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Luckily, Kubernetes and Kafka's combination helps us reduce everyday tasks tremendously by adding myriad capabilities to lessen the complexity of managing clusters.
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Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming apps. It was developed by LinkedIn in 2011 to solve problems with data integration and processing. Kafka uses a publish-subscribe messaging model and is designed to be fast, scalable, and durable. It allows both streaming and storage of data and acts as a central data backbone for large organizations.
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Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms.
Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.
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What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will start with quick recap of how we created systems over the past 20 years and how different architectures evolved from it. The talk will show how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so.
Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
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(Guido Schmutz, Trivadis) Kafka Summit SF 2018
Internet of Things use cases are a perfect match for processing with a streaming platform such as Kafka and the Confluent Platform. Some of the questions to be answered are: How do we feed the data from our devices into Kafka? Do we directly send data to Kafka? Is Kafka accessible from outside the organization over the internet? What if we want to use a more specific IoT protocol such as MQTT or CoAP in between? How would we integrate it with Kafka? How can we enrich IoT streaming data with static data sitting in a traditional system?
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Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
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Introduction to Kafka connect
It covers a brief introduction to Apache Kafka Connect, giving insights about its benefits,use cases, motivation behind building Kafka Connect.And also a short discussion on its architecture.
Scalability, Availability & Stability Patterns
This document provides an overview of patterns for scalability, availability, and stability in distributed systems. It discusses general recommendations like immutability and referential transparency. It covers scalability trade-offs around performance vs scalability, latency vs throughput, and availability vs consistency. It then describes various patterns for scalability including managing state through partitioning, caching, sharding databases, and using distributed caching. It also covers patterns for managing behavior through event-driven architecture, compute grids, load balancing, and parallel computing. Availability patterns like fail-over, replication, and fault tolerance are discussed. The document provides examples of popular technologies that implement many of these patterns.
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Watch this talk here: https://www.confluent.io/online-talks/apache-kafka-architecture-and-fundamentals-explained-on-demand
This session explains Apache Kafka’s internal design and architecture. Companies like LinkedIn are now sending more than 1 trillion messages per day to Apache Kafka. Learn about the underlying design in Kafka that leads to such high throughput.
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How do to big data processing in real time?
What architectures are out there to support this paradigm?
Which one should we choose?
What Advantages / Pitfalls they contain.
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What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will start with quick recap of how we created systems over the past 20 years and how different architectures evolved from it. The talk will show how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so.
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Managing Apache Kafka sometimes could be cumbersome, and that's something that we would like to avoid, especially for developers and data engineers that need to build and develop data pipelines.
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- How to properly configure Kafka Connect and KSQLDB
- Integrate Apache Pinot to answer OLAP queries
Introduction to Apache Kafka
Apache Kafka is an open-source distributed event streaming platform used for building real-time data pipelines and streaming apps. It was developed by LinkedIn in 2011 to solve problems with data integration and processing. Kafka uses a publish-subscribe messaging model and is designed to be fast, scalable, and durable. It allows both streaming and storage of data and acts as a central data backbone for large organizations.
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This document provides an overview of Apache Airflow, an open-source workflow management system. It describes Airflow's key features like workflow definition using directed acyclic graphs (DAGs), rich UI, scheduler, operators for tasks like databases and web services, and use of Jinja templating. The document also discusses Airflow's architecture with parallel execution, UI, command line operations like backfilling, and security features. Airflow is used by over 200 companies for workflows like ETL, analytics, and machine learning pipelines.
Streaming Data Lakes using Kafka Connect + Apache Hudi | Vinoth Chandar, Apac...
Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms.
Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.
Building Event Driven (Micro)services with Apache Kafka
What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to bind services together? Or is it better to use a richer, more loosely-coupled protocol? This talk will start with quick recap of how we created systems over the past 20 years and how different architectures evolved from it. The talk will show how we piece services together in event driven systems, how we use a distributed log (event hub) to create a central, persistent history of events and what benefits we achieve from doing so.
Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk will show the difference between a request-driven and event-driven communication and show when to use which. It highlights how the modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
AWS Real-Time Event Processing
Real-time event processing monitors the incoming data stream and initiates action based on detected events like fraud, error or performance degradation. These events are often used to issue alerts and notifications, take responsive action, or to populate a monitoring dashboard. In this session, we will walk through different use cases for event processing and demonstrate how to build a scalable pipeline for tracking IoT device status. AWS services to be covered include: AWS Lambda and the Kinesis Client Library (KCL).
Introduction to Kafka Streams
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
Dynamic Allocation in Spark
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help boost feelings of calmness, happiness and focus.
Ingesting and Processing IoT Data Using MQTT, Kafka Connect and Kafka Streams...
(Guido Schmutz, Trivadis) Kafka Summit SF 2018
Internet of Things use cases are a perfect match for processing with a streaming platform such as Kafka and the Confluent Platform. Some of the questions to be answered are: How do we feed the data from our devices into Kafka? Do we directly send data to Kafka? Is Kafka accessible from outside the organization over the internet? What if we want to use a more specific IoT protocol such as MQTT or CoAP in between? How would we integrate it with Kafka? How can we enrich IoT streaming data with static data sitting in a traditional system?
This session will provide answers to these and other questions using a fictitious use case of a trucking company. Trucks are constantly sending data about position and driving habits, which can be used to derive real-time information and actions. A large part of the presentation will be a live demo. The demo will show the implementation of the pipeline incrementally: starting with sending the truck movement events directly to Kafka, then adding MQTT to the sensor data ingestion, followed by using Kafka Streams and KSQL to apply stream processing on the information received. The final pipeline will demonstrate the application of Kafka Connect with MQTT and JDBC source connectors for data ingestion and event stream enrichment, and Kafka Streams and KSQL for stream processing. The key takeaway is the live demonstration of a working end-to-end IoT streaming data ingestion pipeline using Kafka technologies.
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Building data pipelines is pretty hard! Building a multi-datacenter active-active real time data pipeline for multiple classes of data with different durability, latency and availability guarantees is much harder.
Real time infrastructure powers critical pieces of Uber (think Surge) and in this talk we will discuss our architecture, technical challenges, learnings and how a blend of open source infrastructure (Apache Kafka and Samza) and in-house technologies have helped Uber scale.
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This document discusses using Domain-Driven Design (DDD) patterns to structure serverless applications. It introduces DDD concepts like bounded contexts, aggregates, repositories, and CQRS. Bounded contexts separate domains into cohesive models that are loosely coupled. Aggregates define transactional boundaries and ensure data integrity. Repositories provide storage and retrieval of aggregates. CQRS separates commands and queries using different data models. Applying these DDD patterns can help organize serverless applications as they grow in complexity.
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It covers a brief introduction to Apache Kafka Connect, giving insights about its benefits,use cases, motivation behind building Kafka Connect.And also a short discussion on its architecture.
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This document provides an overview of patterns for scalability, availability, and stability in distributed systems. It discusses general recommendations like immutability and referential transparency. It covers scalability trade-offs around performance vs scalability, latency vs throughput, and availability vs consistency. It then describes various patterns for scalability including managing state through partitioning, caching, sharding databases, and using distributed caching. It also covers patterns for managing behavior through event-driven architecture, compute grids, load balancing, and parallel computing. Availability patterns like fail-over, replication, and fault tolerance are discussed. The document provides examples of popular technologies that implement many of these patterns.
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Apache Kafka Architecture & Fundamentals Explained
Apache Kafka Architecture & Fundamentals Explained
Unlocking the Power of Lakehouse Architectures with Apache Pulsar and Apache ...
Unlocking the Power of Lakehouse Architectures with Apache Pulsar and Apache ...
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Building Event Driven (Micro)services with Apache Kafka
How to understand and analyze Apache Hive query execution plan for performanc...
How to understand and analyze Apache Hive query execution plan for performanc...
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Building a Streaming Pipeline on Kubernetes Using Kafka Connect, KSQLDB & Apa...
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Building Event Driven (Micro)services with Apache Kafka
Building Event Driven (Micro)services with Apache Kafka
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How Uber scaled its Real Time Infrastructure to Trillion events per day
How Uber scaled its Real Time Infrastructure to Trillion events per day
Shaping serverless architecture with domain driven design patterns
Shaping serverless architecture with domain driven design patterns
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About VisualDNA Architecture @ Rubyslava 2014
Michal Hariš provides an overview of the evolution of VisualDNA's data architecture over the past 3 years. Originally, 10 people managed a single MySQL table holding 50M user profiles. They transitioned to using Cassandra and Hadoop to address scalability issues. Currently, they have a 120 person team using a lambda architecture with Java, Scala, Hadoop, Cassandra, Kafka, Redis, R and AngularJS. Real-time processing of 8.5k events/second is done alongside batch pipelines and machine learning. They have learned lessons around system design, testing, and remote collaboration while addressing challenges such as globally distributed APIs and bottlenecks in their data pipeline.
The need for more agile analytics platforms.
Why you need to seize your own Big Data advantage and leverage a more agile analytics platform. Overview of the Cray Urika-GX system.
Geek Nights Hong Kong
This document provides an overview of Hazelcast, an in-memory distributed computing platform. It discusses Hazelcast's capabilities for distributed caching, data grids, and real-time processing. The document also provides details on Hazelcast's commercial support offerings, recent 3.7 release features including modularity and performance improvements, and roadmap including new client libraries and cloud integration.
Coherence RoadMap 2018
The document provides a roadmap for Oracle Coherence, including upcoming versions, new capabilities, and use cases. Key points include:
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Peek into Neo4j Product Strategy and Roadmap
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🐬 The future of MySQL is Postgres 🐘
Breathing New Life into MySQL Apps
With Advanced Postgres Capabilities
Webinar: What's new in CDAP 3.5?
Cask Webinar
Date: 08/10/2016
Link to video recording: https://www.youtube.com/watch?v=XUkANr9iag0
In this webinar, Nitin Motgi, CTO of Cask, walks through the new capabilities of CDAP 3.5 and explains how your organization can benefit.
Some of the highlights include:
- Enterprise-grade security - Authentication, authorization, secure keystore for storing configurations. Plus integration with Apache Sentry and Apache Ranger.
- Preview mode - Ability to preview and debug data pipelines before deploying them.
- Joins in Cask Hydrator - Capabilities to join multiple data sources in data pipelines
- Real-time pipelines with Spark Streaming - Drag & drop real-time pipelines using Spark Streaming.
- Data usage analytics - Ability to report application usage of data sets.
- And much more!
Modernizing Global Shared Data Analytics Platform and our Alluxio Journey
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
Modernizing Global Shared Data Analytics Platform and our Alluxio Journey
Sandipan Chakraborty, Director of Engineering (Rakuten)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
What’s New in Cloudera Enterprise 6.0: The Inside Scoop 6.14.18
Webinar on Cloudera Enterprise 6.0 where we will discuss how to build new applications on the modern platform for machine learning and analytics. This webinar will take a look at the latest software enhancements and how they’ll help you improve your productivity and innovate new analytics applications.
Modern MySQL Monitoring and Dashboards.
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The Road Most Traveled: A Kafka Story | Heikki Nousiainen, Aiven
When moving to a cloud native architecture Moogsoft knew they needed more scale than Rabbit could provide. Moogsoft moved into Kafka which is known for quick writing and driving heavy event driven workloads on top of niceties such as replayability. Choosing the tool was easy, finding a vendor that ticked all their boxes was not. They needed to ensure scalability, upgradability, builds via existing IAC pipelines, and observability via existing tools. When Moogsoft found Aiven, they were impressed with their offering and ability to scale on demand. During this presentation we will explore how Moogsoft used Aiven for Kafka to manage and scale their data in the cloud.
From monolith to microservices
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These slides share how TransferWise codebase is moving from a monolith architecture to a microservices architecture.
Analytics at the Speed of Thought: Actian Express Overview
Deliver faster insight – reduce query response times to seconds
Analyze more data faster – explore billions of rows of data in seconds
More concurrent users – enable more concurrent BI users to explore more data
Microsoft Azure in der Praxis
.NET Usergroup Oldenburg 26. März 2015 - von Winfried Klinker und Andre Hühn
Microsoft Azure gehört zu den Cloud-Diensten, die Microsoft anbietet. Es umfasst neben dem Hosting von virtuellen Maschinen insbesondere eine große Sammlung an Diensten (wie SQL Azure, Mobile Services, Machine Learning).
Wir geben einen ersten Überblick über die Features von Azure insbesondere für Entwickler. Dabei werden wir sowohl auf die Platform as a Service (PaaS) Angebote wie auch auf die Infrastructe as a Service (IaaS) eingehen. Außerdem geben wir einen Einblick in moderne Cloud Architektur und zeigen Best Practices bei der Cloud Entwicklung auf. Dabei werden Beispiele aus der Praxis zeigen, wie man eine Fehlertolerante und robuste Cloud Lösung erstellen kann.
Über die Sprecher:
Winfried Klinker ist als Software Architekt bei der Firma Sitrion in Oldenburg tätig. Er beschäftigt sich größtenteils mit Cloud Architekturen mit Microsoft Azure vor allem in Bezug auf Backends für mobile Anwendungen.
Andre Hühn ist Team Lead für Entwicklung mobiler Apps bei der Firma Sitrion in Oldenburg und beeinflusst damit die Richtung der Architektur für das Sitrion ONE Produkt.
Billions of Messages in Real Time: Why Paypal & LinkedIn Trust an Engagement ...
(Bruno Simic, Solutions Engineer, Couchbase)
Breakout during Confluent’s streaming event in Munich. This three-day hands-on course focused on how to build, manage, and monitor clusters using industry best-practices developed by the world’s foremost Apache Kafka™ experts. The sessions focused on how Kafka and the Confluent Platform work, how their main subsystems interact, and how to set up, manage, monitor, and tune your cluster.
From Data to Services at the Speed of Business
From Data to Services at the Speed of Business: Applying cloud-native paradigm to combine fast data analytics with microservices architecture for hybrid workloads.
[Cloud OnAir] Talks by DevRel Vol.4 データ管理とデータ ベース 2020年8月27日 放送
The document discusses a Cloud OnAir event about database management and databases. It includes an agenda that covers overviews of Cloud SQL, Cloud Memorystore, Cloud Spanner, and Cloud Firestore updates. Several speakers are also listed that will provide presentations on choosing the right database, database migrations, database modernization, and specific database services like Cloud Spanner and Cloud Bigtable.
Big Trends in Big Data
Big Trends in Big Data, New Apache Hadoop enhancements, New Sql tools and Big Data Real Time computation systems
Best Bigquery ETL Tool
Lyftrondata enables enterprises to load data from 300+ connectors to Google Bigquery in minutes without any engineering requirements. Simply connect, organize, centralize and share your data on Bigquery with zero code data pipeline, ETL & ELT tool.
Microsoft Azure
Azure provides cloud computing services including computing, analytics, networking, storage, and more. It offers virtual machines, databases, websites, and other services that can be accessed from anywhere and scaled up as needed. Azure aims to provide enterprise-grade services that are economical, scalable, and hybrid-ready to work with existing on-premises systems. It has data centers across the world and over 600,000 servers to provide its services globally at scale.
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Oracle 23c New Features For DBAs and Developers.pptx
Most important New features of Oracle 23c for DBAs and Developers. You can get more idea from my youtube channel video from https://youtu.be/XvL5WtaC20A
E-commerce Development Services- Hornet Dynamics
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SMS API Integration in Saudi Arabia| Best SMS API Service
Discover the benefits and implementation of SMS API integration in the UAE and Middle East. This comprehensive guide covers the importance of SMS messaging APIs, the advantages of bulk SMS APIs, and real-world case studies. Learn how CEQUENS, a leader in communication solutions, can help your business enhance customer engagement and streamline operations with innovative CPaaS, reliable SMS APIs, and omnichannel solutions, including WhatsApp Business. Perfect for businesses seeking to optimize their communication strategies in the digital age.
UI5con 2024 - Boost Your Development Experience with UI5 Tooling Extensions
The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
Microservice Teams - How the cloud changes the way we work
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Top Benefits of Using Salesforce Healthcare CRM for Patient Management.pdf
Salesforce Healthcare CRM, implemented by VALiNTRY360, revolutionizes patient management by enhancing patient engagement, streamlining administrative processes, and improving care coordination. Its advanced analytics, robust security, and seamless integration with telehealth services ensure that healthcare providers can deliver personalized, efficient, and secure patient care. By automating routine tasks and providing actionable insights, Salesforce Healthcare CRM enables healthcare providers to focus on delivering high-quality care, leading to better patient outcomes and higher satisfaction. VALiNTRY360's expertise ensures a tailored solution that meets the unique needs of any healthcare practice, from small clinics to large hospital systems.
For more info visit us https://valintry360.com/solutions/health-life-sciences
一比一原版(USF毕业证)旧金山大学毕业证如何办理
USF硕士毕业证成绩单【微信95270640】一比一伪造旧金山大学文凭@假冒USF毕业证成绩单+Q微信95270640办理USF学位证书@仿造USF毕业文凭证书@购买旧金山大学毕业证成绩单USF真实使馆认证/真实留信认证回国人员证明
#一整套旧金山大学文凭证件办理#—包含旧金山大学旧金山大学本科毕业证成绩单学历认证|使馆认证|归国人员证明|教育部认证|留信网认证永远存档教育部学历学位认证查询办理国外文凭国外学历学位认证#我们提供全套办理服务。
一整套留学文凭证件服务：
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二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站永久可查
四：留信认证留学生信息网站永久可查
国外毕业证学位证成绩单办理方法：
1客户提供办理旧金山大学旧金山大学本科毕业证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
教育部文凭学历认证认证的用途：
如果您计划在国内发展那么办理国内教育部认证是必不可少的。事业性用人单位如银行国企公务员在您应聘时都会需要您提供这个认证。其他私营 #外企企业无需提供！办理教育部认证所需资料众多且烦琐所有材料您都必须提供原件我们凭借丰富的经验帮您快速整合材料让您少走弯路。
实体公司专业为您服务如有需要请联系我: 微信95270640奈一次次令他失望山娃今年岁上五年级识得很多字从走出小屋开始山娃就知道父亲的家和工地共有一个很动听的名字——天河工地的底层空空荡荡很宽阔很凉爽在地上铺上报纸和水泥袋父亲和工人们中午全睡在地上地面坑坑洼洼山娃曾多次绊倒过也曾有长铁钉穿透凉鞋刺在脚板上但山娃不怕工地上也常有五六个从乡下来的小学生他们的父母亲也是高楼上的建筑工人小伙伴来自不同省份都操着带有浓重口音的普通话可不知为啥山娃不仅很快与他们熟识了
How Can Hiring A Mobile App Development Company Help Your Business Grow?
ToXSL Technologies is an award-winning Mobile App Development Company in Dubai that helps businesses reshape their digital possibilities with custom app services. As a top app development company in Dubai, we offer highly engaging iOS & Android app solutions. https://rb.gy/necdnt
UI5con 2024 - Keynote: Latest News about UI5 and it’s Ecosystem
Learn about the latest innovations in and around OpenUI5/SAPUI5: UI5 Tooling, UI5 linter, UI5 Web Components, Web Components Integration, UI5 2.x, UI5 GenAI.
Recording:
https://www.youtube.com/live/MSdGLG2zLy8?si=INxBHTqkwHhxV5Ta&t=0
Artificia Intellicence and XPath Extension Functions
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
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Top 9 Trends in Cybersecurity for 2024.pptx
Security and risk management (SRM) leaders face disruptions on technological, organizational, and human fronts. Preparation and pragmatic execution are key for dealing with these disruptions and providing the right cybersecurity program.
Enums On Steroids - let's look at sealed classes !
These are slides for my session"Enums On Steroids - let's look at sealed classes !" - delivered among others, on Devoxx UK 2024 conference
WWDC 2024 Keynote Review: For CocoaCoders Austin
Overview of WWDC 2024 Keynote Address.
Covers: Apple Intelligence, iOS18, macOS Sequoia, iPadOS, watchOS, visionOS, and Apple TV+.
Understandable dialogue on Apple TV+
On-device app controlling AI.
Access to ChatGPT with a guest appearance by Chief Data Thief Sam Altman!
App Locking! iPhone Mirroring! And a Calculator!!
一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理
UMN硕士毕业证成绩单【微信95270640】购买（明尼苏达大学毕业证成绩单硕士学历）Q微信95270640代办UMN学历认证留信网伪造明尼苏达大学学位证书精仿明尼苏达大学本科/硕士文凭证书补办明尼苏达大学 diplomaoffer,Transcript购买明尼苏达大学毕业证成绩单购买UMN假毕业证学位证书购买伪造明尼苏达大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
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国外大学明尼苏达大学明尼苏达大学毕业证offer制作方法（一对一专业服务）
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
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— — -公司理念 【诚信为主】— — —
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此贴长年有效【贴心专线/微-信: 95270640】敬请保留此联系方式以备用！如有不在线请给我们留言！我们将在第一时间给您回复!上散发着一抹抹的光晕而这每处自然形成的细节融合在一起浑然天成的美实在令人心生愉悦小道的周边无秩序的生长着几株艳丽的野花红的粉的紫的虽混乱无章却给这幅美景更增添一份性感夹杂着一份纯洁的妖娆毫无违和感实在给人带来一份悠然幸福的心情如果说现在的审美已经断然拒绝了无声的话那么在树林间飞掠而过的小鸟叽叽咋咋的叫声是否就是这最后的点睛之笔悠然走在林间的小路上宁静与清香一丝丝的盛夏气息吸入身体昔日生活里的繁忙多
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No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
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Enums On Steroids - let's look at sealed classes !
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Need for Speed: Removing speed bumps from your Symfony projects ⚡️
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Hasura
- 2. Intro to Hasura ● globally distributed team, with offices in San Francisco ● make data access fast, secure & scalable ○ a world where data delivery is just another piece of infrastructure ● Written in Haskell ● Support PostgreSQL, MySQL, SQL server & soon BigQuery
- 3. Access data is a bottleneck for developers, Hasura solves this
- 5. Features ● Powerful Querying. ● Subscriptions and Live Queries. ● Dynamic Access Control. ● Write business logic backed by REST APIs. ● Remote Schemas. ● Event Triggers for serverless. ● Adding to an existing Postgres database. ● Admin UI & Schema migrations.
- 6. Benefits Tangible impact on ● Time to market ○ Self-serve APIs (GraphQL + Declarative API engine) ● API Performance ○ 5x-100x faster vs DIY ● Uptime & Reliability ○ Distributed, HA, Fault-tolerant ● Security ○ Easy & Observable Designed for ● New applications ● incremental modernization of existing applications
- 7. The community ● 20K+ Github starts ● 370+ Contributors ● Fastest growing graphQL project ● 100+ million downloads
- 8. My challenges & experience ● Meta data & config management ● Authorization (RBAC & ABAC combination) ● Caching
- 9. Demo