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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.

以下內(nèi)容來自谷歌翻譯：
事件驅(qū)動服務(wù)有許多形狀和大小，從小事件驅(qū)動的功能枪向，浸入事件流屏鳍，直到重，有狀態(tài)的服務(wù)，可以促進(jìn)請求響應(yīng)杆勇。這個實際的談話使用Stream Processing工具來構(gòu)建這種風(fēng)格的系統(tǒng)呕缭。我們還通過一些模式，了解我們?nèi)绾螌⑦@些東西放在一起筒愚。

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.

以下內(nèi)容來自谷歌翻譯：
在資金圈赴蝇，我們正在使用Apache Kafka和Kafka構(gòu)建全球貸款平臺，以處理大量實時處理快速清算時間類似于證券交易所巢掺。在本講座中句伶，我們將概述系統(tǒng)架構(gòu)劲蜻，并總結(jié)邊緣服務(wù)連接，冪等處理和遷移策略的關(guān)鍵結(jié)果考余。

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.

以下內(nèi)容來自谷歌翻譯：
我們正在將三大消費品商品公司之一從批量導(dǎo)向的系統(tǒng)架構(gòu)遷移到流式微服務(wù)平臺先嬉。在這個演講中，我將解釋我們?nèi)绾卫肔ightbend反應(yīng)堆棧和Kafka來實現(xiàn)這一點楚堤，以及API中適合的4個Kafka在我們的建筑疫蔓。另外我解釋為什么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.

以下內(nèi)容來自谷歌翻譯：
快速擴(kuò)展的流處理世界可能是艱巨的身冬，新的概念（各種類型的時間語義衅胀，窗口聚合，更改日志等）和編程框架來掌握酥筝。 KSQL是一個新的開源項目滚躯，旨在簡化所有這些，并使流處理可用于所有人嘿歌。

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.

以下內(nèi)容來自谷歌翻譯：
就像SQL作為聲明式數(shù)據(jù)分析的通用語言一樣掸掏，Apache Beam旨在提供一種便攜式標(biāo)準(zhǔn)，用于在各種平臺上以各種語言表達(dá)強(qiáng)大的無序數(shù)據(jù)處理流水線宙帝。通過干凈地將用戶的處理邏輯與底層執(zhí)行引擎的細(xì)節(jié)分開丧凤，相同的管道將在任何Apache Beam運行時環(huán)境中運行，無論是內(nèi)部部署還是在云中茄唐，在Apache <span class ='no' > Spark </ span>或Apache Flink息裸，或Google Cloud Dataflow等托管服務(wù)。在這個演講中沪编，我會：

簡要介紹Beam模型的功能呼盆，用于數(shù)據(jù)處理和與IO連接器的集成，如Apache Kafka蚁廓。
討論Beam提供的便攜性和易用性的優(yōu)勢访圃。
演示在多個部署場景（例如，Google Cloud上的Apache Flink相嵌，AWS上的Apache Spark腿时，Apache Apex內(nèi)部部署）上運行的相同波束管道。
瞥見Beam將來面臨的一些挑戰(zhàn)饭宾。

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.

以下內(nèi)容來自谷歌翻譯：
Kafka Streams允許構(gòu)建可擴(kuò)展的流應(yīng)用而無需集群批糟。這種“即插即用”方法通過“數(shù)據(jù)即發(fā)”功能擴(kuò)展：交互查詢允許直接查詢應(yīng)用程序內(nèi)部狀態(tài)，從而無需外部數(shù)據(jù)庫來訪問此數(shù)據(jù)看铆。這避免了冗余存儲的數(shù)據(jù)和DB更新延遲徽鼎，并且簡化了整體架構(gòu)，例如用于微服務(wù)。

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.

以下內(nèi)容來自谷歌翻譯：
HITS算法為文檔創(chuàng)建分?jǐn)?shù);一個是“喧囂”否淤，另一個是“權(quán)威”悄但。通常這是作為批處理操作完成的，同時處理所有數(shù)據(jù)石抡。然而檐嚣，仔細(xì)考慮，這可以在使用KStreams和KTables的流式架構(gòu)中實現(xiàn)啰扛，允許以適合特定用例的頻率進(jìn)行有效的實時抽樣排序嚎京。

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.

以下內(nèi)容來自谷歌翻譯：
<3 Python＆想要從Kafka處理數(shù)據(jù)？這個講話會看起來如何使這個真棒隐解。在許多系統(tǒng)中挖藏，傳統(tǒng)的方法包括首先將數(shù)據(jù)讀入JVM，然后將數(shù)據(jù)傳遞給Python厢漩，這可能有點慢，而在糟糕的一天岩臣，幾乎不可能調(diào)試溜嗜。這個演講將會在Spark中如何做得更好，如何在Kafka Streams中執(zhí)行此操作架谎。