellow test industry veteran, Brad Johnson, SOASTA’s VP of Product, as they explore these four key areas where skills and expert tools must intersect to deliver speed and quality in today’s fast moving companies.
About the presenters:
Dan Downing, Managing Principal, Application Testing, Mentora
Dan leads the Enterprise Application Performance Testing practice and serves as the principal consultant for quality assessments and large enterprise projects. He has 30 years technical and leadership experience as programmer, sales engineer, product manager, senior manager, and has led enterprise load testing projects for a variety of industries. Dan is widely regarded as a subject matter expert in load testing and created the 5-Steps of Load Testing methodology taught at Mercury Interactive. He is a frequent presenter at software quality conferences such as STAR, STPCon, and Workshop on Performance and Reliability for which he is one of the organizers.
Brad Johnson, VP Product, SOASTA
Brad Johnson has been supporting testers since the turn of the last century as head of monitoring and test products at Compuware, Mercury Interactive and Borland. He joined the new school of testing in 2009 when he signed on with SOASTA to deliver cloud testing on the CloudTest platform to a skeptical and established software testing market. Now, with the experience of tens-of-thousands of tests and hundreds of companies embracing the cloud, and using the same for mobile test automation, he’s helping expand the horizons of testers everywhere.
The Four Hats of Load and Performance Testing with special guest MentoraSOASTA
Performance testing may be the most critical function to assuring business success and continuity under unexpected application stress conditions. Professionals in this domain develop several key skills to model realistic workloads, develop robust scripts, monitor complex environments, and deliver actionable results.
In this webinar hear how good teams effectively utilize the skills associated with the four hats of performance testing:
- Business Analyst, for effective test planning
- Developer, for creating maintainable scripts
- Systems Engineer, to identify and configure resource monitors
- Data Analyst, to interpret and report results
Dan Downing, Managing Principal at Mentora, is a veteran performance tester, teacher, author, and presenter, with 30 years of enterprise testing expertise. Join Dan and fellow test industry veteran, Brad Johnson, SOASTA’s VP of Product, as they explore these four key areas where skills and expert tools must intersect to deliver speed and quality in today’s fast moving companies.
About the presenters:
Dan Downing, Managing Principal, Application Testing, Mentora
Dan leads the Enterprise Application Performance Testing practice and serves as the principal consultant for quality assessments and large enterprise projects. He has 30 years technical and leadership experience as programmer, sales engineer, product manager, senior manager, and has led enterprise load testing projects for a variety of industries. Dan is widely regarded as a subject matter expert in load testing and created the 5-Steps of Load Testing methodology taught at Mercury Interactive. He is a frequent presenter at software quality conferences such as STAR, STPCon, and Workshop on Performance and Reliability for which he is one of the organizers.
Brad Johnson, VP Product, SOASTA
Brad Johnson has been supporting testers since the turn of the last century as head of monitoring and test products at Compuware, Mercury Interactive and Borland. He joined the new school of testing in 2009 when he signed on with SOASTA to deliver cloud testing on the CloudTest platform to a skeptical and established software testing market. Now, with the experience of tens-of-thousands of tests and hundreds of companies embracing the cloud, and using the same for mobile test automation, he’s helping expand the horizons of testers everywhere.
Oracle Goldengate for Big Data - LendingClub ImplementationVengata Guruswamy
This slide covers the LendingClub use case for implementing real time analytics using Oracle goldengate for Big Data. It covers architecture ,implementation and troubleshooting steps.
More and more data sources today provide a constant data stream, from Internet of Things devices to Social Media streams. It is one thing to collect these events in the velocity they arrive, without losing any single message. An Event Hub and a data flow engine can help here. It’s another thing to do some (complex) analytics on the data. There is always the option to first store them in a data sink of choice, such as a data lake implemented with HDFS/object store, or in a database such as a NoSQL or even an RDBMS, if the volume of events is not too high. Storing a high-volume event stream is feasible and not such a challenge anymore. But doing it adds to the end-to-end latency and it’s a matter of minutes or hours until you can present some results of your analytics. If you need to react fast, you simply can't afford to first store the data and doing the analysis/analytics later. You have to be able to include part of your analytics directly on the data stream. This is called Stream Processing or Stream Analytics. In this talk I will present the important concepts, a Stream Processing solution should support and then dive into some of the most popular frameworks available on the market and how they compare.
LendingClub RealTime BigData Platform with Oracle GoldenGateRajit Saha
LendingClub RealTime BigData Platform with Oracle GoldenGate BigData Adapter. This was presented at Oracle Open World 2017 at San Francisco.
Speaker :
Rajit Saha
Vengata Guruswami
Independent of the source of data, the integration and analysis of event streams gets more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events.
So far this mostly a development experience, with frameworks such as Oracle Event Processing, Apache Storm or Spark Streaming. With Oracle Stream Analytics, analytics on event streams can be put in the hands of the business analyst. It simplifies the implementation of event processing solutions so that every business analyst is able to graphically and decleratively define event stream processing pipelines, without having to write a single line of code or continous query language (CQL). Event Processing is no longer “complex”! This session presents Oracle Stream Analytics directly on some selected demo use cases.
A modern approach to streaming data integration, event processing with a big data (kappa style) data architecture. Key patterns are discussed with pros/cons of newer approaches and open source technologies. Focus on Oracle and GoldenGate technology. OpenWorld 2018 presentation.
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https://arxiv.org/abs/2306.08302
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https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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Presented by Vladimir Iglovikov:
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- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
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Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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