Lambda Architecture in Practice

•Download as PPTX, PDF•

0 likes•813 views

This presentation covers practical implementation of Lambda with different patterns. It also explains how to achieve continuous deployment using lambda.

What ?
A construct in functional programming
language to denote an anonymous
Function definition that is not bound to an
identifier

Ecosystem
• Dynamo
• SQS
• CloudWatch
• Serverless
• Lambada
• Chalice
• Java
• NodeJS
• Python
• AWS Lambda
• Cloud Functions
• Azure Functions
• Openwhisk
Providers Languages
Integrations
Framework
s

Why ?
- Event-Driven
- Elasticity
- Unpredictable
Bursty Traffic
- Lightweight Jobs
- Continuous
- Complex SOA
- Constant traffic
- Persistent
connection

Architecture Patterns
Monolith NanoService Hybrid
 Easy code-share
 More CPU time
 Always warm
 Difficult code-share
 Less CPU time
 Nano deployments
 Easy code-share
 Micro Deployments
 Reasonably warm

Framework extension
Spring
+
Jersey
Jetty
Proxy
1
3
2
4
Lambda EnvironmentAPI gateway
Conventional app deployment in Lambda

Scheduling & Triggers
Health Checks
Scheduled Jobs
Scheduled Warm-up
Triggers
Cloudwatch-Event
SQS
API Gateway

Continuous Delivery
• Plugin creates a shaded jar
• Upload to S3
• Lambda updates with latest code
• API gateway deployment

Limitation
Max response time = 300 sec
No persistent connection
Event payload size = 128K
Deployment size = 50 MB ( 250 MB uncompressed )
Max concurrent executions = 100
Concurrency = Events rate * Avg execution time

* Over 100 million subscribers from over 190 countries enjoy the Netflix service. This leads to over a trillion events flowing through the Keystone stream processing infrastructure to help glean business insights and improve customer experience. The self-serve infrastructure enables the users to focus on extracting insights, and not worry about building out scalable infrastructure. I’ll share our experience building building this platform with Flink, and lessons learnt.

Beaming flink to the cloud @ netflix ff 2016-monal-daxini

Monal Daxini

Netflix is a data driven company and we process over 700 billion streaming events per day with at-least once processing semantics in the cloud. To enable extracting intelligence from this unbounded stream easily we are building Stream Processing as a Service (SPaaS) infrastructure so that the user can focus on extracting value and not have to worry about boilerplate infrastructure and scale. We will share our experience in building a scalable SPaaS using Flink, Apache Beam and Kafka as the foundation layer to process over 1.3 PB of event data without service disruption.

Apache Zeppelin Meetup Christian Tzolov 1/21/16

PivotalOpenSourceHub

Data Pipeline with Kafka

Peerapat Asoktummarungsri

Kafka On YARN (KOYA): An Open Source Initiative to integrate Kafka & YARN

DataWorks Summit

What Crimean War gunboats teach us about the need for schema registries

Alexander Dean

In 1853 Britain’s workshops built 90 new gunboats for the Royal Navy in just 90 days: an astonishing feat of engineering. Industrial standardization made this possible - and in this talk, my first at Strata, I argued that data-sophisticated corporations need a new standardization of their own, in the form of schema registries like Confluent Schema Registry or Snowplow’s own Iglu. Talk abstract: At the start of the Crimean War in 1853, Britain's Royal Navy needed 90 new gunboats ready to fight in the Baltic in just 90 days. Assembling the boats was straightforward - the challenge was to build all of the engine sets in time. Marine engineer John Penn did an unusual thing: he took a pair of reference engines, disassembled them and distributed the pieces to the best machine shops across Britain. These workshops - latter-day micro-services - each built 90 sets of their allocated parts, which were then assembled into the engines for the new gunboats, ready for battle. This was the nineteenth century - how could the Admiralty be certain that the parts from all these independent workshops would come together to form 90 high-powered engines? The answer lay in a crucial piece of standardization: the Whitworth thread, the world’s first national screw thread standard, devised by Sir Joseph Whitworth in 1841. By the time the Royal Navy came knocking, this standard had been adopted by workshops across Britain; John Penn could be confident that engine parts built by any workshop to the Whitworth standard would fit together. In this talk, Snowplow co-founder Alexander Dean will draw on the story of the Crimean War gunboats to argue that our data processing architectures urgently require a standardization of their own, in the form of schema registries. Like the Whitworth screw thread, a schema registry, such as Confluent Schema Registry or Snowplow’s own Iglu, allows enterprises to standardise on a set of business entities which can be used throughout their batch and stream processing architectures. Like the artisanal workshops in 1850s Britain, micro-services can work on narrowly defined data processing tasks, confident that their inputs and outputs will be compatible with their peers. This talk will start with the rationale for putting a schema registry at the heart of your business, before moving on to the practicalities of an implementation, including: a side-by-side comparison of the available registries; best practises about schema versioning; strategies around schema federation across different companies such as Snowplow’s own Iglu Central.

Select Star: Flink SQL for Pulsar Folks - Pulsar Summit NA 2021

StreamNative

SQL is eating the world (again!), and stream processing is no exception. As Flink SQL evolves to power business-critical applications at companies like Yelp, Airbnb or Uber, the Flink and Pulsar communities have been working in close collaboration to bring you the best of both worlds. But where do we stand today? In this talk, we’ll get you up to speed with the latest in streaming SQL with Flink and demo how you can integrate with Apache Pulsar to build unified, elastic data processing pipelines.

Flink forward-2017-netflix keystones-paas

Monal Daxini

The need for gleaning answers from unbounded data streams is moving from nicety to a necessity. Netflix is a data driven company, and has a need to process over 1 trillion events a day amounting to 3 PB of data to derive business insights. To ease extracting insight, we are building a self-serve, scalable, fault-tolerant, multi-tenant "Stream Processing as a Service" platform so the user can focus on data analysis. I'll share our experience using Flink to help build the platform.

Over 100 million subscribers from over 190 countries enjoy the Netflix service. This leads to over a trillion events, amounting to 3 PB, flowing through the Keystone infrastructure to help improve customer experience and glean business insights. The self-serve Keystone stream processing service processes these messages in near real-time with at-least once semantics in the cloud. This enables the users to focus on extracting insights, and not worry about building out scalable infrastructure. I’ll share the details about this platform, and our experience building it.

How Tencent Applies Apache Pulsar to Apache InLong - Pulsar Summit Asia 2021

StreamNative

Apache InLong is a one-stop data streaming platform, it chooses Apache Pulsar to cache data for forwarding sort. Apache Pulsar has great reliability and stability, which helps the InLong to be more confident for users. This session will share Tencent Big Data Team's journal of adopting Pulsar in their core data engine to process tens of billions of data integration. Besides, some problems they encountered during the process and the improvements on Pulsar they have made will also be shared as an example for future Pulsar users.

Lambda Architecture with Spark

Knoldus Inc.

How Disney+ uses fast data ubiquity to improve the customer experience

Martin Zapletal

Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. In this session, you learn how Disney+ built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Kinesis Data Analytics for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. Hear how these services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability. Learn more about re:Invent 2020 at http://bit.ly/3c4NSdY

Streaming Analytics with Spark, Kafka, Cassandra and Akka

Helena Edelson

This talk will address how a new architecture is emerging for analytics, based on Spark, Mesos, Akka, Cassandra and Kafka (SMACK). Popular architecture like Lambda separate layers of computation and delivery and require many technologies which have overlapping functionality. Some of this results in duplicated code, untyped processes, or high operational overhead, let alone the cost (i.e. ETL). I will discuss the problem domain and what is needed in terms of strategies, architecture and application design and code to begin leveraging simpler data flows. We will cover how the particular set of technologies addresses common requirements and how collaboratively they work together to enrich and reinforce each other.

Kafka Summit NYC 2017 - Data Processing at LinkedIn with Apache Kafka

confluent

Netflix Keystone—Cloud scale event processing pipeline

Monal Daxini

fluentd -- the missing log collector

Muga Nishizawa

Using FLiP with influxdb for EdgeAI IoT at Scale

Timothy Spann

Big data pipeline with scala by Rohit Rai, Tuplejump - presented at Pune Scal...Thoughtworks

Uber Real Time Data Analytics

Ankur Bansal

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.

The Netflix Way to deal with Big Data Problems

Monal Daxini

Should you read Kafka as a stream or in batch? Should you even care? | Ido Na...

HostedbyConfluent

Should you consume Kafka in a stream OR batch? When should you choose each one? What is more efficient, and cost effective? In this talk we’ll give you the tools and metrics to decide which solution you should apply when, and show you a real life example with cost & time comparisons. To highlight the differences, we’ll dive into a project we’ve done, transitioning from reading Kafka in a stream to reading it in batch. By turning conventional thinking on its head and reading our multi-petabyte Kafka stream in batch using Spark and Airflow, we’ve achieved a huge cost reduction of 65% while at the same time getting a more scalable and resilient solution. We’ll explore the tradeoffs and give you the metrics and intuition you’ll need to make such decisions yourself. We’ll cover: Costs of processing in stream compared to batch Scaling up for bursts and reprocessing Making the tradeoff between wait times and costs Recovering from outages And much more…

Architecting for Scale

Pooyan Jamshidi

Spark-Streaming-as-a-Service with Kafka and YARN: Spark Summit East talk by J...

Spark Summit

Since April 2016, Spark-as-a-service has been available to researchers in Sweden from the Swedish ICT SICS Data Center at www.hops.site. Researchers work in an entirely UI-driven environment on a platform built with only open-source software. Spark applications can be either deployed as jobs (batch or streaming) or written and run directly from Apache Zeppelin. Spark applications are run within a project on a YARN cluster with the novel property that Spark applications are metered and charged to projects. Projects are also securely isolated from each other and include support for project-specific Kafka topics. That is, Kafka topics are protected from access by users that are not members of the project. In this talk we will discuss the challenges in building multi-tenant Spark streaming applications on YARN that are metered and easy-to-debug. We show how we use the ELK stack (Elasticsearch, Logstash, and Kibana) for logging and debugging running Spark streaming applications, how we use Graphana and Graphite for monitoring Spark streaming applications, and how users can debug and optimize terminated Spark Streaming jobs using Dr Elephant. We will also discuss the experiences of our users (over 120 users as of Sept 2016): how they manage their Kafka topics and quotas, patterns for how users share topics between projects, and our novel solutions for helping researchers debug and optimize Spark applications. To conclude, we will also give an overview on our course ID2223 on Large Scale Learning and Deep Learning, in which 60 students designed and ran SparkML applications on the platform.

Apache Spark on Kubernetes Anirudh Ramanathan and Tim Chen

Databricks

Kubernetes is a fast growing open-source platform which provides container-centric infrastructure. Conceived by Google in 2014, and leveraging over a decade of experience running containers at scale internally, it is one of the fastest moving projects on GitHub with 1000+ contributors and 40,000+ commits. Kubernetes has first class support on Google Cloud Platform, Amazon Web Services, and Microsoft Azure. Unlike YARN, Kubernetes started as a general purpose orchestration framework with a focus on serving jobs. Support for long-running, data intensive batch workloads required some careful design decisions. Engineers across several organizations have been working on Kubernetes support as a cluster scheduler backend within Spark. During this process, we encountered several challenges in translating Spark considerations into idiomatic Kubernetes constructs. In this talk, we describe the challenges and the ways in which we solved them. This talk will be technical and is aimed at people who are looking to run Spark effectively on their clusters. The talk assumes basic familiarity with cluster orchestration and containers.

Architecture of a Kafka camus infrastructure

mattlieber

Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan Volz

Databricks

Today, there are several compliance use cases ‒ archiving, e-discovery, supervision and surveillance, to name a few ‒ that appear naturally suited as Hadoop workloads, but haven’t seen wide adoption. In this session, you’ll learn about common limitations, how Apache Spark helps and some new blueprints for modernizing this architecture and disrupt existing solutions. Additionally, we’ll review the rising role of Apache Spark in this ecosystem, leveraging machine learning and advanced analytics in a space that has traditionally been restricted to fairly rote reporting.

Netflix viewing data architecture evolution - QCon 2014

Philip Fisher-Ogden

Matt Franklin - Apache Software (Geekfest)

W2O Group

AWS and Serverless with Alexa

Rory Preddy

AWS Core services: * The AWS web console: the entry point for configuring your infrastructure in the AWS cloud * The Free Tier and how to setup billing alerts * Elastic Compute Cloud (EC2) instances, and the ease with which you can pick a particular Amazon Machine Image (AMI) for your workload, and spin it up as an instance right away * How to create and deploy a high-availability web application in AWS, with an Elastic Load Balancer (ELB) and a multi-availability-zone Relational-Database-Service (RDS) instance * How CloudFormation can automate all of the above. Serverless Functions: Serverless architecture allows developers to focus on code and their business problem rather than spending time looking after backend infrastructure. Serverless architecture can help developers build scalable, high-performing, and cost-effective applications quickly We will talk about how serverless architecture and AWS Lambda can make things easier, cheaper, and help to accelerate development of projects.

Compute Without Servers – Building Applications with AWS Lambda - Technical 301

Amazon Web Services

AWS Lambda enables developers to build scalable applications without managing servers. Come learn how Lambda's event driven approach helps build backend ingestion systems, real time stream processing, and scalable API backends. We will deep dive into the different approaches that customers have taken to building applications with Lambda, typical architectures that customers use Lambda for, and best practices for authoring, deploying, and managing Lambda functions. Speaker: Ajay Nair, Sr Product Manager Lambda, Amazon Web Services

What's hot

AWS Re-Invent 2017 Netflix Keystone SPaaS - Monal Daxini - Abd320 2017

Monal Daxini

How Tencent Applies Apache Pulsar to Apache InLong - Pulsar Summit Asia 2021

StreamNative

Lambda Architecture with Spark

Knoldus Inc.

How Disney+ uses fast data ubiquity to improve the customer experience

Martin Zapletal

Streaming Analytics with Spark, Kafka, Cassandra and Akka

Helena Edelson

Kafka Summit NYC 2017 - Data Processing at LinkedIn with Apache Kafka

confluent

Netflix Keystone—Cloud scale event processing pipeline

Monal Daxini

fluentd -- the missing log collector

Muga Nishizawa

Using FLiP with influxdb for EdgeAI IoT at Scale

Timothy Spann

Big data pipeline with scala by Rohit Rai, Tuplejump - presented at Pune Scal...Thoughtworks

Uber Real Time Data Analytics

Ankur Bansal

The Netflix Way to deal with Big Data Problems

Monal Daxini

Should you read Kafka as a stream or in batch? Should you even care? | Ido Na...

HostedbyConfluent

Architecting for Scale

Pooyan Jamshidi

Spark-Streaming-as-a-Service with Kafka and YARN: Spark Summit East talk by J...

Spark Summit

Apache Spark on Kubernetes Anirudh Ramanathan and Tim Chen

Databricks

Architecture of a Kafka camus infrastructure

mattlieber

Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan Volz

Databricks

Netflix viewing data architecture evolution - QCon 2014

Philip Fisher-Ogden

Matt Franklin - Apache Software (Geekfest)

W2O Group

What's hot (20)

AWS Re-Invent 2017 Netflix Keystone SPaaS - Monal Daxini - Abd320 2017

How Tencent Applies Apache Pulsar to Apache InLong - Pulsar Summit Asia 2021

Lambda Architecture with Spark

How Disney+ uses fast data ubiquity to improve the customer experience

Streaming Analytics with Spark, Kafka, Cassandra and Akka

Kafka Summit NYC 2017 - Data Processing at LinkedIn with Apache Kafka

Netflix Keystone—Cloud scale event processing pipeline

fluentd -- the missing log collector

Using FLiP with influxdb for EdgeAI IoT at Scale

Big data pipeline with scala by Rohit Rai, Tuplejump - presented at Pune Scal...

Uber Real Time Data Analytics

The Netflix Way to deal with Big Data Problems

Should you read Kafka as a stream or in batch? Should you even care? | Ido Na...

Architecting for Scale

Spark-Streaming-as-a-Service with Kafka and YARN: Spark Summit East talk by J...

Apache Spark on Kubernetes Anirudh Ramanathan and Tim Chen

Architecture of a Kafka camus infrastructure

Archiving, E-Discovery, and Supervision with Spark and Hadoop with Jordan Volz

Netflix viewing data architecture evolution - QCon 2014

Matt Franklin - Apache Software (Geekfest)

Similar to Lambda Architecture in Practice

AWS and Serverless with Alexa

Rory Preddy

Compute Without Servers – Building Applications with AWS Lambda - Technical 301

Amazon Web Services

Getting Started with AWS Lambda and the Serverless Cloud

Amazon Web Services

With AWS Lambda, you can easily build scalable microservices for mobile, web, and IoT applications or respond to events from other AWS services without managing infrastructure. In this session, you’ll see demonstrations and hear more about newly launched features. We’ll show you how to use Lambda to build web, mobile, or IoT backends and voice-enabled apps, and we'll show you how to extend both AWS and third party services by triggering Lambda functions. We’ll also provide productivity and performance tips for getting the most out of your Lambda functions and show how cloud native architectures use Lambda to eliminate “cold servers” and excess capacity without sacrificing scalability or responsiveness.

AWS Lambda and Serverless Cloud

Amazon Web Services

A Walk in the Cloud with AWS Lambda

Amazon Web Services

This presentation is from the AWS Lambda session of Container Days Conference in NYC. AWS Lambda is a new compute service that runs your code in response to events and automatically and dynamically manages infra resources for you. Tara will talk about AWS's event-driven compute strategy and explain how Lambda works to respond to events from various Amazon services. Tara will describe what you need to easily build scalable microservices for mobile, web, and IoT applications that use AWS Lambda as a serverless back-end, how you can expose these services using Amazon API Gateway, and how to extend both AWS and third party services by triggering Lambda functions. She'll also cover the updated Lambda features announced at reInvent 2015, its programming model, and tips on getting the most out of Lambda.

SoCal NodeJS Meetup 20170215_aws_lambda

Stefan Deusch

AWS re:Invent 2016: Accenture Cloud Platform Serverless Journey (ARC202)

Amazon Web Services

Accenture Cloud Platform helps customers manage public and private enterprise cloud resources effectively and securely. In this session, learn how we designed and built new core platform capabilities using a serverless, microservices-based architecture that is based on AWS services such as AWS Lambda and Amazon API Gateway. During our journey, we discovered a number of key benefits, including a dramatic increase in developer velocity, a reduction (to almost zero) of reliance on other teams, reduced costs, greater resilience, and scalability. We describe the (wild) successes we’ve had and the challenges we’ve overcome to create an AWS serverless architecture at scale. Session sponsored by Accenture. AWS Competency Partner

SMC301 The State of Serverless Computing

Amazon Web Services

Join us to learn about the state of serverless computing from Dr. Tim Wagner, General Manager of AWS Lambda. Dr. Wagner discusses the latest developments from AWS Lambda and the serverless computing ecosystem. He talks about how serverless computing is becoming a core component in how companies build and run their applications and services, and he also discusses how serverless computing will continue to evolve.

Deep Dive on Microservices and Docker

Kristana Kane

Getting Started with AWS Lambda and the Serverless Cloud

Amazon Web Services

Serverless Architectures on AWS Lambda

Serhat Can

Deep Dive on AWS Lambda - January 2017 AWS Online Tech Talks

Amazon Web Services

AWS Lambda lets you run code without provisioning or managing servers. You pay only for the compute time you consume - there is no charge when your code is not running. With Lambda, you can run code for virtually any type of application or backend service - all with zero administration. Just upload your code and Lambda takes care of everything required to run and scale your code with high availability. You can set up your code to automatically trigger from other AWS services or call it directly from any web or mobile app. In this session, we dive deep into AWS Lambda to learn about capabilities, features and benefits. Learning Objectives: • Dive deep into AWS Lambda • Learn about the capabilities, features and benefits of AWS Lambda • Learn about the different use cases • Learn how to get started using AWS Lambda

Aws-What You Need to Know_Simon Elisha

Helen Rogers

Getting Started with AWS Lambda and the Serverless Cloud

Amazon Web Services

AWS March 2016 Webinar Series Getting Started with Serverless Architectures

Amazon Web Services

Serverless Architectures allow you to build and run applications and services without having to manage the infrastructure. With serverless architectures on AWS, your application still runs on servers, but all the server management is done by AWS. In this webinar, you will learn how to build applications and services using a serverless architecture. We will discuss how you can use AWS Lambda to run code for any type of application or backend service; use Amazon DynamoDB to store application data with high scalability and redundancy; and use Amazon API Gateway to create and manage secure API endpoints. We will also run through a demo setting up a web application using this architecture, and discuss best practices and patterns used by our customers to run serverless applications. Learning Objectives: • Understand the basics of serverless architectures • Learn how to use Lambda, API Gateway, and DynamoDB to run web applications Who Should Attend: • Developers, web developers

Getting Started with Serverless Architectures

Amazon Web Services

Serverless DevOps to the Rescue - SRV330 - re:Invent 2017

Amazon Web Services

Join this workshop for a crash course in serverless DevOps! This workshops presents a scenario in which you help out Wild Rydes (www.wildrydes.com), the world’s leading unicorn transportation startup! After building the first iteration of its serverless web application, Wild Rydes needs serverless DevOps experts like yourself to help it rapidly build and iterate upon its web app. In this workshop, you’ll help Wild Rydes set up a CI/CD pipeline that enables the company to rapidly build, test, and deploy changes to its serverless application. You’ll also learn to monitor and diagnose issues for its application. This workshop will teach you how to model and deploy serverless apps with the AWS Serverless Application Model. You’ll learn to use AWS CodePipeline and AWS CodeBuild to create a CI/CD pipeline for AWS Lambda and other services. Finally, you’ll learn to use AWS X-Ray to diagnose issues in your Lambda functions. Requirements: Laptop, AWS account, basic Git experience. Recommended: Previous experience with the AWS Management Console and AWS CloudFormation templates, some familiarity with the AWS Developer Tools services, and preferably one of the AWS Associate certifications.

20180111 we bde-bs - serverless url shortener

Luca Bianchi

What's New in AWS Serverless and Containers

Amazon Web Services

What's New in AWS Serverless and Containers 亞馬遜 AWS 於 2018 年 11 月底在美國拉斯維加斯所舉辦的第七屆 AWS re:Invent 2018 大會，在 AWS 客戶、合作夥伴、媒體人士、產業分析師及 AWS 員工共襄盛舉下，與會人數再創新高，超過 5 萬人。會中 AWS 發布超過 20 款雲端方案，且一半以上專攻雲端 AI、機器學習、物聯網，包括對 SageMaker 強化更多進階功能，推出第一款專用的機器學習推論晶片、加入深度的機器學習運算法支援，及其他包括儲存、資料庫、混合雲、邊緣運算 IoT 等解決方案。而具備微型機器學習能力的迷你自駕遙控車 DeepRacer 的現身，驚人之舉不僅抓人眼球，深入客戶體驗的用心，更成功抓住全球使用者的心。為讓您與全球先進技術同步，共享最新趨勢資訊，解決您開發機器學習和發展 AIoT 所遇到的難題，AWS 台灣團隊將於 2019 年 1 月 31 日 (四) 舉辦《AWS re:Invent 2018 Recap 台北》，特別嚴選最適切國內諸位先進和企業需求的內容，從「技術創新」、「AIoT」兩大分組議程，發表 AWS 的新服務和新方案。大會除了邀請亞馬遜 AWS 大中華區首席雲計算企業顧問 (Principal Evangelist) 張俠博士分享 AWS 的解決方案藍圖外，眾多 AWS 資深專家也將分享包含機器學習、深度學習推理加速等新方案，完全託管的文件系統、資料庫，無伺服器、容器技術與安全性，以及大數據與分析、物聯網服務應用、儲存方案等最新技術。歡迎您親臨會場，全方位體驗 AWS 新服務將能為您創造的驚人創新之效益。

AWS Summit Auckland - Getting Started with AWS Lambda and the Serverless Cloud

Amazon Web Services

Similar to Lambda Architecture in Practice (20)

AWS and Serverless with Alexa

Compute Without Servers – Building Applications with AWS Lambda - Technical 301

Getting Started with AWS Lambda and the Serverless Cloud

AWS Lambda and Serverless Cloud

A Walk in the Cloud with AWS Lambda

SoCal NodeJS Meetup 20170215_aws_lambda

AWS re:Invent 2016: Accenture Cloud Platform Serverless Journey (ARC202)

SMC301 The State of Serverless Computing

Deep Dive on Microservices and Docker

Getting Started with AWS Lambda and the Serverless Cloud

Serverless Architectures on AWS Lambda

Deep Dive on AWS Lambda - January 2017 AWS Online Tech Talks

Aws-What You Need to Know_Simon Elisha

Getting Started with AWS Lambda and the Serverless Cloud

AWS March 2016 Webinar Series Getting Started with Serverless Architectures

Getting Started with Serverless Architectures

Serverless DevOps to the Rescue - SRV330 - re:Invent 2017

20180111 we bde-bs - serverless url shortener

What's New in AWS Serverless and Containers

AWS Summit Auckland - Getting Started with AWS Lambda and the Serverless Cloud

Recently uploaded

Encryption in Microsoft 365 - ExpertsLive Netherlands 2024

Albert Hoitingh

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Paul Groth

Monitoring Java Application Security with JDK Tools and JFR Events

Ana-Maria Mihalceanu

Transcript: Selling digital books in 2024: Insights from industry leaders - T...

BookNet Canada

The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more. Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/ Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.

Securing your Kubernetes cluster_ a step-by-step guide to success !

KatiaHIMEUR1

Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster. However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks. In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.

JMeter webinar - integration with InfluxDB and Grafana

RTTS

Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application. In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics. Length: 30 minutes Session Overview ------------------------------------------- During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana: - What out-of-the-box solutions are available for real-time monitoring JMeter tests? - What are the benefits of integrating InfluxDB and Grafana into the load testing stack? - Which features are provided by Grafana? - Demonstration of InfluxDB and Grafana using a practice web application To view the webinar recording, go to: https://www.rttsweb.com/jmeter-integration-webinar

Epistemic Interaction - tuning interfaces to provide information for AI support

Alan Dix

Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024 https://alandix.com/academic/papers/synergy2024-epistemic/ As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.

Elevating Tactical DDD Patterns Through Object Calisthenics

Dorra BARTAGUIZ

After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!

Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality

Inflectra

In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring. Learn about: • The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks. • Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective. • Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification. • Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process. Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.

Key Trends Shaping the Future of Infrastructure.pdf

Cheryl Hung

Essentials of Automations: Optimizing FME Workflows with Parameters

Safe Software

Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place. Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects. Here’s what you’ll gain: - Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows. - Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy. - Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency. - Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity. We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic. Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.

Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...

Ramesh Iyer

In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.

State of ICS and IoT Cyber Threat Landscape Report 2024 preview

Prayukth K V

The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development. The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers: State of global ICS asset and network exposure Sectoral targets and attacks as well as the cost of ransom Global APT activity, AI usage, actor and tactic profiles, and implications Rise in volumes of AI-powered cyberattacks Major cyber events in 2024 Malware and malicious payload trends Cyberattack types and targets Vulnerability exploit attempts on CVEs Attacks on counties – USA Expansion of bot farms – how, where, and why In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East Why are attacks on smart factories rising? Cyber risk predictions Axis of attacks – Europe Systemic attacks in the Middle East Download the full report from here: https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/

FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf

FIDO Alliance

Leading Change strategies and insights for effective change management pdf 1.pdf

OnBoard

Assuring Contact Center Experiences for Your Customers With ThousandEyes

ThousandEyes

From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...

Product School

The Art of the Pitch: WordPress Relationships and Sales

Laura Byrne

Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes? All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.

Accelerate your Kubernetes clusters with Varnish Caching

Thijs Feryn

Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...

Product School

Recently uploaded (20)

Encryption in Microsoft 365 - ExpertsLive Netherlands 2024

To Graph or Not to Graph Knowledge Graph Architectures and LLMs

Monitoring Java Application Security with JDK Tools and JFR Events

Transcript: Selling digital books in 2024: Insights from industry leaders - T...

Securing your Kubernetes cluster_ a step-by-step guide to success !

JMeter webinar - integration with InfluxDB and Grafana

Epistemic Interaction - tuning interfaces to provide information for AI support

Elevating Tactical DDD Patterns Through Object Calisthenics

Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality

Key Trends Shaping the Future of Infrastructure.pdf

Essentials of Automations: Optimizing FME Workflows with Parameters

Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...

State of ICS and IoT Cyber Threat Landscape Report 2024 preview

FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf

Leading Change strategies and insights for effective change management pdf 1.pdf

Assuring Contact Center Experiences for Your Customers With ThousandEyes

From Siloed Products to Connected Ecosystem: Building a Sustainable and Scala...

The Art of the Pitch: WordPress Relationships and Sales

Accelerate your Kubernetes clusters with Varnish Caching

Unsubscribed: Combat Subscription Fatigue With a Membership Mentality by Head...

Lambda Architecture in Practice

1. Lambda Architecture In Practice

2. What ? A construct in functional programming language to denote an anonymous Function definition that is not bound to an identifier

3. Ecosystem • Dynamo • SQS • CloudWatch • Serverless • Lambada • Chalice • Java • NodeJS • Python • AWS Lambda • Cloud Functions • Azure Functions • Openwhisk Providers Languages Integrations Framework s

4. Why ? - Event-Driven - Elasticity - Unpredictable Bursty Traffic - Lightweight Jobs - Continuous - Complex SOA - Constant traffic - Persistent connection

5. Architecture Patterns Monolith NanoService Hybrid  Easy code-share  More CPU time  Always warm  Difficult code-share  Less CPU time  Nano deployments  Easy code-share  Micro Deployments  Reasonably warm

6. Framework extension Spring + Jersey Jetty Proxy 1 3 2 4 Lambda EnvironmentAPI gateway Conventional app deployment in Lambda

7. Scheduling & Triggers Health Checks Scheduled Jobs Scheduled Warm-up Triggers Cloudwatch-Event SQS API Gateway

8. Continuous Delivery • Plugin creates a shaded jar • Upload to S3 • Lambda updates with latest code • API gateway deployment

9. Limitation Max response time = 300 sec No persistent connection Event payload size = 128K Deployment size = 50 MB ( 250 MB uncompressed ) Max concurrent executions = 100 Concurrency = Events rate * Avg execution time

10. Questions ?

Editor's Notes

Huddle SMG
SMG Huddle
Huddle Use case SMG use case

Lambda Architecture in Practice

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Lambda Architecture in Practice

Similar to Lambda Architecture in Practice (20)

More from Navneet kumar

More from Navneet kumar (9)

Recently uploaded

Recently uploaded (20)

Lambda Architecture in Practice

Editor's Notes