Lambda Architecture in Practice

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

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1) Apache InLong is an open source data integration framework that provides automatic, secure, and reliable data transmission. It supports both batch and stream processing using different message queues like Apache Pulsar. 2) Apache Pulsar is used with Apache InLong because it offers very low latency, high throughput, reliable data transmission, and multi-tenancy. KoP allows migrating Kafka workloads to Pulsar. 3) Apache InLong contributes to Apache Pulsar through over 60 contributors and 50 pull requests to the KoP project. It uses Pulsar for auto disaster tolerance, multi-tenancy of data streams, and auditing data streams.

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This document discusses a new approach to building scalable data processing systems using streaming analytics with Spark, Kafka, Cassandra, and Akka. It proposes moving away from architectures like Lambda and ETL that require duplicating data and logic. The new approach leverages Spark Streaming for a unified batch and stream processing runtime, Apache Kafka for scalable messaging, Apache Cassandra for distributed storage, and Akka for building fault tolerant distributed applications. This allows building real-time streaming applications that can join streaming and historical data with simplified architectures that remove the need for duplicating data extraction and loading.

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This document discusses whether it is better to process data using a stream or batch approach. It describes how one company evolved their data pipeline from a micro-batch streaming process to a batch approach. The streaming process was very expensive, costing $400,000 per year to run. It also had issues with wasted resources during idle times, slow processing during bursts of data, and long recovery times from outages. The company rearchitected the process to use discrete time windows run in isolated batch jobs. This new batch approach reduced costs by 60% to $160,000 per year and improved processing efficiency and outage recovery times.

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

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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.

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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.

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mattlieber

This document summarizes the results of a performance evaluation of Kafka and Camus to ingest streaming data into Hadoop. It finds that Kafka can ingest data at rates from 15,000-50,000 messages per second depending on data format (Avro is fastest). Camus can move the data to HDFS at rates from 54,000-662,000 records per second. Once in HDFS, queries on Avro-formatted data are fastest, with count and max aggregation queries completing in under 100 seconds for 20 million records. The customer's goal of 5000 events per second can be easily achieved with this architecture.

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

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This document discusses using Hadoop for archiving, e-discovery, and supervision. It outlines the key components of each task and highlights traditional shortcomings. Hadoop provides strengths like speed, ease of use, and security. An architectural overview shows how Hadoop can be used for ingestion, processing, analysis, and machine learning. Examples demonstrate surveillance use cases. While some obstacles remain, partners can help address areas like user interfaces and compliance storage.

Netflix viewing data architecture evolution - QCon 2014

Philip Fisher-Ogden

Matt Franklin - Apache Software (Geekfest)

W2O Group

The document discusses the potential benefits of container technologies like Docker. It notes that containers offer significantly higher density than virtual machines by avoiding hypervisor overhead. This density improvement can lead to major cost reductions by reducing infrastructure needs. Containers also improve developer efficiency by making development environments portable and disposable. This allows more rapid experimentation and innovation, potentially translating to increased revenue. Technologies like Amazon Lambda take the on-demand aspects of containers even further by abstracting compute resources. The document promotes StackEngine as a solution for managing containers at scale in production environments.

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

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The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including: Material identification: Identify unknown materials, minerals, and contaminants. Quality control: Ensure the quality and consistency of raw materials and finished products. Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds. Food safety testing: Detect contaminants and adulterants in food products. Field analysis: Analyze materials in the field, such as during environmental monitoring or forensic investigations. The Zaitechno Handheld Raman Spectrometer is easy to use and features a user-friendly interface. It is compact and lightweight, making it ideal for field applications. With its rapid analysis capabilities, the Zaitechno Handheld Raman Spectrometer can help you improve efficiency and productivity in your research or quality control workflows.

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How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems. While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure. So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong? ------------------------- This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access. This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world. The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact

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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

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