This document discusses using Fluentd to collect streaming data from Apache Kafka. It presents two approaches: 1) the fluent-plugin-kafka plugin which allows Fluentd to act as a producer and consumer of Kafka topics, and 2) the kafka-fluentd-consumer project which runs a standalone Kafka consumer that sends events to Fluentd. Configuration examples are provided for both approaches. The document concludes that Fluentd and Kafka can work together to build reliable and flexible data pipelines.
Apache Kafka becoming the message bus to transfer huge volumes of data from various sources into Hadoop.
It's also enabling many real-time system frameworks and use cases.
Managing and building clients around Apache Kafka can be challenging. In this talk, we will go through the best practices in deploying Apache Kafka
in production. How to Secure a Kafka Cluster, How to pick topic-partitions and upgrading to newer versions. Migrating to new Kafka Producer and Consumer API.
Also talk about the best practices involved in running a producer/consumer.
In Kafka 0.9 release, we’ve added SSL wire encryption, SASL/Kerberos for user authentication, and pluggable authorization. Now Kafka allows authentication of users, access control on who can read and write to a Kafka topic. Apache Ranger also uses pluggable authorization mechanism to centralize security for Kafka and other Hadoop ecosystem projects.
We will showcase open sourced Kafka REST API and an Admin UI that will help users in creating topics, re-assign partitions, Issuing
Kafka ACLs and monitoring Consumer offsets.
Kafka Streams is a new stream processing library natively integrated with Kafka. It has a very low barrier to entry, easy operationalization, and a natural DSL for writing stream processing applications. As such it is the most convenient yet scalable option to analyze, transform, or otherwise process data that is backed by Kafka. We will provide the audience with an overview of Kafka Streams including its design and API, typical use cases, code examples, and an outlook of its upcoming roadmap. We will also compare Kafka Streams' light-weight library approach with heavier, framework-based tools such as Spark Streaming or Storm, which require you to understand and operate a whole different infrastructure for processing real-time data in Kafka.
ksqlDB: A Stream-Relational Database Systemconfluent
Speaker: Matthias J. Sax, Software Engineer, Confluent
ksqlDB is a distributed event streaming database system that allows users to express SQL queries over relational tables and event streams. The project was released by Confluent in 2017 and is hosted on Github and developed with an open-source spirit. ksqlDB is built on top of Apache Kafka®, a distributed event streaming platform. In this talk, we discuss ksqlDB’s architecture that is influenced by Apache Kafka and its stream processing library, Kafka Streams. We explain how ksqlDB executes continuous queries while achieving fault tolerance and high vailability. Furthermore, we explore ksqlDB’s streaming SQL dialect and the different types of supported queries.
Matthias J. Sax is a software engineer at Confluent working on ksqlDB. He mainly contributes to Kafka Streams, Apache Kafka's stream processing library, which serves as ksqlDB's execution engine. Furthermore, he helps evolve ksqlDB's "streaming SQL" language. In the past, Matthias also contributed to Apache Flink and Apache Storm and he is an Apache committer and PMC member. Matthias holds a Ph.D. from Humboldt University of Berlin, where he studied distributed data stream processing systems.
https://db.cs.cmu.edu/events/quarantine-db-talk-2020-confluent-ksqldb-a-stream-relational-database-system/
Kafka is an open-source message broker that provides high-throughput and low-latency data processing. It uses a distributed commit log to store messages in categories called topics. Processes that publish messages are producers, while processes that subscribe to topics are consumers. Consumers can belong to consumer groups for parallel processing. Kafka guarantees order and no lost messages. It uses Zookeeper for metadata and coordination.
Apache Kafka 0.8 basic training - VerisignMichael Noll
Apache Kafka 0.8 basic training (120 slides) covering:
1. Introducing Kafka: history, Kafka at LinkedIn, Kafka adoption in the industry, why Kafka
2. Kafka core concepts: topics, partitions, replicas, producers, consumers, brokers
3. Operating Kafka: architecture, hardware specs, deploying, monitoring, P&S tuning
4. Developing Kafka apps: writing to Kafka, reading from Kafka, testing, serialization, compression, example apps
5. Playing with Kafka using Wirbelsturm
Audience: developers, operations, architects
Created by Michael G. Noll, Data Architect, Verisign, https://www.verisigninc.com/
Verisign is a global leader in domain names and internet security.
Tools mentioned:
- Wirbelsturm (https://github.com/miguno/wirbelsturm)
- kafka-storm-starter (https://github.com/miguno/kafka-storm-starter)
Blog post at:
http://www.michael-noll.com/blog/2014/08/18/apache-kafka-training-deck-and-tutorial/
Many thanks to the LinkedIn Engineering team (the creators of Kafka) and the Apache Kafka open source community!
Kafka Streams: What it is, and how to use it?confluent
Kafka Streams is a client library for building distributed applications that process streaming data stored in Apache Kafka. It provides a high-level streams DSL that allows developers to express streaming applications as set of processing steps. Alternatively, developers can use the lower-level processor API to implement custom business logic. Kafka Streams handles tasks like fault-tolerance, scalability and state management. It represents data as streams for unbounded data or tables for bounded state. Common operations include transformations, aggregations, joins and table operations.
Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job?
This session explores the DOs and DONTs. Separate sections explain when to use Kafka, when NOT to use Kafka, and when to MAYBE use Kafka.
No matter if you think about open source Apache Kafka, a cloud service like Confluent Cloud, or another technology using the Kafka protocol like Redpanda or Pulsar, check out this slide deck.
A detailed article about this topic:
https://www.kai-waehner.de/blog/2022/01/04/when-not-to-use-apache-kafka/
Apache Kafka is a fast, scalable, durable and distributed messaging system. It is designed for high throughput systems and can replace traditional message brokers. Kafka has better throughput, partitioning, replication and fault tolerance compared to other messaging systems, making it suitable for large-scale applications. Kafka persists all data to disk for reliability and uses distributed commit logs for durability.
Developing Real-Time Data Pipelines with Apache KafkaJoe Stein
Apache Kafka is a distributed streaming platform that allows for building real-time data pipelines and streaming apps. It provides a publish-subscribe messaging system with persistence that allows for building real-time streaming applications. Producers publish data to topics which are divided into partitions. Consumers subscribe to topics and process the streaming data. The system handles scaling and data distribution to allow for high throughput and fault tolerance.
A deep dive into Amazon MSK - ADB206 - Chicago AWS SummitAmazon Web Services
Apache Kafka is a popular, open-source technology for collecting, processing, and analyzing streaming data in real-time. Amazon MSK is a fully managed service that removes the complexities of managing Kafka clusters so that you can focus on building real-time applications. In this session, we provide an overview of Amazon MSK and then discuss how to get started. We then look at some best practices and top tips, and walk through how to decide whether to choose Amazon MSK, Amazon Kinesis, or a mix of both to address your data streaming use cases.
Speaker: Damien Gasparina, Engineer, Confluent
Here's how to fail at Apache Kafka brilliantly!
https://www.meetup.com/Paris-Data-Engineers/events/260694777/
This document compares the architectures of Kafka and Kinesis. Both have similar architectures, with Kafka brokers storing messages in partitions and consumers subscribing to topics. The document finds that Kafka has higher throughput and lower costs than Kinesis due to Kinesis' throughput limits. It also notes headaches with Kinesis' throughput limits and management overhead. The document recommends switching from Kinesis to Kafka for these reasons.
Apache Kafka is a distributed publish-subscribe messaging system that can handle high volumes of data and enable messages to be passed from one endpoint to another. It uses a distributed commit log that allows messages to be persisted on disk for durability. Kafka is fast, scalable, fault-tolerant, and guarantees zero data loss. It is used by companies like LinkedIn, Twitter, and Netflix to handle high volumes of real-time data and streaming workloads.
Running Kafka On Kubernetes With Strimzi For Real-Time Streaming ApplicationsLightbend
In this talk by Sean Glover, Principal Engineer at Lightbend, we will review how the Strimzi Kafka Operator, a supported technology in Lightbend Platform, makes many operational tasks in Kafka easy, such as the initial deployment and updates of a Kafka and ZooKeeper cluster.
See the blog post containing the YouTube video here: https://www.lightbend.com/blog/running-kafka-on-kubernetes-with-strimzi-for-real-time-streaming-applications
Introducing Apache Kafka - a visual overview. Presented at the Canberra Big Data Meetup 7 February 2019. We build a Kafka "postal service" to explain the main Kafka concepts, and explain how consumers receive different messages depending on whether there's a key or not.
Apache Kafka is a distributed messaging system that allows for publishing and subscribing to streams of records, known as topics, in a fault-tolerant and scalable way. It is used for building real-time data pipelines and streaming apps. Producers write data to topics which are committed to disks across partitions and replicated for fault tolerance. Consumers read data from topics in a decoupled manner based on offsets. Kafka can process streaming data in real-time and at large volumes with low latency and high throughput.
Introducing Apache Kafka and why it is important to Oracle, Java and IT profe...Lucas Jellema
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.
Apache Kafka - Scalable Message-Processing and more !Guido Schmutz
Independent of the source of data, the integration of event streams into an Enterprise Architecture gets more and more important in the world of sensors, social media streams and Internet of Things. Events have to be accepted quickly and reliably, they have to be distributed and analysed, often with many consumers or systems interested in all or part of the events. How can me make sure that all these event are accepted and forwarded in an efficient and reliable way? This is where Apache Kafaka comes into play, a distirbuted, highly-scalable messaging broker, build for exchanging huge amount of messages between a source and a target.
This session will start with an introduction into Apache and presents the role of Apache Kafka in a modern data / information architecture and the advantages it brings to the table. Additionally the Kafka ecosystem will be covered as well as the integration of Kafka in the Oracle Stack, with products such as Golden Gate, Service Bus and Oracle Stream Analytics all being able to act as a Kafka consumer or producer.
fluent-plugin-beats at Elasticsearch meetup #14N Masahiro
This document summarizes a presentation about integrating Fluentd with Elastic Beats data collection agents. It introduces Beats, their supported outputs including Elasticsearch, and various third party Beats. It then describes the fluent-plugin-beats plugin which allows Fluentd to receive events from Beats using the Lumberjack protocol. An example configuration is shown. Performance tests show Fluentd can handle over 100,000 events/sec while Filebeat is slower at 18,000 events/sec. The conclusion is that Beats are useful for collection but Fluentd may be better than Filebeat for high volume environments.
What is Kafka & why is it Important? (UKOUG Tech17, Birmingham, UK - December...Lucas Jellema
Fast data arrives in real time and potentially high volume. Rapid processing, filtering and aggregation is required to ensure timely reaction and actual information in user interfaces. Doing so is a challenge, make this happen in a scalable and reliable fashion is even more interesting. This session introduces Apache Kafka as the scalable event bus that takes care of the events as they flow in and Kafka Streams and KSQL for the streaming analytics. Both Java and Node applications are demonstrated that interact with Kafka and leverage Server Sent Events and WebSocket channels to update the Web UI in real time. User activity performed by the audience in the Web UI is processed by the Kafka powered back end and results in live updates on all clients.
This presentation includes a demonstration of remote database synchronization through Twitter.
Over 100 million subscribers from over 190 countries enjoy the Netflix service. This leads to over a trillion events, amounting to 3 PB, flowing through the Keystone infrastructure to help improve customer experience and glean business insights. The self-serve Keystone stream processing service processes these messages in near real-time with at-least once semantics in the cloud. This enables the users to focus on extracting insights, and not worry about building out scalable infrastructure. I’ll share the details about this platform, and our experience building it.
Apache Kafka is a distributed streaming platform. It provides a high-throughput distributed messaging system that can handle trillions of events daily. Many large companies use Kafka for application logging, metrics collection, and powering real-time analytics. The current version is 0.8.2 and upcoming versions will include a new consumer, security features, and support for transactions.
Apache Pulsar: Why Unified Messaging and Streaming Is the Future - Pulsar Sum...StreamNative
Data insights and data-driven strategies create the competitive differentiators companies thrive off today. The need for unified messaging and streaming has never been more apparent.
Pulsar started with the goal of building a global, geo-replicated infrastructure to serve Yahoo!’s messaging needs. With the increased need to process both business events (such as payment request, billing request) and operational events (such as log data, click events, etc), the team at Yahoo! set out to build a true unified infrastructure platform to handle all in-motion data. That technology became Apache Pulsar.
In this talk, Matteo Merli and Sijie Guo will dive into the landscape of unified messaging and streaming, how Pulsar helps companies achieve this vision, and what the future of Pulsar will look like.
Fluentd is a data collector for unified logging that provides a robust core and plugins. It allows for reliable data transfer through error handling and retries. The core handles common concerns like parsing, buffering, and writing data, while plugins handle input, output, and other use cases. Fluentd has a pluggable architecture and processes data through a pipeline of input, parser, filter, buffer, formatter, and output plugins.
Technologies for Data Analytics PlatformN Masahiro
This document discusses building a data analytics platform and summarizes various technologies that can be used. It begins by outlining reasons for analyzing data like reporting, monitoring, and exploratory analysis. It then discusses using relational databases, parallel databases, Hadoop, and columnar storage to store and process large volumes of data. Streaming technologies like Storm, Kafka, and services like Redshift, BigQuery, and Treasure Data are also summarized as options for a complete analytics platform.
Apache Kafka - Scalable Message Processing and more!Guido Schmutz
After a quick overview and introduction of Apache Kafka, this session cover two components which extend the core of Apache Kafka: Kafka Connect and Kafka Streams/KSQL.
Kafka Connects role is to access data from the out-side-world and make it available inside Kafka by publishing it into a Kafka topic. On the other hand, Kafka Connect is also responsible to transport information from inside Kafka to the outside world, which could be a database or a file system. There are many existing connectors for different source and target systems available out-of-the-box, either provided by the community or by Confluent or other vendors. You simply configure these connectors and off you go.
Kafka Streams is a light-weight component which extends Kafka with stream processing functionality. By that, Kafka can now not only reliably and scalable transport events and messages through the Kafka broker but also analyse and process these event in real-time. Interestingly Kafka Streams does not provide its own cluster infrastructure and it is also not meant to run on a Kafka cluster. The idea is to run Kafka Streams where it makes sense, which can be inside a “normal” Java application, inside a Web container or on a more modern containerized (cloud) infrastructure, such as Mesos, Kubernetes or Docker. Kafka Streams has a lot of interesting features, such as reliable state handling, queryable state and much more. KSQL is a streaming engine for Apache Kafka, providing a simple and completely interactive SQL interface for processing data in Kafka.
This document provides an introduction to Apache Kafka, an open-source distributed event streaming platform. It discusses Kafka's history as a project originally developed by LinkedIn, its use cases like messaging, activity tracking and stream processing. It describes key Kafka concepts like topics, partitions, offsets, replicas, brokers and producers/consumers. It also gives examples of how companies like Netflix, Uber and LinkedIn use Kafka in their applications and provides a comparison to Apache Spark.
Automation + dev ops summit hail hydrate! from stream to lakeTimothy Spann
Automation + dev ops summit hail hydrate! from stream to lake
2021
Apache Pulsar, APache NiFi, Apache Flink
StreamNative
https://sessionize.com/app/speaker/session/265189
Tim Spann, Developer Advocate
Building Scalable Big Data Infrastructure Using Open Source Software Presenta...ssuserd3a367
1) StumbleUpon uses open source tools like Kafka, HBase, Hive and Pig to build a scalable big data infrastructure to process large amounts of data from its services in real-time and batch.
2) Data is collected from various services using Kafka and stored in HBase for real-time analytics. Batch processing is done using Pig and data is loaded into Hive for ad-hoc querying.
3) The infrastructure powers various applications like recommendations, ads and business intelligence dashboards.
Hail hydrate! from stream to lake using open sourceTimothy Spann
(VIRTUAL) Hail Hydrate! From Stream to Lake Using Open Source - Timothy J Spann, StreamNative
https://osselc21.sched.com/event/lAPi?iframe=no
A cloud data lake that is empty is not useful to anyone. How can you quickly, scalably and reliably fill your cloud data lake with diverse sources of data you already have and new ones you never imagined you needed. Utilizing open source tools from Apache, the FLiP stack enables any data engineer, programmer or analyst to build reusable modules with low or no code. FLiP utilizes Apache NiFi, Apache Pulsar, Apache Flink and MiNiFi agents to load CDC, Logs, REST, XML, Images, PDFs, Documents, Text, semistructured data, unstructured data, structured data and a hundred data sources you could never dream of streaming before. I will teach you how to fish in the deep end of the lake and return a data engineering hero. Let's hope everyone is ready to go from 0 to Petabyte hero.
https://osselc21.sched.com/event/lAPi/virtual-hail-hydrate-from-stream-to-lake-using-open-source-timothy-j-spann-streamnative
This document provides a summary of the AWS re:Invent 2020 virtual conference and the 145 announcements that were made. It highlights some of the major announcements including new capabilities for Edge, Networking, Compute, Storage, Databases, Analytics, AI, Customer Engagement, and Management & Security services. The next monthly AWS User Group meeting will focus on Security topics such as ransomware response and modernizing controls.
Kafka's growth is exploding, with more than 1/3 of Fortune 500 companies using it. Kafka is a fast, scalable, durable messaging system that is often used for real-time streaming of big data, such as tracking service calls or IoT sensors. It can feed data to systems like Hadoop, Spark, Storm and Flink for real-time analytics and processing. Major companies like LinkedIn, Twitter, Square, Spotify and Netflix rely on Kafka to handle high volumes of data streams. The key reasons for Kafka's popularity are its great performance, which it achieves through techniques like zero-copy, batching, and sequential writes to disk.
Self-Service Data Ingestion Using NiFi, StreamSets & KafkaGuido Schmutz
Many of the Big Data and IoT use cases are based on combining data from multiple data sources and to make them available on a Big Data platform for analysis. The data sources are often very heterogeneous, from simple files, databases to high-volume event streams from sensors (IoT devices). It’s important to retrieve this data in a secure and reliable manner and integrate it with the Big Data platform so that it is available for analysis in real-time (stream processing) as well as in batch (typical big data processing). In past some new tools have emerged, which are especially capable of handling the process of integrating data from outside, often called Data Ingestion. From an outside perspective, they are very similar to a traditional Enterprise Service Bus infrastructures, which in larger organization are often in use to handle message-driven and service-oriented systems. But there are also important differences, they are typically easier to scale in a horizontal fashion, offer a more distributed setup, are capable of handling high-volumes of data/messages, provide a very detailed monitoring on message level and integrate very well with the Hadoop ecosystem. This session will present and compare Apache Flume, Apache NiFi, StreamSets and the Kafka Ecosystem and show how they handle the data ingestion in a Big Data solution architecture.
DATA SUMMIT 24 Building Real-Time Pipelines With FLaNKTimothy Spann
Building Real-Time Pipelines With FLaNK
Timothy Spann, Principal Developer Advocate, Streaming - Cloudera Future of Data meetup, startup grind, AI Camp
The combination of Apache Flink, Apache NiFi, and Apache Kafka for building real-time data processing pipelines is extremely powerful, as demonstrated by this case study using the FLaNK-MTA project. The project leverages these technologies to process and analyze real-time data from the New York City Metropolitan Transportation Authority (MTA). FLaNK-MTA demonstrates how to efficiently collect, transform, and analyze high-volume data streams, enabling timely insights and decision-making.
Apache NiFi
Apache Kafka
Apache Flink
Apache Iceberg
LLM
Generative AI
Slack
Postgresql
Building a company-wide data pipeline on Apache Kafka - engineering for 150 b...LINE Corporation
Yuto Kawamura
Building a company-wide data pipeline on Apache Kafka - engineering for 150 billion messages per day
Summary:
LINE is a messaging service with 200 million monthly active users. Our service architecture evolves daily with various collaborating components. I'll introduce overview of LINE's messaging service architecture,
mainly focusing on our company-wide data pipeline infrastructure built upon Apache Kafka which accepts more than 150 billion messages every day, making it one of the largest in the world. In this talk I will introduce
how we managed such scale keeping it highly reliable to be capable of being an infrastructure to build services.
Fluentd Project Intro at Kubecon 2019 EUN Masahiro
Fluentd is a streaming data collector that can unify logging and metrics collection. It collects data from sources using input plugins, processes and filters the data, and outputs it to destinations using output plugins. It is commonly used for container logging, collecting logs from files or Docker and adding metadata before outputting to Elasticsearch or other targets. Fluentbit is a lightweight version of Fluentd that is better suited for edge collection and forwarding logs to a Fluentd instance for aggregation.
Fluentd v1 provides major improvements over v0.12 including nanosecond event time resolution, multi-core support, Windows support, and new plugin APIs. The new plugin APIs provide well-controlled lifecycles and integrate all output plugins. v1 also introduces a server engine based supervisor, dynamic buffering capabilities, and various plugin helpers. While maintaining compatibility with v0.12 plugins, v1 focuses on ease of use, stability, performance and flexibility.
Fluentd and Distributed Logging at KubeconN Masahiro
This document discusses distributed logging with containers using Fluentd. It notes the challenges of logging in container environments where logs need to be collected from ephemeral containers and transferred to storage. It introduces Fluentd as a flexible data collection tool that can collect logs from containers using various plugins and methods like log drivers, shared volumes, and application libraries. The document discusses deployment patterns for Fluentd including using it for source-side aggregation to buffer and transfer logs more efficiently and for destination-side aggregation to scale log storage.
This document summarizes Fluentd v1.0 and provides details about its new features and release plan. It notes that Fluentd v1.0 will provide stable APIs and compatibility with previous versions while improving plugin APIs, adding Windows and multicore support, and increasing event time resolution to nanoseconds. The release is planned for Q3 2017 to allow feedback on v0.14 before finalizing v1.0 features.
This document summarizes the key features and changes between versions of Fluentd, an open source data collector.
The main points are:
1) Fluentd v1.0 will provide stable APIs and features while remaining compatible with v0.12 and v0.14. It will have no breaking API changes.
2) New features in v0.14 and v1.0 include nanosecond time resolution, multi-core processing, Windows support, improved buffering and plugins, and more.
3) The goals for v1.0 include migrating more plugins to the new APIs, addressing issues, and improving documentation. A release is planned for Q2 2017.
This document summarizes recent updates to Presto, including new data types, connectors, syntax, features, functions, and configuration options. Some key additions are support for DECIMAL, VARCHAR, and new data types; connectors for Redis, MongoDB, and other data sources; transaction support; and a variety of new SQL functions for strings, dates, aggregation, and more. Upcoming work includes prepared statements, a new optimizer, and other performance and usability improvements.
The document summarizes the key features and changes in Fluentd v0.14, including new plugin APIs, plugin storage and helpers, time with nanosecond resolution, a ServerEngine-based supervisor for Windows support, and plans for symmetric multi-core processing, a counter API, and TLS/authentication in future versions. It also benchmarks some performance improvements and outlines the roadmap for Treasure Agent 3.0 based on Fluentd v0.14.
- The document discusses logging for containers using Fluentd, an open source data collector. It describes how Fluentd can provide a unified logging layer, reliably forwarding and aggregating logs from multiple containers and applications in a pluggable way.
- Key points covered include using Fluentd with the new Docker logging drivers to directly collect logs from containers, avoiding performance penalties from other approaches. A demo of Fluentd is also mentioned.
How to create Treasure Data #dotsbigdataN Masahiro
This document provides an overview of Treasure Data's big data analytics platform. It discusses how Treasure Data ingests and processes large amounts of schema-less data from various sources in real-time and at scale. It also describes how Treasure Data stores and indexes the data for fast querying using SQL interfaces while maintaining schema flexibility.
This document provides an overview of Fluentd, an open source data collector. It discusses the key features of Fluentd including structured logging, reliable forwarding, and a pluggable architecture. The document then summarizes the architectures and new features of different Fluentd versions, including v0.10, v0.12, and the upcoming v0.14 and v1 releases. It also discusses Fluentd's ecosystem and plugins as well as Treasure Data's use of Fluentd in its log data collection and analytics platform.
This document summarizes Masahiro Nakagawa's presentation on Fluentd and Embulk. Fluentd is a data collector for unified logging that allows for streaming data transfer based on JSON. It is written in Ruby and uses plugins to collect, process, and output data. Embulk is a bulk loading tool that allows high performance parallel processing of data to load it into various databases and storage systems. Both tools use a pluggable architecture to provide flexibility in handling different data sources and targets.
Treasure Data and AWS - Developers.io 2015N Masahiro
This document discusses Treasure Data's data architecture. It describes how Treasure Data collects and imports log data using Fluentd. The data is stored in columnar format in S3 and metadata is stored in PostgreSQL. Treasure Data uses Presto to enable fast analytics on the large datasets. The document provides details on the import process, storage, partitioning, and optimizations to improve query performance.
Fluentd Unified Logging Layer At FossasiaN Masahiro
Masahiro Nakagawa is a senior software engineer at Treasure Data and the main maintainer of Fluentd. Fluentd is a data collector for unified logging that provides a streaming data transfer based on JSON. It has a simple core with plugins written in Ruby to provide functionality like input/output, buffering, parsing, filtering and formatting of data.
- Treasure Data is a cloud data service that provides data acquisition, storage, and analysis capabilities.
- It collects data from various sources using Fluentd and Embulk and stores it in its own columnar database called Plazma DB.
- It offers various computing frameworks like Hive, Pig, and Presto for analytics and visualization with tools like Tableau.
- Presto is an interactive SQL query engine that can query data in HDFS, Hive, Cassandra and other data stores.
Masahiro Nakagawa introduced Fluentd, an open source data collector. Fluentd provides a unified logging layer and collects data through a streaming data transfer based on JSON. It is written in Ruby and uses a plugin architecture to allow for various input and output functions. Fluentd is used in production environments for log aggregation, metrics collection, and data processing tasks.
This document summarizes Masahiro Nakagawa's presentation on Fluentd at the Data Transfer Middleware Meetup #1. It discusses Fluentd's history and architecture, including the core plugins in v0.10 and new features in v0.12 like filtering and labeling. The roadmap is outlined, with v0.14 adding new plugin APIs and v1 focusing on stability. Other projects like Treasure Agent and fluentd-forwarder that comprise the Fluentd ecosystem are also briefly mentioned.
Fluentd: Unified Logging Layer at CWT2014N Masahiro
The document summarizes Masahiro Nakagawa's presentation on Fluentd at the Cloudera World Tokyo conference. Fluentd is an open source log collector written in Ruby that uses a pluggable architecture and JSON format for log messages. It provides unified logging and data processing capabilities. The presentation covered Fluentd's core functionality, related products from Treasure Data, use cases, and the company's roadmap.
The document discusses Presto, an open source distributed SQL query engine for interactive analysis of large datasets. It provides summaries of Presto's capabilities, architecture, and how it addresses issues with other SQL engines on Hadoop like Hive being too slow. Key points include that Presto allows direct querying of data in HDFS without needing to copy it elsewhere, uses a distributed query execution model rather than MapReduce, and supports many connectors and a PostgreSQL gateway.
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
Finetuning GenAI For Hacking and DefendingPriyanka Aash
Generative AI, particularly through the lens of large language models (LLMs), represents a transformative leap in artificial intelligence. With advancements that have fundamentally altered our approach to AI, understanding and leveraging these technologies is crucial for innovators and practitioners alike. This comprehensive exploration delves into the intricacies of GenAI, from its foundational principles and historical evolution to its practical applications in security and beyond.
Discovery Series - Zero to Hero - Task Mining Session 1DianaGray10
This session is focused on providing you with an introduction to task mining. We will go over different types of task mining and provide you with a real-world demo on each type of task mining in detail.
Top 12 AI Technology Trends For 2024.pdfMarrie Morris
Technology has become an irreplaceable component of our daily lives. The role of AI in technology revolutionizes our lives for the betterment of the future. In this article, we will learn about the top 12 AI technology trends for 2024.
"Making .NET Application Even Faster", Sergey Teplyakov.pptxFwdays
In this talk we're going to explore performance improvement lifecycle, starting with setting the performance goals, using profilers to figure out the bottle necks, making a fix and validating that the fix works by benchmarking it. The talk will be useful for novice and seasoned .NET developers and architects interested in making their application fast and understanding how things work under the hood.
This PDF delves into the aspects of information security from a forensic perspective, focusing on privacy leaks. It provides insights into the methods and tools used in forensic investigations to uncover and mitigate privacy breaches in mobile and cloud environments.
The Zaitechno Handheld Raman Spectrometer is a powerful and portable tool for rapid, non-destructive chemical analysis. It utilizes Raman spectroscopy, a technique that analyzes the vibrational fingerprint of molecules to identify their chemical composition. This handheld instrument allows for on-site analysis of materials, making it ideal for a variety of applications, including:
Material identification: Identify unknown materials, minerals, and contaminants.
Quality control: Ensure the quality and consistency of raw materials and finished products.
Pharmaceutical analysis: Verify the identity and purity of pharmaceutical compounds.
Food safety testing: Detect contaminants and adulterants in food products.
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2. Who are you?
• Masahiro Nakagawa
• github: @repeatedly
• Treasure Data Inc.
• Fluentd / td-agent developer
• Fluentd Enterprise support
• I love OSS :)
• D Language, MessagePack, The organizer of several meetups, etc…
3. Fluentd
• Pluggable streaming event collector
• Lightweight, robust and flexible
• Lots of plugins on rubygems
• Used by AWS, GCP, MS and more companies
• Resources
• http://www.fluentd.org/
• Webinar: https://www.youtube.com/watch?v=6uPB_M7cbYk
6. Push vs Pull
• Push:
• Easy to transfer data to multiple destinations
• Hard to control stream ratio in multiple streams
• Pull:
• Easy to control stream flow / ratio
• Should manage consumers correctly
7. There are 2 ways
• fluent-plugin-kafka
• kafka-fluentd-consumer
8. fluent-plugin-kafka
• Input / Output plugin for kafka
• https://github.com/htgc/fluent-plugin-kafka
• in_kafka, in_kafka_group, out_kafka, out_kafka_buffered
• Pros
• Easy to use and output support
• Cons
• Performance is not primary
9. Configuration example
<source>
@type kafka
topics web,system
format json
add_prefix kafka.
# more options
</source>
<match kafka.**>
@type kafka_buffered
output_data_type msgpack
default_topic metrics
compression_codec gzip
required_acks 1
</match>
https://github.com/htgc/fluent-plugin-kafka#usage
10. kafka fluentd consumer
• Stand-alone kafka consumer for fluentd
• https://github.com/treasure-data/kafka-fluentd-consumer
• Send cosumed events to fluentd’s in_forward
• Pros
• High performance and Java API features
• Cons
• Need Java runtime
11. Run consumer
• Edit log4j and fluentd-consumer properties
• Run following command:
$ java
-Dlog4j.configuration=file:///path/to/log4j.properties
-jar path/to/kafka-fluentd-consumer-0.2.1-all.jar
path/to/fluentd-consumer.properties
12. Properties example
fluentd.tag.prefix=kafka.event.
fluentd.record.format=regexp # default is json
fluentd.record.pattern=(?<text>.*) # for regexp format
fluentd.consumer.topics=app.* # can use Java Rege
fluentd.consumer.topics.pattern=blacklist # default is whitelist
fluentd.consumer.threads=5
https://github.com/treasure-data/kafka-fluentd-consumer/blob/master/config/fluentd-consumer.properties
13. With Fluentd example
<source>
@type forward
</source>
<source>
@type exec
command java -
Dlog4j.configuration=file:///path/to/
log4j.properties -jar /path/to/kafka-
fluentd-consumer-0.2.1-all.jar /path/
to/config/fluentd-
consumer.properties
tag dummy
format json
</source>
https://github.com/treasure-data/kafka-fluentd-consumer#run-kafka-consumer-for-fluentd-via-in_exec
14. Conclusion
• Kafka is now becomes important component
on data platform
• Fluentd can communicate with Kafka
• Fluentd plugin and kafka consumer
• Building reliable and flexible data pipeline with
Fluentd and Kafka