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IMPACT Final Conference - Claus Gravenhorst
IMPACT Final Conference - Claus Gravenhorst
IMPACT Centre of Competence
The IMPACT Interoperability Framework - Workflows for OCR and beyond Better, faster, cheaper. Solutions of the IMPACT Centre of Competence and future challenges, The British Library, 24-25 October 2011, London, United Kingdom.
The IMPACT Interoperability Framework - Workflows for OCR and beyond
The IMPACT Interoperability Framework - Workflows for OCR and beyond
cneudecker
We present I², an interactive development environment for real-time analysis pipelines, which is based on Apache Flink and Apache Zeppelin. The sheer amount of available streaming data frequently makes it impossible to visualize all data points at the same time. I² coordinates running Flink jobs and corresponding visualizations such that only the currently depicted data points are processed in Flink and transferred towards the front end. We show how Flink jobs can adapt to changed visualization properties at runtime to allow interactive data exploration on high bandwidth data streams. Moreover, we present a data reduction technique which minimizes data transfer while providing loss free time-series plots. We show I² in a live demonstration in which we replay recorded sensor data from a football match (ca. 12k event/s). I² was first presented at EDBT'17 where it was awarded as best demonstration. The demonstration is available as open source at github.com/TU-Berlin-DIMA/i2.
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
Jonas Traub
Stream processing has been traditionally associated with realtime analytics. Modern stream processors, like Apache Flink, however, go far beyond that and give us a new approach to build applications and services as a whole. This talk shows how to build applications on *data streams*, *state*, and *snaphots* (point-in-time views of application state) using Apache Flink. Rather than separating computation (application) and state (database), Flink manages the application logic and state as a tight pair and uses snapshots for consistent view onto the application and its state. With features like Flink's queryable state, the stream processor and database effectively become one. This application pattern has many interesting properties: Aside from having fewer moving parts, it supports very high event rates because of its tight integration between computation and state, and its simple concurrency and recovery model. At the same time, it exposes a powerful consistency model, allows for seamless forking/updating/rollback of online applications, generalizes across historic and real-time data, and easily incorporates event time semantics and handling of late data. Finally, it allows applications to be defined in an easy way via streaming SQL.
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Building Applications with Streams and Snapshots
J On The Beach
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.
Flink Forward Berlin 2018: Xingcan Cui - "Stream Join in Flink: from Discrete...
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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.
Monitoring Flink with Prometheus
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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.
Flink Forward Berlin 2018: Viktor Klang - Keynote "The convergence of stream ...
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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.
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IMPACT Final Conference - Claus Gravenhorst
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The IMPACT Interoperability Framework - Workflows for OCR and beyond Better, faster, cheaper. Solutions of the IMPACT Centre of Competence and future challenges, The British Library, 24-25 October 2011, London, United Kingdom.
The IMPACT Interoperability Framework - Workflows for OCR and beyond
The IMPACT Interoperability Framework - Workflows for OCR and beyond
cneudecker
We present I², an interactive development environment for real-time analysis pipelines, which is based on Apache Flink and Apache Zeppelin. The sheer amount of available streaming data frequently makes it impossible to visualize all data points at the same time. I² coordinates running Flink jobs and corresponding visualizations such that only the currently depicted data points are processed in Flink and transferred towards the front end. We show how Flink jobs can adapt to changed visualization properties at runtime to allow interactive data exploration on high bandwidth data streams. Moreover, we present a data reduction technique which minimizes data transfer while providing loss free time-series plots. We show I² in a live demonstration in which we replay recorded sensor data from a football match (ca. 12k event/s). I² was first presented at EDBT'17 where it was awarded as best demonstration. The demonstration is available as open source at github.com/TU-Berlin-DIMA/i2.
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
I²: Interactive Real-Time Visualization for Streaming Data with Apache Flink ...
Jonas Traub
Stream processing has been traditionally associated with realtime analytics. Modern stream processors, like Apache Flink, however, go far beyond that and give us a new approach to build applications and services as a whole. This talk shows how to build applications on *data streams*, *state*, and *snaphots* (point-in-time views of application state) using Apache Flink. Rather than separating computation (application) and state (database), Flink manages the application logic and state as a tight pair and uses snapshots for consistent view onto the application and its state. With features like Flink's queryable state, the stream processor and database effectively become one. This application pattern has many interesting properties: Aside from having fewer moving parts, it supports very high event rates because of its tight integration between computation and state, and its simple concurrency and recovery model. At the same time, it exposes a powerful consistency model, allows for seamless forking/updating/rollback of online applications, generalizes across historic and real-time data, and easily incorporates event time semantics and handling of late data. Finally, it allows applications to be defined in an easy way via streaming SQL.
Building Applications with Streams and Snapshots
Building Applications with Streams and Snapshots
J On The Beach
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.
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Monitoring Flink with Prometheus
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Maximilian Bode
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.
Flink Forward Berlin 2018: Viktor Klang - Keynote "The convergence of stream ...
Flink Forward Berlin 2018: Viktor Klang - Keynote "The convergence of stream ...
Flink Forward
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.
Flink Forward Berlin 2018: Shriya Arora - "Taming large-state to join dataset...
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Flink started with the mission to unify batch and stream processing. We believe that Flink’s architecture is uniquely positioned to be a great engine for streaming, batch and AI workloads at the same time. We will talk about the work we did in this direction.
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Flink Forward Berlin 2018: Xiaowei Jiang - Keynote: "Unified Engine for Data ...
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http://flink-forward.org/kb_sessions/declarative-stream-processing-with-streamsql-and-cep/ Complex event processing (CEP) and stream analytics are commonly treated as distinct classes of stream processing applications. While CEP workloads identify patterns from event streams in near real-time, stream analytics queries ingest and aggregate high-volume streams. Both types of use cases have very different requirements which resulted in diverging system designs. CEP systems excel at low-latency processing whereas engines for stream analytics achieve high throughput. Recent advances in open source stream processing yielded systems that can process several millions of events per second at sub-second latency. Systems like Apache Flink enable applications that include typical CEP features as well as heavy aggregations. In this talk we will show how Apache Flink unifies CEP and stream analytics workloads. Guided by examples, we introduce Flink’s CEP-enriched StreamSQL interface and discuss how queries are compiled, optimized, and executed on Flink.
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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: Gregory Fee - "Bootstrapping State In Apach...
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Last year we (TouK) introduced Flink in one of the biggest polish telcoms in the domain of real time marketing and fraud detection. One of the most significant problems in adoption was lack of programming skills at our client - the users were supposed to be analytics/business people. Therefore, we developed a custom platform - TouK Nussknacker - which allows users to design processes with GUI by drawing diagrams. Our project is going to be open-sourced soon - this will happen before Flink Forward. We believe it can make stream processing with Flink more accessible in many use cases, especially in companies that don't have their own development teams. During the talk I’m going to describe architecture of our platform, why we made certain design decisions and about our future plans. I’ll also describe our experiences - when being able to use GUI is great and when it’s better to develop jobs as normal code. If time permits I’ll also show a quick demo of our solution.
Flink Forward Berlin 2017: Maciek Próchniak - TouK Nussknacker - creating Fli...
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One of the main characteristics of the good streaming pipeline is correctness for event time processing. Real challenges become when such pipeline should be resilient to different types of failures. In this talk, we describe how Criteo runs Flink on one of the biggest Yarn clusters in Europe and computes 100k messages per second to acknowledge revenue of our platform within the delay of 5 minutes. Real-time revenue monitoring system calculates data under 1% of discrepancies and minimizes business impact in case of revenue anomalies.
Flink Forward Berlin 2018: Oleksandr Nitavskyi - "Data lossless event time st...
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Flink Forward San Francisco 2019: Real-time Processing with Flink for Machine...
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Flink Forward Berlin 2018: Raj Subramani - "A streaming Quantitative Analytic...
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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.
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Last year we (TouK) introduced Flink in one of the biggest polish telcoms in the domain of real time marketing and fraud detection. One of the most significant problems in adoption was lack of programming skills at our client - the users were supposed to be analytics/business people. Therefore, we developed a custom platform - TouK Nussknacker - which allows users to design processes with GUI by drawing diagrams. Our project is going to be open-sourced soon - this will happen before Flink Forward. We believe it can make stream processing with Flink more accessible in many use cases, especially in companies that don't have their own development teams. During the talk I’m going to describe architecture of our platform, why we made certain design decisions and about our future plans. I’ll also describe our experiences - when being able to use GUI is great and when it’s better to develop jobs as normal code. If time permits I’ll also show a quick demo of our solution.
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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.
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The presentation explores the development and application of artificial intelligence (AI) from its inception to its current status in the modern world. The term "artificial intelligence" was first coined by John McCarthy in 1956 to describe efforts to develop computer programs capable of performing tasks that typically require human intelligence. This concept was first introduced at a conference held at Dartmouth College, where programs demonstrated capabilities such as playing chess, proving theorems, and interpreting texts. In the early stages, Alan Turing contributed to the field by defining intelligence as the ability of a being to respond to certain questions intelligently, proposing what is now known as the Turing Test to evaluate the presence of intelligent behavior in machines. As the decades progressed, AI evolved significantly. The 1980s focused on machine learning, teaching computers to learn from data, leading to the development of models that could improve their performance based on their experiences. The 1990s and 2000s saw further advances in algorithms and computational power, which allowed for more sophisticated data analysis techniques, including data mining. By the 2010s, the proliferation of big data and the refinement of deep learning techniques enabled AI to become mainstream. Notable milestones included the success of Google's AlphaGo and advancements in autonomous vehicles by companies like Tesla and Waymo. A major theme of the presentation is the application of generative AI, which has been used for tasks such as natural language text generation, translation, and question answering. Generative AI uses large datasets to train models that can then produce new, coherent pieces of text or other media. The presentation also discusses the ethical implications and the need for regulation in AI, highlighting issues such as privacy, bias, and the potential for misuse. These concerns have prompted calls for comprehensive regulations to ensure the safe and equitable use of AI technologies. Artificial intelligence has also played a significant role in healthcare, particularly highlighted during the COVID-19 pandemic, where it was used in drug discovery, vaccine development, and analyzing the spread of the virus. The capabilities of AI in healthcare are vast, ranging from medical diagnostics to personalized medicine, demonstrating the technology's potential to revolutionize fields beyond just technical or consumer applications. In conclusion, AI continues to be a rapidly evolving field with significant implications for various aspects of society. The development from theoretical concepts to real-world applications illustrates both the potential benefits and the challenges that come with integrating advanced technologies into everyday life. The ongoing discussion about AI ethics and regulation underscores the importance of managing these technologies responsibly to maximize their their benefits while minimizing potential harms.
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IMPACT Final Conference - Clemens Neudecker
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The IMPACT Interoperability
Framework: Workflows for OCR and beyond Clemens Neudecker, KB National Library of the Netherlands 2 nd IMPACT Conference, British Library, London 24/25 October 2011
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