Building a Streaming Microservice Architecture: with Apache Spark Structured ...Databricks
As we continue to push the boundaries of what is possible with respect to pipeline throughput and data serving tiers, new methodologies and techniques continue to emerge to handle larger and larger workloads
Accelerate Your ML Pipeline with AutoML and MLflowDatabricks
Building ML models is a time consuming endeavor that requires a thorough understanding of feature engineering, selecting useful features, choosing an appropriate algorithm, and performing hyper-parameter tuning. Extensive experimentation is required to arrive at a robust and performant model. Additionally, keeping track of the models that have been developed and deployed may be complex. Solving these challenges is key for successfully implementing end-to-end ML pipelines at scale.
In this talk, we will present a seamless integration of automated machine learning within a Databricks notebook, thus providing a truly unified analytics lifecycle for data scientists and business users with improved speed and efficiency. Specifically, we will show an app that generates and executes a Databricks notebook to train an ML model with H2O’s Driverless AI automatically. The resulting model will be automatically tracked and managed with MLflow. Furthermore, we will show several deployment options to score new data on a Databricks cluster or with an external REST server, all within the app.
Productionalizing Models through CI/CD Design with MLflowDatabricks
Often times model deployment and integration consists of several moving parts that require intricate steps woven together. Automating this pipeline and feedback loop can be incredibly challenging, especially in lieu of varying model development techniques.
Best Practices: How to Analyze IoT Sensor Data with InfluxDBInfluxData
InfluxDB is the purpose-built time series platform. Its high ingest capability makes it perfect for collecting, storing and analyzing time-stamped data from sensors — down to the nanosecond. The InfluxDB platform has everything developers need: the data collection agent, the database, visualization tools, and data querying and scripting language. Join this webinar as Brian Gilmore provides a product overview; he will also deep-dive with some helpful tips and ticks. Stick around for a live demo and Q&A time.
Join this webinar as Brian Gilmore dives into:
The basics of time series data and applications
A platform overview — learn about InfluxDB, Telegraf, and Flux
InfluxDB use case examples — start collecting data at the edge and use your preferred IoT protocol (i.e. MQTT)
Dynamic Rule-based Real-time Market Data AlertsFlink Forward
Flink Forward San Francisco 2022.
At Bloomberg, we deal with high volumes of real-time market data. Our clients expect to be notified of any anomalies in this market data, which may indicate volatile movements in the markets, notable trades, forthcoming events, or system failures. The parameters for these alerts are always evolving and our clients can update them dynamically. In this talk, we'll cover how we utilized the open source Apache Flink and Siddhi SQL projects to build a distributed, scalable, low-latency and dynamic rule-based, real-time alerting system to solve our clients' needs. We'll also cover the lessons we learned along our journey.
by
Ajay Vyasapeetam & Madhuri Jain
Building a Streaming Microservice Architecture: with Apache Spark Structured ...Databricks
As we continue to push the boundaries of what is possible with respect to pipeline throughput and data serving tiers, new methodologies and techniques continue to emerge to handle larger and larger workloads
Accelerate Your ML Pipeline with AutoML and MLflowDatabricks
Building ML models is a time consuming endeavor that requires a thorough understanding of feature engineering, selecting useful features, choosing an appropriate algorithm, and performing hyper-parameter tuning. Extensive experimentation is required to arrive at a robust and performant model. Additionally, keeping track of the models that have been developed and deployed may be complex. Solving these challenges is key for successfully implementing end-to-end ML pipelines at scale.
In this talk, we will present a seamless integration of automated machine learning within a Databricks notebook, thus providing a truly unified analytics lifecycle for data scientists and business users with improved speed and efficiency. Specifically, we will show an app that generates and executes a Databricks notebook to train an ML model with H2O’s Driverless AI automatically. The resulting model will be automatically tracked and managed with MLflow. Furthermore, we will show several deployment options to score new data on a Databricks cluster or with an external REST server, all within the app.
Productionalizing Models through CI/CD Design with MLflowDatabricks
Often times model deployment and integration consists of several moving parts that require intricate steps woven together. Automating this pipeline and feedback loop can be incredibly challenging, especially in lieu of varying model development techniques.
Best Practices: How to Analyze IoT Sensor Data with InfluxDBInfluxData
InfluxDB is the purpose-built time series platform. Its high ingest capability makes it perfect for collecting, storing and analyzing time-stamped data from sensors — down to the nanosecond. The InfluxDB platform has everything developers need: the data collection agent, the database, visualization tools, and data querying and scripting language. Join this webinar as Brian Gilmore provides a product overview; he will also deep-dive with some helpful tips and ticks. Stick around for a live demo and Q&A time.
Join this webinar as Brian Gilmore dives into:
The basics of time series data and applications
A platform overview — learn about InfluxDB, Telegraf, and Flux
InfluxDB use case examples — start collecting data at the edge and use your preferred IoT protocol (i.e. MQTT)
Dynamic Rule-based Real-time Market Data AlertsFlink Forward
Flink Forward San Francisco 2022.
At Bloomberg, we deal with high volumes of real-time market data. Our clients expect to be notified of any anomalies in this market data, which may indicate volatile movements in the markets, notable trades, forthcoming events, or system failures. The parameters for these alerts are always evolving and our clients can update them dynamically. In this talk, we'll cover how we utilized the open source Apache Flink and Siddhi SQL projects to build a distributed, scalable, low-latency and dynamic rule-based, real-time alerting system to solve our clients' needs. We'll also cover the lessons we learned along our journey.
by
Ajay Vyasapeetam & Madhuri Jain
This talk presents 3 programming situations where typeclasses and generics are not adequate: evolving serialization protocols, data generation, modular applications. A library, registry, can be used to help with those 3 situations by giving us the means to wire and rewire code at will.
Serverless Streaming Data Processing using Amazon Kinesis AnalyticsAmazon Web Services
by Adrian Hornsby, Technical Evanglist, AWS
As more and more organizations strive to gain real-time insights into their business, streaming data has become ubiquitous. Typical streaming data analytics solutions require specific skills and complex infrastructure. However, with Amazon Kinesis Analytics, you can analyze streaming data in real-time with standard SQL—there is no need to learn new programming languages or processing frameworks. In this session, we dive deep into the capabilities of Amazon Kinesis Analytics using real-world examples. We’ll present an end-to-end streaming data solution using Amazon Kinesis Streams for data ingestion, Amazon Kinesis Analytics for real-time processing, and Amazon Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Amazon Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system.
Fundamentals Big Data and AI ArchitectureGuido Schmutz
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Distributed computing with Ray. Find your hyper-parameters, speed up your Pan...Jan Margeta
In this talk we will explore Ray - a high-performance and low latency distributed execution framework which will allow you to run your Python code on multiple cores, and scale the same code from your laptop to a large cluster.
Ray uses several interesting ideas like actors, fast zero-copy shared-memory object store, or bottom-up scheduling. Moreover, on top of a succinct API, Ray builds tools to your Pandas pipelines faster, tools that find you the best hyper-parameters for your machine learning models, or train state of the art reinforcement learning algorithms, and much more. Come to the talk and learn some more.
Updated the talk with Kubernetes
https://www.pydays.at/
Frame - Feature Management for Productive Machine LearningDavid Stein
Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018.
Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand.
Cardinality Estimation through Histogram in Apache Spark 2.3 with Ron Hu and ...Databricks
Apache Spark 2.2 shipped with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Skewed data distributions are often inherent in many real world applications. In order to deal with skewed distributions effectively, we added equal-height histograms to Apache Spark 2.3. Leveraging reliable statistics and histogram helps Spark make better decisions in picking the most optimal query plan for real world scenarios.
In this talk, we’ll take a deep dive into how Spark’s Cost-Based Optimizer estimates the cardinality and size of each database operator. Specifically, for skewed distribution workload such as TPC-DS, we will show histogram’s impact on query plan change, hence leading to performance gain.
Architecting Snowflake for High Concurrency and High PerformanceSamanthaBerlant
Cloud Data Warehousing juggernaut Snowflake has raced out ahead of the pack to deliver a data management platform from which a wealth of new analytics can be run. Using Snowflake as a traditional data warehouse has some obvious cost advantages over a hardware solution. But the real value of Snowflake as a data platform lies in its ability to support a high-concurrency analytics platform using Kyligence Cloud, powered by Apache Kylin.
In this presentation, Senior Solutions Architect Robert Hardaway will describe a modern data service architecture using precomputation and distributed indexes to provide interactive analytics to hundreds or even thousands of users running against very large Snowflake datasets (TBs to PBs).
End-to-end Data Governance with Apache Avro and AtlasDataWorks Summit
Aeolus is Comcast’s new internal Big Data system for providing access to an integrated view of a wide variety of high-quality, near-real-time and batch data. Such integration can enable data scientists to uncover otherwise hidden trends, anomalies, and powerful predictors of business successes and failures. But integrating data across silos in a large enterprise is fraught with peril. There typically are few standards on naming conventions and data representation, and spotty documentation at best. The old rule of thumb often applies: 70% of the analysts’ time goes into data wrangling, while only 30% goes toward the actual analyses and simulations. The goal of the Athene Data Governance Platform within Aeolus is to invert this ratio. This talk will explain how Comcast is using Apache Avro and Atlas for end-to-end data governance, the challenges faced, and methods used to address these challenges.
Avro provides a lingua franca for data representation, data integration, and schema evolution. All data published for community consumption must have an associated avro schema in Atlas. Every step in its journey through Aeolus, in flight or at rest, is captured in Atlas. Atlas’ extensibility has allowed us to add or update various entity types (e.g., avro schemas, kafka topics, object store pseudo-directories) and lineage types (e.g., storing streaming data in object storage; embellishing and re-publishing streaming data; performing aggregations and other transformations on data at rest; and evolution of schemas with compatibility flags). Transformation services notify Atlas of lineage links via custom asynchronous kafka messaging.
Atlas provides self-service data discovery and lineage browsing and querying, via full-text search, DSL query language, or gremlin graph query language. Example queries: “Where is data from kafka topic X stored?” “Display the journey of data currently stored in pseudo-directory X since it entered the Aeolus system”. “Show me all earlier versions of schema S, and whether they are forward/backward compatible with each other.”
FlinkDTW: Time-series Pattern Search at Scale Using Dynamic Time Warping - Ch...Flink Forward
DTW: Dynamic Time Warping is a well-known method to find patterns within a time-series. It has the possibility to find a pattern even if the data are distorted. It can be used to detect trends in sell, defect in machine signals in the industry, medicine for electro-cardiograms, DNA…
Most of the implementations are usually very slow, but a very efficient open source implementation (best paper SIGKDD 2012) is implemented in C. It can be easily ported in other language, as Java, so that it can be then easily used in Flink.
We present how we did some slight modifications so that we can use with Flink at even greater scale to return the TopK best matches on past data or streaming data.
Reproducible AI using MLflow and PyTorchDatabricks
Model reproducibility is becoming the next frontier for successful AI models building and deployments for both Research and Production scenarios. In this talk, we will show you how to build reproducible AI models and workflows using PyTorch and MLflow that can be shared across your teams, with traceability and speed up collaboration for AI projects.
Kafka streams windowing behind the curtain confluent
Kafka Streams Windowing Behind the Curtain, Neil Buesing, Principal Solutions Architect, Rill
https://www.meetup.com/TwinCities-Apache-Kafka/events/279316299/
Unmanned Aerial Vehicles (UAVs) are aircrafts that fly without any humans being onboard. They are either remotely piloted, or piloted by an onboard computer. This kind of aircrafts can be used in different military missions such as surveillance, reconnaissance, battle damage assessment, communications relay, minesweeping, hazardous substances detection and radar jamming. However they can be used in other than military missions like detection of hazardous objects on train rails and investigation of infected areas. Aircrafts that are able of hovering and vertical flying can also be used for indoor missions like counter terrorist operations.
Bringing complex event processing to Spark streamingDataWorks Summit
Complex event processing (CEP) is about identifying business opportunities and threats in real time by detecting patterns in data and taking appropriate automated action. Example business use cases for CEP include location-based marketing, smart inventories, targeted ads, Wi-Fi offloading, fraud detection, churn prediction, fleet management, predictive maintenance, security incident event management, and many more. While Spark Streaming provides a distributed resilient framework for ingesting events in real time, effort is still needed to build CEP applications. This is because CEP use cases require correlation of events, which in turn requires us to treat every incoming event as a discrete occurrence in time. Spark Streaming treats the entire batch of events as single occurrence. Many CEP use cases also require alerts to be fired even when there is no incoming event. An example of such use case is to fire an alert when an order-shipped event is NOT received within the SLA times following an order-received event. At Oracle we have adopted a few neat techniques like running continuous query engines as long running tasks, using empty batches as triggers, etc. to bring complex event processing to Spark Streaming.
Join us to learn more on CEP for Spark, the fastest growing data processing platform in the world.
Speakers
Prabhu Thukkaram, Senior Director, Product Development, Oracle
Hoyong Park, Architect, Oracle
22nd Athens Big Data Meetup - 1st Talk - MLOps Workshop: The Full ML Lifecycl...Athens Big Data
Title: MLOps Workshop: The Full ML Lifecycle - How to Use ML in Production
Speakers: Spyros Cavadias (https://www.linkedin.com/in/spyros-cavadias/), Konstantinos Pittas (https://www.linkedin.com/in/konstantinos-pittas-83310270/), Thanos Gkinakos (https://www.linkedin.com/in/thanos-gkinakos-03582a128/)
Date: Saturday, December 17, 2022
Event: https://www.meetup.com/athens-big-data/events/289927468/
This talk presents 3 programming situations where typeclasses and generics are not adequate: evolving serialization protocols, data generation, modular applications. A library, registry, can be used to help with those 3 situations by giving us the means to wire and rewire code at will.
Serverless Streaming Data Processing using Amazon Kinesis AnalyticsAmazon Web Services
by Adrian Hornsby, Technical Evanglist, AWS
As more and more organizations strive to gain real-time insights into their business, streaming data has become ubiquitous. Typical streaming data analytics solutions require specific skills and complex infrastructure. However, with Amazon Kinesis Analytics, you can analyze streaming data in real-time with standard SQL—there is no need to learn new programming languages or processing frameworks. In this session, we dive deep into the capabilities of Amazon Kinesis Analytics using real-world examples. We’ll present an end-to-end streaming data solution using Amazon Kinesis Streams for data ingestion, Amazon Kinesis Analytics for real-time processing, and Amazon Kinesis Firehose for persistence. We review in detail how to write SQL queries using streaming data and discuss best practices to optimize and monitor your Amazon Kinesis Analytics applications. Lastly, we discuss how to estimate the cost of the entire system.
Fundamentals Big Data and AI ArchitectureGuido Schmutz
The right architecture is key for any IT project. This is especially the case for big data projects, where there are no standard architectures which have proven their suitability over years. This session discusses the different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Streaming Analytics architecture as well as Lambda and Kappa architecture and presents the mapping of components from both Open Source as well as the Oracle stack onto these architectures.
The right architecture is key for any IT project. This is valid in the case for big data projects as well, but on the other hand there are not yet many standard architectures which have proven their suitability over years.
This session discusses different Big Data Architectures which have evolved over time, including traditional Big Data Architecture, Event Driven architecture as well as Lambda and Kappa architecture.
Each architecture is presented in a vendor- and technology-independent way using a standard architecture blueprint. In a second step, these architecture blueprints are used to show how a given architecture can support certain use cases and which popular open source technologies can help to implement a solution based on a given architecture.
Distributed computing with Ray. Find your hyper-parameters, speed up your Pan...Jan Margeta
In this talk we will explore Ray - a high-performance and low latency distributed execution framework which will allow you to run your Python code on multiple cores, and scale the same code from your laptop to a large cluster.
Ray uses several interesting ideas like actors, fast zero-copy shared-memory object store, or bottom-up scheduling. Moreover, on top of a succinct API, Ray builds tools to your Pandas pipelines faster, tools that find you the best hyper-parameters for your machine learning models, or train state of the art reinforcement learning algorithms, and much more. Come to the talk and learn some more.
Updated the talk with Kubernetes
https://www.pydays.at/
Frame - Feature Management for Productive Machine LearningDavid Stein
Presented at the ML Platforms Meetup at Pinterest HQ in San Francisco on August 16, 2018.
Abstract: At LinkedIn we observed that much of the complexity in our machine learning applications was in their feature preparation workflows. To address this problem, we built Frame, a shared virtual feature store that provides a unified abstraction layer for accessing features by name. Frame removes the need for feature consumers to deal directly with underlying data sources, which are often different across computing environments. By simplifying feature preparation, Frame has made ML applications at LinkedIn easier to build, modify, and understand.
Cardinality Estimation through Histogram in Apache Spark 2.3 with Ron Hu and ...Databricks
Apache Spark 2.2 shipped with a state-of-art cost-based optimization framework that collects and leverages a variety of per-column data statistics (e.g., cardinality, number of distinct values, NULL values, max/min, avg/max length, etc.) to improve the quality of query execution plans. Skewed data distributions are often inherent in many real world applications. In order to deal with skewed distributions effectively, we added equal-height histograms to Apache Spark 2.3. Leveraging reliable statistics and histogram helps Spark make better decisions in picking the most optimal query plan for real world scenarios.
In this talk, we’ll take a deep dive into how Spark’s Cost-Based Optimizer estimates the cardinality and size of each database operator. Specifically, for skewed distribution workload such as TPC-DS, we will show histogram’s impact on query plan change, hence leading to performance gain.
Architecting Snowflake for High Concurrency and High PerformanceSamanthaBerlant
Cloud Data Warehousing juggernaut Snowflake has raced out ahead of the pack to deliver a data management platform from which a wealth of new analytics can be run. Using Snowflake as a traditional data warehouse has some obvious cost advantages over a hardware solution. But the real value of Snowflake as a data platform lies in its ability to support a high-concurrency analytics platform using Kyligence Cloud, powered by Apache Kylin.
In this presentation, Senior Solutions Architect Robert Hardaway will describe a modern data service architecture using precomputation and distributed indexes to provide interactive analytics to hundreds or even thousands of users running against very large Snowflake datasets (TBs to PBs).
End-to-end Data Governance with Apache Avro and AtlasDataWorks Summit
Aeolus is Comcast’s new internal Big Data system for providing access to an integrated view of a wide variety of high-quality, near-real-time and batch data. Such integration can enable data scientists to uncover otherwise hidden trends, anomalies, and powerful predictors of business successes and failures. But integrating data across silos in a large enterprise is fraught with peril. There typically are few standards on naming conventions and data representation, and spotty documentation at best. The old rule of thumb often applies: 70% of the analysts’ time goes into data wrangling, while only 30% goes toward the actual analyses and simulations. The goal of the Athene Data Governance Platform within Aeolus is to invert this ratio. This talk will explain how Comcast is using Apache Avro and Atlas for end-to-end data governance, the challenges faced, and methods used to address these challenges.
Avro provides a lingua franca for data representation, data integration, and schema evolution. All data published for community consumption must have an associated avro schema in Atlas. Every step in its journey through Aeolus, in flight or at rest, is captured in Atlas. Atlas’ extensibility has allowed us to add or update various entity types (e.g., avro schemas, kafka topics, object store pseudo-directories) and lineage types (e.g., storing streaming data in object storage; embellishing and re-publishing streaming data; performing aggregations and other transformations on data at rest; and evolution of schemas with compatibility flags). Transformation services notify Atlas of lineage links via custom asynchronous kafka messaging.
Atlas provides self-service data discovery and lineage browsing and querying, via full-text search, DSL query language, or gremlin graph query language. Example queries: “Where is data from kafka topic X stored?” “Display the journey of data currently stored in pseudo-directory X since it entered the Aeolus system”. “Show me all earlier versions of schema S, and whether they are forward/backward compatible with each other.”
FlinkDTW: Time-series Pattern Search at Scale Using Dynamic Time Warping - Ch...Flink Forward
DTW: Dynamic Time Warping is a well-known method to find patterns within a time-series. It has the possibility to find a pattern even if the data are distorted. It can be used to detect trends in sell, defect in machine signals in the industry, medicine for electro-cardiograms, DNA…
Most of the implementations are usually very slow, but a very efficient open source implementation (best paper SIGKDD 2012) is implemented in C. It can be easily ported in other language, as Java, so that it can be then easily used in Flink.
We present how we did some slight modifications so that we can use with Flink at even greater scale to return the TopK best matches on past data or streaming data.
Reproducible AI using MLflow and PyTorchDatabricks
Model reproducibility is becoming the next frontier for successful AI models building and deployments for both Research and Production scenarios. In this talk, we will show you how to build reproducible AI models and workflows using PyTorch and MLflow that can be shared across your teams, with traceability and speed up collaboration for AI projects.
Kafka streams windowing behind the curtain confluent
Kafka Streams Windowing Behind the Curtain, Neil Buesing, Principal Solutions Architect, Rill
https://www.meetup.com/TwinCities-Apache-Kafka/events/279316299/
Unmanned Aerial Vehicles (UAVs) are aircrafts that fly without any humans being onboard. They are either remotely piloted, or piloted by an onboard computer. This kind of aircrafts can be used in different military missions such as surveillance, reconnaissance, battle damage assessment, communications relay, minesweeping, hazardous substances detection and radar jamming. However they can be used in other than military missions like detection of hazardous objects on train rails and investigation of infected areas. Aircrafts that are able of hovering and vertical flying can also be used for indoor missions like counter terrorist operations.
Bringing complex event processing to Spark streamingDataWorks Summit
Complex event processing (CEP) is about identifying business opportunities and threats in real time by detecting patterns in data and taking appropriate automated action. Example business use cases for CEP include location-based marketing, smart inventories, targeted ads, Wi-Fi offloading, fraud detection, churn prediction, fleet management, predictive maintenance, security incident event management, and many more. While Spark Streaming provides a distributed resilient framework for ingesting events in real time, effort is still needed to build CEP applications. This is because CEP use cases require correlation of events, which in turn requires us to treat every incoming event as a discrete occurrence in time. Spark Streaming treats the entire batch of events as single occurrence. Many CEP use cases also require alerts to be fired even when there is no incoming event. An example of such use case is to fire an alert when an order-shipped event is NOT received within the SLA times following an order-received event. At Oracle we have adopted a few neat techniques like running continuous query engines as long running tasks, using empty batches as triggers, etc. to bring complex event processing to Spark Streaming.
Join us to learn more on CEP for Spark, the fastest growing data processing platform in the world.
Speakers
Prabhu Thukkaram, Senior Director, Product Development, Oracle
Hoyong Park, Architect, Oracle
22nd Athens Big Data Meetup - 1st Talk - MLOps Workshop: The Full ML Lifecycl...Athens Big Data
Title: MLOps Workshop: The Full ML Lifecycle - How to Use ML in Production
Speakers: Spyros Cavadias (https://www.linkedin.com/in/spyros-cavadias/), Konstantinos Pittas (https://www.linkedin.com/in/konstantinos-pittas-83310270/), Thanos Gkinakos (https://www.linkedin.com/in/thanos-gkinakos-03582a128/)
Date: Saturday, December 17, 2022
Event: https://www.meetup.com/athens-big-data/events/289927468/
This is an updated version of my "Add more fun to your functional programming with RXJS". It includes a bit more background information on Reactive programming.
Get that Corner Office with Angular 2 and ElectronLukas Ruebbelke
These are the slides from my workshop at ng-conf 2016 on Angular 2 and Electron. Pull down the demo repository and work through the branches. Check out http://onehungrymind.com/ for additional resources.
Regional Anesthesia in the Prevention of Persistent Postsurgical PainEdward R. Mariano, MD
Persistent postsurgical pain (PPSP), or chronic pain that develops after surgery, occurs more frequently than one may expect: up to 50% after relatively common operations. For anesthesiologists, surgeons, and pain physicians, there is an urgent need to discover methods to prevent the development of PPSP which is considered one of the more dreaded adverse outcomes following elective surgery.
RAD Studioで始めるマルチデバイス・クロスプラットフォーム開発ワークショップKaz Aiso
RAD Studioは、Windows、Mac、iOS、Androidの4つのプラットフォーム向けのネイティブアプリケーションを効率的に構築できるビジュアル開発ツールです。統合開発環境のRAD Studioを用いることで、C++またはDelphiといった開発言語を使用し、複数デバイス向けの高性能アプリケーションを、一つのソースコードと、一つの基本UI設計ですばやく開発することができます。 このセミナーでは、マルチデバイス/モバイル開発のポイントを抑えながら、RAD Studioを用いてどのように実際の開発作業を行うのかを、演習を通して学ぶことができます。
Microservices are the new black. You've heard about them, you've read about them, you may have even implemented a few, but sooner or later you'll run into the age-old conundrum: How do I break my monolith apart? Where do I draw service boundaries?
In this talk you will learn several widely-applicable strategies for decomposing your monolithic application, along with their respective risks and the appropriate mitigation strategies. These techniques are widely used at Wix, took us a long time to develop and have proven consistently effective; hopefully they will help you avoid the same battle scars.
El Equipo y el Equipo de Alto Desempeño
Los Equipos de alto desempeño EAD (Equipos de Alto Desempeño) es un término bastante utilizado al referirse a equipos que trascienden los límites de lo ordinario. Comencemos por destacar que los EAD como cualquier otro sistema humano es dinámico, y esto le permite evolucionar basado en sus componentes, relaciones e interacciones con el entorno, dentro de esa dinámica de evolución estará fluctuando entre estados o etapas dentro del ciclo de vida, generando diferentes niveles de productividad, respuestas al cambio y respuestas emocionales a sus individuos. Es sobre esta sistema complejo que siento la necesidad de escribir, basándome en valiosas investigaciones en la materia, y mi propia experiencia de éxitos y aprendizajes dentro de este viaje del conocimiento hacia la construcción de EAD.
Espero con esto lograr entregarte herramientas que incrementen las oportunidades de éxito en la construcción
de EAD, alcanzando resultados asombrosos, incrementando la rentabilidad de las empresas, con software/sistemas
de calidad, construidos eficientemente de la mano de equipos felices y orgullosos de su trabajo.
https://mm.tt/873957143?t=Qty9FIDxys
How I Learnt to Stop Worrying and Love my Agile TeamDipesh Pala
As we reflect back on our numerous struggles with making Agile Teams more efficient and operate like well-oiled machines, we are often overwhelmed with wondering how we didn’t learn the lessons faster or earlier. Life is too short to learn from just our own mistakes – we have to learn from others’ mistakes as well.
In this session, Dipesh will be drawing upon more than a decade of Agile experiences in multiple organizations across nine countries to share stories and challenges of transitioning into an Agile Leader, while also focusing on what we in the Agile community are struggling with most.
There has been a lot written about techniques for creating great Agile teams. Dipesh will take these theories a bit further, and look into how Leaders can build great teams, not by using a new method or management style, but rather by understanding their own Agile team dynamics and behaviour.
You will learn about the assumptions and challenges surrounding self-organizing Agile teams and how to build a stronger team of Servant Leaders.
If you are a leader or an aspiring leader of an Agile team, this session will provide clear implications for where to focus your efforts so that you do not worry about the wrong things. You will be inspired by knowing how to establish trust within the teams that is required to embrace uncertainty and ambiguity while confidently making better decisions.
An Open Talk at DeveloperWeek Austin 2017 by Kimberly Wilkins (@dba_denizen), Principal Engineer - Databases at ObjectRocket. Featuring new use cases like Bitcoin, AI, IoT, and all the cool things.
NoSQL, as many of you may already know, is basically a database used to manage huge sets of unstructured data, where in the data is not stored in tabular relations like relational databases. Most of the currently existing Relational Databases have failed in solving some of the complex modern problems like:
• Continuously changing nature of data - structured, semi-structured, unstructured and polymorphic data.
• Applications now serve millions of users in different geo-locations, in different timezones and have to be up and running all the time, with data integrity maintained
• Applications are becoming more distributed with many moving towards cloud computing.
NoSQL plays a vital role in an enterprise application which needs to access and analyze a massive set of data that is being made available on multiple virtual servers (remote based) in the cloud infrastructure and mainly when the data set is not structured. Hence, the NoSQL database is designed to overcome the Performance, Scalability, Data Modelling and Distribution limitations that are seen in the Relational Databases.
(DAT204) NoSQL? No Worries: Build Scalable Apps on AWS NoSQL ServicesAmazon Web Services
In this session, we discuss the benefits of NoSQL databases and take a tour of the main NoSQL services offered by AWS—Amazon DynamoDB and Amazon ElastiCache. Then, we hear from two leading customers, Expedia and Mapbox, about their use cases and architectural challenges, and how they addressed them using AWS NoSQL services, including design patterns and best practices. You will walk out of this session having a better understanding of NoSQL and its powerful capabilities, ready to tackle your database challenges with confidence.
Igor Anishchenko
Odessa Java TechTalks
Lohika - September, 2012
This session starts with a high-level look at all that the Spring Data project has to offer.
Then we’ll dive deeper into a few select Spring Data modules, including Spring Data JPA, Spring Data MongoDB and Spring Data Redis.
Implementing a data access layer of an application has been cumbersome for quite a while. Too much boilerplate code had to be written!
Spring Data is a project that makes it easier to build Spring-powered applications that use new data, offering a reasonably consistent programming model regardless of which type of database you choose.
In addition to supporting the new “NoSQL” databases such as document and graph databases, Spring Data also greatly simplifies working with RDBMS-oriented datastores using JPA -simplifies the development of creating a JPA-based data access layer.
BDA402 Deep Dive: Log Analytics with Amazon Elasticsearch ServiceAmazon Web Services
Everything generates logs. Applications, infrastructure, security ... everything. Keeping track of the flood of log data is a big challenge, yet critical to your ability to understand your systems and troubleshoot (or prevent) issues. In this session, we will use both Amazon CloudWatch and application logs to show you how to build an end-to-end log analytics solution. First, we cover how to configure an Amazon Elaticsearch Service domain and ingest data into it using Amazon Kinesis Firehose, demonstrating how easy it is to transform data with Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data and configure a secure analytics environment. We demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we dive deep into the Elasticsearch query DSL and review approaches for generating custom, ad-hoc reports.
Data & Analytics - Session 2 - Introducing Amazon RedshiftAmazon Web Services
Amazon Redshift is a fast and powerful, fully managed, petabyte-scale data warehouse service in the cloud. This presentation will give an introduction to the service and its pricing before diving into how it delivers fast query performance on data sets ranging from hundreds of gigabytes to a petabyte or more.
Steffen Krause, Technical Evangelist, AWS
Padraic Mulligan, Architect and Lead Developer and Mike McCarthy, CTO, Skillspage
A Presentation on MongoDB Introduction - HabilelabsHabilelabs
It is Scalable High-Performance Open-source, Document-orientated database.
Built for Speed - the performance of traditional key-value stores while maintaining functionality of traditional RDBMS.
Slides for a talk.
Talk abstract:
In the dark of the night, if you listen carefully enough, you can hear databases cry. But why? As developers, we rarely consider what happens under the hood of widely used abstractions such as databases. As a consequence, we rarely think about the performance of databases. This is especially true to less widespread, but often very useful NoSQL databases.
In this talk we will take a close look at NoSQL database performance, peek under the hood of the most frequently used features to see how they affect performance and discuss performance issues and bottlenecks inherent to all databases.
Storage options for Analytics are not one size fits all. To deliver the best solution, you need to understand the use case, performance requirements, and users of the system. This session will break down the options you have in Azure to build a data analytics ecosystem, and explain why everyone's talking about data lakes and where's best to build your data warehouse.
C* Summit 2013: Cassandra at eBay Scale by Feng Qu and Anurag JambhekarDataStax Academy
We have seen rapid adoption of C* at eBay in past two years. We have made tremendous efforts to integrate C* into existing database platforms, including Oracle, MySQL, Postgres, MongoDB, XMP etc.. We also scale C* to meet business requirement and encountered technical challenges you only see at eBay scale, 100TB data on hundreds of nodes. We will share our experience of deployment automation, managing, monitoring, reporting for both Apache Cassandra and DataStax enterprise.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
4. • One user, their persistent filesystem
• For small datasets (<1,000 items), loading /
saving JSON and filtering it in memory is fine.
Do I need a database?
5. • Chrome has great key-value storage options,
and we can manually maintain indexes.
Do I need a relational
database?
KEY VALUE
A {“name”:”My first document..
B {“name”:”Another note…
C {“name”: “Favorite note…
RECENT_IDS [“B”, “C”]
6. LocalStorage
• Synchronous
• Strings only
• Retrieve keys
• <10MB (#8337)
Key Value Storage
IndexedDB
• Sync or async
• Strings, JSON
• Retrieve keys, ranges
• Indexed scan for key
• < 1/3 free disk space
Or use the Filesystem!
7. • But… I need full-text search
• But… I want to query and sort by arbitrary fields
• But… I want to support millions of notes
• But… I may need more than 1/3 of the available
disk space. (IndexedDB limit)
🤔
8. • De-facto standard for relational storage in client-
side applications (macOS, iOS, Android, etc.)
• Builds everywhere, no dependencies
• Simple, fast, reliable
• Open source, great documentation
11. But… for Electron?
• Most JavaScript database wrappers were built
for server-side NodeJS.
• Heavy focus on querying, connection pools,
etc., limited APIs for connecting models to
views.
12. • CoreData (iOS): NSFetchedResultsController
• YapDatabase (iOS): YapDatabaseView
• AndroidSQLite (Android): View “Cursors”
“Give me the notes matching this query, and let
me know if the results change.”
13. Electron-RxDB
• Observable object store built on SQLite:
CoreData for Electron
• Built to power the Nylas N1 mail client,
tuned for performance
18. • Database is an EventEmitter, broadcasts events
when transactions are committed.
• Queries return RxJS Observables that emit new
result sets as transactions are committed.
• Optimizations prevent RxDB from re-running SQL
queries in common cases
SQLite 💖 RxJS
19. Nylas N1
• RxDB provides live “slices” of 1GB+ of mail data
• Views bind to Flux / Redux stores for application
state, RxDB queries for data.
• Many features (mail rules, notifications, etc.)
implemented with database listeners.
20. Built for Electron
• Multi-window support
• Always builds SQLite for Electron
• Example Electron app: “Notes”