Stream Data Hackathon

Confluent, gegründet von den Entwicklern der Open-Source-Lösung Apache Kafka®, bietet die führende Streaming-Plattform, welche es Unternehmen ermöglicht, den Wert ihrer Daten zu maximieren. Die Plattform von Confluent versetzt führende Unternehmen aus Einzelhandel, Logistik, Industrie, Finanzdienstleistungen, Technologie und Medien in die Lage, Daten aus isolierten Systemen in eine Echtzeit-Datenpipeline zu übertragen, wo sie sofort darauf reagieren können. Confluent, unterstützt von Benchmark, Index Ventures und Sequoia, hat seinen Sitz in Palo Alto, Kalifornien.Mehr Informationen finden Sie unterwww.confluent.io.Confluent Open Source und eine kostenlose Testversion von Confluent Enterprise können hier heruntergeladen werden:www.confluent.io/download.

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<H3>Stream Data Hackathon</H3>
Join the Bay Area Apache Kafka community for our first Stream Data Hackathon! This inaugural hackathon will focus Kafka’s new high-level tools for real-time data integration and stream processing: Kafka Connect and Kafka Streams. Kafka Connect abstracts away the common problems every connector needs to solve, allowing connector developers to focus on the details of the system they are importing to or exporting from. Kafka Streams is a lightweight library for stream processing that leverages Kafka as its internal messaging layer, is agnostic to resource and configuration management tools, and is built on important concepts such as properly distinguishing between event time and processing time, windowing support, and simple yet efficient management of application state. Combined, they form a powerful toolset for building real-time data pipelines.
You come with great ideas for connectors, stream processing applications, and a laptop. We’ll provide food, drinks, guidance from Kafka developers, and prizes.
WHEN: Monday, April 25, 2016 from 6:00 PM to 10:00 PM (PDT)
WHERE: Hilton San Francisco Union Square – Imperial B Ballroom – 333 O’Farrell Street, San Francisco, CA 94102 –<A TARGET="_blank" HREF="https://www.eventbrite.com/e/kafka-connector-hackathon-tickets-22755586572#map-target"> View Map</A>
**Note: Attending the Stream Data Hackathon does not include access to the Kafka Summit. If you would like to attend Kafka Summit, please register <A TARGET="_blank" HREF="http://kafka-summit.org/register/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">here</A>.**
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<H3>About Kafka Connect</H3>
Kafka Connect is a tool for scalably and reliably streaming data between Apache Kafka and other data systems. It makes it simple to quickly define connectors that move large streaming datasets into and out of Kafka. Kafka Connect can ingest entire databases or collect metrics from all your application servers into Kafka topics, making the data available for stream processing with low latency. An export connector can deliver data from Kafka topics into secondary indexes like Elasticsearch or into batch systems such as Hadoop for offline analysis.
Kafka Connect abstracts away the common problems every connector to Kafka needs to solve: schema management, fault tolerance, partitioning, offset management and delivery semantics, operations, and monitoring. This allows connector developers to focus on the details specific to the system they are copying data from, while relying on Kafka Connect to solve the hard problems. Connect users can pick from a repository of open-source connectors without having to worry about interoperability as well as  have a single system to manage, monitor, and deploy several connectors on.
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<H3>About Kafka Streams</H3>
Kafka Streams is a library for building streaming applications, specifically applications that transform input Kafka topics into output Kafka topics. Kafka Streams has a very low barrier to entry, easy for application development, and a natural DSL for writing stream processing applications. It achieves this unique feature set by working directly with Kafka and leveraging the existing distributed, fault tolerant clients. By implementing stream processing as a library instead of a framework, it remains agnostic to resource management and configuration tools so it is easily adopted in any organization. And because it builds upon important concepts for stream processing such as properly distinguishing between event time and processing time, windowing support, and simple yet efficient management of application state, you get all the modern features you expect in a lightweight library.
 
<H3>Project Ideas</H3>
Interested in the hackathon but not sure what to build? No problem. Here are a few ideas to get the creative juices flowing:
Graphite Source Connector - Make your metrics available at scale in real time. If your organization already standardizes on graphite for metrics, you can stream them into Kafka and make them available for complex alerting applications or to buffer them for delivery to batch storage systems for offline analysis.
Real-time Alerting - Build a Kafka Streams application that consumes metrics or logs from Kafka, performs aggregations, and can trigger alerts on certain conditions (e.g. a metric’s 1 minute average exceeds a configurable threshold).
Slack Source Connector - The hottest thing in chat since IRC. Connect to the <A TARGET="_blank" HREF="https://api.slack.com/rtm" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Slack Real Time Message API</A> to stream message contents into Kafka. Downstream applications could aggregate and analyze this data.
Twitter Trending Topics - Using data imported by a <A TARGET="_blank" HREF="http://connectors.confluent.io" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Twitter connector</A>, analyze and aggregate tweets to detect trending topics. This might be accomplished by computing popular topics over multiple time periods, then joining these data sets to find terms/hashtags that are significantly more popular recently (e.g. last hour vs last day).
PagerDuty Sink Connector - Use the <A TARGET="_blank" HREF="https://developer.pagerduty.com/documentation/integration/events" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">PagerDuty Event API</A> to trigger alerts for your ops team directly from events generated in Kafka. This could be the final stage in an alerting system (which you might build with the Graphite connector described above).
Get creative! Databases and message are obvious targets for connectors, but you can connect all sorts of systems, from loading <A TARGET="_blank" HREF="https://github.com/guozhangwang/kafka/commit/6c6b83fe0c26f72314f7aaf783958637fa2e078a" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Wikipedia</A> edits into Kafka to creating JIRA tickets from messages in a Kafka topic. Kafka Streams applications could leverage and combine data from any set of <A TARGET="_blank" HREF="http://connectors.confluent.io" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">available connectors</A>.
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Still not sure what to build? During registration you can include systems you’re interested in and we’ll help connect you with other participants so you can work in a team to come up with and implement a project.
 
**Note: Attending the Stream Data Hackathon does not include access to the Kafka Summit. If you would like to attend Kafka Summit, please register <A TARGET="_blank" HREF="http://kafka-summit.org/register/" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">here</A>.**

Hilton San Francisco Union Square - Imperial B Ballroom

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Stream Data Hackathon

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