Streaming Analytics with Spark, Kafka, Cassandra and Akka by Helena EdelsonSpark Summit
Streaming Analytics with Spark, Kafka, Cassandra, and Akka discusses rethinking architectures for streaming analytics. The document discusses:
1) The need to build scalable, fault-tolerant systems to handle massive amounts of streaming data from different sources with varying structures.
2) An example use case of profiling cyber threat actors using streaming machine data to detect intrusions and security breaches.
3) Rethinking architectures by moving away from ETL pipelines and dual batch/stream systems like Lambda architecture toward unified stream processing with Spark Streaming, Kafka, Cassandra and Akka. This simplifies analytics and eliminates duplicate code and systems.
Apache Flink Training: DataStream API Part 1 BasicFlink Forward
The document provides an overview of Apache Flink's DataStream API for stream processing. It discusses key concepts like stream execution environments, data types (including tuples), transformations (such as map, filter, grouping), data sources (files, sockets, collections), sinks, and fault tolerance through checkpointing. The document also contains examples of a WordCount application using the DataStream API in Java.
Companion slides for Stormpath CTO and Co-Founder Les REST API Security Webinar. This presentation covers all the RESTful best practices learned building the Stormpath APIs. This webinar is full of best practices learned building the Stormpath API and supporting authentication for thousands of projects. Topics Include:
- HTTP Authentication
- Choosing a Security Protocol
- Generating & Managing API Keys
- Authorization & Scopes
- Token Authentication with JSON Web Tokens (JWTs)
- Much more...
Stormpath is a User Management API that reduces development time with instant-on, scalable user infrastructure. Stormpath's intuitive API and expert support make it easy for developers to authenticate, manage and secure users and roles in any application.
This document discusses using Thanos for long term storage of metrics data collected by Prometheus at Ticketmaster. It describes how Thanos provides global visibility of metrics across datacenters, high availability of data through replication, and increased data retention compared to using just Prometheus by storing data in object storage like S3. The architecture includes Thanos query and store nodes, compaction of data, and integration with Prometheus through the store API. Thanos was chosen over alternatives like Cortex due to its simpler deployment model and minimal dependencies.
This document provides an overview of Splunk, including how to install Splunk, configure licenses, perform searches, set up alerts and reports, and manage deployments. It discusses indexing data, extracting fields, tagging events, and using the web interface. The goal is to get users started with the basic functions of Splunk like searching, reporting and monitoring.
Dokumen tersebut membahas pengantar manajemen proyek, yang mencakup definisi proyek dan manajemen proyek, tiga batasan utama manajemen proyek yaitu waktu, biaya dan kualitas, serta area keahlian manajemen proyek. Juga dibahas mengenai konteks manajemen proyek, rencana strategis, portofolio, program, dan perbedaan antara manajemen program dan portofolio.
This document provides an overview of Grafana, an open source metrics dashboard and graph editor for Graphite, InfluxDB and OpenTSDB. It discusses Grafana's features such as rich graphing, time series querying, templated queries, annotations, dashboard search and export/import. The document also covers Grafana's history and alternatives. It positions Grafana as providing richer features than Graphite Web and highlights features like multiple y-axes, unit formats, mixing graph types, thresholds and tooltips.
Streaming Analytics with Spark, Kafka, Cassandra and Akka by Helena EdelsonSpark Summit
Streaming Analytics with Spark, Kafka, Cassandra, and Akka discusses rethinking architectures for streaming analytics. The document discusses:
1) The need to build scalable, fault-tolerant systems to handle massive amounts of streaming data from different sources with varying structures.
2) An example use case of profiling cyber threat actors using streaming machine data to detect intrusions and security breaches.
3) Rethinking architectures by moving away from ETL pipelines and dual batch/stream systems like Lambda architecture toward unified stream processing with Spark Streaming, Kafka, Cassandra and Akka. This simplifies analytics and eliminates duplicate code and systems.
Apache Flink Training: DataStream API Part 1 BasicFlink Forward
The document provides an overview of Apache Flink's DataStream API for stream processing. It discusses key concepts like stream execution environments, data types (including tuples), transformations (such as map, filter, grouping), data sources (files, sockets, collections), sinks, and fault tolerance through checkpointing. The document also contains examples of a WordCount application using the DataStream API in Java.
Companion slides for Stormpath CTO and Co-Founder Les REST API Security Webinar. This presentation covers all the RESTful best practices learned building the Stormpath APIs. This webinar is full of best practices learned building the Stormpath API and supporting authentication for thousands of projects. Topics Include:
- HTTP Authentication
- Choosing a Security Protocol
- Generating & Managing API Keys
- Authorization & Scopes
- Token Authentication with JSON Web Tokens (JWTs)
- Much more...
Stormpath is a User Management API that reduces development time with instant-on, scalable user infrastructure. Stormpath's intuitive API and expert support make it easy for developers to authenticate, manage and secure users and roles in any application.
This document discusses using Thanos for long term storage of metrics data collected by Prometheus at Ticketmaster. It describes how Thanos provides global visibility of metrics across datacenters, high availability of data through replication, and increased data retention compared to using just Prometheus by storing data in object storage like S3. The architecture includes Thanos query and store nodes, compaction of data, and integration with Prometheus through the store API. Thanos was chosen over alternatives like Cortex due to its simpler deployment model and minimal dependencies.
This document provides an overview of Splunk, including how to install Splunk, configure licenses, perform searches, set up alerts and reports, and manage deployments. It discusses indexing data, extracting fields, tagging events, and using the web interface. The goal is to get users started with the basic functions of Splunk like searching, reporting and monitoring.
Dokumen tersebut membahas pengantar manajemen proyek, yang mencakup definisi proyek dan manajemen proyek, tiga batasan utama manajemen proyek yaitu waktu, biaya dan kualitas, serta area keahlian manajemen proyek. Juga dibahas mengenai konteks manajemen proyek, rencana strategis, portofolio, program, dan perbedaan antara manajemen program dan portofolio.
This document provides an overview of Grafana, an open source metrics dashboard and graph editor for Graphite, InfluxDB and OpenTSDB. It discusses Grafana's features such as rich graphing, time series querying, templated queries, annotations, dashboard search and export/import. The document also covers Grafana's history and alternatives. It positions Grafana as providing richer features than Graphite Web and highlights features like multiple y-axes, unit formats, mixing graph types, thresholds and tooltips.
Event-Driven Stream Processing and Model Deployment with Apache Kafka, Kafka ...Kai Wähner
Talk from Kafka Summit San Francisco 2019 (https://kafka-summit.org/sessions/event-driven-model-serving-stream-processing-vs-rpc-kafka-tensorflow/). Video recording will be available for free on the Summit website.
Event-based stream processing is a modern paradigm to continuously process incoming data feeds, e.g. for IoT sensor analytics, payment and fraud detection, or logistics. Machine Learning / Deep Learning models can be leveraged in different ways to do predictions and improve the business processes. Either analytic models are deployed natively in the application or they are hosted in a remote model server. In the latter you combine stream processing with RPC / Request-Response paradigm instead of direct doing direct inference within the application. This talk discusses the pros and cons of both approaches and shows examples of stream processing vs. RPC model serving using Kubernetes, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving. The trade-offs of using a public cloud service like AWS or GCP for model deployment are also discussed and compared to local hosting for offline predictions directly “at the edge”.
Key takeaways
• Machine Learning / Deep Learning models can be used in different ways to do predictions. Scalability and loose coupling are important success factors
• Stream processing vs. RPC / Request-Response for model serving has many trade-offs – learn about alternatives and best practices for your different scenarios
• Understand the alternatives and trade-offs of model deployment in modern infrastructures like Kubernetes or Cloud Services like AWS or GCP
• See live demos with Java, gRPC, Apache Kafka, KSQL and TensorFlow Serving to understand the trade-offs
Grafana is an open source analytics and monitoring tool that uses InfluxDB to store time series data and provide visualization dashboards. It collects metrics like application and server performance from Telegraf every 10 seconds, stores the data in InfluxDB using the line protocol format, and allows users to build dashboards in Grafana to monitor and get alerts on metrics. An example scenario is using it to collect and display load time metrics from a QA whitelist VM.
Dokumen ini memberikan deskripsi perancangan perangkat lunak untuk proyek <nama proyek> yang disusun untuk <nama pelanggan>. Dokumen ini memuat tujuan penulisan, lingkup masalah yang ditangani aplikasi, rancangan lingkungan implementasi, dekomposisi fungsional dan fisik modul, serta deskripsi rinci modul-modul yang terdapat dalam sistem.
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
Sistem Informasi Posko Keamanan bertujuan untuk mengembangkan aplikasi digitalisasi administrasi posko keamanan yang sebelumnya masih dilakukan secara manual. Aplikasi ini diharapkan dapat mempermudah pencatatan, rekap, dan penyimpanan data secara real-time serta meningkatkan transparansi dan ramah lingkungan melalui laporan digital. Proyek ini akan menghasilkan software, dokumentasi, dan laporan dengan anggaran Rp150 juta dan dijad
Dokumen tersebut membahas tentang pengertian performa sistem komputer yang dapat diukur berdasarkan beberapa parameter seperti execution time, throughput, dan MIPS. Performa dapat didefinisikan berdasarkan kecepatan eksekusi program atau kapasitas menyelesaikan pekerjaan. Peningkatan performa dapat dicapai dengan menurunkan execution time atau meningkatkan throughput dengan menambah sumber daya komputasi.
Dokumen tersebut memberikan pengantar singkat tentang bahasa pemrograman Python. Ia menjelaskan bahwa Python sering digunakan untuk aplikasi web, perangkat lunak, ilmu data, dan machine learning. Dokumen tersebut juga menyebutkan beberapa manfaat menggunakan Python seperti mudah dipelajari dan portabel di berbagai sistem operasi.
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
The columnar roadmap: Apache Parquet and Apache ArrowJulien Le Dem
This document discusses Apache Parquet and Apache Arrow, open source projects for columnar data formats. Parquet is an on-disk columnar format that optimizes I/O performance through compression and projection pushdown. Arrow is an in-memory columnar format that maximizes CPU efficiency through vectorized processing and SIMD. It aims to serve as a standard in-memory format between systems. The document outlines how Arrow builds on Parquet's success and provides benefits like reduced serialization overhead and ability to share functionality through its ecosystem. It also describes how Parquet and Arrow representations are integrated through techniques like vectorized reading and predicate pushdown.
This document provides an overview of Apache NiFi and dataflow. It begins with an introduction to the challenges of moving data effectively within and between systems. It then discusses Apache NiFi's key features for addressing these challenges, including guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document outlines NiFi's architecture and components like repositories and extension points. It also previews a live demo and invites attendees to further discuss Apache NiFi at a Birds of a Feather session.
Designing Event-Driven Applications with Apache NiFi, Apache Flink, Apache Spark
DevNexus 2022 Atlanta
https://devnexus.com/presentations/7150/
This talk is a quick overview of the How, What and WHY of Apache Pulsar, Apache Flink and Apache NiFi. I will show you how to design event-driven applications that scale the cloud native way.
This talk was done live in person at DevNexus across from the booth in room 311
Tim Spann
Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
Apache Flink 101 - the rise of stream processing and beyondBowen Li
This document provides an overview and summary of Apache Flink. It discusses how Flink enables stateful stream processing and beyond. Key points include that Flink allows for stateful computations over event streams in an expressive, scalable, fault-tolerant way through layered APIs. It also supports batch processing, machine learning, and serving as a stream processor that unifies streaming and batch. The document highlights many use cases of Flink at Alibaba and how it powers critical systems like real-time analytics and recommendations.
Sharing is Caring: Toward Creating Self-tuning Multi-tenant Kafka (Anna Povzn...HostedbyConfluent
Deploying Kafka to support multiple teams or even an entire company has many benefits. It reduces operational costs, simplifies onboarding of new applications as your adoption grows, and consolidates all your data in one place. However, this makes applications sharing the cluster vulnerable to any one or few of them taking all cluster resources. The combined cluster load also becomes less predictable, increasing the risk of overloading the cluster and data unavailability.
In this talk, we will describe how to use quota framework in Apache Kafka to ensure that a misconfigured client or unexpected increase in client load does not monopolize broker resources. You will get a deeper understanding of bandwidth and request quotas, how they get enforced, and gain intuition for setting the limits for your use-cases.
While quotas limit individual applications, there must be enough cluster capacity to support the combined application load. Onboarding new applications or scaling the usage of existing applications may require manual quota adjustments and upfront capacity planning to ensure high availability.
We will describe the steps we took toward solving this problem in Confluent Cloud, where we must immediately support unpredictable load with high availability. We implemented a custom broker quota plugin (KIP-257) to replace static per broker quota allocation with dynamic and self-tuning quotas based on the available capacity (which we also detect dynamically). By learning our journey, you will have more insights into the relevant problems and techniques to address them.
Ringkasan dokumen tersebut adalah:
Dokumen tersebut membahas pentingnya literasi digital di era digital saat ini, termasuk manfaat dan kemampuan apa saja yang dibutuhkan untuk memiliki literasi digital yang baik.
Buku ini membahas sejarah Internet di dunia dan Indonesia, termasuk sejarah provider Internet dan pengelolaan domain di Indonesia. Buku ini juga menjelaskan perangkat keras untuk mengakses Internet seperti modem dan router. "
This document provides an overview and comparison of Informix's streaming technologies: Change Data Capture (CDC), Smart Triggers, Asynchronous Triggers, and V-II Socket Streaming. CDC processes database transaction logs to capture all changes and send them to clients. Smart Triggers use selective triggers and filtering to capture specific data changes. Asynchronous Triggers use post-commit triggers to route data to user-defined routines. V-II Socket Streaming sends triggered data to MQTT brokers but is not officially supported. The document also includes code examples and diagrams demonstrating how these technologies integrate with applications.
While many companies struggled to maintain their figures over the last year to eighteen months, others have grown - even in these tough economic times. One significant factor in their success appears to be the level of engagement with customers and stakeholders.
Sally Falkow (APR) Social Media Strategist at Expansion Plus, and Rebecca Lieb, VP North America, Econsultancy will discuss research that shows how important engagement has become and how it is tied to financial success.
They'll present case studies that show that this applies just as much to small and medium businesses as it does to large corporations.
The document is a newsletter from HILLS, Inc. that provides information on their various programs and trips for clients. It outlines their adventure clubs, evening social programs, weekend trips, week long vacation trips, and international vacations. The programs involve a variety of outdoor and indoor recreational, social, and community service activities designed for clients of varying abilities. Details are provided on locations, dates, prices, and level of physical ability required for each. A spotlight is also given to a client named David who has lost weight and become more active through HILLS' programs.
Event-Driven Stream Processing and Model Deployment with Apache Kafka, Kafka ...Kai Wähner
Talk from Kafka Summit San Francisco 2019 (https://kafka-summit.org/sessions/event-driven-model-serving-stream-processing-vs-rpc-kafka-tensorflow/). Video recording will be available for free on the Summit website.
Event-based stream processing is a modern paradigm to continuously process incoming data feeds, e.g. for IoT sensor analytics, payment and fraud detection, or logistics. Machine Learning / Deep Learning models can be leveraged in different ways to do predictions and improve the business processes. Either analytic models are deployed natively in the application or they are hosted in a remote model server. In the latter you combine stream processing with RPC / Request-Response paradigm instead of direct doing direct inference within the application. This talk discusses the pros and cons of both approaches and shows examples of stream processing vs. RPC model serving using Kubernetes, Apache Kafka, Kafka Streams, gRPC and TensorFlow Serving. The trade-offs of using a public cloud service like AWS or GCP for model deployment are also discussed and compared to local hosting for offline predictions directly “at the edge”.
Key takeaways
• Machine Learning / Deep Learning models can be used in different ways to do predictions. Scalability and loose coupling are important success factors
• Stream processing vs. RPC / Request-Response for model serving has many trade-offs – learn about alternatives and best practices for your different scenarios
• Understand the alternatives and trade-offs of model deployment in modern infrastructures like Kubernetes or Cloud Services like AWS or GCP
• See live demos with Java, gRPC, Apache Kafka, KSQL and TensorFlow Serving to understand the trade-offs
Grafana is an open source analytics and monitoring tool that uses InfluxDB to store time series data and provide visualization dashboards. It collects metrics like application and server performance from Telegraf every 10 seconds, stores the data in InfluxDB using the line protocol format, and allows users to build dashboards in Grafana to monitor and get alerts on metrics. An example scenario is using it to collect and display load time metrics from a QA whitelist VM.
Dokumen ini memberikan deskripsi perancangan perangkat lunak untuk proyek <nama proyek> yang disusun untuk <nama pelanggan>. Dokumen ini memuat tujuan penulisan, lingkup masalah yang ditangani aplikasi, rancangan lingkungan implementasi, dekomposisi fungsional dan fisik modul, serta deskripsi rinci modul-modul yang terdapat dalam sistem.
Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안SANG WON PARK
Apache Kafak의 빅데이터 아키텍처에서 역할이 점차 커지고, 중요한 비중을 차지하게 되면서, 성능에 대한 고민도 늘어나고 있다.
다양한 프로젝트를 진행하면서 Apache Kafka를 모니터링 하기 위해 필요한 Metrics들을 이해하고, 이를 최적화 하기 위한 Configruation 설정을 정리해 보았다.
[Apache kafka 모니터링을 위한 Metrics 이해 및 최적화 방안]
Apache Kafka 성능 모니터링에 필요한 metrics에 대해 이해하고, 4가지 관점(처리량, 지연, Durability, 가용성)에서 성능을 최적화 하는 방안을 정리함. Kafka를 구성하는 3개 모듈(Producer, Broker, Consumer)별로 성능 최적화를 위한 …
[Apache Kafka 모니터링을 위한 Metrics 이해]
Apache Kafka의 상태를 모니터링 하기 위해서는 4개(System(OS), Producer, Broker, Consumer)에서 발생하는 metrics들을 살펴봐야 한다.
이번 글에서는 JVM에서 제공하는 JMX metrics를 중심으로 producer/broker/consumer의 지표를 정리하였다.
모든 지표를 정리하진 않았고, 내 관점에서 유의미한 지표들을 중심으로 이해한 내용임
[Apache Kafka 성능 Configuration 최적화]
성능목표를 4개로 구분(Throughtput, Latency, Durability, Avalibility)하고, 각 목표에 따라 어떤 Kafka configuration의 조정을 어떻게 해야하는지 정리하였다.
튜닝한 파라미터를 적용한 후, 성능테스트를 수행하면서 추출된 Metrics를 모니터링하여 현재 업무에 최적화 되도록 최적화를 수행하는 것이 필요하다.
Sistem Informasi Posko Keamanan bertujuan untuk mengembangkan aplikasi digitalisasi administrasi posko keamanan yang sebelumnya masih dilakukan secara manual. Aplikasi ini diharapkan dapat mempermudah pencatatan, rekap, dan penyimpanan data secara real-time serta meningkatkan transparansi dan ramah lingkungan melalui laporan digital. Proyek ini akan menghasilkan software, dokumentasi, dan laporan dengan anggaran Rp150 juta dan dijad
Dokumen tersebut membahas tentang pengertian performa sistem komputer yang dapat diukur berdasarkan beberapa parameter seperti execution time, throughput, dan MIPS. Performa dapat didefinisikan berdasarkan kecepatan eksekusi program atau kapasitas menyelesaikan pekerjaan. Peningkatan performa dapat dicapai dengan menurunkan execution time atau meningkatkan throughput dengan menambah sumber daya komputasi.
Dokumen tersebut memberikan pengantar singkat tentang bahasa pemrograman Python. Ia menjelaskan bahwa Python sering digunakan untuk aplikasi web, perangkat lunak, ilmu data, dan machine learning. Dokumen tersebut juga menyebutkan beberapa manfaat menggunakan Python seperti mudah dipelajari dan portabel di berbagai sistem operasi.
We want to present multiple anti patterns utilizing Redis in unconventional ways to get the maximum out of Apache Spark.All examples presented are tried and tested in production at Scale at Adobe. The most common integration is spark-redis which interfaces with Redis as a Dataframe backing Store or as an upstream for Structured Streaming. We deviate from the common use cases to explore where Redis can plug gaps while scaling out high throughput applications in Spark.
Niche 1 : Long Running Spark Batch Job – Dispatch New Jobs by polling a Redis Queue
· Why?
o Custom queries on top a table; We load the data once and query N times
· Why not Structured Streaming
· Working Solution using Redis
Niche 2 : Distributed Counters
· Problems with Spark Accumulators
· Utilize Redis Hashes as distributed counters
· Precautions for retries and speculative execution
· Pipelining to improve performance
The columnar roadmap: Apache Parquet and Apache ArrowJulien Le Dem
This document discusses Apache Parquet and Apache Arrow, open source projects for columnar data formats. Parquet is an on-disk columnar format that optimizes I/O performance through compression and projection pushdown. Arrow is an in-memory columnar format that maximizes CPU efficiency through vectorized processing and SIMD. It aims to serve as a standard in-memory format between systems. The document outlines how Arrow builds on Parquet's success and provides benefits like reduced serialization overhead and ability to share functionality through its ecosystem. It also describes how Parquet and Arrow representations are integrated through techniques like vectorized reading and predicate pushdown.
This document provides an overview of Apache NiFi and dataflow. It begins with an introduction to the challenges of moving data effectively within and between systems. It then discusses Apache NiFi's key features for addressing these challenges, including guaranteed delivery, data buffering, prioritized queuing, and data provenance. The document outlines NiFi's architecture and components like repositories and extension points. It also previews a live demo and invites attendees to further discuss Apache NiFi at a Birds of a Feather session.
Designing Event-Driven Applications with Apache NiFi, Apache Flink, Apache Spark
DevNexus 2022 Atlanta
https://devnexus.com/presentations/7150/
This talk is a quick overview of the How, What and WHY of Apache Pulsar, Apache Flink and Apache NiFi. I will show you how to design event-driven applications that scale the cloud native way.
This talk was done live in person at DevNexus across from the booth in room 311
Tim Spann
Tim Spann is a Developer Advocate for StreamNative. He works with StreamNative Cloud, Apache Pulsar, Apache Flink, Flink SQL, Apache NiFi, MiniFi, Apache MXNet, TensorFlow, Apache Spark, big data, the IoT, machine learning, and deep learning. Tim has over a decade of experience with the IoT, big data, distributed computing, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Architect at AirisData, a Senior Field Engineer at Pivotal and a Team Leader at HPE. He blogs for DZone, where he is the Big Data Zone leader, and runs a popular meetup in Princeton on big data, the IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as IoT Fusion, Strata, ApacheCon, Data Works Summit Berlin, DataWorks Summit Sydney, and Oracle Code NYC. He holds a BS and MS in computer science.
Apache Flink 101 - the rise of stream processing and beyondBowen Li
This document provides an overview and summary of Apache Flink. It discusses how Flink enables stateful stream processing and beyond. Key points include that Flink allows for stateful computations over event streams in an expressive, scalable, fault-tolerant way through layered APIs. It also supports batch processing, machine learning, and serving as a stream processor that unifies streaming and batch. The document highlights many use cases of Flink at Alibaba and how it powers critical systems like real-time analytics and recommendations.
Sharing is Caring: Toward Creating Self-tuning Multi-tenant Kafka (Anna Povzn...HostedbyConfluent
Deploying Kafka to support multiple teams or even an entire company has many benefits. It reduces operational costs, simplifies onboarding of new applications as your adoption grows, and consolidates all your data in one place. However, this makes applications sharing the cluster vulnerable to any one or few of them taking all cluster resources. The combined cluster load also becomes less predictable, increasing the risk of overloading the cluster and data unavailability.
In this talk, we will describe how to use quota framework in Apache Kafka to ensure that a misconfigured client or unexpected increase in client load does not monopolize broker resources. You will get a deeper understanding of bandwidth and request quotas, how they get enforced, and gain intuition for setting the limits for your use-cases.
While quotas limit individual applications, there must be enough cluster capacity to support the combined application load. Onboarding new applications or scaling the usage of existing applications may require manual quota adjustments and upfront capacity planning to ensure high availability.
We will describe the steps we took toward solving this problem in Confluent Cloud, where we must immediately support unpredictable load with high availability. We implemented a custom broker quota plugin (KIP-257) to replace static per broker quota allocation with dynamic and self-tuning quotas based on the available capacity (which we also detect dynamically). By learning our journey, you will have more insights into the relevant problems and techniques to address them.
Ringkasan dokumen tersebut adalah:
Dokumen tersebut membahas pentingnya literasi digital di era digital saat ini, termasuk manfaat dan kemampuan apa saja yang dibutuhkan untuk memiliki literasi digital yang baik.
Buku ini membahas sejarah Internet di dunia dan Indonesia, termasuk sejarah provider Internet dan pengelolaan domain di Indonesia. Buku ini juga menjelaskan perangkat keras untuk mengakses Internet seperti modem dan router. "
This document provides an overview and comparison of Informix's streaming technologies: Change Data Capture (CDC), Smart Triggers, Asynchronous Triggers, and V-II Socket Streaming. CDC processes database transaction logs to capture all changes and send them to clients. Smart Triggers use selective triggers and filtering to capture specific data changes. Asynchronous Triggers use post-commit triggers to route data to user-defined routines. V-II Socket Streaming sends triggered data to MQTT brokers but is not officially supported. The document also includes code examples and diagrams demonstrating how these technologies integrate with applications.
While many companies struggled to maintain their figures over the last year to eighteen months, others have grown - even in these tough economic times. One significant factor in their success appears to be the level of engagement with customers and stakeholders.
Sally Falkow (APR) Social Media Strategist at Expansion Plus, and Rebecca Lieb, VP North America, Econsultancy will discuss research that shows how important engagement has become and how it is tied to financial success.
They'll present case studies that show that this applies just as much to small and medium businesses as it does to large corporations.
The document is a newsletter from HILLS, Inc. that provides information on their various programs and trips for clients. It outlines their adventure clubs, evening social programs, weekend trips, week long vacation trips, and international vacations. The programs involve a variety of outdoor and indoor recreational, social, and community service activities designed for clients of varying abilities. Details are provided on locations, dates, prices, and level of physical ability required for each. A spotlight is also given to a client named David who has lost weight and become more active through HILLS' programs.
Making social media monitoring and analytics work for your brandMarketwired
The document discusses social media monitoring challenges and solutions provided by MAP and Heartbeat products. It outlines the 5 W's of business intelligence from social data - what, when, where, who, and why people are talking. MAP is for historical research and analytics while Heartbeat is for real-time monitoring. Both products analyze sentiment, demographics, and geolocations of social conversations. The document provides examples of how companies leverage social insights for various business goals.
Gerakan Gamelan Lovers bertujuan untuk melestarikan budaya Gamelan di kalangan generasi muda dengan memberikan informasi, mengadakan aktivitas mengenalkan Gamelan modern, dan mempromosikan kebanggaan akan budaya Indonesia melalui kaos dan desain grafis di kota.
This document appears to be records from a class project where students destroyed bridges with varying weights. It lists students' names alongside the weights of bridges they destroyed, with Andrew destroying the heaviest bridge at 35 pounds. The document questions where Evan is and declares Andrew as the ruler, suggesting he destroyed the most bridges.
The document discusses engineering design tips and past prize amounts for various engineering competitions. It lists prize amounts from 2011 ranging from $17 to $170,000. It provides tips for choosing hollow tubes or solid rods for structures and considering force to strength ratios. Arch bridges are noted to gain their strength from compression forces, making hollow tubes more efficient for arch designs.
The document discusses social media usage statistics in Croatia and opportunities and challenges for VIPnet telecommunications company to engage with social media. It notes that 70% of Croatian households have internet access but VIPnet currently lacks a social media presence. The document recommends that over the next 3 years, VIPnet should establish a social media team, become an accepted member of the Croatian online community, and have the best online reputation and social media presence in Croatia.
The document discusses several people, places, and events. It provides clues about notable figures like Samuel Beckett, Conrad Hunte, PJ Antony, and Jamsetji Tata. It also identifies structures like Mysore Palace and locations like Florence. Events mentioned include the Suez Canal crisis and the Sino-Indian War of 1962. Works and concepts discussed include Hitopadesha, Dante's Divine Comedy, and the UN peacekeeping force.
Encouraging Sustainability: Use of LEED to Enhance Focus on SustainabilityDaniel Haddock
Describes the implementation of an initiative at American Water to obtain LEED certification for four new water treatment facilities in Indiana & Illinois. Discussion of the wider benefits of the initiative in terms of introducing concepts of sustainability to employees across the utility organization.
The Gruvi app aims to help users discover and share entertainment experiences by tapping into friends' recommendations on Facebook. It will recommend movies based on users' tastes, show what friends like, and help plan movie nights. This addresses a problem in the film industry where new movies have difficulty gaining exposure. Gruvi will identify early fans, engage them through campaigns, and track engagement metrics. This will help studios market films more effectively and deliver a return on investment. The app plans to expand across Europe and integrate with other services to reach users whenever they make film choices. Revenue projections show the company needs €250,000 to break even in Europe.
This document provides definitions for common parts of speech including interjections, articles, nouns, pronouns, verbs, adjectives, adverbs, prepositions, and conjunctions. Examples are given for each part of speech to illustrate their meaning and use in language.
This business presentation describes the Aaachoo! opportunity, a digital product business with a low $29.95/month investment, no inventory requirements, and global market reach. It claims participants can earn $80,000 per month through Aaachoo!'s compensation plan which involves recruiting others into a matrix structure to earn commissions. The presentation encourages viewers to enroll now on the free trial and upgrade their account to get started sharing the opportunity with others.
This document discusses the Sakai open academic environment. It describes widgets everywhere, embedding gadgets in different sites, and accessing gadgets from a browser toolbar on any website. It also mentions the November 2010 state of play, and lists the administrative IT, library IT, research IT, and academic computing teams involved in the project.
Leveraging the semantic web meetup, Semantic Search, Schema.org and moreBarbaraStarr2009
A history and description of the adoption of Semantic Search by the major search and social engines. Covers schema.org, the knowledege graph and status to date (july 30, 2013). Presented From a Search Engine Point of View.
4. DC/DRC Router
• Router DC dan DRC diconfigur
menggunakan VRRP. Pada saat
gagal mengakses kesalah satu
router maka router yg lain akan
otomatis aktif.
• Load Balancer Aplikasi (HAproxy)
• Jika server aplikasi lebih dari satu,
lebih baik digunakan load
balancer yang berguna untuk
membagi beban (load) secara
merata.
5. DC/DRC Server Aplikasi
• File aplikasi(.php) dan file hasil
olah aplikasi (.xls,.pdf,.doc)
ditempatkan di file server
dengan metode folder sharing
(NFS)
• Opsi 1 : Aplikasi melakukan
pengecekan koneksi ke salah
satu server database, jika hasil
pengecekan ok, maka aplikasi
akan melakukan koneksi ke
database tersebut.
6. DC/DRC Server Aplikasi
• Opsi di atas, berarti aplikasi
dibuatkan pernyataan
kondisi(Conditional Statements)
jika server Database 1 gagal
diarahkan ke server database
yang lain.
• Opsi 2 : Menggunakan Load
Balancer Database (HAproxy).
Aplikasi akan melakukan koneksi
hanya ke salah satu IP virtual,
yang kemudian load balancer
akan mengarahkan dabatase yang
diprioritaskan.
7. DC/DRC Server Database
• Server database akan
melakukan sinkronisasi
secara otomatis, jika salah
satu database datanya
berubah maka server yg lain
akan tersinkron atas
perubahan tersebut.
• Semua server database akan
mempunyai dua peran
sekaligus sebagai master dan
sebagai slave.
9. Node DC dan Node DRC
• Folder Data di storage server (DC) akan disinkron
otomatis(realtime) menggunakan tool btsync dengan folder
Data di storage server DRC.
• Salah satu database server di DC akan disinkron secara
otomatis dengan salah satu database server di DRC. Yang
berarti, jika ada perubahan data di DC, data akan mengalir
(Sinkronisasi) ke DRC.
11. Module Aplikasi DRC
(Berbasis Web)
• Module Scheduler Backup
– Module ini berfungsi untuk konfigurasi penjadwalan
backup secara otomatis berdasarkan waktu tertentu
• Module Backup/Restore Database
– Module ini berfungsi untuk melakukan eksekusi
backup/restore secara manual oleh pengguna.
• File Manager
– Module ini digunakan untuk melihat daftar
kesejarahan backup database.