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

Koji Kawamura

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

Nifi

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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.

Building Data Pipelines for Solr with Apache NiFi

Bryan Bende

Extending the Yahoo Streaming Benchmark + MapR Benchmarks

Jamie Grier

Introduction to data flow management using apache nifi

Anshuman Ghosh

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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Running Apache NiFi with Apache Spark : Integration Options

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

Storage Requirements and Options for Running Spark on Kubernetes

Tendances (20)

Migrating pipelines into Docker

What will be new in Apache NiFi 1.2.0

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Real Time Monitoring requires a high scalable infrastructure of message bus, database, distributed event processing and scalable analytics engine. By bringing together leading open source projects of Apache Kafka, Apache HBase, Apache Storm and Apache Hive, the Hortonworks Data Platform offers a comprehensive Real Time Analysis platform. In this session, we will provide an in-depth overview all the key technology components and demonstrate a working solution for monitoring a fleet of trucks. Audience: Developers, Architects and System Engineers from the Hortonworks Technology Partner community. Recording: https://hortonworks.webex.com/hortonworks/lsr.php?RCID=0278dc8aa49a9991e1ce436c71f53d30

Hortonworks Technical Workshop: Real Time Monitoring with Apache Hadoop

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Using Apache NiFi with Apache Pulsar for Fast Data On-Ramp https://pulsar-summit.org/event/europe-2023/schedule https://pulsar-summit.org/event/europe-2023/sessions/europe-2023-using-apache-nifi-with-apache-pulsar-for-fast-data-on-ramp 12:30 PM - 1:00 PM, CEST , May 23 Using Apache Nifi with Apache Pulsar for Fast Data On-Ramp As the Pulsar communities grows, more and more connectors will be added. To enhance the availability of sources and sinks and to make use of the greater Apache Streaming community, joining forces between Apache NiFi and Apache Pulsar is a perfect fit. Apache NiFi also adds the benefits of ELT, ETL, data crunching, transformation, validation and batch data processing. Once data is ready to be an event, NiFi can launch it into Pulsar at light speed. Timothy Spann Principal Developer Advocate for Data in Motion @ Cloudera

Using Apache NiFi with Apache Pulsar for Fast Data On-Ramp

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Using the low-latency Apache Pulsar we can build up millions of streams of concurrent data and join them in real time with Apache Flink. We need an ultra-low latency database that can support these workloads to build next-generation IoT, financial and instant analytical transit applications By sinking data into ScyllaDB we enable amazingly fast applications that can grow to any size and join with existing data sources. The next generation of apps is being built now, you must choose the right low-latency scalable platform for these massively data-intensive applications. ScyllaDB + Pulsar + Flink is that platform. Choose Open, Choose Fast, and Make the right choice.

Sink Your Teeth into Streaming at Any Scale

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Tesla ingests trillions of events every day from hundreds of unique data sources through our streaming data platform. Find out how we developed a set of high-throughput, non-blocking primitives that allow us to transform and ingest data into a variety of data stores with minimal development time. Additionally, we will discuss how these primitives allowed us to completely migrate the streaming platform in just a few months. Finally, we will talk about how we scale team size sub-linearly to data volumes, while continuing to onboard new use cases.

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confluent

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gagravarr

OSSNA Building Modern Data Streaming Apps https://ossna2023.sched.com/event/1Jt05/virtual-building-modern-data-streaming-apps-with-open-source-timothy-spann-streamnative Timothy Spann Cloudera Principal Developer Advocate Data in Motion In my session, I will show you some best practices I have discovered over the last seven years in building data streaming applications, including IoT, CDC, Logs, and more. In my modern approach, we utilize several open-source frameworks to maximize all the best features. We often start with Apache NiFi as the orchestrator of streams flowing into Apache Pulsar. From there, we build streaming ETL with Apache Spark and enhance events with Pulsar Functions for ML and enrichment. We make continuous queries against our topics with Flink SQL. We will stream data into various open-source data stores, including Apache Iceberg, Apache Pinot, and others. We use the best streaming tools for the current applications with the open source stack - FLiPN. https://www.flipn.app/ Updates: This will be in-person with live coding based on feedback from the crowd. This will also include new data stores, new sources, and data relevant to and from the Vancouver area. This will also include updates to the platforms and inclusion of Apache Iceberg, Apache Pinot and some other new tech. https://github.com/tspannhw/SpeakerProfile Tim Spann is a Principal Developer Advocate for Cloudera. He works with Apache Kafka, 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, messaging, streaming technologies, and Java programming. Previously, he was a Principal DataFlow Field Engineer at Cloudera, a Senior Solutions Engineer at Hortonworks, 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, Cloud, IoT, deep learning, streaming, NiFi, the blockchain, and Spark. Tim is a frequent speaker at conferences such as ApacheCon, DeveloperWeek, Pulsar Summit and many more. He holds a BS and MS in computer science. Timothy J Spann Cloudera Principal Developer Advocate Hightstown, NJ Websitehttps://datainmotion.dev/

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In this talk we will walk through how Apache Kafka and Apache Accumulo can be used together to orchestrate a de-coupled, real-time distributed and reactive request/response system at massive scale. Multiple data pipelines can perform complex operations for each message in parallel at high volumes with low latencies. The final result will be inline with the initiating call. The architecture gains are immense. They allow for the requesting system to receive a response without the need for direct integration with the data pipeline(s) that messages must go through. By utilizing Apache Kafka and Apache Accumulo, these gains sustain at scale and allow for complex operations of different messages to be applied to each response in real-time.

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Talk Abstract In this talk we will walk through how Apache Kafka and Apache Accumulo can be used together to orchestrate a de-coupled, real-time distributed and reactive request/response system at massive scale. Multiple data pipelines can perform complex operations for each message in parallel at high volumes with low latencies. The final result will be inline with the initiating call. The architecture gains are immense. They allow for the requesting system to receive a response without the need for direct integration with the data pipeline(s) that messages must go through. By utilizing Apache Kafka and Apache Accumulo, these gains sustain at scale and allow for complex operations of different messages to be applied to each response in real-time. Speaker Joe Stein Principal Consultant, Big Data Open Source Security, LLC Joe Stein is an Apache Kafka committer and PMC member. Joe is the Founder and Principal Architect of Big Data Open Source Security LLC a professional services and product solutions company. Joe has been a developer, architect and technologist professionally for 15 years now having built back end systems that supported over one hundred million unique devices a day processing trillions of events. He blogs and hosts a podcast about Hadoop and related systems at All Things Hadoop and tweets @allthingshadoop

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Apache Storm

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Events are playing an increasingly important role in modern application architecture. They represent fast, streaming data, they fuel the interaction between microservices, they are at the core of CQRS and event sourcing. Apache Kafka has quickly emerged as the de facto standard event platform: open source, cross technology, reliable and extremely scalable and available on any platform, in Docker and from the major cloud platforms- including Oracle Cloud’s Event Hub service. This session explains the what, why and how of Apache Kafka. What role does it play, how is it used and what are challenges and tricks for real life applications. How does it fit in with Oracle Database and Fusion Middleware and with Oracle Public Cloud? In several demos, Kafka is seen at work - in real time streaming event analysis through KSQL, in CQRS and microservices scenarios and with user interfaces updated in real time through events and HTML5 server sent events. This presentation includes a demonstration of remote database synchronization through Twitter.

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The driving question behind redesigns of countless data collection architectures has often been, ?how can we make the data available to our analytical systems faster?? Increasingly, the go-to solution for this data collection problem is Apache Flume. In this talk, architectures and techniques for designing a low-latency Flume-based data collection and delivery system to enable Hadoop-based analytics are explored. Techniques for getting the data into Flume, getting the data onto HDFS and HBase, and making the data available as quickly as possible are discussed. Best practices for scaling up collection, addressing de-duplication, and utilizing a combination streaming/batch model are described in the context of Flume and Hadoop ecosystem components.

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ADR, or Architecture Decision Record, is a valuable tool in software development for several reasons. It provides a centralized location for documenting and tracking architectural decisions, aiding both current and future team members. ADRs enhance communication among team members by documenting the rationale behind architectural decisions, especially beneficial during onboarding of new team members or when revisiting decisions. They serve as a knowledge base, enabling teams to learn from past decisions and refine their decision-making process. Additionally, ADRs contribute to transparency by helping stakeholders understand the reasons behind specific architectural choices. As with any other tool or process, introducing them into an organization can face several obstacles, and overcoming these challenges is crucial for successful implementation. In this talk I go through some common problems and our way of solving them.

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This presentation covers the following topics: What is logging? The purpose of logging: Debugging The purpose of logging: Security The purpose of logging: Stats & analytics Traditional logging Traditional logging: Advantages Traditional logging: Disadvantages The solution: Large-scale logging Large-scale logging: Core principles Large-scale logging: Solution types Large-scale logging: Cloud vs on-prem Large-scale logging: Operational complexity Large-scale logging: Security Large-scale logging: Costs Large-scale logging: On-prem comparison - Elasticsearch - Grafana Loki - VictoriaLogs On-prem comparison: Setup and operation On-prem comparison: Costs On-prem comparison: Full-text search support On-prem comparison: How to efficiently query 100TB of logs? On-prem comparison: Integration with CLI tools VictoriaLogs for large-scale logging VictoriaLogs demo instance - Ingestion rate: 3600 messages / minute - The number of log messages: 1.1 billion - Uncompressed log messages’ size: 1.5TB - Compressed log messages’ size: 23GB - Compression ratio: 47x - Memory usage: 150MB VictoriaLogs CLI integration demo - Which errors have occurred in all the apps during the last hour? - How many errors have occurred during the last hour? - Which apps generated the most of errors during the last hour? - The number of per-minute errors for the last 10 minutes - Status codes for the last hour - Non-200 status codes for the last week - Top client IPs for the last 4 weeks with 404 and 500 response status codes - Per-month stats for the given IP across all the logs Large-scale logging solution MUST provide excellent CLI integration VictoriaLogs: (temporary) drawbacks VictoriaLogs: Recap - Easy to setup and operate - The lowest RAM usage and disk space usage (up to 30x less than Elasticsearch and Grafana Loki) - Fast full-text search - Excellent integration with traditional command-line tools for log analysis - Accepts logs from popular log shippers (Filebeat, Fluentbit, Logstash, Vector, Promtail, Grafana Agent) - Open source and free to use!

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At the recent Microsoft Ignite 2023 conference, Microsoft unveiled a groundbreaking strategy that will redefine enterprise work management. The plan involves integrating Microsoft’s key planning tools, Microsoft To Do, Microsoft Planner, and Microsoft Project for the web into a unified experience called “Microsoft Planner.” What does this new strategy from Microsoft mean for current users? Join us and learn how best to take advantage of this announcement while gaining a clear path on how to elevate the current state of Microsoft Planner from a basic task manager to a comprehensive tool for Enterprise Work Management using OnePlan. Learn how OnePlan’s integration with Microsoft Planner allows for strategic alignment with business goals through advanced features like strategic planning, portfolio management, resource management, financial management, and more!

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