Introduction to Apache NiFi And Storm

Summarizes new capabilities added to Apache NiFi 1.2.0 (soon to be released). Disclaimer: - The contents in this slide deck are derived from Apache NiFi JIRA issues which is labeled with next release target 1.2.0 and source code available at Github (already merged into master branch), however it does NOT mean these are guaranteed to be released and still are subjects to change. - The motivation of this presentation is share what have been introduced into the project since the latest Apache NiFi 1.1.2 release. - The contents are created from information available under Apache NiFi project, however, the way summarize it is solely done with my personal thoughts and not a consensus built among Apache NiFi community.

What will be new in Apache NiFi 1.2.0

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Introduction to Apache NiFi 1.11.4

Apache NiFi, Storm and Kafka augment each other in modern enterprise architectures. NiFi provides a coding free solution to get many different formats and protocols in and out of Kafka and compliments Kafka with full audit trails and interactive command and control. Storm compliments NiFi with the capability to handle complex event processing.   Join us to learn how Apache NiFi, Storm and Kafka can augment each other for creating a new dataplane connecting multiple systems within your enterprise with ease, speed and increased productivity. https://www.brighttalk.com/webcast/9573/224063

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Apache NiFi is an easy to use, powerful, and reliable system to process and distribute data. It supports highly configurable directed graphs of data routing, transformation, and system mediation logic. Some of NiFi's key features include a web-based user interface for monitoring and controlling data flows, guaranteed delivery, data provenance, and easy extensibility through custom processor development. These features make NiFi a perfect candidate for building production quality data pipelines that interact with Apache Solr. This talk will demonstrate how to use a NiFi processor that delivers data to a Solr update handler, as well as a processor for extracting data from Solr on regular intervals for delivery to down-stream systems. In addition we will show how these processors can be combined with other built-in NiFi processors to solve a variety of use cases, including log aggregation, and indexing messages received from Kafka.

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The amount of information coming from a Cloud deployment, that could be used to have a better situational awareness, and operate it efficiently is huge. Tools as the ones provided by Apache foundation can be used to build a solution to that challenge. Nowadays Cloud deployments are pervasive in businesses, with scalability and multi tenancy as their core capabilities. This means that these deployments can grow easily beyond 1000 nodes and efficient operation of these huge clusters requires real time log analysis, metrics, events and configuration data. Performing correlation and finding patterns, not just to get to root causes but also to predict failures and reduce risk requires tools that go beyond current solutions. In the prototype developed by Red Hat and KEEDIO (keedio.com), we managed to address the above challenges with the use of Big Data tools like Apache NiFi, Apache Kafka and Apache Flink, that enabled us to process the constant stream of syslog messages (RFC5424) produced by the Infrastructure as a Service, provided by OpenStack services, and also detect common failure patterns that could arise and generate alerts as needed. This session is an (Intermediate) talk in our Apache Nifi and Data Science track. It focuses on Apache Flink, Apache Nifi, Apache Kafka and is geared towards Architect, Data Scientist, Data Analyst, Developer / Engineer audiences. Speaker Miguel Perez Colino, Senior Design Product Manager, Red Hat Suneel Marthi, Senior Principal Engineer, Red Hat

Cloud Operations with Streaming Analytics using Apache NiFi and Apache Flink

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LLAP (Live Long and Process) is the newest query acceleration engine for Hive 2.0, which entered GA in 2017. LLAP brings into light a new set of trade-offs and optimizations that allows for efficient and secure multi-user BI systems on the cloud. In this talk, we discuss the specifics of building a modern BI engine within those boundaries, designed to be fast and cost-effective on the public cloud. The focus of the LLAP cache is to speed up common BI query patterns on the cloud, while avoiding most of the operational administration overheads of maintaining a caching layer, with an automatically coherent cache with intelligent eviction and support for custom file formats from text to ORC, and explore the possibilities of combining the cache with a transactional storage layer which supports online UPDATE and DELETES without full data reloads. LLAP by itself, as a relational data layer, extends the same caching and security advantages to any other data processing framework. We overview the structure of such a hybrid system, where both Hive and Spark use LLAP to provide SQL query acceleration on the cloud with new, improved concurrent query support and production-ready tools and UI. Speaker Sergey Shelukin, Member of Technical Staff, Hortonworks

LLAP: Building Cloud First BI

In a world of serverless computing users tend to be frugal when it comes to expenditure on compute, storage and other resources. Paying for the same when they aren’t in use becomes a significant factor. Offering Spark as service on cloud presents very unique challenges. Running Spark on Kubernetes presents a lot of challenges especially around storage and persistence. Spark workloads have very unique requirements of Storage for intermediate data, long time persistence, Share file system and requirements become very tight when it same need to be offered as a service for enterprise to mange GDPR and other compliance like ISO 27001 and HIPAA certifications. This talk covers challenges involved in providing Serverless Spark Clusters share the specific issues one can encounter when running large Kubernetes clusters in production especially covering the scenarios related to persistence. This talk will help people using Kubernetes or docker runtime in production and help them understand various storage options available and which is more suitable for running Spark workloads on Kubernetes and what more can be done

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