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Building Event-Driven Services with Stateful Streams

by Benjamin Stopford, Engineer, Confluent
video, slide
Event Driven Services come in many shapes and sizes from tiny event driven functions that dip into an event stream, right through to heavy, stateful services which can facilitate request response. This practical talk makes the case for building this style of system using Stream Processing tools. We also walk through a number of patterns for how we actually put these things together.

下面的內(nèi)容來自機器翻譯:
事件驅(qū)動服務具有許多形式和尺寸亿昏，從小事件驅(qū)動的功能進入事件流聚蝶，直到沉重嘹叫，有狀態(tài)的服務蓬痒，這可以方便請求響應。這個實際的談話使得使用流處理工具來構建這種類型的系統(tǒng)成為可能闯睹。我們也通過一些模式來解釋我們?nèi)绾螌⑦@些東西放在一起阀参。

Building Stateful Financial Applications with Kafka Streams

by Charles Reese, Senior Software Engineer, Funding Circle
video, slide
At Funding Circle, we are building a global lending platform with Apache Kafka and Kafka Streams to handle high volume, real-time processing with rapid clearing times similar to a stock exchange. In this talk, we will provide an overview of our system architecture and summarize key results in edge service connectivity, idempotent processing, and migration strategies.

下面的內(nèi)容來自機器翻譯:
在Funding Circle，我們正在與Apache Kafka和Kafka Streams建立一個全球性的貸款平臺瞻坝，以處理大批量蛛壳，實時的處理杏瞻，快速的結(jié)算時間與證券交易所類似。在本次演講中衙荐，我們將概述我們的系統(tǒng)架構捞挥，并總結(jié)邊緣服務連接，冪等處理和遷移策略的關鍵成果忧吟。

Fast Data in Supply Chain Planning

by Jeroen Soeters, Lead Developer, ThoughtWorks
video, slide
We are migrating one of the top 3 consumer packaged goods companies from a batch-oriented systems architecture to a streaming micro services platform. In this talk I’ll explain how we leverage the Lightbend reactive stack and Kafka to achieve this and how the 4 Kafka APIs fit in our architecture. Also I explain why Kafka Streams <3 Enterprise Integration Patterns.

下面的內(nèi)容來自機器翻譯:
我們正在將三大消費品公司之一從批處理系統(tǒng)架構遷移到流式微服務平臺砌函。在這個演講中，我將解釋我們?nèi)绾卫肔ightbend反應堆和Kafka來實現(xiàn)這個目標溜族，以及4個Kafka API如何適應我們的架構讹俊。另外我解釋了為什么Kafka Streams <3企業(yè)集成模式。

Kafka Stream Processing for Everyone with KSQL

by Nick Dearden, Director of Engineering, Confluent
video, slide
The rapidly expanding world of stream processing can be daunting, with new concepts (various types of time semantics, windowed aggregates, changelogs, and so on) and programming frameworks to master. KSQL is a new open-source project which aims to simplify all this and make stream processing available to everyone.

下面的內(nèi)容來自機器翻譯:
隨著新概念（各種類型的時間語義煌抒，窗口聚合仍劈，更新日志等）和編程框架的掌握，流處理的迅速發(fā)展的世界將變得艱巨寡壮。 KSQL是一個新的開源項目贩疙，旨在簡化所有這些工作，并為每個人提供流處理况既。

Portable Streaming Pipelines with Apache Beam

by Frances Perry, Software Engineer, Google
video, slide
Much as SQL stands as a lingua franca for declarative data analysis, Apache Beam aims to provide a portable standard for expressing robust, out-of-order data processing pipelines in a variety of languages across a variety of platforms. By cleanly separating the user’s processing logic from details of the underlying execution engine, the same pipelines will run on any Apache Beam runtime environment, whether it’s on-premise or in the cloud, on open source frameworks like Apache Spark or Apache Flink, or on managed services like Google Cloud Dataflow. In this talk, I will:

Briefly, introduce the capabilities of the Beam model for data processing and integration with IO connectors like Apache Kafka.
Discuss the benefits Beam provides regarding portability and ease-of-use.
Demo the same Beam pipeline running on multiple runners in multiple deployment scenarios (e.g. Apache Flink on Google Cloud, Apache Spark on AWS, Apache Apex on-premise).
Give a glimpse at some of the challenges Beam aims to address in the future.

下面的內(nèi)容來自機器翻譯:
就像SQL作為聲明性數(shù)據(jù)分析的通用語言一樣这溅，Apache Beam旨在提供一種便攜式標準，用于在各種平臺上以各種語言表示健壯的棒仍，無序的數(shù)據(jù)處理管道悲靴。通過將用戶的處理邏輯與基礎執(zhí)行引擎的細節(jié)完全分離，相同的管道將運行在任何Apache Beam運行時環(huán)境（無論是內(nèi)部部署還是云中）莫其，Apache Spark或Apache Flink等開放源代碼框架上癞尚，還是像谷歌云數(shù)據(jù)流管理的服務。在這個演講中榜配，我會：

簡而言之否纬，介紹Beam模型的功能吕晌，用于數(shù)據(jù)處理和IO連接器（如Apache Kafka）的集成蛋褥。
討論Beam提供的有關便攜性和易用性的好處。
在多個部署場景（例如睛驳，Google Cloud上的Apache Flink烙心，AWS上的Apache Spark，Apache Apex內(nèi)部部署）上演示運行在多個運行器上的相同Beam管道乏沸。
瞥見梁在未來要解決的一些挑戰(zhàn)淫茵。

Query the Application, Not a Database: “Interactive Queries” in Kafka’s Streams API

by Matthias Sax, Engineer, Confluent
video, slide
Kafka Streams allows to build scalable streaming apps without a cluster. This “Cluster-to-go” approach is extended by a “DB-to-go” feature: Interactive Queries allows to directly query app internal state, eliminating the need for an external DB to access this data. This avoids redundantly stored data and DB update latency, and simplifies the overall architecture, e.g., for micro-services.

下面的內(nèi)容來自機器翻譯:
Kafka Streams允許在沒有群集的情況下構建可擴展的流式應用程序。這種“Cluster-to-go”方法通過“DB-to-go”功能進行擴展：交互式查詢允許直接查詢應用程序內(nèi)部狀態(tài)蹬跃，無需外部數(shù)據(jù)庫來訪問這些數(shù)據(jù)匙瘪。這避免了冗余存儲的數(shù)據(jù)和數(shù)據(jù)庫更新等待時間，并且簡化了整體架構，例如對于微服務丹喻。

Real-Time Document Rankings with Kafka Streams

by Hunter Kelly, Senior Software/Data Engineer, Zalando
video, slide
The HITS algorithm creates a score for documents; one is “hubbiness”, the other is “authority”. Usually this is done as a batch operation, working on all the data at once. However, with careful consideration, this can be implemented in a streaming architecture using KStreams and KTables, allowing efficient real time sampling of rankings at a frequency appropriate to the specific use case.

下面的內(nèi)容來自機器翻譯:
HITS算法為文檔創(chuàng)建分數(shù);一個是“喧囂”薄货，一個是“權威”。通常這是作為批處理操作完成的碍论，一次處理所有的數(shù)據(jù)谅猾。然而，經(jīng)過慎重的考慮鳍悠，這可以在使用KStreams和KTables的流式架構中實現(xiàn)税娜，從而以適合特定用例的頻率對排名進行高效的實時采樣。

Streaming Processing in Python – 10 ways to avoid summoning Cuthulu

by Holden Karau, Principal Software Engineer, IBM
video, slide
<3 Python & want to process data from Kafka? This talk will look how to make this awesome. In many systems the traditional approach involves first reading the data into the JVM and then passing the data to Python, which can be a little slow, and on a bad day results in almost impossible to debug. This talk will look at how to be more awesome in Spark & how to do this in Kafka Streams.

下面的內(nèi)容來自機器翻譯:
<3 Python＆想要處理來自Kafka的數(shù)據(jù)藏研？這個演講將看看如何使這個真棒敬矩。在許多系統(tǒng)中，傳統(tǒng)的方法是首先將數(shù)據(jù)讀入JVM遥倦，然后將數(shù)據(jù)傳遞給Python谤绳，這可能會稍微慢一點，而在糟糕的一天中袒哥，幾乎不可能調(diào)試缩筛。這個演講將討論如何在Spark中更加棒，以及如何在Kafka Streams中做到這一點堡称。