Building a robust, responsive, secure data service for healthcare is tricky. For starters, healthcare data lends itself to multiple models:
• Document representation for patient profile view or update
• Graph representation to query relationships between patients, providers, and medications
• Search representation for advanced lookups
Keeping these different systems up to date requires an architecture that can synchronize them in real time as data is updated. Furthermore, meeting audit requirements in Healthcare requires the ability to apply granular cross-datacenter replication policies to data and be able to provide detailed lineage information for each record. This post will describe how stream-first architectures can solve these challenges, and look at how this has been implemented at a Health Information Network provider.
This talk will go over the Kafka API with these design patterns:
• Turning the database upside down
• Event Sourcing , Command Query Responsibity Separation , Polyglot Persistence
• Kappa Architecture
Advanced Threat Detection on Streaming DataCarol McDonald
The document discusses using a stream processing architecture to enable real-time detection of advanced threats from large volumes of streaming data. The solution ingests data using fast distributed messaging like Kafka or MapR Streams. Complex event processing with Storm and Esper is used to detect patterns. Data is stored in scalable NoSQL databases like HBase and analyzed using machine learning. The parallelized, partitioned architecture allows for high performance and scalability.
NoSQL Application Development with JSON and MapR-DBMapR Technologies
NoSQL databases are being used everywhere by startups and Global 2000 companies alike for data environments that require cost-effective scaling. These environments also typically need to represent data in a more flexible way than is practical with relational databases.
How Big Data is Reducing Costs and Improving Outcomes in Health CareCarol McDonald
There is no better example of the important role that data plays in our lives than in matters of our health and our healthcare. There’s a growing wealth of health-related data out there, and it’s playing an increasing role in improving patient care, population health, and healthcare economics.
Join this talk to hear how MapR customers are using big data and advanced analytics to address a myriad of healthcare challenges—from patient to payer.
We will cover big data healthcare trends and production use cases that demonstrate how to deliver data-driven healthcare applications
Build a Time Series Application with Apache Spark and Apache HBaseCarol McDonald
This document discusses using Apache Spark and Apache HBase to build a time series application. It provides an overview of time series data and requirements for ingesting, storing, and analyzing high volumes of time series data. The document then describes using Spark Streaming to process real-time data streams from sensors and storing the data in HBase. It outlines the steps in the lab exercise, which involves reading sensor data from files, converting it to objects, creating a Spark Streaming DStream, processing the DStream, and saving the data to HBase.
Streaming patterns revolutionary architectures Carol McDonald
This document discusses streaming data architectures and patterns. It begins with an overview of streams, their core components, and why streaming is useful for real-time analytics on big data sources like sensor data. Common streaming patterns are then presented, including event sourcing, the duality of streams and databases, command query responsibility separation, and using streams to materialize multiple views of the data. Real-world examples of streaming architectures in retail and healthcare are also briefly described. The document concludes with a discussion of scalability, fault tolerance, and data recovery capabilities of streaming systems.
Applying Machine Learning to Live Patient DataCarol McDonald
This document discusses applying machine learning to live patient data for real-time anomaly detection. It describes using streaming data from medical devices like EKGs to build a machine learning model for identifying anomalies. The streaming data is processed using Spark Streaming and enriched with cluster assignments from a pre-trained K-means model before being sent to a dashboard for real-time monitoring of patient vitals.
Advanced Threat Detection on Streaming DataCarol McDonald
The document discusses using a stream processing architecture to enable real-time detection of advanced threats from large volumes of streaming data. The solution ingests data using fast distributed messaging like Kafka or MapR Streams. Complex event processing with Storm and Esper is used to detect patterns. Data is stored in scalable NoSQL databases like HBase and analyzed using machine learning. The parallelized, partitioned architecture allows for high performance and scalability.
NoSQL Application Development with JSON and MapR-DBMapR Technologies
NoSQL databases are being used everywhere by startups and Global 2000 companies alike for data environments that require cost-effective scaling. These environments also typically need to represent data in a more flexible way than is practical with relational databases.
How Big Data is Reducing Costs and Improving Outcomes in Health CareCarol McDonald
There is no better example of the important role that data plays in our lives than in matters of our health and our healthcare. There’s a growing wealth of health-related data out there, and it’s playing an increasing role in improving patient care, population health, and healthcare economics.
Join this talk to hear how MapR customers are using big data and advanced analytics to address a myriad of healthcare challenges—from patient to payer.
We will cover big data healthcare trends and production use cases that demonstrate how to deliver data-driven healthcare applications
Build a Time Series Application with Apache Spark and Apache HBaseCarol McDonald
This document discusses using Apache Spark and Apache HBase to build a time series application. It provides an overview of time series data and requirements for ingesting, storing, and analyzing high volumes of time series data. The document then describes using Spark Streaming to process real-time data streams from sensors and storing the data in HBase. It outlines the steps in the lab exercise, which involves reading sensor data from files, converting it to objects, creating a Spark Streaming DStream, processing the DStream, and saving the data to HBase.
Streaming patterns revolutionary architectures Carol McDonald
This document discusses streaming data architectures and patterns. It begins with an overview of streams, their core components, and why streaming is useful for real-time analytics on big data sources like sensor data. Common streaming patterns are then presented, including event sourcing, the duality of streams and databases, command query responsibility separation, and using streams to materialize multiple views of the data. Real-world examples of streaming architectures in retail and healthcare are also briefly described. The document concludes with a discussion of scalability, fault tolerance, and data recovery capabilities of streaming systems.
Applying Machine Learning to Live Patient DataCarol McDonald
This document discusses applying machine learning to live patient data for real-time anomaly detection. It describes using streaming data from medical devices like EKGs to build a machine learning model for identifying anomalies. The streaming data is processed using Spark Streaming and enriched with cluster assignments from a pre-trained K-means model before being sent to a dashboard for real-time monitoring of patient vitals.
Fast Cars, Big Data How Streaming can help Formula 1Carol McDonald
This document discusses how streaming data and analytics can help Formula 1 racing teams. It provides examples of the large volume of sensor data collected from Formula 1 cars during races. The document demonstrates how streaming this data using Apache Kafka and analyzing it in real-time with tools like Apache Spark and Apache Flink can help teams with tasks like predictive maintenance, race strategy optimization, and driver coaching. It also discusses storing the streaming data in databases like Apache Drill and MapR-DB for ad-hoc querying and analysis.
We’re in the midst of an exciting paradigm shift in terms of how we process events data in real time to better react to business opportunities or risk. To stay ahead of your competition, you need the ability to react to business-critical events as they happen. These critical events are created through diverse sources such as social interaction, machine sensors, or a customer transaction. How can you understand the meaning and context of these events that ultimately define your business?
This document provides an overview of Apache Spark, including:
- A refresher on MapReduce and its processing model
- An introduction to Spark, describing how it differs from MapReduce in addressing some of MapReduce's limitations
- Examples of how Spark can be used, including for iterative algorithms and interactive queries
- Resources for free online training in Hadoop, MapReduce, Hive and using HBase with MapReduce and Hive
Free Code Friday - Machine Learning with Apache SparkMapR Technologies
In this Free Code Friday webinar, you’ll get an overview of machine learning with Apache Spark’s MLlib, and you’ll also learn how MLlib decision trees can be used to predict flight delays.
How Spark is Enabling the New Wave of Converged Cloud Applications MapR Technologies
Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single, general-purpose compute engine.
But is Spark alone sufficient for developing cloud-based big data applications? What are the other required components for supporting big data cloud processing? How can you accelerate the development of applications which extend across Spark and other frameworks such as Kafka, Hadoop, NoSQL databases, and more?
Applying Machine learning to IOT: End to End Distributed Distributed Pipeline...Carol McDonald
This discusses the architecture of an end-to-end application that combines streaming data with machine learning to do real-time analysis and visualization of where and when Uber cars are clustered, so as to analyze and visualize the most popular Uber locations.
The document discusses how big data has enabled new opportunities by changing scaling laws and problem landscapes. Specifically, linearly scaling costs with big data now make it feasible to process large amounts of data, opening up many problems that were previously impossible or too difficult. This has created many "green field" opportunities where simple approaches can solve important problems. Two examples discussed are using log analysis to detect security threats and using transaction histories to find a common point of compromise for a data breach.
Structured Streaming Data Pipeline Using Kafka, Spark, and MapR-DBCarol McDonald
This document discusses building a streaming data pipeline using Apache technologies like Kafka, Spark Streaming, and MapR-DB. It describes collecting streaming data with Kafka, organizing the data into topics, and processing the streams in Spark Streaming. The streaming data can then be stored in MapR-DB and queried using Spark SQL. An example uses a streaming payment dataset to demonstrate parsing the data, transforming it into a Dataset, and continuously aggregating values with Spark Streaming.
With the general availability of the MapR Converged Data Platform 5.2, we’d like to invite our customers and partners to this webinar in which members of the MapR product team will share details about this exciting new release.
MapR 5.2: Getting More Value from the MapR Converged Data PlatformMapR Technologies
End of maintenance for MapR 4.x is coming in January, so now is a good time to plan your upgrade. Please join us to learn about the recent developments during the past year in the MapR Platform that will make the upgrade effort this year worthwhile.
Drill can query JSON data stored in various data sources like HDFS, HBase, and Hive. It allows running SQL queries over JSON data without requiring a fixed schema. The document describes how Drill enables ad-hoc querying of JSON-formatted Yelp business review data using SQL, providing insights faster than traditional approaches.
You’re not the only one still loading your data into data warehouses and building marts or cubes out of it. But today’s data requires a much more accessible environment that delivers real-time results. Prepare for this transformation because your data platform and storage choices are about to undergo a re-platforming that happens once in 30 years.
With the MapR Converged Data Platform (CDP) and Cisco Unified Compute System (UCS), you can optimize today’s infrastructure and grow to take advantage of what’s next. Uncover the range of possibilities from re-platforming by intimately understanding your options for density, performance, functionality and more.
This document discusses connecting internet of things (IoT) devices to business intelligence (BI) systems. It describes how IoT data from devices like connected cars, smart homes and cities can be analyzed in real-time for operational efficiency, predictive maintenance and self-driving vehicles. The document outlines an example use case of connecting a Raspberry Pi to a vehicle's OBD-II port to log driving data and integrate it with MapR's distributed database platform for real-time analytics and visualization with Grafana and QlikView. It also discusses extending this to optimize home heating/cooling using IoT thermostats like Nest based on vehicle location data.
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...Debraj GuhaThakurta
Event: TDWI Accelerate Seattle, October 16, 2017
Topic: Distributed and In-Database Analytics with R
Presenter: Debraj GuhaThakurta
Description: How to develop scalable and in-DB analytics using R in Spark and SQL-Server
Predicting Flight Delays with Spark Machine LearningCarol McDonald
Apache Spark's MLlib makes machine learning scalable and easier with ML pipelines built on top of DataFrames. In this webinar, we will go over an example from the ebook Getting Started with Apache Spark 2.x.: predicting flight delays using Apache Spark machine learning.
From the Hadoop Summit 2015 Session with Ted Dunning:
Just when we thought the last mile problem was solved, the Internet of Things is turning the last mile problem of the consumer internet into the first mile problem of the industrial internet. This inversion impacts every aspect of the design of networked applications. I will show how to use existing Hadoop ecosystem tools, such as Spark, Drill and others, to deal successfully with this inversion. I will present real examples of how data from things leads to real business benefits and describe real techniques for how these examples work.
Predicting failure in power networks, detecting fraudulent activities in payment card transactions, and identifying next logical products targeted at the right customer at the right time all require machine learning around massive data sets. This form of artificial intelligence requires complex self-learning algorithms, rapid data iteration for advanced analytics and a robust big data architecture that’s up to the task.
Learn how you can quickly exploit your existing IT infrastructure and scale operations in line with your budget to enjoy advanced data modeling, without having to invest in a large data science team.
This document discusses how Spark can be used for production scale applications. It provides examples of companies using Spark and MapR in production for tasks like security analytics, genomics research, and customer analytics. It also outlines key issues to consider when taking Spark to production and describes how MapR provides the performance, reliability, support and data services needed for mission critical Spark applications.
This document discusses common patterns for running Apache Kafka across multiple data centers. It describes stretched clusters, active/passive, and active/active cluster configurations. For each pattern, it covers how to handle failures and recover consumer offsets when switching data centers. It also discusses considerations for using Kafka with other data stores in a multi-DC environment and future work like timestamp-based offset seeking.
Building Stream Infrastructure across Multiple Data Centers with Apache KafkaGuozhang Wang
To manage the ever-increasing volume and velocity of data within your company, you have successfully made the transition from single machines and one-off solutions to large distributed stream infrastructures in your data center, powered by Apache Kafka. But what if one data center is not enough? I will describe building resilient data pipelines with Apache Kafka that span multiple data centers and points of presence, and provide an overview of best practices and common patterns while covering key areas such as architecture guidelines, data replication, and mirroring as well as disaster scenarios and failure handling.
Join us to learn practical applications of the Streaming API, as well as technical implementation concerns. We?ll start by creating server-side Apex methods and then we?ll implement some basic JavaScript handlers to accept the real-time data updates. Finally, we?ll create a beautiful interface using Bootstrap to notify the user of a change. You'll walk away feeling comfortable with saying, ?Yes, we can do real-time updates in Force.com,? and have the documentation and examples to back that up.
Fast Cars, Big Data How Streaming can help Formula 1Carol McDonald
This document discusses how streaming data and analytics can help Formula 1 racing teams. It provides examples of the large volume of sensor data collected from Formula 1 cars during races. The document demonstrates how streaming this data using Apache Kafka and analyzing it in real-time with tools like Apache Spark and Apache Flink can help teams with tasks like predictive maintenance, race strategy optimization, and driver coaching. It also discusses storing the streaming data in databases like Apache Drill and MapR-DB for ad-hoc querying and analysis.
We’re in the midst of an exciting paradigm shift in terms of how we process events data in real time to better react to business opportunities or risk. To stay ahead of your competition, you need the ability to react to business-critical events as they happen. These critical events are created through diverse sources such as social interaction, machine sensors, or a customer transaction. How can you understand the meaning and context of these events that ultimately define your business?
This document provides an overview of Apache Spark, including:
- A refresher on MapReduce and its processing model
- An introduction to Spark, describing how it differs from MapReduce in addressing some of MapReduce's limitations
- Examples of how Spark can be used, including for iterative algorithms and interactive queries
- Resources for free online training in Hadoop, MapReduce, Hive and using HBase with MapReduce and Hive
Free Code Friday - Machine Learning with Apache SparkMapR Technologies
In this Free Code Friday webinar, you’ll get an overview of machine learning with Apache Spark’s MLlib, and you’ll also learn how MLlib decision trees can be used to predict flight delays.
How Spark is Enabling the New Wave of Converged Cloud Applications MapR Technologies
Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single, general-purpose compute engine.
But is Spark alone sufficient for developing cloud-based big data applications? What are the other required components for supporting big data cloud processing? How can you accelerate the development of applications which extend across Spark and other frameworks such as Kafka, Hadoop, NoSQL databases, and more?
Applying Machine learning to IOT: End to End Distributed Distributed Pipeline...Carol McDonald
This discusses the architecture of an end-to-end application that combines streaming data with machine learning to do real-time analysis and visualization of where and when Uber cars are clustered, so as to analyze and visualize the most popular Uber locations.
The document discusses how big data has enabled new opportunities by changing scaling laws and problem landscapes. Specifically, linearly scaling costs with big data now make it feasible to process large amounts of data, opening up many problems that were previously impossible or too difficult. This has created many "green field" opportunities where simple approaches can solve important problems. Two examples discussed are using log analysis to detect security threats and using transaction histories to find a common point of compromise for a data breach.
Structured Streaming Data Pipeline Using Kafka, Spark, and MapR-DBCarol McDonald
This document discusses building a streaming data pipeline using Apache technologies like Kafka, Spark Streaming, and MapR-DB. It describes collecting streaming data with Kafka, organizing the data into topics, and processing the streams in Spark Streaming. The streaming data can then be stored in MapR-DB and queried using Spark SQL. An example uses a streaming payment dataset to demonstrate parsing the data, transforming it into a Dataset, and continuously aggregating values with Spark Streaming.
With the general availability of the MapR Converged Data Platform 5.2, we’d like to invite our customers and partners to this webinar in which members of the MapR product team will share details about this exciting new release.
MapR 5.2: Getting More Value from the MapR Converged Data PlatformMapR Technologies
End of maintenance for MapR 4.x is coming in January, so now is a good time to plan your upgrade. Please join us to learn about the recent developments during the past year in the MapR Platform that will make the upgrade effort this year worthwhile.
Drill can query JSON data stored in various data sources like HDFS, HBase, and Hive. It allows running SQL queries over JSON data without requiring a fixed schema. The document describes how Drill enables ad-hoc querying of JSON-formatted Yelp business review data using SQL, providing insights faster than traditional approaches.
You’re not the only one still loading your data into data warehouses and building marts or cubes out of it. But today’s data requires a much more accessible environment that delivers real-time results. Prepare for this transformation because your data platform and storage choices are about to undergo a re-platforming that happens once in 30 years.
With the MapR Converged Data Platform (CDP) and Cisco Unified Compute System (UCS), you can optimize today’s infrastructure and grow to take advantage of what’s next. Uncover the range of possibilities from re-platforming by intimately understanding your options for density, performance, functionality and more.
This document discusses connecting internet of things (IoT) devices to business intelligence (BI) systems. It describes how IoT data from devices like connected cars, smart homes and cities can be analyzed in real-time for operational efficiency, predictive maintenance and self-driving vehicles. The document outlines an example use case of connecting a Raspberry Pi to a vehicle's OBD-II port to log driving data and integrate it with MapR's distributed database platform for real-time analytics and visualization with Grafana and QlikView. It also discusses extending this to optimize home heating/cooling using IoT thermostats like Nest based on vehicle location data.
TWDI Accelerate Seattle, Oct 16, 2017: Distributed and In-Database Analytics ...Debraj GuhaThakurta
Event: TDWI Accelerate Seattle, October 16, 2017
Topic: Distributed and In-Database Analytics with R
Presenter: Debraj GuhaThakurta
Description: How to develop scalable and in-DB analytics using R in Spark and SQL-Server
Predicting Flight Delays with Spark Machine LearningCarol McDonald
Apache Spark's MLlib makes machine learning scalable and easier with ML pipelines built on top of DataFrames. In this webinar, we will go over an example from the ebook Getting Started with Apache Spark 2.x.: predicting flight delays using Apache Spark machine learning.
From the Hadoop Summit 2015 Session with Ted Dunning:
Just when we thought the last mile problem was solved, the Internet of Things is turning the last mile problem of the consumer internet into the first mile problem of the industrial internet. This inversion impacts every aspect of the design of networked applications. I will show how to use existing Hadoop ecosystem tools, such as Spark, Drill and others, to deal successfully with this inversion. I will present real examples of how data from things leads to real business benefits and describe real techniques for how these examples work.
Predicting failure in power networks, detecting fraudulent activities in payment card transactions, and identifying next logical products targeted at the right customer at the right time all require machine learning around massive data sets. This form of artificial intelligence requires complex self-learning algorithms, rapid data iteration for advanced analytics and a robust big data architecture that’s up to the task.
Learn how you can quickly exploit your existing IT infrastructure and scale operations in line with your budget to enjoy advanced data modeling, without having to invest in a large data science team.
This document discusses how Spark can be used for production scale applications. It provides examples of companies using Spark and MapR in production for tasks like security analytics, genomics research, and customer analytics. It also outlines key issues to consider when taking Spark to production and describes how MapR provides the performance, reliability, support and data services needed for mission critical Spark applications.
This document discusses common patterns for running Apache Kafka across multiple data centers. It describes stretched clusters, active/passive, and active/active cluster configurations. For each pattern, it covers how to handle failures and recover consumer offsets when switching data centers. It also discusses considerations for using Kafka with other data stores in a multi-DC environment and future work like timestamp-based offset seeking.
Building Stream Infrastructure across Multiple Data Centers with Apache KafkaGuozhang Wang
To manage the ever-increasing volume and velocity of data within your company, you have successfully made the transition from single machines and one-off solutions to large distributed stream infrastructures in your data center, powered by Apache Kafka. But what if one data center is not enough? I will describe building resilient data pipelines with Apache Kafka that span multiple data centers and points of presence, and provide an overview of best practices and common patterns while covering key areas such as architecture guidelines, data replication, and mirroring as well as disaster scenarios and failure handling.
Join us to learn practical applications of the Streaming API, as well as technical implementation concerns. We?ll start by creating server-side Apex methods and then we?ll implement some basic JavaScript handlers to accept the real-time data updates. Finally, we?ll create a beautiful interface using Bootstrap to notify the user of a change. You'll walk away feeling comfortable with saying, ?Yes, we can do real-time updates in Force.com,? and have the documentation and examples to back that up.
SAMI - Samsung Developer Conference - Nov 2014Jerome Dubreuil
The document discusses Samsung Artificial Intelligence (SAMI), an open platform for connecting devices and building applications using device data. SAMI provides APIs and services for registering device types, connecting devices, and accessing live and historical device data. This allows developers to focus on applications while SAMI handles data storage, processing and APIs. Examples are provided of a home sensor device type manifest and an application accessing SAMI data to monitor and visualize sensor readings.
This document discusses what constitutes a platform as a service (PaaS). It explains that historically, PaaS solutions were proprietary and hosted on-premises, but now there are many open source options. When choosing a PaaS, it's important to understand your requirements and consider it as a foundational building block. A good PaaS encapsulates engineering principles and ensures repeatability, predictability and consistency across the software development lifecycle, including source control, dependencies, testing, continuous integration, and deployment. It also covers key areas like service discovery, configuration, persistence, and security. The document provides examples of the technologies and tools the author uses to build their own PaaS.
Micro gateways are APIs that can filter access, enforce authentication and plans, and gather metrics for APIs. Traditionally, gateways were large, complex systems but newer Node.js tools allow for smaller, lighter "micro" gateways that can provide the same functionality with less overhead. API Connect from IBM allows developers to easily create and manage micro gateways for APIs.
Interoperable Web Services with JAX-WS and WSITCarol McDonald
The document provides an overview of Carol McDonald's presentation on Sun's web services stack. The key points are:
- Metro is Sun's implementation of JAX-WS for developing web services. WSIT provides reliability, security, and transactions using WS-* specifications.
- JAX-WS allows developing web services by annotating POJOs. The WSDL is generated automatically.
- WSIT adds features like reliable messaging, security, and transactions to web services using standards like WS-ReliableMessaging and WS-Security.
- The presentation demonstrates creating and consuming a web service using JAX-WS and configuring reliable messaging and security using WSIT.
Making Scrum Work Inside Small Businesses Laszlo Szalvay
This document discusses how entrepreneurs can benefit from using Scrum. It recommends that entrepreneurs incorporate a culture of learning and questioning, use both qualitative and quantitative metrics like cash on hand and number of happy customers and employees, and do Scrum at the organizational level across functions like marketing, sales, and executives. Adopting these agile practices can help entrepreneurs build learning organizations that continuously solve problems and adapt.
This document provides an overview of Elasticsearch and Kibana. It describes how Elasticsearch is a search and analytics engine built on Apache Lucene that allows for distributed indexing, replication, and querying. Kibana is a visualization tool that allows users to visualize Elasticsearch data. The document then discusses MapR-DB integration with Elasticsearch, including how MapR-DB tables can be replicated to Elasticsearch indexes. It provides information on default data conversion and custom conversion from MapR-DB to Elasticsearch, as well as gateway configuration and monitoring of the replication process.
Querying the Internet of Things: Streaming SQL on Kafka/Samza and Storm/TridentJulian Hyde
This document discusses streaming SQL and how it can be used to query streaming data sources like IoT devices, web servers, and databases. Some key points discussed include:
- Streaming SQL extends standard SQL to work over both streaming and static data sources. It allows queries to be executed continuously over streaming data.
- The replay principle states that streaming queries should produce the same results as equivalent non-streaming queries over the same static data. Techniques like watermarks and monotonic columns help ensure this.
- Windowing functions allow aggregating over sliding windows of records in a stream. Various window types like tumbling and hopping windows are described.
- Apache Calcite is an open source framework that can optimize streaming SQL queries
Big Data Streams Architectures. Why? What? How?Anton Nazaruk
With a current zoo of technologies and different ways of their interaction it's a big challenge to architect a system (or adopt existed one) that will conform to low-latency BigData analysis requirements. Apache Kafka and Kappa Architecture in particular take more and more attention over classic Hadoop-centric technologies stack. New Consumer API put significant boost in this direction. Microservices-based streaming processing and new Kafka Streams tend to be a synergy in BigData world.
The document discusses machine learning techniques including classification, clustering, and collaborative filtering. It provides examples of algorithms used for each technique, such as Naive Bayes, k-means clustering, and alternating least squares for collaborative filtering. The document then focuses on using Spark for machine learning, describing MLlib and how it can be used to build classification and regression models on Spark, including examples predicting flight delays using decision trees. Key steps discussed are feature extraction, splitting data into training and test sets, training a model, and evaluating performance on test data.
Innovation in the Data Warehouse - StampedeCon 2016StampedeCon
Enterprise Holding’s first started with Hadoop as a POC in 2013. Today, we have clusters on premises and in the cloud. This talk will explore our experience with Big Data and outline three common big data architectures (batch, lambda, and kappa). Then, we’ll dive into the decision points to necessary for your own cluster, for example: cloud vs on premises, physical vs virtual, workload, and security. These decisions will help you understand what direction to take. Finally, we’ll share some lessons learned with the pieces of our architecture worked well and rant about those which didn’t. No deep Hadoop knowledge is necessary, architect or executive level.
Building a Node.js API backend with LoopBack in 5 MinutesRaymond Feng
LoopBack is an open source API framework built on top of Express optimized for mobile and web. Connect to multiple data sources, write business logic in Node.js, glue on top of your existing services and data, connect using JS, iOS & Android SDKs.
Node Architecture Implications for In-Memory Data Analytics on Scale-in ClustersAhsan Javed Awan
While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics. Recent studies propose scale-in clusters with in-storage processing devices to process big data analytics with Spark However the proposal is based solely on the memory bandwidth characterization of in-memory data analytics and also does not shed light on the specification of host CPU and memory. Through empirical evaluation of in-memory data analytics with Apache Spark on an Ivy Bridge dual socket server, we have found that (i) simultaneous multi-threading is effective up to 6 cores (ii) data locality on NUMA nodes can improve the performance by 10% on average, (iii) disabling next-line L1-D prefetchers can reduce the execution time by up to 14%, (iv) DDR3 operating at 1333 MT/s is sufficient and (v) multiple small executors can provide up to 36% speedup over single large executor
Rapid API Development with LoopBack/StrongLoopRaymond Camden
This document discusses how the speaker used to develop websites by focusing heavily on an application server that handled all database access, HTML generation, and other tasks, while the client-side was limited. Now, with improved client-side capabilities and the rise of mobile apps, the speaker focuses on building APIs with Node.js frameworks like Express and LoopBack that allow clients to directly access and render data without heavy server-side processing. The speaker demonstrates how to quickly create RESTful APIs and applications with LoopBack.
NoSQL HBase schema design and SQL with Apache Drill Carol McDonald
The document provides an overview of HBase, including:
- HBase is a column-oriented NoSQL database modeled after Google's Bigtable. It is designed to handle large volumes of sparse data across clusters in a distributed fashion.
- Data in HBase is stored in tables containing rows, column families, columns, and versions. Tables are partitioned into regions distributed across region servers. The HMaster manages the cluster and Zookeeper coordinates operations.
- Common operations on HBase include put (insert/update), get, scan, and delete. The meta table stored in Zookeeper maps rows to their regions. This allows clients to efficiently access data in HBase's distributed architecture.
Kappa Architecture is an alternative to Lambda Architecture that simplifies real-time data processing. It uses a distributed log like Kafka to store all input data immutably to allow reprocessing from the beginning if the processing code changes. This avoids having to maintain separate batch and real-time processing systems. The ASPgems team has implemented Kappa Architecture for several clients using Kafka, Spark Streaming, and Cassandra to provide real-time analytics and metrics in sectors like telecommunications, IoT, insurance, and energy.
This presentation provides an introduction to Apache Kafka and describes best practices for working with fast data streams in Kafka and MapR Streams.
The code examples used during this talk are available at github.com/iandow/design-patterns-for-fast-data.
Author:
Ian Downard
Presented at the Portland Java User Group on Tuesday, October 18 2016.
Design Patterns for working with Fast Data in KafkaIan Downard
Apache Kafka is an open-source message broker project that provides a platform for storing and processing real-time data feeds. In this presentation Ian Downard describes the concepts that are important to understand in order to effectively use the Kafka API. He describes how to prepare a development environment from scratch, how to write a basic publish/subscribe application, and how to run it on a variety of cluster types, including simple single-node clusters, multi-node clusters using Heroku’s “Kafka as a Service”, and enterprise-grade multi-node clusters using MapR’s Converged Data Platform.
Video: https://vimeo.com/188045894
Ian also discusses strategies for working with "fast data" and how to maximize the throughput of your Kafka pipeline. He describes which Kafka configurations and data types have the largest impact on performance and provide some useful JUnit tests, combined with statistical analysis in R, that can help quantify how various configurations effect throughput.
How Spark is Enabling the New Wave of Converged ApplicationsMapR Technologies
Apache Spark has become the de-facto compute engine of choice for data engineers, developers, and data scientists because of its ability to run multiple analytic workloads with a single compute engine. Spark is speeding up data pipeline development, enabling richer predictive analytics, and bringing a new class of applications to market.
Handling the Extremes: Scaling and Streaming in FinanceMapR Technologies
This document discusses how streaming platforms can handle large volumes of data for financial applications. It provides examples of messaging platforms and use cases for fraud detection and email filtering. The key benefits discussed are the ability to horizontally scale applications, replicate data across clusters, and index data dynamically for different consumers.
The document discusses the layers of an Internet of Things (IoT) solution for temperature monitoring using open source technologies. It covers sensors, devices, protocols, messaging, computation, storage and dashboards. A demo is shown of collecting temperature data from Arduino sensors using MQTT and storing it in InfluxDB for analysis and visualization in dashboards. Big data technologies like Kafka and Spark Streaming are used to handle high volumes of IoT data.
Evolving Beyond the Data Lake: A Story of Wind and RainMapR Technologies
This document discusses how companies are increasingly investing in next-generation technologies like big data, cloud computing, and software/hardware related to these areas. It notes that 90% of data will be on next-gen technologies within four years. It then discusses how a converged data platform can help organizations gain insights from both historical and real-time data through applications that combine operational and analytical uses. Key benefits include the ability to seamlessly access and analyze both types of data.
This document summarizes a presentation about using streams as a system of record. The presentation covers how streams can serve as the authoritative data source by persisting events immutably over time. It also demonstrates how to version a real-time data pipeline using MapR streams and StreamSets to ensure different application versions do not interfere with each other. The document includes an agenda, explanations of key concepts, examples, and an announcement of a demo of MapR and StreamSets.
This document discusses Apache Kafka and message queuing systems. It provides an overview of Kafka, including how producers and consumers work, and details on topics, partitions, and Zookeeper. It then discusses performance, production issues, and what improvements are planned for future Kafka releases. The document also reviews the Kafka community and integrations with other technologies.
Building Cloud-Native App Series - Part 2 of 11
Microservices Architecture Series
Event Sourcing & CQRS,
Kafka, Rabbit MQ
Case Studies (E-Commerce App, Movie Streaming, Ticket Booking, Restaurant, Hospital Management)
Streaming Goes Mainstream: New Architecture & Emerging Technologies for Strea...MapR Technologies
This document summarizes Ellen Friedman's presentation on streaming data and architectures. The key points are:
1) Streaming data is becoming mainstream as technologies for distributed storage and stream processing mature. Real-time insights from streaming data provide more value than static batch analysis.
2) MapR Streams is part of MapR's converged data platform for message transport and can support use cases like microservices with its distributed, durable messaging capabilities.
3) Apache Flink is a popular open source stream processing framework that provides accurate, low-latency processing of streaming data through features like windowing, event-time semantics, and state management.
HUG Italy meet-up with Fabian Wilckens, MapR EMEA Solutions ArchitectSpagoWorld
The presentation supported the speech "Think differently – Stream-based Microservice Architecture for Next-Generation Applications" by Fabian Wilckens (EMEA Solutions Architect, MapR Technologies Inc.) at the HUG Italy meet-up supported by Engineering Group's SpagoBI Labs, which took place in Milan, Italy on March 17th, 2016. Read more: http://bit.ly/1UydNuz
The document discusses using Apache Kafka for event detection pipelines. It describes how Kafka can be used to decouple data pipelines and ingest events from various source systems in real-time. It then provides an example use case of using Kafka, Hadoop, and machine learning for fraud detection in consumer banking, describing the online and offline workflows. Finally, it covers some of the challenges of building such a system and considerations for deploying Kafka.
Open Source Bristol 30 March 2022
https://www.meetup.com/Open-Source-Bristol/events/284198269/
18:35 // 'Building a Scalable Event Streaming and Messaging Platform using Apache Pulsar for Fintech' // Tim Spann and John Kinson
Today, companies are adopting Apache Pulsar, an open-source messaging and event streaming platform. Pulsar’s scalability and cloud-native capabilities make it uniquely positioned to meet a range of emerging business needs, including AdTech, fraud detection, IoT analytics, microservices development, and payment processing.
Tim Spann and John Kinson will share insights into the modern data streaming landscape, how Apache Pulsar fits into it, and how it can be used for Fintech. John will also talk about the origins of StreamNative as a Commercial Open Source Software company, and how that has shaped the go-to-market strategy.
Stream data from Apache Kafka for processing with Apache ApexApache Apex
Meetup presentation: How Apache Apex consumes from Kafka topics for real-time time processing and analytics. Learn about features of the Apex Kafka Connector, which is one of the most popular operators in the Apex Malhar operator library, and powers several production use cases. We explain the advanced features this operator provides for high throughput, low latency ingest and how it enables fault tolerant topologies with exactly once processing semantics.
Uber has one of the largest Kafka deployment in the industry. To improve the scalability and availability, we developed and deployed a novel federated Kafka cluster setup which hides the cluster details from producers/consumers. Users do not need to know which cluster a topic resides and the clients view a "logical cluster". The federation layer will map the clients to the actual physical clusters, and keep the location of the physical cluster transparent from the user. Cluster federation brings us several benefits to support our business growth and ease our daily operation. In particular, Client control. Inside Uber there are a large of applications and clients on Kafka, and it's challenging to migrate a topic with live consumers between clusters. Coordinations with the users are usually needed to shift their traffic to the migrated cluster. Cluster federation enables much control of the clients from the server side by enabling consumer traffic redirection to another physical cluster without restarting the application. Scalability: With federation, the Kafka service can horizontally scale by adding more clusters when a cluster is full. The topics can freely migrate to a new cluster without notifying the users or restarting the clients. Moreover, no matter how many physical clusters we manage per topic type, from the user perspective, they view only one logical cluster. Availability: With a topic replicated to at least two clusters we can tolerate a single cluster failure by redirecting the clients to the secondary cluster without performing a region-failover. This also provides much freedom and alleviates the risks for us to carry out important maintenance on a critical cluster. Before the maintenance, we mark the cluster as a secondary and migrate off the live traffic and consumers. We will present the details of the architecture and several interesting technical challenges we overcame.
Hortonworks DataFlow (HDF) 3.3 - Taking Stream Processing to the Next LevelHortonworks
The HDF 3.3 release delivers several exciting enhancements and new features. But, the most noteworthy of them is the addition of support for Kafka 2.0 and Kafka Streams.
https://hortonworks.com/webinar/hortonworks-dataflow-hdf-3-3-taking-stream-processing-next-level/
IoT Ingestion & Analytics using Apache Apex - A Native Hadoop PlatformApache Apex
Internet of Things (IoT) devices are becoming more ubiquitous in consumer, business and industrial landscapes. They are being widely used in applications ranging from home automation to the industrial internet. They pose a unique challenge in terms of the volume of data they produce, and the velocity with which they produce it, and the variety of sources they need to handle. The challenge is to ingest and process this data at the speed at which it is being produced in a real-time and fault tolerant fashion. Apache Apex is an industrial grade, scalable and fault tolerant big data processing platform that runs natively on Hadoop. In this deck, you will see how Apex is being used in IoT applications and also see how the enterprise features such as dimensional analytics, real-time dashboards and monitoring play a key role.
Presented by Pramod Immaneni, Principal Architect at DataTorrent and PPMC member Apache Apex, on BrightTALK webinar on Apr 6th, 2016
First presentation for Savi's sponsorship of the Washington DC Spark Interactive. Discusses tips and lessons learned using Spark Streaming (24x7) to ingest and analyze Industrial Internet of Things (IIoT) data as part of a Lambda Architecture
The Evolution of Trillion-level Real-time Messaging System in BIGO - Puslar ...StreamNative
This document discusses BIGO's evolution from using open-source Kafka to Apache Pulsar for its real-time messaging system. It describes the challenges BIGO faced with Kafka as data scales rapidly grew, including poor scalability and degraded I/O performance. BIGO chose Pulsar for its lightweight horizontal scalability, excellent read-write isolation, and ability to support over a million topics. Typical application scenarios discussed include high throughput event tracking, lightweight traffic balancing, and high performance catch-up reads for machine learning tasks. Future work may involve optimizations for different read/write models and combining SSD and HDD storage.
MyHeritage Kakfa use cases - Feb 2014 Meetup Ran Levy
MyHeritage uses Kafka as a messaging system to handle two main use cases: indexing data to their search system and reporting statistics to their business intelligence system. The document provides an overview of Kafka, describing it as a fast, scalable, durable, distributed messaging system. It then details MyHeritage's implementation, including using Kafka to handle event streaming from producers to consumers that process the data for indexing and reporting. The summary emphasizes that Kafka is very fast, scalable, and extensively used at MyHeritage to handle their high scale systems.
Similar to Streaming Patterns Revolutionary Architectures with the Kafka API (20)
Introduction to machine learning with GPUsCarol McDonald
The document provides an introduction to machine learning concepts including supervised and unsupervised learning. It discusses classification and regression as examples of supervised learning techniques and clustering as an example of unsupervised learning. It also provides an overview of deep learning using neural networks and examples of convolutional neural networks and recurrent neural networks. The document emphasizes how GPUs have accelerated machine learning by enabling parallel processing.
Analyzing Flight Delays with Apache Spark, DataFrames, GraphFrames, and MapR-DBCarol McDonald
Apache Spark GraphX made it possible to run graph algorithms within Spark, GraphFrames integrates GraphX and DataFrames and makes it possible to perform Graph pattern queries without moving data to a specialized graph database.
This presentation will help you get started using Apache Spark GraphFrames Graph Algorithms and Graph Queries with MapR-DB JSON document database.
Applying Machine Learning to IOT: End to End Distributed Pipeline for Real-Ti...Carol McDonald
This document discusses using Apache technologies like Kafka, Spark, and HBase to build an end-to-end machine learning pipeline for real-time analysis of Uber trip data. It provides an example of using K-means clustering on streaming Uber trip data to identify geographic patterns and visualize them in a dashboard. The document also provides background on machine learning, streaming data, Spark, and why combining IoT with machine learning is useful for applications like predictive maintenance, smart cities, healthcare, and more.
Demystifying AI, Machine Learning and Deep LearningCarol McDonald
Deep learning, machine learning, artificial intelligence - all buzzwords and representative of the future of analytics. In this talk we will explain what is machine learning and deep learning at a high level with some real world examples. The goal of this is not to turn you into a data scientist, but to give you a better understanding of what you can do with machine learning. Machine learning is becoming more accessible to developers, and Data scientists work with domain experts, architects, developers and data engineers, so it is important for everyone to have a better understanding of the possibilities. Every piece of information that your business generates has potential to add value. This and future posts are meant to provoke a review of your own data to identify new opportunities.
This document provides an introduction to GraphX, which is an Apache Spark component for graphs and graph-parallel computations. It describes different types of graphs like regular graphs, directed graphs, and property graphs. It shows how to create a property graph in GraphX by defining vertex and edge RDDs. It also demonstrates various graph operators that can be used to perform operations on graphs, such as finding the number of vertices/edges, degrees, longest paths, and top vertices by degree. The goal is to introduce the basics of representing and analyzing graph data with GraphX.
This document provides an introduction to machine learning techniques including classification and clustering. It discusses supervised learning algorithms like decision trees and how they can be used for classification problems like predicting customer churn. Unsupervised learning techniques like clustering are also introduced. The remainder of the document demonstrates how to use Spark ML and Spark SQL to build a machine learning pipeline to predict customer churn using decision trees on telecom customer data. Key steps discussed include data loading, feature extraction, model training, cross validation, and evaluation.
This document discusses machine learning techniques in Spark including classification, clustering, and collaborative filtering. It provides examples of building classification models with Spark including vectorizing data, training models, evaluating models, and making predictions. Clustering and collaborative filtering are also introduced. The document demonstrates collaborative filtering with Spark using alternating least squares to build a recommendation model from user ratings data.
This document provides an overview of Apache Spark Streaming. It discusses why Spark Streaming is useful for processing time series data in near-real time. It then explains key concepts of Spark Streaming like data sources, transformations, and output operations. Finally, it provides an example of using Spark Streaming to process sensor data in real-time and save results to HBase.
Machine Learning Recommendations with SparkCarol McDonald
Collaborative filtering algorithms recommend items to users based on the preferences of similar users. They work by building a model from user preference data on many items. The model can then be used to predict item preferences for new users based on similarities to other users with similar preferences. Alternating least squares (ALS) is an iterative collaborative filtering algorithm that approximates the user-item rating matrix as the product of two dense matrices to discover latent features of users and items.
This document provides an overview of Apache Spark, including:
- What Spark is and how it differs from MapReduce by running computations in memory for improved performance on iterative algorithms.
- Examples of Spark's core APIs like RDDs (Resilient Distributed Datasets) and transformations like map, filter, reduceByKey.
- How Spark programs are executed through a DAG (Directed Acyclic Graph) and translated to physical execution plans with stages and tasks.
The document discusses new TeMIP products for network management on Windows NT platforms. TeMIP Alarm Handling for Windows NT allows real-time alarm monitoring and analysis on Windows NT clients. The TeMIP Access Library Toolkit enables development of custom applications on Windows NT that can access TeMIP resources. Both products are part of the TeMIP V3.2A release and provide scalability, performance, standards compliance and other benefits for telecommunications network management.
This document provides an overview and objectives of a session on getting started with HBase application development. It discusses why NoSQL and HBase are needed due to limitations of relational databases in scaling horizontally to handle big data. It provides an introduction to the HBase data model, architecture, and basic operations like put, get, scan, and delete. It explains how HBase stores data in a sorted map structure and how writes flow through the write ahead log, memstore, and are flushed to HFiles on disk.
The Comprehensive Guide to Validating Audio-Visual Performances.pdfkalichargn70th171
Ensuring the optimal performance of your audio-visual (AV) equipment is crucial for delivering exceptional experiences. AV performance validation is a critical process that verifies the quality and functionality of your AV setup. Whether you're a content creator, a business conducting webinars, or a homeowner creating a home theater, validating your AV performance is essential.
These are the slides of the presentation given during the Q2 2024 Virtual VictoriaMetrics Meetup. View the recording here: https://www.youtube.com/watch?v=hzlMA_Ae9_4&t=206s
Topics covered:
1. What is VictoriaLogs
Open source database for logs
● Easy to setup and operate - just a single executable with sane default configs
● Works great with both structured and plaintext logs
● Uses up to 30x less RAM and up to 15x disk space than Elasticsearch
● Provides simple yet powerful query language for logs - LogsQL
2. Improved querying HTTP API
3. Data ingestion via Syslog protocol
* Automatic parsing of Syslog fields
* Supported transports:
○ UDP
○ TCP
○ TCP+TLS
* Gzip and deflate compression support
* Ability to configure distinct TCP and UDP ports with distinct settings
* Automatic log streams with (hostname, app_name, app_id) fields
4. LogsQL improvements
● Filtering shorthands
● week_range and day_range filters
● Limiters
● Log analytics
● Data extraction and transformation
● Additional filtering
● Sorting
5. VictoriaLogs Roadmap
● Accept logs via OpenTelemetry protocol
● VMUI improvements based on HTTP querying API
● Improve Grafana plugin for VictoriaLogs -
https://github.com/VictoriaMetrics/victorialogs-datasource
● Cluster version
○ Try single-node VictoriaLogs - it can replace 30-node Elasticsearch cluster in production
● Transparent historical data migration to object storage
○ Try single-node VictoriaLogs with persistent volumes - it compresses 1TB of production logs from
Kubernetes to 20GB
● See https://docs.victoriametrics.com/victorialogs/roadmap/
Try it out: https://victoriametrics.com/products/victorialogs/
Streamlining End-to-End Testing Automation with Azure DevOps Build & Release Pipelines
Automating end-to-end (e2e) test for Android and iOS native apps, and web apps, within Azure build and release pipelines, poses several challenges. This session dives into the key challenges and the repeatable solutions implemented across multiple teams at a leading Indian telecom disruptor, renowned for its affordable 4G/5G services, digital platforms, and broadband connectivity.
Challenge #1. Ensuring Test Environment Consistency: Establishing a standardized test execution environment across hundreds of Azure DevOps agents is crucial for achieving dependable testing results. This uniformity must seamlessly span from Build pipelines to various stages of the Release pipeline.
Challenge #2. Coordinated Test Execution Across Environments: Executing distinct subsets of tests using the same automation framework across diverse environments, such as the build pipeline and specific stages of the Release Pipeline, demands flexible and cohesive approaches.
Challenge #3. Testing on Linux-based Azure DevOps Agents: Conducting tests, particularly for web and native apps, on Azure DevOps Linux agents lacking browser or device connectivity presents specific challenges in attaining thorough testing coverage.
This session delves into how these challenges were addressed through:
1. Automate the setup of essential dependencies to ensure a consistent testing environment.
2. Create standardized templates for executing API tests, API workflow tests, and end-to-end tests in the Build pipeline, streamlining the testing process.
3. Implement task groups in Release pipeline stages to facilitate the execution of tests, ensuring consistency and efficiency across deployment phases.
4. Deploy browsers within Docker containers for web application testing, enhancing portability and scalability of testing environments.
5. Leverage diverse device farms dedicated to Android, iOS, and browser testing to cover a wide range of platforms and devices.
6. Integrate AI technology, such as Applitools Visual AI and Ultrafast Grid, to automate test execution and validation, improving accuracy and efficiency.
7. Utilize AI/ML-powered central test automation reporting server through platforms like reportportal.io, providing consolidated and real-time insights into test performance and issues.
These solutions not only facilitate comprehensive testing across platforms but also promote the principles of shift-left testing, enabling early feedback, implementing quality gates, and ensuring repeatability. By adopting these techniques, teams can effectively automate and execute tests, accelerating software delivery while upholding high-quality standards across Android, iOS, and web applications.
Stork Product Overview: An AI-Powered Autonomous Delivery FleetVince Scalabrino
Imagine a world where instead of blue and brown trucks dropping parcels on our porches, a buzzing drove of drones delivered our goods. Now imagine those drones are controlled by 3 purpose-built AI designed to ensure all packages were delivered as quickly and as economically as possible That's what Stork is all about.
Secure-by-Design Using Hardware and Software Protection for FDA ComplianceICS
This webinar explores the “secure-by-design” approach to medical device software development. During this important session, we will outline which security measures should be considered for compliance, identify technical solutions available on various hardware platforms, summarize hardware protection methods you should consider when building in security and review security software such as Trusted Execution Environments for secure storage of keys and data, and Intrusion Detection Protection Systems to monitor for threats.
What’s new in VictoriaMetrics - Q2 2024 UpdateVictoriaMetrics
These slides were presented during the virtual VictoriaMetrics User Meetup for Q2 2024.
Topics covered:
1. VictoriaMetrics development strategy
* Prioritize bug fixing over new features
* Prioritize security, usability and reliability over new features
* Provide good practices for using existing features, as many of them are overlooked or misused by users
2. New releases in Q2
3. Updates in LTS releases
Security fixes:
● SECURITY: upgrade Go builder from Go1.22.2 to Go1.22.4
● SECURITY: upgrade base docker image (Alpine)
Bugfixes:
● vmui
● vmalert
● vmagent
● vmauth
● vmbackupmanager
4. New Features
* Support SRV URLs in vmagent, vmalert, vmauth
* vmagent: aggregation and relabeling
* vmagent: Global aggregation and relabeling
* vmagent: global aggregation and relabeling
* Stream aggregation
- Add rate_sum aggregation output
- Add rate_avg aggregation output
- Reduce the number of allocated objects in heap during deduplication and aggregation up to 5 times! The change reduces the CPU usage.
* Vultr service discovery
* vmauth: backend TLS setup
5. Let's Encrypt support
All the VictoriaMetrics Enterprise components support automatic issuing of TLS certificates for public HTTPS server via Let’s Encrypt service: https://docs.victoriametrics.com/#automatic-issuing-of-tls-certificates
6. Performance optimizations
● vmagent: reduce CPU usage when sharding among remote storage systems is enabled
● vmalert: reduce CPU usage when evaluating high number of alerting and recording rules.
● vmalert: speed up retrieving rules files from object storages by skipping unchanged objects during reloading.
7. VictoriaMetrics k8s operator
● Add new status.updateStatus field to the all objects with pods. It helps to track rollout updates properly.
● Add more context to the log messages. It must greatly improve debugging process and log quality.
● Changee error handling for reconcile. Operator sends Events into kubernetes API, if any error happened during object reconcile.
See changes at https://github.com/VictoriaMetrics/operator/releases
8. Helm charts: charts/victoria-metrics-distributed
This chart sets up multiple VictoriaMetrics cluster instances on multiple Availability Zones:
● Improved reliability
● Faster read queries
● Easy maintenance
9. Other Updates
● Dashboards and alerting rules updates
● vmui interface improvements and bugfixes
● Security updates
● Add release images built from scratch image. Such images could be more
preferable for using in environments with higher security standards
● Many minor bugfixes and improvements
● See more at https://docs.victoriametrics.com/changelog/
Also check the new VictoriaLogs PlayGround https://play-vmlogs.victoriametrics.com/
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...kalichargn70th171
Visual testing plays a vital role in ensuring that software products meet the aesthetic requirements specified by clients in functional and non-functional specifications. In today's highly competitive digital landscape, users expect a seamless and visually appealing online experience. Visual testing, also known as automated UI testing or visual regression testing, verifies the accuracy of the visual elements that users interact with.
Strengthening Web Development with CommandBox 6: Seamless Transition and Scal...Ortus Solutions, Corp
Join us for a session exploring CommandBox 6’s smooth website transition and efficient deployment. CommandBox revolutionizes web development, simplifying tasks across Linux, Windows, and Mac platforms. Gain insights and practical tips to enhance your development workflow.
Come join us for an enlightening session where we delve into the smooth transition of current websites and the efficient deployment of new ones using CommandBox 6. CommandBox has revolutionized web development, consistently introducing user-friendly enhancements that catalyze progress in the field. During this presentation, we’ll explore CommandBox’s rich history and showcase its unmatched capabilities within the realm of ColdFusion, covering both major variations.
The journey of CommandBox has been one of continuous innovation, constantly pushing boundaries to simplify and optimize development processes. Regardless of whether you’re working on Linux, Windows, or Mac platforms, CommandBox empowers developers to streamline tasks with unparalleled ease.
In our session, we’ll illustrate the simple process of transitioning existing websites to CommandBox 6, highlighting its intuitive features and seamless integration. Moreover, we’ll unveil the potential for effortlessly deploying multiple websites, demonstrating CommandBox’s versatility and adaptability.
Join us on this journey through the evolution of web development, guided by the transformative power of CommandBox 6. Gain invaluable insights, practical tips, and firsthand experiences that will enhance your development workflow and embolden your projects.
Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...The Third Creative Media
"Navigating Invideo: A Comprehensive Guide" is an essential resource for anyone looking to master Invideo, an AI-powered video creation tool. This guide provides step-by-step instructions, helpful tips, and comparisons with other AI video creators. Whether you're a beginner or an experienced video editor, you'll find valuable insights to enhance your video projects and bring your creative ideas to life.
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
In this infographic, we have explored cost-effective strategies for iOS app development, focusing on building high-quality apps within a budget. Key points covered include prioritizing essential features, leveraging existing tools and libraries, adopting cross-platform development approaches, optimizing for a Minimum Viable Product (MVP), and integrating with cloud services and third-party APIs. By implementing these strategies, businesses and developers can create functional and engaging iOS apps while minimizing development costs and time-to-market.