DevOps is such a dynamic and flexible part of an organization that it is the absolute hardest thing to get documented, and the place where it really makes a difference to burnout if you have managed the miracle.
Non-technical demonstration of grlc intended for users without prior knowledge of SPARQL or github. See grlc.io and SALAD best paper awarded research: http://datalegend.net/assets/paper7.pdf
El documento describe el MERCOSUR, un tratado firmado en 1991 entre Argentina, Brasil, Paraguay y Uruguay para crear un mercado común en América del Sur. Los objetivos del MERCOSUR incluyen la libre circulación de bienes, servicios y factores productivos, así como la adopción de una política comercial y arancel externo común entre los países miembros. El documento explica la estructura y funciones de los principales órganos del MERCOSUR como el Consejo del Mercado Común y el Grupo Mercado Común.
El documento proporciona información sobre el Mercosur, incluyendo su historia, países miembros y asociados, órganos principales y objetivos. El Mercosur se estableció en 1985 y actualmente incluye a Argentina, Brasil, Paraguay y Uruguay como miembros plenos, así como varios países asociados. Sus órganos clave son el Consejo del Mercado Común, el Grupo Mercado Común y la Comisión de Comercio del Mercosur. Los objetivos del bloque son promover el libre comercio y la integra
El MERCOSUR es un acuerdo de integración económica y comercial entre países de América del Sur firmado en 1991 por Argentina, Brasil, Paraguay y Uruguay. Actualmente está integrado por esos cuatro países más Venezuela, y Bolivia se encuentra en proceso de adhesión. El objetivo del MERCOSUR es promover el comercio entre los países miembros y fortalecer su posición económica en el mundo a través de la integración.
Este documento describe el Mercado Común del Sur (MERCOSUR), un bloque económico y comercial formado por Argentina, Brasil, Paraguay, Uruguay y Venezuela. El MERCOSUR tiene como objetivo principal promover la libre circulación de bienes, servicios y capitales entre los países miembros a través de la eliminación de barreras arancelarias y no arancelarias. El documento también explica algunas de las instituciones creadas por el MERCOSUR como el Fondo para la Convergencia Estructural y el Programa de Apoyo al Sector Educat
La conferencia de Potsdam en 1945 dividió Alemania en cuatro zonas administradas por los aliados, dando inicio a la Guerra Fría y la división de Alemania en las zonas occidental y oriental. Esta división culminó con la construcción del Muro de Berlín en 1961, pero finalmente cayó en 1989 abriendo el camino para la reunificación alemana el 3 de octubre de 1990.
Mercosur is a trading bloc in South America consisting of Brazil, Argentina, Paraguay and Uruguay. It was created in 1991 with the goal of promoting free trade and the fluid movement of goods, people, and currency between member states. Key objectives include eliminating customs duties and lifting restrictions on the movement of goods. Mercosur has established a common external tariff and aims to integrate member economies and develop common institutions. It currently functions as a customs union. India has a preferential trade agreement with Mercosur that came into effect in 2009.
El documento describe el Mercosur, un bloque económico sudamericano formado en 1995 y compuesto por Argentina, Brasil, Paraguay, Uruguay y Venezuela. Explica que el Mercosur busca la liberación económica entre sus países miembros a través de una unión aduanera, mientras adopta una postura proteccionista hacia mercados externos. También proporciona detalles sobre la economía, el producto interno bruto y las exportaciones e importaciones de cada uno de los países miembros.
Non-technical demonstration of grlc intended for users without prior knowledge of SPARQL or github. See grlc.io and SALAD best paper awarded research: http://datalegend.net/assets/paper7.pdf
El documento describe el MERCOSUR, un tratado firmado en 1991 entre Argentina, Brasil, Paraguay y Uruguay para crear un mercado común en América del Sur. Los objetivos del MERCOSUR incluyen la libre circulación de bienes, servicios y factores productivos, así como la adopción de una política comercial y arancel externo común entre los países miembros. El documento explica la estructura y funciones de los principales órganos del MERCOSUR como el Consejo del Mercado Común y el Grupo Mercado Común.
El documento proporciona información sobre el Mercosur, incluyendo su historia, países miembros y asociados, órganos principales y objetivos. El Mercosur se estableció en 1985 y actualmente incluye a Argentina, Brasil, Paraguay y Uruguay como miembros plenos, así como varios países asociados. Sus órganos clave son el Consejo del Mercado Común, el Grupo Mercado Común y la Comisión de Comercio del Mercosur. Los objetivos del bloque son promover el libre comercio y la integra
El MERCOSUR es un acuerdo de integración económica y comercial entre países de América del Sur firmado en 1991 por Argentina, Brasil, Paraguay y Uruguay. Actualmente está integrado por esos cuatro países más Venezuela, y Bolivia se encuentra en proceso de adhesión. El objetivo del MERCOSUR es promover el comercio entre los países miembros y fortalecer su posición económica en el mundo a través de la integración.
Este documento describe el Mercado Común del Sur (MERCOSUR), un bloque económico y comercial formado por Argentina, Brasil, Paraguay, Uruguay y Venezuela. El MERCOSUR tiene como objetivo principal promover la libre circulación de bienes, servicios y capitales entre los países miembros a través de la eliminación de barreras arancelarias y no arancelarias. El documento también explica algunas de las instituciones creadas por el MERCOSUR como el Fondo para la Convergencia Estructural y el Programa de Apoyo al Sector Educat
La conferencia de Potsdam en 1945 dividió Alemania en cuatro zonas administradas por los aliados, dando inicio a la Guerra Fría y la división de Alemania en las zonas occidental y oriental. Esta división culminó con la construcción del Muro de Berlín en 1961, pero finalmente cayó en 1989 abriendo el camino para la reunificación alemana el 3 de octubre de 1990.
Mercosur is a trading bloc in South America consisting of Brazil, Argentina, Paraguay and Uruguay. It was created in 1991 with the goal of promoting free trade and the fluid movement of goods, people, and currency between member states. Key objectives include eliminating customs duties and lifting restrictions on the movement of goods. Mercosur has established a common external tariff and aims to integrate member economies and develop common institutions. It currently functions as a customs union. India has a preferential trade agreement with Mercosur that came into effect in 2009.
El documento describe el Mercosur, un bloque económico sudamericano formado en 1995 y compuesto por Argentina, Brasil, Paraguay, Uruguay y Venezuela. Explica que el Mercosur busca la liberación económica entre sus países miembros a través de una unión aduanera, mientras adopta una postura proteccionista hacia mercados externos. También proporciona detalles sobre la economía, el producto interno bruto y las exportaciones e importaciones de cada uno de los países miembros.
The document discusses the "Seven Righteous Fights" that technical writers should focus on. It lists the seven fights as localization, security, extensibility, documentation, affordance, acceptance, and accessibility. For each fight, it provides brief explanations and examples of tactics to address them such as writing style guides, testing with users, and cultivating inclusion. The overarching message is to avoid technical debt and prioritize building software that is inclusive and usable for all people.
This history of DevOps up to the current day including some useful things to know published along the way told in the Twitter Conversations of those that showed us the way
devopsdays Warsaw 2018 - Chaos while deploying MLThiago de Faria
AI is such a buzzword, with its futuristic implementations and sophisticated machine learning algorithms (Hello, Deep learning!). We are using ML when we need external data to reach a working product because it would be impossible to solve it with the regular for/if/loops. What are the next steps? Moreover, what about Test, Release, and Deployment? We always value data and call our organizations “data-driven,” but now the impact is even more significant. If you are using an ML component, misused/dirty/problematic data will affect not your internal reports as before… but your application deployment and quality of service. Let’s hear discuss some AI implementations stories (its advantages/problems) finding common mistakes and future challenges for such a hyped theme.
Codemotion Milan 2018 - AI with a devops mindset: experimentation, sharing an...Thiago de Faria
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be in real life with a live demo!
Thiago de Faria - AI with a devops mindset - experimentation, sharing and eas...Codemotion
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be on the real life with a live demo!
Data Con LA 2020
Description
Coming from a grand belief of data democratization, I believe that in order for any team to be successful collaborators, it has to be data centric and data should be accessible to all.
*To ensure that your non software or software engineering centric team has maximum efficiency, data should be visible, data lake should be accessible.
*Form a database for analytics summaries, talk about the different technologies(SQL, NoSQL) cost of deployment, need, team driven structure. Build an API for this database for external/inter team crosstalk.
*Build analytics and visual layer on top of it. Flask/Django/Node, etc.., to enable the team to have high visibility in their analysis, and to ensure a higher turnaround of data.
*Talk about an easy way of enabling the team to run code, could be local/cloud, JupyterHub is a great way of doing so, talk about the tremendous value added in that and the potential it enables
*Talk about the common tools user for version control/CICD/Coding technologies, etc..
*Finally summarize the value of the mixture of all these tools and technologies in order to ensure the maximum efficiency.
Speaker
Nawar Khabbaz, Rivian, Data Engineer
Déjà 10 ans de Software Craft ! Comment vos pratiques ont-elles évolué durant cette décennie ? Au-delà de la dette technique dont Arnaud Lemaire avait parlé l’an passé, au-delà du Clean Code, de TDD et de BDD, 10 ans après le Craft doit se préoccuper désormais des environnements d’aujourd’hui, avec plus de distribué, des microservices, du Cloud et même (et ce n’est même pas un troll) des transformations digitales ! Mais alors, est-ce vraiment encore du Craft ? Venez juger par vous-mêmes avec Cyrille sous le soleil de Sunny Tech !
This document discusses NoSQL databases and frameworks for using them with Java applications. It summarizes the advantages of NoSQL databases, different types including key-value, column-oriented, document and graph databases. It also discusses frameworks like NoSQL Endgame that aim to provide a common API for working with multiple NoSQL databases from Java code. However, it notes that fully supporting all NoSQL databases and scenarios is still a challenge for such frameworks.
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Codemotion Berlin 2018 - AI with a devops mindset: experimentation, sharing a...Thiago de Faria
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be on the real life with a live demo!
Thiago de Faria - AI with a devops mindset - experimentation, sharing and eas...Codemotion
The document discusses applying a DevOps mindset to machine learning. It notes traditional problems in ML like data scientists not being engineers, lack of versioning and scaling issues. Applying continuous integration/deployment practices from DevOps can help address these by facilitating collaboration, versioning of code and data, continuous evaluation, packaging, deployment and model serving. This requires culture change and bridging the gap between data scientists and engineers. DevOps practices may help achieve the collaboration, automation and monitoring needed between ML development and operations.
The document discusses the challenges of managing large volumes of data from various sources in a traditional divided approach. It argues that Hadoop provides a solution by allowing all data to be stored together in a single system and processed as needed. This addresses the problems caused by keeping data isolated in different silos and enables new types of analysis across all available data.
When it all goes wrong | PGConf EU 2019 | Will LeinweberCitus Data
This document summarizes a presentation about troubleshooting Postgres performance problems. It discusses how to determine if the issue is with the database, system resources, or the application. It provides examples of common problems like running out of CPU, memory, disk, or parallelism. It also recommends tools to diagnose issues like perf, gdb, iostat, iotop, htop, bwm-ng, and pg_stat_statements. Finally, it discusses setting boundaries around economics, workload, performance, and errors to avoid instability.
When it all goes wrong (with Postgres) | RailsConf 2019 | Will LeinweberCitus Data
You're woken up in the middle of the night to your phone. Your app is down and you're on call to fix it. Eventually you track it down to "something with the db," but what exactly is wrong? And of course, you're sure that nothing changed recently…
Knowing what to fix, and even where to start looking, is a skill that takes a long time to develop. Especially since Postgres normally works very well for months at a time, not letting you get practice!
In this talk, I'll share not only the more common failure cases and how to fix them, but also a general approach to efficiently figuring out what's wrong in the first place.
Version Control in AI/Machine Learning by DatmoNicholas Walsh
Starting with outlining the history of conventional version control before diving into explaining QoDs (Quantitative Oriented Developers) and the unique problems their ML systems pose from an operations perspective (MLOps). With the only status quo solutions being proprietary in-house pipelines (exclusive to Uber, Google, Facebook) and manual tracking/fragile "glue" code for everyone else.
Datmo works to solve this issue by empowering QoDs in two ways: making MLOps manageable and simple (rather than completely abstracted away) as well as reducing the amount of glue code so to ensure more robust pipelines.
Hadoop at the Center: The Next Generation of HadoopAdam Muise
This document discusses Hortonworks' approach to addressing challenges around managing large volumes of diverse data. It presents Hortonworks' Hadoop Data Platform (HDP) as a solution for consolidating siloed data into a central data lake on a single cluster. This allows different data types and workloads like batch, interactive, and real-time processing to leverage shared services for security, governance and operations while preserving existing tools. The HDP also enables new use cases for analytics like real-time personalization and segmentation using diverse data sources.
WJAX 2013 Slides online: Big Data beyond Apache Hadoop - How to integrate ALL...Kai Wähner
Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data. Apache Hadoop is the open source defacto standard for implementing big data solutions on the Java platform. Hadoop consists of its kernel, MapReduce, and the Hadoop Distributed Filesystem (HDFS). A challenging task is to send all data to Hadoop for processing and storage (and then get it back to your application later), because in practice data comes from many different applications (SAP, Salesforce, Siebel, etc.) and databases (File, SQL, NoSQL), uses different technologies and concepts for communication (e.g. HTTP, FTP, RMI, JMS), and consists of different data formats using CSV, XML, binary data, or other alternatives. This session shows different open source frameworks and products to solve this challenging task. Learn how to use every thinkable data with Hadoop – without plenty of complex or redundant boilerplate code.
This document discusses Writables and WritableComparables in Hadoop. It explains that Writables allow data to be serialized for efficient transfer between nodes in a Hadoop cluster. The document outlines how to create custom Writable types by implementing the Writable interface and its methods like write and readFields. It also discusses that WritableComparable is needed for keys to allow sorting, by implementing the Comparable interface and compareTo method. The document shows how to create a custom WritableComparable type to allow efficient data transfer and sorting of custom data types in Hadoop.
YOUR OPEN SOURCE PROJECT IS LIKE A STARTUP, TREAT IT LIKE ONE, EYAR ZILBERMAN...DevOpsDays Tel Aviv
The document provides tips for marketing an open source project on GitHub. It recommends explaining the motivation and purpose of the project, researching similar existing projects, and developing the project to be easy to install, use, and contribute to. Key steps include writing a good README, publishing the project in relevant communities, and submitting it to curated lists to help users discover the project. The goal is to build an active community of contributors and users to support the long-term success of the open source project.
The document discusses GraphQL as an alternative to REST APIs. It begins by describing some common issues with REST APIs, such as over-fetching and under-fetching data. It then introduces GraphQL as a query language that allows clients to get precisely the data they need in a single request. Key benefits of GraphQL include no breaking changes, no over-fetching or under-fetching, and being transport agnostic. The document also covers GraphQL concepts like schemas, queries, mutations, and errors. It provides examples of how problems like rate limiting and error handling are approached differently in GraphQL compared to REST. In the end, it acknowledges that GraphQL has a learning curve but can help solve real problems for
More Related Content
Similar to Fear of the Bus - Heidi Waterhouse - DevOpsDays Tel Aviv 2016
The document discusses the "Seven Righteous Fights" that technical writers should focus on. It lists the seven fights as localization, security, extensibility, documentation, affordance, acceptance, and accessibility. For each fight, it provides brief explanations and examples of tactics to address them such as writing style guides, testing with users, and cultivating inclusion. The overarching message is to avoid technical debt and prioritize building software that is inclusive and usable for all people.
This history of DevOps up to the current day including some useful things to know published along the way told in the Twitter Conversations of those that showed us the way
devopsdays Warsaw 2018 - Chaos while deploying MLThiago de Faria
AI is such a buzzword, with its futuristic implementations and sophisticated machine learning algorithms (Hello, Deep learning!). We are using ML when we need external data to reach a working product because it would be impossible to solve it with the regular for/if/loops. What are the next steps? Moreover, what about Test, Release, and Deployment? We always value data and call our organizations “data-driven,” but now the impact is even more significant. If you are using an ML component, misused/dirty/problematic data will affect not your internal reports as before… but your application deployment and quality of service. Let’s hear discuss some AI implementations stories (its advantages/problems) finding common mistakes and future challenges for such a hyped theme.
Codemotion Milan 2018 - AI with a devops mindset: experimentation, sharing an...Thiago de Faria
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be in real life with a live demo!
Thiago de Faria - AI with a devops mindset - experimentation, sharing and eas...Codemotion
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be on the real life with a live demo!
Data Con LA 2020
Description
Coming from a grand belief of data democratization, I believe that in order for any team to be successful collaborators, it has to be data centric and data should be accessible to all.
*To ensure that your non software or software engineering centric team has maximum efficiency, data should be visible, data lake should be accessible.
*Form a database for analytics summaries, talk about the different technologies(SQL, NoSQL) cost of deployment, need, team driven structure. Build an API for this database for external/inter team crosstalk.
*Build analytics and visual layer on top of it. Flask/Django/Node, etc.., to enable the team to have high visibility in their analysis, and to ensure a higher turnaround of data.
*Talk about an easy way of enabling the team to run code, could be local/cloud, JupyterHub is a great way of doing so, talk about the tremendous value added in that and the potential it enables
*Talk about the common tools user for version control/CICD/Coding technologies, etc..
*Finally summarize the value of the mixture of all these tools and technologies in order to ensure the maximum efficiency.
Speaker
Nawar Khabbaz, Rivian, Data Engineer
Déjà 10 ans de Software Craft ! Comment vos pratiques ont-elles évolué durant cette décennie ? Au-delà de la dette technique dont Arnaud Lemaire avait parlé l’an passé, au-delà du Clean Code, de TDD et de BDD, 10 ans après le Craft doit se préoccuper désormais des environnements d’aujourd’hui, avec plus de distribué, des microservices, du Cloud et même (et ce n’est même pas un troll) des transformations digitales ! Mais alors, est-ce vraiment encore du Craft ? Venez juger par vous-mêmes avec Cyrille sous le soleil de Sunny Tech !
This document discusses NoSQL databases and frameworks for using them with Java applications. It summarizes the advantages of NoSQL databases, different types including key-value, column-oriented, document and graph databases. It also discusses frameworks like NoSQL Endgame that aim to provide a common API for working with multiple NoSQL databases from Java code. However, it notes that fully supporting all NoSQL databases and scenarios is still a challenge for such frameworks.
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Codemotion Berlin 2018 - AI with a devops mindset: experimentation, sharing a...Thiago de Faria
AI is the buzzword while ML is the underlying component... but when do we use ML? To solve problems that machines can find patterns without explicitly programming them to do so. But do you have a team building an ML model? How far are they from the IT team? Do they know how to deploy and serve that? Testing? And sharing what they have done? That's where a devops mindset comes in: reduce the batch size, continuous-everything and a culture of failure/experimentation are vital for your data team! In the end, I will show how the workflow of a data scientist can be on the real life with a live demo!
Thiago de Faria - AI with a devops mindset - experimentation, sharing and eas...Codemotion
The document discusses applying a DevOps mindset to machine learning. It notes traditional problems in ML like data scientists not being engineers, lack of versioning and scaling issues. Applying continuous integration/deployment practices from DevOps can help address these by facilitating collaboration, versioning of code and data, continuous evaluation, packaging, deployment and model serving. This requires culture change and bridging the gap between data scientists and engineers. DevOps practices may help achieve the collaboration, automation and monitoring needed between ML development and operations.
The document discusses the challenges of managing large volumes of data from various sources in a traditional divided approach. It argues that Hadoop provides a solution by allowing all data to be stored together in a single system and processed as needed. This addresses the problems caused by keeping data isolated in different silos and enables new types of analysis across all available data.
When it all goes wrong | PGConf EU 2019 | Will LeinweberCitus Data
This document summarizes a presentation about troubleshooting Postgres performance problems. It discusses how to determine if the issue is with the database, system resources, or the application. It provides examples of common problems like running out of CPU, memory, disk, or parallelism. It also recommends tools to diagnose issues like perf, gdb, iostat, iotop, htop, bwm-ng, and pg_stat_statements. Finally, it discusses setting boundaries around economics, workload, performance, and errors to avoid instability.
When it all goes wrong (with Postgres) | RailsConf 2019 | Will LeinweberCitus Data
You're woken up in the middle of the night to your phone. Your app is down and you're on call to fix it. Eventually you track it down to "something with the db," but what exactly is wrong? And of course, you're sure that nothing changed recently…
Knowing what to fix, and even where to start looking, is a skill that takes a long time to develop. Especially since Postgres normally works very well for months at a time, not letting you get practice!
In this talk, I'll share not only the more common failure cases and how to fix them, but also a general approach to efficiently figuring out what's wrong in the first place.
Version Control in AI/Machine Learning by DatmoNicholas Walsh
Starting with outlining the history of conventional version control before diving into explaining QoDs (Quantitative Oriented Developers) and the unique problems their ML systems pose from an operations perspective (MLOps). With the only status quo solutions being proprietary in-house pipelines (exclusive to Uber, Google, Facebook) and manual tracking/fragile "glue" code for everyone else.
Datmo works to solve this issue by empowering QoDs in two ways: making MLOps manageable and simple (rather than completely abstracted away) as well as reducing the amount of glue code so to ensure more robust pipelines.
Hadoop at the Center: The Next Generation of HadoopAdam Muise
This document discusses Hortonworks' approach to addressing challenges around managing large volumes of diverse data. It presents Hortonworks' Hadoop Data Platform (HDP) as a solution for consolidating siloed data into a central data lake on a single cluster. This allows different data types and workloads like batch, interactive, and real-time processing to leverage shared services for security, governance and operations while preserving existing tools. The HDP also enables new use cases for analytics like real-time personalization and segmentation using diverse data sources.
WJAX 2013 Slides online: Big Data beyond Apache Hadoop - How to integrate ALL...Kai Wähner
Big data represents a significant paradigm shift in enterprise technology. Big data radically changes the nature of the data management profession as it introduces new concerns about the volume, velocity and variety of corporate data. Apache Hadoop is the open source defacto standard for implementing big data solutions on the Java platform. Hadoop consists of its kernel, MapReduce, and the Hadoop Distributed Filesystem (HDFS). A challenging task is to send all data to Hadoop for processing and storage (and then get it back to your application later), because in practice data comes from many different applications (SAP, Salesforce, Siebel, etc.) and databases (File, SQL, NoSQL), uses different technologies and concepts for communication (e.g. HTTP, FTP, RMI, JMS), and consists of different data formats using CSV, XML, binary data, or other alternatives. This session shows different open source frameworks and products to solve this challenging task. Learn how to use every thinkable data with Hadoop – without plenty of complex or redundant boilerplate code.
This document discusses Writables and WritableComparables in Hadoop. It explains that Writables allow data to be serialized for efficient transfer between nodes in a Hadoop cluster. The document outlines how to create custom Writable types by implementing the Writable interface and its methods like write and readFields. It also discusses that WritableComparable is needed for keys to allow sorting, by implementing the Comparable interface and compareTo method. The document shows how to create a custom WritableComparable type to allow efficient data transfer and sorting of custom data types in Hadoop.
Similar to Fear of the Bus - Heidi Waterhouse - DevOpsDays Tel Aviv 2016 (20)
YOUR OPEN SOURCE PROJECT IS LIKE A STARTUP, TREAT IT LIKE ONE, EYAR ZILBERMAN...DevOpsDays Tel Aviv
The document provides tips for marketing an open source project on GitHub. It recommends explaining the motivation and purpose of the project, researching similar existing projects, and developing the project to be easy to install, use, and contribute to. Key steps include writing a good README, publishing the project in relevant communities, and submitting it to curated lists to help users discover the project. The goal is to build an active community of contributors and users to support the long-term success of the open source project.
The document discusses GraphQL as an alternative to REST APIs. It begins by describing some common issues with REST APIs, such as over-fetching and under-fetching data. It then introduces GraphQL as a query language that allows clients to get precisely the data they need in a single request. Key benefits of GraphQL include no breaking changes, no over-fetching or under-fetching, and being transport agnostic. The document also covers GraphQL concepts like schemas, queries, mutations, and errors. It provides examples of how problems like rate limiting and error handling are approached differently in GraphQL compared to REST. In the end, it acknowledges that GraphQL has a learning curve but can help solve real problems for
MICROSERVICES ABOVE THE CLOUD - DESIGNING THE INTERNATIONAL SPACE STATION FOR...DevOpsDays Tel Aviv
Robert Barron discusses the construction and operation of the International Space Station (ISS). The ISS was built in stages over many flights from 1998-2011, with modules being delivered and assembled to create the permanently inhabited outpost in space. It currently consists of multiple modules from the US, Russia, Europe, Japan and Canada which provide living quarters, research labs, docking ports, and more. Managing resources like oxygen generation and spacesuits on the ISS requires redundant systems to ensure resiliency in the challenging space environment.
THE (IR)RATIONAL INCIDENT RESPONSE: HOW PSYCHOLOGICAL BIASES AFFECT INCIDENT ...DevOpsDays Tel Aviv
Have you ever felt you took every wrong turn possible in the process of mitigating a production incident? Did you go through a 3-hour hell during incident response and felt the incident wasn’t complex enough to justify the horrors you’ve experienced? Did it cause you to question your engineering or problem-solving skills?
Well, it’s only partially you. Our brain is wired to make decision-making simpler. In doing so, it exposes itself to biases, heuristics, and other quirks that may seem like “bad decisions” in hindsight.
In this talk, through real-life outages, we’ll project those psychological principles onto the world of production monitor, and incident management. As a responder, you’ll learn why those behavioral patterns emerge during production incidents and what can be done to limit their effect, and as a manager, you’ll learn how to enable and encourage a healthy environment to better support those patterns.
The word observable entered the English language roughly 400 years ago, but the concepts of what it means to see, comprehend, and understand something have been debated since time immemorial. Starting in the 19th century, a series of postulates and criteria coalesced into control theory, and it is from this body of knowledge that we gained the word “observability”. Today, with the advent of complex, interconnected computer systems, that word has taken on new meanings and connotations—some useful, some detrimental, and some just plain confusing.
In this talk, we’ll mix a little history, a touch of philosophy, and a healthy dose of reality, to demystify what observability means to us as professional computer people. We’ll tear through the marketing material and unearth foundational principles that will help us to build better infrastructure, write better software, and promote healthier business practices. Finally, we’ll explore some potential new avenues for discussion and understanding.
NUDGE AND SLUDGE: DRIVING SECURITY WITH DESIGN // J. WOLFGANG GOERLICH, Duo S...DevOpsDays Tel Aviv
Security people say users are the weakest link. But are they? When complying with security becomes too burdensome, users take shortcuts, find workarounds, and end up jeopardizing security. Blaming users is lazy and easy. Making security usable is time consuming and challenging. How does design research help us understand our customers? What patterns and principles drive secure behavior? How can we build empathy with customers and make the right thing to do the easiest thing to do? This session explores these questions, and provides examples of how design thinking and research can help us be more secure. We will walk through our creation of core user personas, design principles, and how these inform and direct our design choices and intent. Don’t blame your users anymore. Come learn how to be part of a future where usability leads security.
(Ignite) TAKE A HIKE: PREVENTING BATTERY CORROSION - LEAH VOGEL, CHEGGDevOpsDays Tel Aviv
This is for you, you rockstar, ninja coffee drinking workaholic who doesn’t know what a vacation day looks like. Even though you love your job and are dedicated and are super important, you need a break too.
We tend to think that working all the time is an effective practice while the truth is that finding the time for self care and recharging your batteries is beneficial for both you and your company. Additionally, if you’re a leader, you’re responsible for the wellbeing of your team. In this talk I’ll discuss the importance of taking time off of work and creating a positive culture surrounding vacation time.
BUILDING A DR PLAN FOR YOUR CLOUD INFRASTRUCTURE FROM THE GROUND UP, MOSHE BE...DevOpsDays Tel Aviv
This is a story about taking the cloud infrastructure of a successful company, that is still managed as infrastructure of a startup company, and rebuilding it to support the growing business requirements, especially around disaster recovery and business continuity. In the session I will share Next Insurance’s journey - where we started, where we are now and what we learned on the way so far. I will talk about how we managed to build our proven DR plans, and actually execute them in our DR drills. I will also talk about why we decided that the only way to prove your DR plan works is to continue running your business in the DR account and make it your production account, and go on to build your next DR account. If you are a part of a company that is about to embark on a similar journey, this session might equip you with some very useful insights on how to think about such a challenge, and some very useful and practical tips on how to execute it.
THE THREE DISCIPLINES OF CI/CD SECURITY, DANIEL KRIVELEVICH, Cider SecurityDevOpsDays Tel Aviv
CI/CD pipelines are quickly becoming the path of least resistance for would-be attackers into sensitive internal systems, gaining access to critical data, with minimal effort.
In the InfoSec world when we talk about CI/CD security often times this focuses on specific aspects of securing your pipeline - scanning the code, protecting secrets, securely managing code deployments, or even authentication and authorization mechanisms, but we rarely talk about all of these together.
After years of being in the trenches and realizing that the attack surface is growing and the threat landscape becoming more and more complex, it has become increasingly apparent that security teams need to adapt and modify strategies to keep up with the new reality of CI/CD protection, without compromising developer velocity.
In this talk I would like to propose a new way of thinking about CI/CD security - that encompasses the three disciplines that comprise CI/CD security - security in the pipeline, of the pipeline, and around the pipeline. Partial coverage of any or all of these disciplines simply will not cut it with the continuously evolving risk landscape. Security engineers need to address each of these aspects in their entirety to provide the full scope of coverage that modern organizations need, and I will take a deep dive on the challenges each introduce, and the approaches and techniques for mitigating them based on adversarial sec research.
The last two decades have been all about SaaS, with advantages that cannot be overstated. Except SaaS isn’t always an option, nor is it always the right choice: businesses in tightly regulated industries, or where information security is paramount, for example, will not - often can not - consider any software that isn’t under their control. For many software enterprises, this leads to the dreaded inevitability of on-premise deployment.
Fortunately, the situation today is dramatically different to a scant few years ago, let alone a decade or two: the same technologies that enable SaaS have also radically transformed on-prem deployment. Modern tools like Docker, Consul, ELK and Kubernetes - to name a few - can be leveraged to completely transform the experience for both customers and vendors. In this talk we’ll contrast the challenges and advantages of SaaS and on-prem, see how things have evolved in recent history, and see how modern on-prem deployment can be, if not pleasurable, at least relatively painless.
CONFIGURATION MANAGEMENT IN THE CLOUD NATIVE ERA, SHAHAR MINTZ, EggPackDevOpsDays Tel Aviv
Configuration Management is at the core of Ops. It’s the biggest enabler of any compute operation, small and big. In the past decade, we have switched from thinking about the machines we are configuring, to think about the software and services we are controlling. With that change of mindset, so did the tools we are using. Traditional tools like Puppet, chef, salt and Ansible are slowly declining while new tools such as Terraform, Pulumi, Helm and Kustomize are on the rise. In this talk I will try to describe the pain-points and the opportunities of this transformation as well as suggesting a future direction based on tools developed at the big-tech companies (Mainly facebook and google).
SOLVING THE DEVOPS CRISIS, ONE PERSON AT A TIME, CHRISTINA BABITSKI, DeveleapDevOpsDays Tel Aviv
We all know how hard it is to find DevOps engineers, and creating a diverse team despite gender and ethnicity bias? Nearly impossible. At this talk we will show our tools and methods implemented in the Develeap hiring process that overcome this inherited bias.
About 2 years ago we faced a crisis in our DevOps consulting company - the market demand was higher than we could supply. The traditional recruiting process depending on CV and artificial credentials was not working. So we came up with an alternative solution, and since then - we are growing exponentially and diversely. In this talk we will show the practical tools we deployed in order to increase our capacity, and we will show how these tools overcome the inherited bias in the process.
OPTIMIZING PERFORMANCE USING CONTINUOUS PRODUCTION PROFILING ,YONATAN GOLDSCH...DevOpsDays Tel Aviv
Everyone wants observability into their system, but find themselves with too many vendors and tools, each with its own API, SDK, agent and collectors.
With the increasing complexity of modern applications, continuous profiling methods and tools are gaining popularity among the Developer and Engineering communities. In this session, we cover what continuous profiling entails and why you should implement a profiler into your tech stack (if you haven’t done so already). We’ll then bring theory to practice and demonstrate a real-life scenario using gProfiler, a free open-source continuous profiling tool, covering Linux servers on multiple architectures (such as Graviton).
HOW TO SCALE YOUR ONCALL OPERATION, AND SURVIVE TO TELL, ANTON DRUKHDevOpsDays Tel Aviv
“Being oncall sucks. But it doesn’t have to!” We all heard this one before. Why is it though, that oncall still remains the biggest scar for many? What can a modern Engineering org do to rein the oncall dragons, and actually help people grow as professionals as they go oncall?
In this talk, I will present the main reasons why oncall is difficult in modern orgs, and describe ways to mitigate these hardships. The idea is that oncall is often the ‘backroom’ of an org, where all the technical and organizational debt take their toll. Be it unwieldy systems or broken processes between teams, oncall checks all the ‘weak boxes’. Therefore, the only way to win at oncall is to sort out your debts, starting with the organizational ones.
I will dive into the detail of the oncall rotation at Snyk as the org scaled from 1 to 220 people, what worked well about it, and what was less than perfect. I will discuss the decisions made to turn oncall into a building block of the org, and show a path to rein oncall in your organization as well.
Github Copilot and tools that help us code better are cool. But I’m lucky if I spend 90 minutes a day writing code. We really need to optimize the hours we spend reviewing code, updating tickets and tracing where our code is deployed. Learn how I save an hour a day streamlining non-coding tasks.
This talk is unique because 99% of developer productivity tools and hacks are about coding faster, better, smarter. And yet the vast majority of our time is spent doing all of this other stuff. After I started focusing on optimizing the 10 hours I spend every day on non-coding tasks, I found I my productivity went up and my frustration at annoying stuff went way down. I cover how to save time by reducing cognitive load and by cutting menial, non-coding tasks that we have to perform 10-50 times every day. For example:
Bug or hotfix comes through and you want to start working on it right away so you create a branch and start fixing. What you don’t do is create a Jira ticket but then later your boss/PM/CSM yells at your due to lack of visibility. I share how I automated ticket creation in Slack by correlating Github to Jira.
You have 20 minutes until your next meeting and you open a pull request and start a review. But you get pulled away half way through and when you come back the next day you forgot everything and have to start over. Huge waste of time. I share an ML job I wrote that tells me how long the review will take so I can pick PRs that fit the amount of time I have.
You build. You ship it. You own it. Great. But after I merge my code I never know where it actually is. Did the CI job fail? Is it release under feature flag? Did it just go GA to everyone? I share a bot I wrote that personally tells me where my code is in the pipeline after it leaves my hands so I can actually take full ownership without spending tons of time figuring out what code is in what release.
FLYING BLIND - ACCESSIBILITY IN MONITORING, FEU MOUREK, IcingaDevOpsDays Tel Aviv
Do you know what it feels like to navigate as someone who can’t distinguish between green and red - looking at those badges that tell you whether something is broken or a-okay? I’ll give you a quick look into what it feels like with some examples from the monitoring tool Icinga Web 2.
We all tend to forget, that not everyone sees the world like we do. In this talk I’ll be walking you through different views in Icinga Web 2 with side-by-side comparisons for the default views and how different kinds of vision impairments affect those. The talks also features a few suggestions on how to improve colour schemes and making websites and webapps better to navigate with screen readers!
(Ignite) WHAT'S BURNING THROUGH YOUR CLOUD BILL - GIL BAHAT, CIDER SECURITYDevOpsDays Tel Aviv
Recent years have exposed startups to a major plague - cloud overspend. No vaccine appears to exist, plethora of tools and consultants fail to stop the bleeding. And yet, some companies manage to stay safe. What makes them different? Is it the tools? Is it the mindset? Is it developer training?
In this session we will examine the cultural factors involved in sound and responsible financial management in the cloud. We will also look at relevant system design elements and product design elements which enable us to spend wisely while our business runs smoothly.
Following this session, you should be better versed in cost-aware system design and some of the cultural and structural requirements to keeping your cloud bill low.
In every development process there is the question, do we invest enough on quality? Do we need to invest more? Every team knows about the dilemma of how many tests is the right amount of tests we should write. Is 80% test coverage is good enough? Maybe 90%? 100%? Should we invest more time in unit testing? Are we wasting too much time on unit-testing? Should we invest time on a faster rollback mechanism?
WIIFM
“Without data, you’re just another person with an opinion” - W. Edwards Deming
SLO Driven Development is a framework that helps the developers focus on impact and balance of every aspect of the dev process. When working currently with SLI, SLA, SLO and error budget you can learn where to invest in the development process.
Let’s talk about the importance of good SLOs and how they can help us improve our day2day
In this talk, I will share do's and don'ts on how to onboard successfully in a remote or hybrid setup including moving to a leadership role, speaking from my own journey onboarding remotely in the midst of a global pandemic.
I will share the tips that worked for me for successful onboarding, how I was able to be productive, impactful, and make a good impression on others. The key issues as an “onbordee” that I will talk about are how to create relationships, make yourself visible in the company, time management, and more.
Since I started working in Augury over 100 new employees have joined the company. Each month I give a session that is part of their general onboarding process. This became a crucial step due to the fact that we are now a hybrid company and a lot of people are onboarding remotely or in a hybrid setup for the first time in their lives.
I joined the company as a backend developer and a few months into my role, the squad leader position in my squad was up for grabs and I was fortunate enough to grab it :) This is my first official leadership role, which I also needed to onboard into in a hybrid setup. I will share the process that I built for myself on “How to lead”. Also, a word or two on the process we built as a squad on how we work in a hybrid setup, what are we optimizing for when we do meet and how to include new members of the team.
DON'T PANIC: GETTING YOUR INFRASTRUCTURE DRIFT UNDER CONTROL, ERAN BIBI, FireflyDevOpsDays Tel Aviv
In your ever-changing Infrastructure, some changes are intentional while others are not.
Drift is what happens whenever the real-world state of your infrastructure differs from the state defined in your configuration. This can happen for many reasons, sometimes it happens when adding or removing resources, other times when changing resource definitions upon resource termination or failure, and even when changes have been made manually or via other automation tools.
While Terraform itself can detect drifts, in most cases, you will be informed about it too late: just before you are about to deploy new changes to your infrastructure. What’s interesting about Terraform though, is that you can apply changes in two separate and distinct steps of “Planning” and “Applying”. This means that you have full visibility of what Terraform is planning on doing beforehand, and if you are satisfied with the changes, you can choose to apply them.
So how does this work? When something is changed intentionally, it will appear in the source code, and the Terraform plan will not do anything. However, if any part of the infrastructure has been changed manually, Terraform’s plan will identify this, and alert you to the change. In other words, if your IaC drifted from its expected state, then Terraform’s plan will, in fact, detect it.
Applying this simple solution can empower DevOps and developer velocity, with the reassurance and context for unexpected changes in your IaC, in near real-time. This talk will showcase real-world examples, and practical ways to apply this in your production environments while doing so safely and at the pace of your engineering cycles.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
Northern Engraving | Nameplate Manufacturing Process - 2024Northern Engraving
Manufacturing custom quality metal nameplates and badges involves several standard operations. Processes include sheet prep, lithography, screening, coating, punch press and inspection. All decoration is completed in the flat sheet with adhesive and tooling operations following. The possibilities for creating unique durable nameplates are endless. How will you create your brand identity? We can help!
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
"Choosing proper type of scaling", Olena SyrotaFwdays
Imagine an IoT processing system that is already quite mature and production-ready and for which client coverage is growing and scaling and performance aspects are life and death questions. The system has Redis, MongoDB, and stream processing based on ksqldb. In this talk, firstly, we will analyze scaling approaches and then select the proper ones for our system.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
AppSec PNW: Android and iOS Application Security with MobSFAjin Abraham
Mobile Security Framework - MobSF is a free and open source automated mobile application security testing environment designed to help security engineers, researchers, developers, and penetration testers to identify security vulnerabilities, malicious behaviours and privacy concerns in mobile applications using static and dynamic analysis. It supports all the popular mobile application binaries and source code formats built for Android and iOS devices. In addition to automated security assessment, it also offers an interactive testing environment to build and execute scenario based test/fuzz cases against the application.
This talk covers:
Using MobSF for static analysis of mobile applications.
Interactive dynamic security assessment of Android and iOS applications.
Solving Mobile app CTF challenges.
Reverse engineering and runtime analysis of Mobile malware.
How to shift left and integrate MobSF/mobsfscan SAST and DAST in your build pipeline.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Fear of the Bus - Heidi Waterhouse - DevOpsDays Tel Aviv 2016
1. @wiredferret #docsxdevop
Fear of the Bus
Documentation and Devops
DevOpsDays Tel Aviv 2016
Heidi Waterhouse
@wiredferret
h.waterhouse@gmail.com
heidiwaterhouse.com
16. Secret information risks
What could happen if the data is lost?
What could happen if the data is found
by good actors?
What happens if the data is found by
bad actors?
37. @wiredferret #docsxdevop
Fear of the Bus
Documentation and Devops
DevOpsDays Tel Aviv 2016
Heidi Waterhouse
@wiredferret
h.waterhouse@gmail.com
heidiwaterhouse.com
Editor's Notes
The title of this talk is derived from some black humor in some companies and IT departments. Would the company fold if any single person got hit by a bus? Sometimes it's less gruesome, and we say "won the lottery", but the principle is the same -- is there anyone in your organization who is literally impossible to replace?If so, I hope you have them insured well enough to dissolve the company gracefully.
No matter how much someone loves a job or company, there are things that can happen that make them suddenly unavailable -- accidents, medical emergencies, natural disasters, even telecom breakdowns.
Most operations and devops people are running flat-out to keep up with their work and don't feel that they have the time to talk to someone about their rapidly-changing docs or write the docs themselves.
That means a lot of mission-critical information gets stored in people's memories or in their messy wiki pages. Essentially, the human is working as a file pointer. But that is volatile memory, RAM.But if something happens to disrupt your power you lose the pointers, and even if the data still exists, you can't find it and it's effectively gone.
The way to insulate ourselves as organizations is to make sure that no one is irreplaceable. We have to have some way to pick up their tasks, capture their understanding, and keep going with our organization.
Core principlesEveryone can touch the docsContent matters more than presentationDocs reduce burnout and onboarding stress because everyone is empowered to get answers
Roughly speaking, there are three categories of information we need to capture:AutomatableStructuralSecret
Automate the things you can automate. If you build a new style of server, build it with a script and write down descriptions of things that change as part of the process of creation. Automatable data is both a safety net against losing information, and a way to streamline your workflow. Any time you are spending time repeating a process that could be automated, you are boring your team and allowing for the introduction of errors.
Automation is a lot like teaching your children a chore. At first it's easier to do something yourself rather than automating or teaching it, but over time, your initial investment will pay off.
Create a structure that encourages the creation of templates.
That means that you have a predictable file structure, and a consistently updated set of templates to use.
Templates are both a file organization system, and a way that we actually write information. It's much easier to fill out a form or a template than to accurately remember all the information you're supposed to include in something. A set of structures reduces the cognitive load of documenting something by quite a lot.
For example, when we fill out a bug in a bug tracking system, it's much easier to check all the required and recommended fields than it is to free form remember all the things that we are supposed to remember to include. If I had to do that, I would forget something every time.A little-know secret of template use is that you need to store a copy of the template somewhere people can't find it, because no matter how you teach them not to do it, and lock the file, someone will inevitably use your master template to fill out instead of a copy. So do yourself a favor and hide a clean copy somewhere no one else will look for it.
Structural information is also where we keep information about workflow. What is the series of steps that need to happen for something to be released? Have you made that into a punch list? If you need to terminate an employee, how many people have to be notified, and what do they need to do about it? That's not just about HR cutting their last paycheck, but about revoking physical and network access, activating post-employment requirements, and making sure everything is done correctly. If you don't store structural information in a place people can find it, they do ad hoc work and again, things get missed.
Secret information is the hardest kind of thing to document, because you have to do threat modeling on it. If you're in an organization that does maturity models of security and has standing security policies, you probably have someone who handles this for you, but if you're with a smaller group, then you will need to do it yourself.
What could happen if the data is lost?What could happen if the data is found by good actors?What happens if the data is found by bad actors?
Secret information is the kind we are most likely to skip documenting because it seems risky, but also the kind that is most important to have exist somewhere outside a single person. For example, what will happen if you don't have a way to access your current website because the host username and password were configured by a marketing person who has left the company? What if it's the password to your Microsoft support account? You don't always want to publish this on an open internal wiki, but you do need to figure where to keep it so the right people can get to it.
No matter what type of information you're documenting, it needs to be findable, usable, and current.
Happily, because we are talking about the devops world, we don't have to worry about making anything pretty, fancy, or user-friendly. It's internal so it only needs to be functional.
Collecting information doesn't just mean telling people they should write down what they're doing, it means making it easy to write things, almost easier than not writing them. It means removing all the obstacles and giving people an easy way to do the right thing. It's a cultural change.
I suggest that you start with what I call a documentation audit. What do you already have and where is it?
If you can, install some internal analytics. What pages are people accessing most? You'll want to be sure to address the content on those first.
Collecting information doesn't have to be about writing a lot of new information, it means getting people to share their personal folders, desktop documents, and sometimes their post-it notes.
Start the documentation audit by listing all your current documents in a spreadsheet. This doesn't have to be fancy. Make columns for the tasks that you expect your devops team to be able to support, like "Deploy to production" or "Respond to AWS outage". Decide what kind of documents you need for any task. I suggest:Make sure each of your tasks has all the required documents. You will find that there are some that are almost complete and some that are very sketchy. If a task requires Secret information, be sure you put a reference to that in the dependencies document.
It doesn't do you any good to collect information if no one can find it. You're going to need to use structure, metadata, and internal training to make sure people can actually find the carefully collected documentation you're using.
One of my favorite recommendations is to put everything in one big flat file. This works in organizations up to a certain size. The advantage is that you can just put new information at the top and push old information down without losing it. When you want to know something, you just search on it. This doesn't even have to be in a wiki or anything that fancy, as long as it's a single hosted file somewhere and not something people are using on their individual systems.
If what you care about is making sure things get documented somewhere, use one big page. It's ugly, and it goes against every design principle, but who cares? It's findable, searchable, everyone can have a single bookmark, and no one has excuses.If you're worried about someone using old info, you can archive things after a year. If you have a devops procedure that has been unchanged for a year, I am frankly astonished.Bonus points if you have a hack afternoon every month or two to update, put in anchor tags and headings, and clean things up as a team
Search is magic and amazing and I think over a decade of completely searchable email has changed how we think about data storage.
That said, the reason search works for email is because presumably we read the message at some point and we know what keywords we are looking for.
When more than one person is building a collection of documentation, you tend to have keyword fragmentation, just because everyone is coming from a different background. If someone is using the wrong keyword, they won't find the thing they need. For example, what would you call this screen? (BSOD). For years and years, if you search for these terms, you would not get any answers from the Microsoft site, because that's not what Microsoft thinks of that screen as. They do now, and if you search for it, it's not directly in the text of the help, but it is in the meta text so that you can find it and see what Microsoft calls it.
18F, a startup embedded in our General Services Administration, uses Slack in an interesting way to collect and find information. When someone says something in a slack message that should be retained, they attach a unique emoji that doesn't get used for anything else. Then you can search on that emoji and add whatever it is next to to the documentation you're trying to build.
When I was talking about this at another conference, I found the guy who actually did it. His name is Mike Bland, and he even uploaded the hubot plugin to do it: https://github.com/mbland/hubot-slack-github-issues
Every team has their own idiosyncratic ways of communicating. It doesn't matter. What does matter is figuring out what your team is doing and putting documents in their existing path.
Information has a shelf life. It goes stale, or worse than stale, it gets rotten.
If I could do it, we would tag all information with a date and when it exceeds a certain age, it gets tossed. But that's a problem for lots of reasons, so instead we have to set things up so that people can "smell" that the data is old and make a considered choice whether or not they want to consume it.If I say this at enough conferences, someone will build it for me. I want a wiki style sheet that gradually ages data by turning it lighter and lighter, until by a year old, it's white text on a white background. You could still read it if you highlighted it, but it would be obvious that this information was worn out and should either be updated or disposed of.
I have another whole talk about how keeping data is expensive and increases our security threat surface, but I'm not going into that today.
What I am going to say is that if someone, like a new hire, is looking at your internal documentation, you don't want them to be able to execute on one thing that was absolutely right two years ago and will now bring down servers, and all the people who are already there know not to use Current_Build_Script, because we all use New_Build_Script.
Nothing says that, we just know it. If you don't use it, throw it away, or at least archive it so it doesn't reach out from the past like the hand of a zombie.
All the hardest problems in computers are peopleDocumentation reduces burnout and daily interruptions, and vastly decreases the cost of on boarding.
How do you test that you're doing it right?
Some of you familiar with the concept of the chaos monkey. The idea is that you select a person on the team, have them hand over their work phone, and take a day off. This is especially nice if they had a nasty on-call weekend. Who wouldn't enjoy a day off that was actually….off? Block them on slack and email, don't let anyone call their home phone. They're not here.
Handle a set of stressful actions when the world is not actually on fire, when people aren't actually emotionally compromised. If you can't remember where Maria keeps the build files when she's out for the day, you sure as heck won't remember when she's in the hospital and you're waiting to hear if it all went ok.
Devops docs save time, money, and having to post pictures of the Hindenberg where your uptime stats used to be.