Flink Forward Berlin 2018: Viktor Klang - Keynote "The convergence of stream processing and microservice architecture"
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Two of the main software architectural trends in software development this decade has been the move to streaming data processing, and the move to microservice architecture. Both of these architectures are driven by the needs of managing and mining knowledge from ever increasing volumes of data in a close to real-time fashion—all while being reactive: responsive under failure, and responsive under load. I'm here to tell you that these two trends are converging, and a fusion of the two is both logical and inevitable. In this session we will talk about what a fused approach to stream processing and microservices could look like, what opportunities exist—what software development for business software can look like in the following decade.
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As software grows more and more interconnected, and with several generations of software having to interoperate, a new take on the integration of systems is needed—ad hoc, unversioned, and unreplicated scripts just won’t suffice, and the traditional Enterprise Service Bus (ESB) concept has experienced stability, reliability, performance, and scalability problems.
In this webinar, Viktor explores a new take on Enterprise Integration Patterns:
First, he will explore the Reactive Streams standard, an orchestration layer where transformations are standalone, composable, reusable, and—most importantly—using asynchronous flow-control—back pressure—to maintain predictable, stable, behavior over time.
Furthermore, he will go through how one-off workloads relate to continuous, and batch, workloads, and how they can be addressed by that very same orchestration layer.
Finally, he will review how this type of design achieves resilience, scalability, and ultimately—responsiveness.
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Flink Forward San Francisco 2018 keynote: Srikanth Satya - "Stream Processin...
Stream Processing in conjunction with a Consistent, Durable, Reliable stream storage is kicking the revolution up a notch in Big Data processing. This modern paradigm is enabling a new generation of data middleware that delivers on the streaming promise of a simplified and unified programming model. From data ingest, transformation, and messaging to search, time series and more, a robust streaming data ecosystem means we’ll all be able to more quickly build applications that solve problems we could not solve before.
Flink Forward Berlin 2018: Krzysztof Zarzycki & Alexey Brodovshuk - "Assistin...
Nowadays many companies become data rich and intensive. They have millions of users generating billions of interactions and events per day. These massive streams of complex events can be processed and reacted upon to e.g. offer new products, next best actions, communicate to users or detect frauds, and quicker we can do it, the higher value we can generate. Our presentation will be based on our recent experience in building a real-time data analytics platform for telco events. This platform has been jointly built by GetInData and Kcell - the leading telco in Kazakhstan - in just a few months and it currently runs in production at the scale of 10M subscribers and 160K events per second on a still small cluster. It's used as a backbone for personalized marketing campaigns, detecting frauds, cross-sell & up-sell by following the behavior of millions of users in real-time and reacting to it instantly. We will share how we build such platform using current best of breed open-source projects like Flink, Kafka, and Nifi. We won't skimp on the details how we designed and optimized our Flink applications for high load and performance. We will also describe challenges that we faced during development and try to provide some tips what one should pay attention to when developing similar solutions, not only for telco, but also for banks, e-commerce, IoT and other industries.
Flink Forward Berlin 2018: Wei-Che (Tony) Wei - "Lessons learned from Migrati...
In modern applications of streaming frameworks, stateful streaming is arguably one of the most important usage cases. Flink, as a well-supported streaming framework for stateful streaming, readily helps developers spend less efforts on system deployment and focus more on the business logic. Nevertheless, upgrading from an existing production system to a new one with stateful streaming can still be a challenging task for any development team. In this talk, we will share our experience in migrating an existing system at Appier (an AI-based startup specialized with B2B solutions) to stateful streaming with Flink. We will first discuss how stateful streaming matches our business logic and its potential benefits. Then, we review the obstacles that we have encountered during migration, and present our solutions to conquer them. We hope that our experience and tips shared in this talk hints future users to prepare themselves towards applying Flink in their production systems more painlessly.
Flink Forward Berlin 2018: Stephan Ewen - Keynote: "Unlocking the next wave o...
Stream Processing as helped to turn many monolithic database-centric applications into fast, scalable, and flexible real time applications. However, there are still entire classes of applications that are built against databases, because today's streaming processing model is not yet rich enough to support these applications.
We present what we believe is still missing in today's stream processing models, and how we envision to evolve stream processing for the next wave of application to be able to move to a stream processing architecture.
Flink Forward Berlin 2018: Xingcan Cui - "Stream Join in Flink: from Discrete...
As a distributed stream processing engine, Flink provides users with convenient operators to manipulate data on the fly. Among all these operators, join could be the most complicated one as it requires the capability to cross-analyze various sources simultaneously. In this talk, we aim to give a comprehensive introduction to the stream join in Flink. Specifically, we'll first provide an overview of the different join types and which of them are currently supported by Flink DataStream and Table & SQL APIs. Then we'll discuss some key points when performing distributed stream join. After that, we'd like to focus the rationale and implementation details of the time-windowed join launched in version 1.4. Since there are still a lot of improvements can be made, we'll end our talk by sharing some proposals for the future work.
Scaling stream data pipelines with Pravega and Apache Flink
Extracting insights out of continuously generated data requires a stream processor with powerful data analytics features such as Apache Flink. A stream data pipeline with Flink typically includes a storage component to ingest and serve the data. Pravega is a stream store that ingests and stores stream data permanently, making the data available for tail, catch-up, and historical reads. One important challenge for such stream data pipelines is coping with the variations in the workload. Daily cycles and seasonal spikes might require the provisioning of the application to adapt accordingly. Pravega has a feature called stream scaling, which enables the capacity offered for the ingestion of events of a stream to grow and shrink over time according to workload. Such a feature is useful when the application downstream has the ability of accommodating such changes and also scale its provisioning accordingly. In this presentation, we introduce stream scaling in Pravega and how Flink jobs leverage this feature to rescale stateful jobs according to variations in the workload.
Flink Forward San Francisco 2018: - Jinkui Shi and Radu Tudoran "Flink real-t...
CloudStream service is a Full Management Service in Huawei Cloud. Support several features, such as On-Demand Billing, easy-to-use Stream SQL in online SQL editor, test Stream SQL in real-time style, Multi-tenant, security isolation and so on. We choose Apache Flink as streaming compute platform. Inside of CloudStream Cluster, Flink job can run on Yarn, Mesos, Kubernetes. We also have extended Apache Flink to meet IoT scenario needs. There are specialized tests on Flink reliability with college cooperation. Finally continuously improve the infrastructure around CS including open source projects and cloud services. CloudStream is different with any other real-time analysis cloud service. The development process can also be shared at architecture and principles.
Monitoring Flink with Prometheus
Video: https://data-artisans.com/flink-forward-berlin/resources/monitoring-flink-with-prometheus
Live Demo Code: https://github.com/mbode/flink-prometheus-example
Prometheus is a cloud-native monitoring system prioritizing reliability and simplicity – and Flink works really well with it! This session will show you how to leverage the Flink metrics system together with Pronetheus to improve the observability of your jobs. There will be a live demo establishing how everything ties in together. The talk is aimed at people already building and running Flink jobs who would like to gain more insight into them. It is fine if you are not familiar with Prometheus yet as the basic concepts will be introduced. If you have ever wondered how you could use modern monitoring tools to be alerted in the middle of the night in case your Flink job‘s 99th percentile end-to-end latency degraded for some reason, this might just be the talk you are looking for.
Flink Forward San Francisco 2018: Dave Torok & Sameer Wadkar - "Embedding Fl...
Operationalizing Machine Learning models is never easy. Our team at Comcast has been challenged with operationalizing predictive ML models to improve customer care experiences. Using Apache Flink we have been able to apply real-time streaming to all aspects of the Machine Learning lifecycle. This includes data feature exploration and preparation by data scientists, deploying live models to serve near-real-time predictions, and validating results for model retraining and iteration. We will share best practices and lessons learned from Flink’s role in our operationalized lifecycle including:
• Executing as the “Prediction Pipeline” – a model container environment for near-real-time streaming and batch predictions
• Preparing streaming features and data sets for model training, as input for production model predictions, and for a continually-updated customer context
• Using connected streams and savepoints for “Live in the Dark”, multi-variant testing, and validation scenarios
• Incorporating Flink’s Queryable State as an approach to the online “Feature Store” – a data catalog for reuse by multiple models and use cases
• Enabling versioned models, versioned feature sets, and versioned data through DevOps approaches.
Flink Forward San Francisco 2018: Gregory Fee - "Bootstrapping State In Apach...
Apache Flink is a popular stream computing framework for real-time stream computing. Many stream compute algorithms require trailing data in order to compute the intended result. One example is computing the number of user logins in the last 7 days. This creates a dilemma where the results of the stream program are incomplete until the runtime of the program exceeds 7 days. The alternative is to bootstrap the program using historic data to seed the state before shifting to use real-time data. This talk will discuss alternatives to bootstrap programs in Flink. Some alternatives rely on technologies exogenous to the stream program, such as enhancements to the pub/sub layer, that are more generally applicable to other stream compute engines. Other alternatives include enhancements to Flink source implementations. Lyft is exploring another alternative using orchestration of multiple Flink programs. The talk will cover why Lyft pursued this alternative and future directions to further enhance bootstrapping support in Flink.
Flink Forward San Francisco 2018: Andrew Torson - "Extending Flink metrics: R...
Flink metrics module allows to use Dropwizard-like metrics and reporters in Flink pipelines. It opens a rich opportunity to not only monitor health of Flink pipelines but also attach real-time business intelligence metrics to run alongside existing Flink data jobs, thus avoiding a need to build a separate BI data-flow infrastructure. However, most of the work still resides with application developers: they have to define how those metrics are registered, calculated and attached to Flink functional operators – and then Flink will do the computation and reporting. The most challenging task is to add BI-like metrics, with key dimensions dynamically extracted from streamed data and metric values likely needed to be aggregated over multiple Flink tasks. We present a toolkit that extends Flink metrics and allows to decorate existing Flink operators with both simple health-check and complex BI-like metrics, updated and observable in real-time through IT monitoring and BI visualization dashboards. This work is based on a real-world production use-case of computing retail merchandise prices in real-time for the Walmart e-commerce catalog using Flink.
dA Platform Overview
dA Platform is a production-ready platform for stream processing with Apache Flink®. The Platform includes open source Apache Flink, a stateful stream processing and event-driven application framework, and dA Application Manager, a central deployment and management component. dA Platform schedules clusters on Kubernetes, deploys stateful Flink applications, and controls these applications and their state.
Flink Forward San Francisco 2018: David Reniz & Dahyr Vergara - "Real-time m...
“Customer experience is the next big battle ground for telcos,” proclaimed recently Amit Akhelikar, Global Director of Lynx Analytics at TM Forum Live! Asia in Singapore. But, how to fight in this battle? A common approach has been to keep “under control” some well-known network quality indicators, like dropped calls, radio access congestion, availability, and so on; but this has proven not to be enough to keep customers happy, like a siege weapon is not enough to conquer a city. But, what if it were possible to know how customers perceive services, at least most demanded ones, like web browsing or video streaming? That would be like a squad of archers ready to battle. And even having that, how to extract value of it and take actions in no time, giving our skilled archers the right targets? Meet CANVAS (Customer And Network Visualization and AnaltyticS), one of the first LATAM implementations of a Flink-based stream processing use case for a telco, which successfully combines leading and innovative technologies like Apache Hadoop, YARN, Kafka, Nifi, Druid and advanced visualizations with Flink core features like non-trivial stateful stream processing (joins, windows and aggregations on event time) and CEP capabilities for alarm generation, delivering a next-generation tool for SOC (Service Operation Center) teams.
Flink Forward Berlin 2017: Mihail Vieru - A Materialization Engine for Data I...
In Zalando's microservice architecture, each service continuously generates streams of events for the purposes of inter-service communication or data integration. Some of these events describe business processes, e.g. a customer has placed an order or a parcel has been shipped. Out of this, the need to materialize event streams from the central event bus into persistent cloud storage evolved. The temporarily persisted data is then integrated into our relational data warehouse. In this talk we present a materialization engine backed by Apache Flink. We show how we employ Flink’s RESTful API, custom accumulators and stoppable sources to provide another API abstraction layer for deploying, monitoring and controlling our materialization jobs. Our jobs compact event streams depending on event properties and transform their complex JSON structures into flat files for easier integration into the data warehouse.
Flink Forward San Francisco 2018: Xu Yang - "Alibaba’s common algorithm platf...
There are many batch and stream scenarios in Alibaba, and many data analysts are non-technical, like to use GUI or script tool to deal with data to help business decisions. We’d like to share our experiences on developing algorithms on Apache Flink and build web UI and client to help people easily use algorithms on data analysis, training and inferencing with machine learning model.
The Past, Present, and Future of Apache Flink
Stream processing still evolves and changes at a speed that can make it hard to keep up with the developments. Being at the forefront of stream processing technology, the evolution of Apache Flink has mirrored many of these developments and continues to do so.
We will take you on a journey through the major milestones of stream processing technology in past years, diving into the latest additions that Apache Flink and other communities introduced to the stream processing landscape, such as Streamng SQL, Time Versioned Tables, cluster-library-duality, language portability, etc.
We will take a sneak peek into our crystal ball and present in what the Flink community is working on next.
A stream: Ad-hoc Shared Stream Processing - Jeyhun Karimov, DFKI GmbH
In the last decade, many distributed stream processing engines (SPEs) were developed to perform continuous queries on massive online data. The central design principle of these engines is to handle queries that potentially run forever on data streams with a query-at-a-time model, i.e., each query is optimized and executed separately. In many real applications, streams are not only processed with long-running queries, but also thousands of short-running ad-hoc queries. To support this efficiently, it is essential to share resources and computation for stream ad-hoc queries in a multi-user environment.
The goal of this talk is to bridge the gap between stream processing and ad-hoc queries in SPEs by sharing computation and resources. We define three main requirements for ad-hoc shared stream processing: (1) Integration: Ad-hoc query processing should be a composable layer which can extend stream operators, such as join, aggregation, and window operators; (2) Consistency: Ad-hoc query creation and deletion must be performed in a consistent manner and ensure exactly-once semantics and correctness; (3) Performance: In contrast to state-of-the-art SPEs, ad-hoc SPE should not only maximize data throughput but also query throughout via incremental computation and resource sharing. Based on these requirements, we have developed AStream, an ad-hoc, shared computation stream processing framework.
To the best of our knowledge, AStream is the first system that supports distributed ad-hoc stream processing. AStream is built on top of Apache Flink. Our experiments show that AStream shows comparable results to Flink for single query deployments and outperforms it in orders of magnitude with multiple queries.
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...
Streaming engines like Apache Flink are redefining ETL and data processing. Data can be extracted, transformed, filtered and written out in real-time with an ease matching that of batch processing. However the real challenge of matching the prowess of batch ETL remains in doing joins, in maintaining state and to have the data be paused or rested dynamically. Netflix has a microservices architecture. Different microservices serve and record different kind of user interactions with the product. Some of these live services generate millions of events per second, all carrying meaningful but often partial information. Things start to get exciting when we want to combine the events coming from one high-traffic microservice to another. Joining these raw events generates rich datasets that are used to train the machine learning models that serve Netflix recommendations. Historically we have done this joining of large volume data-sets in batch. However we asked ourselves if the data is being generated in real-time, why must it not be processed downstream in real time? Why wait a full day to get information from an event that was generated a few mins ago? In this talk, we will share how we solved a complex join of two high-volume event streams using Flink. We will talk about maintaining large state, fault tolerance of a stateful application and strategies for failure recovery.
Running Flink in Production: The good, The bad and The in Between - Lakshmi ...
The streaming platform team at Lyft has been running Flink jobs in production for more than a year now, powering critical use cases like improving pickup ETA accuracy, dynamic pricing, generating machine learning features for fraud detection, real-time analytics among many others. Broadly, the jobs fall into two abstraction layers: applications (Flink jobs that run on the native platform) and analytics (that leverage Dryft, Lyft’s fully managed data processing engine). This talk will give an overview of the platform architecture, deployment model and user experience. The talk will also dive deeper into some of the challenges and the lessons that were learnt, running Flink jobs at scale, specifically around scaling Flink connectors, dealing with event time skew (source synchronization) and highlight common patterns of problems observed across several Flink jobs. Finally, the talk will give insights into how we are re-architecting the streaming platform @ Lyft using a Kubernetes based deployment.
Flink Forward Berlin 2017 Keynote: Ferd Scheepers - Taking away customer fric...
As many industries, banking is undergoing a fundamental change because of the software revolution. No longer are banks competing only on interest rates and having the best traders, these days customer experience and having the best engineers are the focus. In this changing world, banks compete with new start-ups, the so-called Fintechs, and with large platform organisations such as Google, Facebook and Apple. At ING, we believe that staying ahead of the game means changing how we interact with our customers, no longer a traditional model of waiting for the customers to come to the bank through our website or apps, but to actively reach out to the customer with information that is relevant to him or her in order to make their financial life frictionless. Many of these changes are driven by reacting to all events that are relevant to the customer, and using streaming analytics to be able to reach out to the customer in milliseconds after the event occurs. Apache Flink is key for ING to achieve this. This presentation addresses how ING approaches the challenge, the role that Apache Flink plays, and the consequences regulations have on how we work with Open Source in general, and with Apache Flink (and data Artisans) in particular. This keynote takes place at Kino 3.
What's hot (20)
Flink Forward San Francisco 2018 keynote: Srikanth Satya - "Stream Processin...
Flink Forward San Francisco 2018 keynote: Srikanth Satya - "Stream Processin...
Flink Forward Berlin 2018: Krzysztof Zarzycki & Alexey Brodovshuk - "Assistin...
Flink Forward Berlin 2018: Krzysztof Zarzycki & Alexey Brodovshuk - "Assistin...
Flink Forward Berlin 2018: Wei-Che (Tony) Wei - "Lessons learned from Migrati...
Flink Forward Berlin 2018: Wei-Che (Tony) Wei - "Lessons learned from Migrati...
Flink Forward Berlin 2018: Stephan Ewen - Keynote: "Unlocking the next wave o...
Flink Forward Berlin 2018: Stephan Ewen - Keynote: "Unlocking the next wave o...
Flink Forward Berlin 2018: Xingcan Cui - "Stream Join in Flink: from Discrete...
Flink Forward Berlin 2018: Xingcan Cui - "Stream Join in Flink: from Discrete...
Scaling stream data pipelines with Pravega and Apache Flink
Scaling stream data pipelines with Pravega and Apache Flink
Flink Forward San Francisco 2018: - Jinkui Shi and Radu Tudoran "Flink real-t...
Flink Forward San Francisco 2018: - Jinkui Shi and Radu Tudoran "Flink real-t...
Flink Forward San Francisco 2018: Dave Torok & Sameer Wadkar - "Embedding Fl...
Flink Forward San Francisco 2018: Dave Torok & Sameer Wadkar - "Embedding Fl...
Flink Forward San Francisco 2018: Gregory Fee - "Bootstrapping State In Apach...
Flink Forward San Francisco 2018: Gregory Fee - "Bootstrapping State In Apach...
Flink Forward San Francisco 2018: Andrew Torson - "Extending Flink metrics: R...
Flink Forward San Francisco 2018: Andrew Torson - "Extending Flink metrics: R...
Flink Forward San Francisco 2018: David Reniz & Dahyr Vergara - "Real-time m...
Flink Forward San Francisco 2018: David Reniz & Dahyr Vergara - "Real-time m...
Flink Forward Berlin 2017: Mihail Vieru - A Materialization Engine for Data I...
Flink Forward Berlin 2017: Mihail Vieru - A Materialization Engine for Data I...
Flink Forward San Francisco 2018: Xu Yang - "Alibaba’s common algorithm platf...
Flink Forward San Francisco 2018: Xu Yang - "Alibaba’s common algorithm platf...
A stream: Ad-hoc Shared Stream Processing - Jeyhun Karimov, DFKI GmbH
A stream: Ad-hoc Shared Stream Processing - Jeyhun Karimov, DFKI GmbH
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...
Running Flink in Production: The good, The bad and The in Between - Lakshmi ...
Running Flink in Production: The good, The bad and The in Between - Lakshmi ...
Flink Forward Berlin 2017 Keynote: Ferd Scheepers - Taking away customer fric...
Flink Forward Berlin 2017 Keynote: Ferd Scheepers - Taking away customer fric...
Similar to Flink Forward Berlin 2018: Viktor Klang - Keynote "The convergence of stream processing and microservice architecture"
DEVNET-1175 OpenDaylight Service Function Chaining
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Revitalizing Enterprise Integration with Reactive Streams
With Viktor Klang, Deputy CTO Lightbend, Inc.
As software grows more and more interconnected, and with several generations of software having to interoperate, a new take on the integration of systems is needed—ad hoc, unversioned, and unreplicated scripts just won’t suffice, and the traditional Enterprise Service Bus (ESB) concept has experienced stability, reliability, performance, and scalability problems.
In this webinar, Viktor explores a new take on Enterprise Integration Patterns:
First, he will explore the Reactive Streams standard, an orchestration layer where transformations are standalone, composable, reusable, and—most importantly—using asynchronous flow-control—back pressure—to maintain predictable, stable, behavior over time.
Furthermore, he will go through how one-off workloads relate to continuous, and batch, workloads, and how they can be addressed by that very same orchestration layer.
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Introducing Events and Stream Processing into Nationwide Building Society (Ro...
Facing Open Banking regulation, rapidly increasing transaction volumes and increasing customer expectations, Nationwide took the decision to take load off their back-end systems through real-time streaming of data changes into Kafka. Hear about how Nationwide started their journey with Kafka, from their initial use case of creating a real-time data cache using Change Data Capture, Kafka and Microservices to how Kafka allowed them to build a stream processing backbone used to reengineer the entire banking experience including online banking, payment processing and mortgage applications. See a working demo of the system and what happens to the system when the underlying infrastructure breaks. Technologies covered include: Change Data Capture, Kafka (Avro, partitioning and replication) and using KSQL and Kafka Streams Framework to join topics and process data.
Building high performance microservices in finance with Apache Thrift
Apache Roadshow Chicago Talk on May 14, 2019
In this talk we’ll look at the ways Apache Thrift can solve performance problems commonly facing next generation applications deployed in performance sensitive capital markets and banking environments. The talk will include practical examples illustrating the construction, performance and resource utilization benefits of Apache Thrift. Apache Thrift is a high-performance cross platform RPC and serialization framework designed to make it possible for organizations to specify interfaces and application wide data structures suitable for serialization and transport over a wide variety of schemes. Due to the unparalleled set of languages supported by Apache Thrift, these interfaces and structs have similar interoperability to REST type services with an order of magnitude improvement in performance. Apache Thrift services are also a perfect fit for container technology, using considerably fewer resources than traditional application server style deployments. Decomposing applications into microservices, packaging them into containers and orchestrating them on systems like Kubernetes can bring great value to an organization; however, it can also take a very fast monolithic application and turn it into a high latency web of slow, resource hungry services. Apache Thrift is a perfect solution to the performance and resource ills of many microservice based endeavors.
Introducing Events and Stream Processing into Nationwide Building Society
Watch this talk here: https://www.confluent.io/online-talks/introducing-events-and-stream-processing-nationwide-building-society
Open Banking regulations compel the UK’s largest banks, and building societies to enable their customers to share personal information with other regulated companies securely. As a result companies such as Nationwide Building Society are re-architecting their processes and infrastructure around customer needs to reduce the risk of losing relevance and the ability to innovate.
In this online talk, you will learn why, when facing Open Banking regulation and rapidly increasing transaction volumes, Nationwide decided to take load off their back-end systems through real-time streaming of data changes into Apache Kafka®. You will hear how Nationwide started their journey with Apache Kafka®, beginning with the initial use case of creating a real-time data cache using Change Data Capture, Confluent Platform and Microservices. Rob Jackson, Head of Application Architecture, will also cover how Confluent enabled Nationwide to build the stream processing backbone that is being used to re-engineer the entire banking experience including online banking, payment processing and mortgage applications.
View now to:
-Explore the technologies used by Nationwide to meet the challenges of Open Banking
-Understand how Nationwide is using KSQL and Kafka Streams Framework to join topics and process data.
-Learn how Confluent Platform can enable enterprises such as Nationwide to embrace the event streaming paradigm
-See a working demo of the Nationwide system and what happens when the underlying infrastructure breaks.
Openshift serverless Solution
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Demo:
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2 Auto scale: https://www.youtube.com/watch?v=9hBKws1Exvc
3 Tekton Pipeline: https://www.youtube.com/watch?v=TxYSzjY7Dps
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These are a series of presentations and knowledge collected from the web to help knowledge sharing at the government of Puerto Rico, created with the hope of helping transform government culture by engaging key personnel in diverse areas of central government IT. We discussed design and development methodologies as well as implementation, network and server technologies that led to the successful launch of the most popular online service in PR.gov, in the hope that the knowledge is retained and used to prevent problems that have plagued digital services of the past.
How did Puerto Rico build the New Good standing Certificate Online Service? How did it scale to handle millions of visitors while having 0 licensing costs? This is the technical overview of the design, philosophy and implementation.
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Note on attribution: some content such as logos and designs were used from the web. Rights remain with their original authors. Thanks for sharing with the world.
Modern Cloud-Native Streaming Platforms: Event Streaming Microservices with K...
Microservices, events, containers, and orchestrators are dominating our vernacular today. As operations teams adapt to support these technologies in production, cloud-native platforms like Cloud Foundry and Kubernetes have quickly risen to serve as force multipliers of automation, productivity and value. Kafka is providing developers a critically important component as they build and modernize applications to cloud-native architecture. This talk will explore:
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• What kind of workloads are best suited for this combination?
• Tips to determine the path forward for legacy monoliths in your application portfolio
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Full lifecycle of a microservice
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Microservices have emerged as a widely discussed and adopted way to build modern and scalable applications. They are easier to build, manage and maintain than monoliths due to smaller code bases; they isolate complexity, allowing for smaller, more agile teams to create services, and they are flexible—allowing the use of various platforms, programming languages, and tools since these choices affect only an individual service and a small team at a time.
Move fast and make things with microservices
1. How to apply microservices patterns & anti-patterns to design the right architecture
2. Why & how to build a core framework to ensure consistency & manage complexity
3. What are the challenges in adopting gRPC for inter-service communication
4. How to orchestrate & manage microservices at scale with Kubernetes
5. How to leverage Cloud Native ecosystem to move fast & avoid vendor lock-in
Databus - LinkedIn's Change Data Capture Pipeline
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https://github.com/linkedin/databus
as presented at Eventbrite - May 07 2013
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http://www.ldp4j.org/PortoTechHub - Hail Hydrate! From Stream to Lake with Apache Pulsar and Friends
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https://portotechhub.com/conference-2021/
Timothy Spann
Developer Advocate
StreamNative
A cloud data lake that is empty is not useful to anyone.
How can you quickly, scalably and reliably fill your cloud data lake with diverse sources of data you already have and new ones you never imagined you needed. Utilizing open source tools from Apache, the FLiP stack enables any data engineer, programmer or analyst to build reusable modules with low or no code. FLiP utilizes Apache NiFi, Apache Pulsar, Apache Flink and MiNiFi agents to load CDC, Logs, REST, XML, Images, PDFs, Documents, Text, semistructured data, unstructured data, structured data and a hundred data sources you could never dream of streaming before.
I will teach you how to fish in the deep end of the lake and return a data engineering hero. Let's hope everyone is ready to go from 0 to Petabyte hero.
TRACK RIBEIRA Fri 07:00 — 50 min
19-Nov-2021
Meetup Openshift Geneva 03/10
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Modern Cloud-Native Streaming Platforms: Event Streaming Microservices with A...
Microservices, events, containers, and orchestrators are dominating our vernacular today. As operations teams adapt to support these technologies in production, cloud-native platforms like Pivotal Cloud Foundry and Kubernetes have quickly risen to serve as force multipliers of automation, productivity and value.
Apache Kafka® is providing developers a critically important component as they build and modernize applications to cloud-native architecture.
This talk will explore:
• Why cloud-native platforms and why run Apache Kafka on Kubernetes?
• What kind of workloads are best suited for this combination?
• Tips to determine the path forward for legacy monoliths in your application portfolio
• Demo: Running Apache Kafka as a Streaming Platform on Kubernetes
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Building real time data-driven products
This presentation will describe how to go beyond a "Hello world" stream application and build a real-time data-driven product. We will present architectural patterns, go through tradeoffs and considerations when deciding on technology and implementation strategy, and describe how to put the pieces together. We will also cover necessary practical pieces for building real products: testing streaming applications, and how to evolve products over time.
Presented at highloadstrategy.com 2016 by Øyvind Løkling (Schibsted Products & Technology), joint work with Lars Albertsson (independent, www.mapflat.com).
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The Table API is one of the most actively developed components of Flink in recent time. Inspired by databases and SQL, it encapsulates concepts many developers are familiar with. It can be used with both bounded and unbounded streams in a unified way. But from afar it can be difficult to keep track of what this API is capable of and how it relates to Flink's other APIs. In this talk, we will explore the current state of Table API. We will show how it can be used as a batch processor, a changelog processor, or a streaming ETL tool with many built-in functions and operators for deduplicating, joining, and aggregating data. By comparing it to the DataStream API we will highlight differences and elaborate on when to use which API. We will demonstrate hybrid pipelines in which both APIs interact with one another and contribute their unique strengths. Finally, we will take a look at some of the most recent additions as a first step to stateful upgrades.
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Based on the new Flink-Pulsar connector, we implemented Flink's TableAPI and Catalog to help users to interact with the Pulsar cluster via Flink SQL easily. We would like to go through the design and implementation of the SQL connector in the following aspects:
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What if my data is already in order? Stream Processing has given us an elegant and powerful solution for running analytic queries and logic over high volumes of continuously arriving data. However, in both Apache Flink and Apache Beam, the notion of time-ordering is baked in at a very low level, making it difficult to express computations that are interested in a semantic-, rather than time-ordering of the data. In financial services, what often matters the most about the data moving between systems is not when the data was created, but in what order, to the extent that many institutions engineer a global sequencing over all data entering and produced by their systems to achieve complete determinism. How, then, can financial institutions and others best employ Stream Processing on streams of data that are already ordered? I will cover various techniques that can make this work, as well as seek input from the community on how Flink might be improved to better support these use-cases.
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Flink Forward San Francisco 2022.
In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling.
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Goldman Sachs's Data Lake platform serves as the firm's centralized data platform, ingesting 140K (and growing!) batches per day of Datasets of varying shape and size. Powered by Flink and using metadata configured by platform users, ingestion applications are generated dynamically at runtime to extract, transform, and load data into centralized storage where it is then exported to warehousing solutions such as Sybase IQ, Snowflake, and Amazon Redshift. Data Latency is one of many key considerations as producers and consumers have their own commitments to satisfy. Consumers range from people/systems issuing queries, to applications using engines like Spark, Hive, and Presto to transform data into refined Datasets. Apache Iceberg allows our applications to not only benefit from consistency guarantees important when running on eventually consistent storage like S3, but also allows us the opportunity to improve our batch processing patterns with its scalability-focused features.
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Flink Forward Berlin 2018: Viktor Klang - Keynote "The convergence of stream processing and microservice architecture"
- 1. The convergence of stream processing & microservice architecture √Deputy CTO, Lightbend @viktorklang / v@lightbend.com / viktorklang.com
- 3. Overview
- 4. History of Data Processing 4 Offline Batch processing 4 MapReduce 4 Lambda Architecture
- 5. History of Services 4 Client-Server 4 CORBA & DCOM 4 EJB & N-Tier 4 SOA & ESB 4 HTTP-API & REST
- 6. Today
- 7. Catch of the day Live1 Stream Processing & Microservice Architecture 1 Near real-time
- 8. Shared requirements: 4 Available 4 Elastically scalable 4 Resilient & self-healing 4 Loosely coupled & upgradeable
- 9. Reactive Systems2 Available (responsive), under failure (resilience) and under load (elasticity), by being message-driven. 2 https://www.reactivemanifesto.org/
- 10. Reactive Streams3 Provides a standard for asynchronous stream processing with non-blocking back pressure. 3 www.reactive-streams.com & java.util.concurrent.Flow (JDK9+)
- 11. Ingredients 4 Stream processing stages: Stateless & Stateful 4 Microservices: RPC-style & Evented 4 Domain Events with Schema/Codec
- 12. #define Events4 4 Immutable recordings of observations 4 Can be disregarded but not be retracted once accepted 4 Can be invalidated but (typically) not be deleted 4 Knowledge accrues with every processed event 4 Learn more: https://speakerdeck.com/jboner/designing-events-first-microservices
- 14. Realizationevented services are stream processing stages
- 16. Is this the solution? 4 Alice: So we we lift evented services into the stream processing pipeline … 4 Bob: … and then we're done! 4 <record scratch> 4 Narrator: They're forgetting the request-reply use- case which RPC services excel at.
- 17. Services (rpc)
- 18. The improved solution 4 lift the service into a stream stage 4 lift input Events into Commands and submit 4 capture responses & convert into output Events
- 19. What if we could derive microservices from data streams?
- 23. Unification 4 Microservices are a part of a streaming pipeline 4 A pipeline can now be exposed as Microservices 4 We can independently upgrade parts of the pipeline
- 24. Developing converged applications 4 Deployment Orchestration 4 Events-First DDD & EventStorming 4 (Meta)Data-driven Continuous Deployment 4 Use of the right tools for each component
- 25. Beyond
- 27. End of stream
- 28. Explore what we do at Lightbend: 4 Lightbend Fast Data Platform 4 https://www.lightbend.com/products/fast-data- platform 4 Lightbend Reactive Platform 4 https://www.lightbend.com/products/reactive-platform 4 Lightbend Enterprise Suite 4 https://www.lightbend.com/products/enterprise-suite
- 29. Thank you for attending! The convergence of stream processing & microservice architecture √ Deputy CTO, Lightbend @viktorklang / v@lightbend.com / viktorklang.com