Uses the example of correct, high-througput, grouping and counting of streaming events as a backdrop for exploring the state-of-the art features of Apache Flink
Jamie Grier - Robust Stream Processing with Apache FlinkFlink Forward
http://flink-forward.org/kb_sessions/robust-stream-processing-with-apache-flink/
In this hands on talk and demonstration I’ll give a very short introduction to stream processing and then dive into writing code and demonstrating the features in Apache Flink that make truly robust stream processing possible. We’ll focus on correctness and robustness in stream processing. During this live demo we’ll be developing a realtime analytics application and modifying it on the fly based on the topics we’re working though. We’ll exercise Flink’s unique features, demonstrate fault-recovery, clearly explain and demonstrate why Event Time is such an important concept in robust stateful stream processing and talk about and demonstrate the features you need in a stream processor in production. Some of the topics covered will be: – Stateful Stream Processing – Event Time vs. Processing Time – Fault tolerance – State management in the face of faults – Savepoints – Data re-processing – Planned downtime and upgrades
http://flink-forward.org/kb_sessions/keynote-tba-2/
The past 12 months saw the data streaming ecosystem mature and grow tremendously with new open source projects and products being offered in the market, and more large-scale production applications of streaming data. It is now understood that streaming data is not a fad, but a growing industry that is here to stay.
Apache Flink was one of the pioneering communities advocating that stream processing is a great fit for the continuous nature of data production, and that batch processing can be seen and efficiently performed as a special case of stream processing. Flink saw tremendous growth since the last Flink Forward conference, with the project boasting now more than 200 contributors from several companies, several production installations and broad adoption.
In this talk, we discuss several large-scale stream processing use cases that we see at data Artisans. Additionally, we discuss what this accelerated growth means for Flink, how we can sustain this growth moving forward, as well as a vision for the next big directions in Flink.
Uses the example of correct, high-througput, grouping and counting of streaming events as a backdrop for exploring the state-of-the art features of Apache Flink
Jamie Grier - Robust Stream Processing with Apache FlinkFlink Forward
http://flink-forward.org/kb_sessions/robust-stream-processing-with-apache-flink/
In this hands on talk and demonstration I’ll give a very short introduction to stream processing and then dive into writing code and demonstrating the features in Apache Flink that make truly robust stream processing possible. We’ll focus on correctness and robustness in stream processing. During this live demo we’ll be developing a realtime analytics application and modifying it on the fly based on the topics we’re working though. We’ll exercise Flink’s unique features, demonstrate fault-recovery, clearly explain and demonstrate why Event Time is such an important concept in robust stateful stream processing and talk about and demonstrate the features you need in a stream processor in production. Some of the topics covered will be: – Stateful Stream Processing – Event Time vs. Processing Time – Fault tolerance – State management in the face of faults – Savepoints – Data re-processing – Planned downtime and upgrades
http://flink-forward.org/kb_sessions/keynote-tba-2/
The past 12 months saw the data streaming ecosystem mature and grow tremendously with new open source projects and products being offered in the market, and more large-scale production applications of streaming data. It is now understood that streaming data is not a fad, but a growing industry that is here to stay.
Apache Flink was one of the pioneering communities advocating that stream processing is a great fit for the continuous nature of data production, and that batch processing can be seen and efficiently performed as a special case of stream processing. Flink saw tremendous growth since the last Flink Forward conference, with the project boasting now more than 200 contributors from several companies, several production installations and broad adoption.
In this talk, we discuss several large-scale stream processing use cases that we see at data Artisans. Additionally, we discuss what this accelerated growth means for Flink, how we can sustain this growth moving forward, as well as a vision for the next big directions in Flink.
http://flink-forward.org/kb_sessions/flink-and-beam-current-state-roadmap/
It is no secret that the Dataflow model, which evolved from Google’s MapReduce, Flume, and MillWheel, has been a major influence to Apache Flink’s streaming API. The essentials of this model are captured in Apache Beam. Beam provides the Dataflow API with the option to deploy to various backends (e.g. Flink, Spark). In this talk we will examine the current state of the Flink Runner. Beam’s Runners manage the translation of the Beam API into the backend API. The Beam project itself has made an effort to summarize the capabilities of each Runner to provide an overview of the supported API concepts. From all open sources backends, Flink is currently the Runner which supports the most features. We will look at the supported Beam features and their counterpart in Flink. Further, we will look at potential improvements and upcoming features of the Flink Runner.
Achieving end-to-end visibility into complex event-sourcing transactions usin...HostedbyConfluent
Event-sourcing systems usage like Kafka is growing rapidly among Node.js applications. Building systems around an event-driven architecture simplifies horizontal scalability in distributed computing models and makes them more resilient to failure. With these advantages, we face new challenges - how to get visibility into these complex processes.
Event-driven architecture is async by nature. Tracking the communication between different components is both extremely difficult and important when debugging or figuring out bottlenecks in the system.
In this talk, I will present ways to achieve end-to-end and granular visibility into complex event-sourcing transactions using distributed tracing. I will use open-source tools like OpenTelemetry, Jaeger, and Zipkin to showcase a complex Node.js system using Kafka.
Thomas Lamirault_Mohamed Amine Abdessemed -A brief history of time with Apac...Flink Forward
Many use cases in the telecommunication industry require producing counters, quality metrics, and alarms in a streaming fashion with very low latency. Most of this metrics are only valuable when they’re made available as soon as the associated events happened. In our company we are looking for a system able to produce this kind of real-time indicator, which must handle massive amounts of data (400,000 eps) with often peak loads (like New Year’s Eve) or out-of-order events like massive network disorder. Low latency and flexible window management with specific watermark emission are also a must-haves. Heterogeneous format, multiple flow correlation, and the possibility of late data arrival are other challenges. Flink being already widely used at Bouygues Telecom for real-time data integration, its features made it the evident candidate for the future System. In this talk, we'll present a real use case of streaming analytics using Flink, Kafka & HBase along with other legacy systems.
Javier Lopez_Mihail Vieru - Flink in Zalando's World of Microservices - Flink...Flink Forward
http://flink-forward.org/kb_sessions/flink-in-zalandos-world-of-microservices/
In this talk we present Zalando’s microservices architecture, introduce Saiki – our next generation data integration and distribution platform on AWS and show how we employ stream processing with Apache Flink for near-real time business intelligence.
Zalando is one of the largest online fashion retailers in Europe. In order to secure our future growth and remain competitive in this dynamic market, we are transitioning from a monolithic to a microservices architecture and from a hierarchical to an agile organization.
We first have a look at how business intelligence processes have been working inside Zalando for the last years and present our current approach – Saiki. It is a scalable, cloud-based data integration and distribution infrastructure that makes data from our many microservices readily available for analytical teams.
We no longer live in a world of static data sets, but are instead confronted with endless streams of events that constantly inform us about relevant happenings from all over the enterprise. The processing of these event streams enables us to do near-real time business intelligence. In this context we have evaluated Apache Flink vs. Apache Spark in order to choose the right stream processing framework. Given our requirements, we decided to use Flink as part of our technology stack, alongside with Kafka and Elasticsearch.
With these technologies we are currently working on two use cases: a near real-time business process monitoring solution and streaming ETL.
Monitoring our business processes enables us to check if technically the Zalando platform works. It also helps us analyze data streams on the fly, e.g. order velocities, delivery velocities and to control service level agreements.
On the other hand, streaming ETL is used to relinquish resources from our relational data warehouse, as it struggles with increasingly high loads. In addition to that, it also reduces the latency and facilitates the platform scalability.
Finally, we have an outlook on our future use cases, e.g. near-real time sales and price monitoring. Another aspect to be addressed is to lower the entry barrier of stream processing for our colleagues coming from a relational database background.
Announcing the next-generation dA Platform 2, which includes open source Apache Flink and the new Application Manager. dA Platform 2 makes it easier than ever to operationalize your Flink-powered stream processing applications in production.
Flink Forward SF 2017: Scott Kidder - Building a Real-Time Anomaly-Detection ...Flink Forward
Mux uses Apache Flink to identify anomalies in the distribution & playback of digital video for major video streaming websites. Scott Kidder will describe the Apache Flink deployment at Mux leveraging Docker, AWS Kinesis, Zookeeper, HDFS, and InfluxDB. Deploying a Flink application in a zero-downtime production environment can be tricky, so unit- & behavioral-testing, application packaging, upgrade, and monitoring strategies will be covered as well.
Real-Time Dynamic Data Export Using the Kafka Ecosystemconfluent
(Preston Thompson, Braze) Kafka Summit SF 2018
If you collect billions of data points every day and create billions more sending and tracking messages, then you know you need to get your infrastructure right. Our clients use Braze to engage their users over their lifecycle via push notifications, emails, in-app messages and more. Using our Currents product, clients can enable multiple configurable integrations to export this event data in real time to a variety of third-party systems, allowing them to tightly integrate with the rest of their operations and understand the impacts of their engagement strategy.
We use Kafka and the Kafka ecosystem to power this high volume real-time export. As you’d expect in a big data environment, we take data collected from a variety of sources—our SDKs, email partner APIs, our own systems—and produce it to Kafka, with topics for each type of event (about 30 types). Kafka Streams filters and transforms this data according to the configurations set by our clients. Clients can choose which types of events should be sent to which third-party systems. Kafka Connect helps to export the data to third-party systems in real time using custom developed connectors. We run a connector instance for each integration for each customer that consumes from the integration-specific topic. On top of it all, we built a service to manage the pipeline. The service provides configurations to the Streams application and also creates topics for new integrations and uses the Connect REST API to create and manage connectors.
In this talk, I will discuss:
-How we started our journey in designing this large-scale streaming architecture
-Why streaming technologies were necessary to solve our technology and business issues
-The lessons we learned along the way that can help you with your Kafka-based architecture
Flink Forward San Francisco 2018: Gregory Fee - "Bootstrapping State In Apach...Flink Forward
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.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1VhSzmy.
Robert Metzger provides an overview of the Apache Flink internals and its streaming-first philosophy, as well as the programming APIs. Filmed at qconlondon.com.
Robert Metzger is a PMC member at the Apache Flink project and a cofounder and software engineer at data Artisans. He is the author of many Flink components including the Kafka and YARN connectors.
Flink Forward San Francisco 2019: Elastic Data Processing with Apache Flink a...Flink Forward
Elastic Data Processing with Apache Flink and Apache Pulsar
More and more applications are using Flink for low-latency data processing. Flink unifies batch and stream processing using one computation engine. However in reality, in order to really unify batch and stream processing, it requires a data system offers one unified data representation for both batch and streaming data. Nowadays, streaming data is typically stored in a log storage or messaging system, while batch data is stored in distributed filesystem and object stores. That means that data scientists still need write two different computing jobs to access same data stored in different data systems.
Apache Pulsar is the next generation messaging and streaming data system. It was originally built at Yahoo, and has graduated from Apache Incubator and become a Top-Level-Project. Pulsar separates messaging serving and data storage into two layers. Such layered architecture provides high throughput and low-latency while ensuring high availability and scalability. Pulsar’s segment centric storage design along with layered architecture makes Pulsar a perfect unbounded streaming data system, which can well fit into Flink’s computation model.
In this talk, Sijie Guo from Apache Pulsar PMC, will introduce Pulsar and its layered architecture and segment-centric storage, detailing how this architecture can well integrate with Flink to provide elastic unified batch and stream processing.
Flink Forward San Francisco 2018 keynote: Srikanth Satya - "Stream Processin...Flink Forward
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 San Francisco 2019: Building production Flink jobs with Airstre...Flink Forward
AirStream is a realtime stream computation framework that supports Flink as one of its processing engines. It allows engineers and data scientists at Airbnb to easily leverage Flink to build real time data pipelines and feedback loops. Multiple mission critical applications have been built on top of it. In this talk, we will start with an overview of AirStream, and describe how we have designed Airstream to leverage SQL support in Flink to allow users to easily build real time data pipelines. We will go over a few production use cases such as building a user activity profiler and building user identity mapping in realtime. We will also cover how we have integrated Airstream into the data infrastructure ecosystem at Airbnb through easily configurable connectors such as Kafka and Hive that allow users to easily leverage these components in their pipelines.
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.
Flink Forward San Francisco 2019: Apache Beam portability in the times of rea...Flink Forward
Apache Beam was open sourced by the big data team at Google in 2016, and has become an active community with participants from all over. Beam is a framework to define data processing workflows and run them on various runners (Flink included). In this talk, I will talk about some cool things you can do with Beam + Flink such as running pipelines written in Go and Python; then I’ll mention some cool tools in the Beam ecosystem. Finally, we’ll wrap up with some cool things we expect to be able to do soon - and how you can get involved.
Disaster Recovery for Multi-Region Apache Kafka Ecosystems at Uberconfluent
Speaker: Yupeng Fu, Staff Engineer, Uber
High availability and reliability are important requirements to Uber services, and the services shall tolerate datacenter failures in a region and fail over to another region. In this talk, we will present the active-active Apache Kafka® at Uber and how it facilitates disaster discovery across regions for Uber services. In particular, we will highlight the key components including topic replication, topic aggregation, offsets sync and then walk through several use cases of their disaster recovery strategy using active-active Kafka. Lastly, we will present several interesting challenges and the future work planned.
Yupeng Fu is a staff engineer in Uber Data Org leading the streaming data platform. Previously, he worked at Alluxio and Palantir, building distributed data analysis and storage platforms. Yupeng holds a B.S. and an M.S. from Tsinghua University and did his Ph.D. research on databases at UCSD.
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.
In this talk, we describe the design and implementation of the Python Streaming API support that has been submitted for inclusion in mainline Flink. Python is one of the most popular programming languages for data analysis. Its readability emphasizes development productivity and as a scripting language, it does not require a compilation nor complex development environment setup. Flink already has support for Python APIs for batch programming and unfortunately, the mechanism used to support batch programs (i.e., DataSet APIs) do does not work for Streaming API. We describe the limitations with the batch implementation and provide insights into how we solved this using Jython. We will walk through some of the examples programs using the new Python API and compare programmability and performance with the Java and Scala streaming APIs.
Siphon - Near Real Time Databus Using Kafka, Eric Boyd, Nitin Kumarconfluent
Siphon is a highly available and reliable distributed pub/sub system built using Apache Kafka. It is used to publish, discover and subscribe to near real-time data streams for operational and product intelligence. Siphon is used as a “Databus” by a variety of producers and subscribers in Microsoft, and is compliant with security and privacy requirements. It has a built-in Auditing and Quality control. This session will provide an overview of the use of Kafka at Microsoft, and then deep dive into Siphon. We will describe an important business scenario and talk about the technical details of the system in the context of that scenario. We will also cover the design and implementation of the service, the scale, and real world production experiences from operating the service in the Microsoft cloud environment.
http://flink-forward.org/kb_sessions/flink-and-beam-current-state-roadmap/
It is no secret that the Dataflow model, which evolved from Google’s MapReduce, Flume, and MillWheel, has been a major influence to Apache Flink’s streaming API. The essentials of this model are captured in Apache Beam. Beam provides the Dataflow API with the option to deploy to various backends (e.g. Flink, Spark). In this talk we will examine the current state of the Flink Runner. Beam’s Runners manage the translation of the Beam API into the backend API. The Beam project itself has made an effort to summarize the capabilities of each Runner to provide an overview of the supported API concepts. From all open sources backends, Flink is currently the Runner which supports the most features. We will look at the supported Beam features and their counterpart in Flink. Further, we will look at potential improvements and upcoming features of the Flink Runner.
Achieving end-to-end visibility into complex event-sourcing transactions usin...HostedbyConfluent
Event-sourcing systems usage like Kafka is growing rapidly among Node.js applications. Building systems around an event-driven architecture simplifies horizontal scalability in distributed computing models and makes them more resilient to failure. With these advantages, we face new challenges - how to get visibility into these complex processes.
Event-driven architecture is async by nature. Tracking the communication between different components is both extremely difficult and important when debugging or figuring out bottlenecks in the system.
In this talk, I will present ways to achieve end-to-end and granular visibility into complex event-sourcing transactions using distributed tracing. I will use open-source tools like OpenTelemetry, Jaeger, and Zipkin to showcase a complex Node.js system using Kafka.
Thomas Lamirault_Mohamed Amine Abdessemed -A brief history of time with Apac...Flink Forward
Many use cases in the telecommunication industry require producing counters, quality metrics, and alarms in a streaming fashion with very low latency. Most of this metrics are only valuable when they’re made available as soon as the associated events happened. In our company we are looking for a system able to produce this kind of real-time indicator, which must handle massive amounts of data (400,000 eps) with often peak loads (like New Year’s Eve) or out-of-order events like massive network disorder. Low latency and flexible window management with specific watermark emission are also a must-haves. Heterogeneous format, multiple flow correlation, and the possibility of late data arrival are other challenges. Flink being already widely used at Bouygues Telecom for real-time data integration, its features made it the evident candidate for the future System. In this talk, we'll present a real use case of streaming analytics using Flink, Kafka & HBase along with other legacy systems.
Javier Lopez_Mihail Vieru - Flink in Zalando's World of Microservices - Flink...Flink Forward
http://flink-forward.org/kb_sessions/flink-in-zalandos-world-of-microservices/
In this talk we present Zalando’s microservices architecture, introduce Saiki – our next generation data integration and distribution platform on AWS and show how we employ stream processing with Apache Flink for near-real time business intelligence.
Zalando is one of the largest online fashion retailers in Europe. In order to secure our future growth and remain competitive in this dynamic market, we are transitioning from a monolithic to a microservices architecture and from a hierarchical to an agile organization.
We first have a look at how business intelligence processes have been working inside Zalando for the last years and present our current approach – Saiki. It is a scalable, cloud-based data integration and distribution infrastructure that makes data from our many microservices readily available for analytical teams.
We no longer live in a world of static data sets, but are instead confronted with endless streams of events that constantly inform us about relevant happenings from all over the enterprise. The processing of these event streams enables us to do near-real time business intelligence. In this context we have evaluated Apache Flink vs. Apache Spark in order to choose the right stream processing framework. Given our requirements, we decided to use Flink as part of our technology stack, alongside with Kafka and Elasticsearch.
With these technologies we are currently working on two use cases: a near real-time business process monitoring solution and streaming ETL.
Monitoring our business processes enables us to check if technically the Zalando platform works. It also helps us analyze data streams on the fly, e.g. order velocities, delivery velocities and to control service level agreements.
On the other hand, streaming ETL is used to relinquish resources from our relational data warehouse, as it struggles with increasingly high loads. In addition to that, it also reduces the latency and facilitates the platform scalability.
Finally, we have an outlook on our future use cases, e.g. near-real time sales and price monitoring. Another aspect to be addressed is to lower the entry barrier of stream processing for our colleagues coming from a relational database background.
Announcing the next-generation dA Platform 2, which includes open source Apache Flink and the new Application Manager. dA Platform 2 makes it easier than ever to operationalize your Flink-powered stream processing applications in production.
Flink Forward SF 2017: Scott Kidder - Building a Real-Time Anomaly-Detection ...Flink Forward
Mux uses Apache Flink to identify anomalies in the distribution & playback of digital video for major video streaming websites. Scott Kidder will describe the Apache Flink deployment at Mux leveraging Docker, AWS Kinesis, Zookeeper, HDFS, and InfluxDB. Deploying a Flink application in a zero-downtime production environment can be tricky, so unit- & behavioral-testing, application packaging, upgrade, and monitoring strategies will be covered as well.
Real-Time Dynamic Data Export Using the Kafka Ecosystemconfluent
(Preston Thompson, Braze) Kafka Summit SF 2018
If you collect billions of data points every day and create billions more sending and tracking messages, then you know you need to get your infrastructure right. Our clients use Braze to engage their users over their lifecycle via push notifications, emails, in-app messages and more. Using our Currents product, clients can enable multiple configurable integrations to export this event data in real time to a variety of third-party systems, allowing them to tightly integrate with the rest of their operations and understand the impacts of their engagement strategy.
We use Kafka and the Kafka ecosystem to power this high volume real-time export. As you’d expect in a big data environment, we take data collected from a variety of sources—our SDKs, email partner APIs, our own systems—and produce it to Kafka, with topics for each type of event (about 30 types). Kafka Streams filters and transforms this data according to the configurations set by our clients. Clients can choose which types of events should be sent to which third-party systems. Kafka Connect helps to export the data to third-party systems in real time using custom developed connectors. We run a connector instance for each integration for each customer that consumes from the integration-specific topic. On top of it all, we built a service to manage the pipeline. The service provides configurations to the Streams application and also creates topics for new integrations and uses the Connect REST API to create and manage connectors.
In this talk, I will discuss:
-How we started our journey in designing this large-scale streaming architecture
-Why streaming technologies were necessary to solve our technology and business issues
-The lessons we learned along the way that can help you with your Kafka-based architecture
Flink Forward San Francisco 2018: Gregory Fee - "Bootstrapping State In Apach...Flink Forward
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.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1VhSzmy.
Robert Metzger provides an overview of the Apache Flink internals and its streaming-first philosophy, as well as the programming APIs. Filmed at qconlondon.com.
Robert Metzger is a PMC member at the Apache Flink project and a cofounder and software engineer at data Artisans. He is the author of many Flink components including the Kafka and YARN connectors.
Flink Forward San Francisco 2019: Elastic Data Processing with Apache Flink a...Flink Forward
Elastic Data Processing with Apache Flink and Apache Pulsar
More and more applications are using Flink for low-latency data processing. Flink unifies batch and stream processing using one computation engine. However in reality, in order to really unify batch and stream processing, it requires a data system offers one unified data representation for both batch and streaming data. Nowadays, streaming data is typically stored in a log storage or messaging system, while batch data is stored in distributed filesystem and object stores. That means that data scientists still need write two different computing jobs to access same data stored in different data systems.
Apache Pulsar is the next generation messaging and streaming data system. It was originally built at Yahoo, and has graduated from Apache Incubator and become a Top-Level-Project. Pulsar separates messaging serving and data storage into two layers. Such layered architecture provides high throughput and low-latency while ensuring high availability and scalability. Pulsar’s segment centric storage design along with layered architecture makes Pulsar a perfect unbounded streaming data system, which can well fit into Flink’s computation model.
In this talk, Sijie Guo from Apache Pulsar PMC, will introduce Pulsar and its layered architecture and segment-centric storage, detailing how this architecture can well integrate with Flink to provide elastic unified batch and stream processing.
Flink Forward San Francisco 2018 keynote: Srikanth Satya - "Stream Processin...Flink Forward
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 San Francisco 2019: Building production Flink jobs with Airstre...Flink Forward
AirStream is a realtime stream computation framework that supports Flink as one of its processing engines. It allows engineers and data scientists at Airbnb to easily leverage Flink to build real time data pipelines and feedback loops. Multiple mission critical applications have been built on top of it. In this talk, we will start with an overview of AirStream, and describe how we have designed Airstream to leverage SQL support in Flink to allow users to easily build real time data pipelines. We will go over a few production use cases such as building a user activity profiler and building user identity mapping in realtime. We will also cover how we have integrated Airstream into the data infrastructure ecosystem at Airbnb through easily configurable connectors such as Kafka and Hive that allow users to easily leverage these components in their pipelines.
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.
Flink Forward San Francisco 2019: Apache Beam portability in the times of rea...Flink Forward
Apache Beam was open sourced by the big data team at Google in 2016, and has become an active community with participants from all over. Beam is a framework to define data processing workflows and run them on various runners (Flink included). In this talk, I will talk about some cool things you can do with Beam + Flink such as running pipelines written in Go and Python; then I’ll mention some cool tools in the Beam ecosystem. Finally, we’ll wrap up with some cool things we expect to be able to do soon - and how you can get involved.
Disaster Recovery for Multi-Region Apache Kafka Ecosystems at Uberconfluent
Speaker: Yupeng Fu, Staff Engineer, Uber
High availability and reliability are important requirements to Uber services, and the services shall tolerate datacenter failures in a region and fail over to another region. In this talk, we will present the active-active Apache Kafka® at Uber and how it facilitates disaster discovery across regions for Uber services. In particular, we will highlight the key components including topic replication, topic aggregation, offsets sync and then walk through several use cases of their disaster recovery strategy using active-active Kafka. Lastly, we will present several interesting challenges and the future work planned.
Yupeng Fu is a staff engineer in Uber Data Org leading the streaming data platform. Previously, he worked at Alluxio and Palantir, building distributed data analysis and storage platforms. Yupeng holds a B.S. and an M.S. from Tsinghua University and did his Ph.D. research on databases at UCSD.
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.
In this talk, we describe the design and implementation of the Python Streaming API support that has been submitted for inclusion in mainline Flink. Python is one of the most popular programming languages for data analysis. Its readability emphasizes development productivity and as a scripting language, it does not require a compilation nor complex development environment setup. Flink already has support for Python APIs for batch programming and unfortunately, the mechanism used to support batch programs (i.e., DataSet APIs) do does not work for Streaming API. We describe the limitations with the batch implementation and provide insights into how we solved this using Jython. We will walk through some of the examples programs using the new Python API and compare programmability and performance with the Java and Scala streaming APIs.
Siphon - Near Real Time Databus Using Kafka, Eric Boyd, Nitin Kumarconfluent
Siphon is a highly available and reliable distributed pub/sub system built using Apache Kafka. It is used to publish, discover and subscribe to near real-time data streams for operational and product intelligence. Siphon is used as a “Databus” by a variety of producers and subscribers in Microsoft, and is compliant with security and privacy requirements. It has a built-in Auditing and Quality control. This session will provide an overview of the use of Kafka at Microsoft, and then deep dive into Siphon. We will describe an important business scenario and talk about the technical details of the system in the context of that scenario. We will also cover the design and implementation of the service, the scale, and real world production experiences from operating the service in the Microsoft cloud environment.
Es un portafolio de planos donde se muestra cada uno de los planos de conjunto y de despiece. donde se exponen los diferentes planos de cada integrante.
Mahesh will act in Mani’s Movie: Suhasini telugustop.com
After Aagadu prince Mahesh Babu is given a nod to start working with the teams of Maniratnam’s film’s shoot. But there was news that Mahesh backed out from the film due to non-availability of call sheets as he will be busy with Koratala Shiva’s project.But now the latest news is that Mahesh is back again to the
Distributed Time Travel for Feature Generation at Netflixsfbiganalytics
Learning is an analytic process of exploring the past in order to predict the future. Hence, being able to travel back in time to create feature is critical for machine learning projects to be successful. At Netflix, we spend significant time and effort experimenting with new features and new ways of building models. This involves generating features for our members from different regions over multiple days. To enable this, we built a time machine using Apache Spark that computes features for any arbitrary time in the recent past. The first step of building this time machine is to snapshot the data from various micro services on a regular basis. We built a general purpose workflow orchestration and scheduling framework optimized for machine learning pipelines and used it to run the snapshot and model training workflows. Snapshot data is then consumed by feature encoders to compute various features for offline experimentation and model training. Crucially, the same feature encoders are used in both offline model building and online scoring for production or A/B tests. Building this time machine helped us try new ideas quickly without placing stress on production services and without having to wait for data accumulation of the newly-implemented features. Moreover, building it with Apache Spark empowered us to both scale up the data size by an order of magnitude and train and validate the models in less time. Finally, using Apache Zeppelin notebook, we are able to interactively prototype features and run experiments.
Speaker Bio: DB Tsai
DB Tsai is an Apache Spark committer and a Senior Research Engineer working on Personalized Recommendation Algorithms at Netflix. Prior to joining Netflix, DB was a Lead Machine Learning Engineer at Alpine Data Labs, where He implemented several algorithms including Linear Regression and Binary/Multinomial Logistic Regression with Elastici-Net (L1/L2) regularization using LBFGS/OWL-QN optimizers in Apache Spark. DB was a Ph.D. candidate in Applied Physics at Stanford University. He holds a Master’s degree in Electrical Engineering from Stanford University.
One of the key elements when implementing changes in organizations is to be clearly aware about the impact of changes when they actually happen.
By means of a Supply Chain Simulation, as a game, participants of this workshop experience and measure the economic impact and managerial benefits achieved through the Pull System.
Aljoscha Krettek offers a very short introduction to stream processing before diving into writing code and demonstrating the features in Apache Flink that make truly robust stream processing possible, with a focus on correctness and robustness in stream processing.
All of this will be done in the context of a real-time analytics application that we’ll be modifying on the fly based on the topics we’re working though, as Aljoscha exercises Flink’s unique features, demonstrates fault recovery, clearly explains why event time is such an important concept in robust, stateful stream processing, and covers the features you need in a stream processor to do robust, stateful stream processing in production.
We’ll also use a real-time analytics dashboard to visualize the results we’re computing in real time, allowing us to easily see the effects of the code we’re developing as we go along.
Topics include:
* Apache Flink
* Stateful stream processing
* Event time versus processing time
* Fault tolerance
* State management in the face of faults
* Savepoints
* Data reprocessing
An evening with Jay Kreps; author of Apache Kafka, Samza, Voldemort & Azkaban.Data Con LA
Jay Kreps, Open Source Visionary and Co Founder of Confluent and several open source projects will be visiting LA. I have asked him to come present at our group. He will present his vision and will answer questions regarding Kafka and other projects
Bio:-
Jay is the co-founder and CEO at Confluent a company built around realtime data streams and the open source messaging system Apache Kafka. He is the original author of several of open source projects including Apache Kafka, Apache Samza, Voldemort, and Azkaban.
Hail hydrate! from stream to lake using open sourceTimothy Spann
(VIRTUAL) Hail Hydrate! From Stream to Lake Using Open Source - Timothy J Spann, StreamNative
https://osselc21.sched.com/event/lAPi?iframe=no
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.
https://osselc21.sched.com/event/lAPi/virtual-hail-hydrate-from-stream-to-lake-using-open-source-timothy-j-spann-streamnative
A Practical Guide to Selecting a Stream Processing Technology confluent
Presented by Michael Noll, Product Manager, Confluent.
Why are there so many stream processing frameworks that each define their own terminology? Are the components of each comparable? Why do you need to know about spouts or DStreams just to process a simple sequence of records? Depending on your application’s requirements, you may not need a full framework at all.
Processing and understanding your data to create business value is the ultimate goal of a stream data platform. In this talk we will survey the stream processing landscape, the dimensions along which to evaluate stream processing technologies, and how they integrate with Apache Kafka. Particularly, we will learn how Kafka Streams, the built-in stream processing engine of Apache Kafka, compares to other stream processing systems that require a separate processing infrastructure.
Extending the Yahoo Streaming BenchmarkJamie Grier
This presentation covers describes my own benchmarking of Apache Storm and Apache Flink based on the work started by Yahoo! It shows the incredible performance of Apache Flink
Data Stream Processing with Apache FlinkFabian Hueske
This talk is an introduction into Stream Processing with Apache Flink. I gave this talk at the Madrid Apache Flink Meetup at February 25th, 2016.
The talk discusses Flink's features, shows it's DataStream API and explains the benefits of Event-time stream processing. It gives an outlook on some features that will be added after the 1.0 release.
Stinger.Next by Alan Gates of HortonworksData Con LA
ver the last 13 months the Apache Hive community, which included 145 developers and 44 companies working together through the Stinger initiative, delivered 390,000 lines of code and 1600 resolved JIRA tickets. This is only the beginning. The Hive community has already started the next phase of extending the Speed, Scale, and SQL compliance in Hive. As Hadoop 2.0 with YARN evolves to enable a dizzying array of powerful engines that allow us to interact with ever growing data in new ways, well known tools such as SQL need to scale with it. This session will provide a technical illustration of the challenges facing SQL on Hadoop today and what the road ahead looks like as the user community drives more innovation. Stinger.next is the next multi-phase initiative to evolve Hive as the de facto SQL engine for Hadoop designed to deliver Speed, Scale and better SQL.
How Tencent Applies Apache Pulsar to Apache InLong —— A Streaming Data Integr...StreamNative
As the largest provider of Internet products and services in China, Tencent serves billions of users across the world. Such huge number of users has brought unprecedented value to the big data generated.
Serves as the front line of Tencent Big Data, Apache InLong is a one-stop streaming data integration solution which is mainly responsible for data collection, distribution, preprocessing and management.
Apache InLong choose pulsar as its data middleware for its high reliability and other capabilities like multi-tenancy, read-write separation, cross-regional replication and flexible fault tolerance.
Tencent Big Data Team will share their 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.
Building high performance microservices in finance with Apache ThriftRX-M Enterprises LLC
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.
In this training webinar, Samantha Wang will walk you through the basics of Telegraf. Telegraf is the open source server agent which is used to collect metrics from your stacks, sensors and systems. It is InfluxDB’s native data collector that supports nearly 300 inputs and outputs. Learn how to send data from a variety of systems, apps, databases and services in the appropriate format to InfluxDB. Discover tips and tricks on how to write your own plugins. The know-how learned here can be applied to a multitude of use cases and sectors. This one-hour session will include the training and time for live Q&A.
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).
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.
Capital One Delivers Risk Insights in Real Time with Stream Processingconfluent
Speakers: Ravi Dubey, Senior Manager, Software Engineering, Capital One + Jeff Sharpe, Software Engineer, Capital One
Capital One supports interactions with real-time streaming transactional data using Apache Kafka®. Kafka helps deliver information to internal operation teams and bank tellers to assist with assessing risk and protect customers in a myriad of ways.
Inside the bank, Kafka allows Capital One to build a real-time system that takes advantage of modern data and cloud technologies without exposing customers to unnecessary data breaches, or violating privacy regulations. These examples demonstrate how a streaming platform enables Capital One to act on their visions faster and in a more scalable way through the Kafka solution, helping establish Capital One as an innovator in the banking space.
Join us for this online talk on lessons learned, best practices and technical patterns of Capital One’s deployment of Apache Kafka.
-Find out how Kafka delivers on a 5-second service-level agreement (SLA) for inside branch tellers.
-Learn how to combine and host data in-memory and prevent personally identifiable information (PII) violations of in-flight transactions.
-Understand how Capital One manages Kafka Docker containers using Kubernetes.
Watch the recording: https://videos.confluent.io/watch/6e6ukQNnmASwkf9Gkdhh69?.
Similar to Robust Stream Processing With Apache Flink (20)
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe 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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan 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.
Welocme to ViralQR, your best QR code generator.ViralQR
Welcome to ViralQR, your best QR code generator available on the market!
At ViralQR, we design static and dynamic QR codes. Our mission is to make business operations easier and customer engagement more powerful through the use of QR technology. Be it a small-scale business or a huge enterprise, our easy-to-use platform provides multiple choices that can be tailored according to your company's branding and marketing strategies.
Our Vision
We are here to make the process of creating QR codes easy and smooth, thus enhancing customer interaction and making business more fluid. We very strongly believe in the ability of QR codes to change the world for businesses in their interaction with customers and are set on making that technology accessible and usable far and wide.
Our Achievements
Ever since its inception, we have successfully served many clients by offering QR codes in their marketing, service delivery, and collection of feedback across various industries. Our platform has been recognized for its ease of use and amazing features, which helped a business to make QR codes.
Our Services
At ViralQR, here is a comprehensive suite of services that caters to your very needs:
Static QR Codes: Create free static QR codes. These QR codes are able to store significant information such as URLs, vCards, plain text, emails and SMS, Wi-Fi credentials, and Bitcoin addresses.
Dynamic QR codes: These also have all the advanced features but are subscription-based. They can directly link to PDF files, images, micro-landing pages, social accounts, review forms, business pages, and applications. In addition, they can be branded with CTAs, frames, patterns, colors, and logos to enhance your branding.
Pricing and Packages
Additionally, there is a 14-day free offer to ViralQR, which is an exceptional opportunity for new users to take a feel of this platform. One can easily subscribe from there and experience the full dynamic of using QR codes. The subscription plans are not only meant for business; they are priced very flexibly so that literally every business could afford to benefit from our service.
Why choose us?
ViralQR will provide services for marketing, advertising, catering, retail, and the like. The QR codes can be posted on fliers, packaging, merchandise, and banners, as well as to substitute for cash and cards in a restaurant or coffee shop. With QR codes integrated into your business, improve customer engagement and streamline operations.
Comprehensive Analytics
Subscribers of ViralQR receive detailed analytics and tracking tools in light of having a view of the core values of QR code performance. Our analytics dashboard shows aggregate views and unique views, as well as detailed information about each impression, including time, device, browser, and estimated location by city and country.
So, thank you for choosing ViralQR; we have an offer of nothing but the best in terms of QR code services to meet business diversity!
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.
2. Who am I?
• Director of Applications Engineering at data
Artisans
• Previously working on streaming computation at
Twitter, Gnip and Boulder Imaging
• Involved in various kinds of stream processing for
about a decade
• High-speed video, social media streaming, general
frameworks for stream processing
3. Overview
• What is Apache Flink?
• What is Stateful Stream Processing?
• Windowed computation over streams
• Robust Time Handling (Event Time vs Processing Time)
• Robust Failure Handling
• Robust Planned Downtime Handling
• Robust Reprocessing