Cloudera Engineering Blog · Flume Posts
Learn about the near real-time data ingest architecture for transforming and enriching data streams using Apache Flume, Apache Kafka, and RocksDB at Santander UK.
Cloudera Professional Services has been working with Santander UK to build a near real-time (NRT) transactional analytics system on Apache Hadoop. The objective is to capture, transform, enrich, count, and store a transaction within a few seconds of a card purchase taking place. The system receives the bank’s retail customer card transactions and calculates the associated trend information aggregated by account holder and over a number of dimensions and taxonomies. This information is then served securely to Santander’s “Spendlytics” app (see below) to enable customers to analyze their latest spending patterns.
To design effective fraud-detection architecture, look no further than the human brain (with some help from Spark Streaming and Apache Kafka).
At its core, fraud detection is about detection whether people are behaving “as they should,” otherwise known as catching anomalies in a stream of events. This goal is reflected in diverse applications such as detecting credit-card fraud, flagging patients who are doctor shopping to obtain a supply of prescription drugs, or identifying bullies in online gaming communities.
Evaluating which streaming architectural pattern is the best match to your use case is a precondition for a successful production deployment.
The Apache Hadoop ecosystem has become a preferred platform for enterprises seeking to process and understand large-scale data in real time. Technologies like Apache Kafka, Apache Flume, Apache Spark, Apache Storm, and Apache Samza are increasingly pushing the envelope on what is possible. It is often tempting to bucket large-scale streaming use cases together but in reality they tend to break down into a few different architectural patterns, with different components of the ecosystem better suited for different problems.
Thanks to Sam Shuster, Software Engineer at Edmunds.com, for the guest post below about his company’s use case for Spark Streaming, SparkOnHBase, and Morphlines.
Every year, the Super Bowl brings parties, food and hopefully a great game to appease everyone’s football appetites until the fall. With any event that brings in around 114 million viewers with larger numbers each year, Americans have also grown accustomed to commercials with production budgets on par with television shows and with entertainment value that tries to rival even the game itself.
The new integration between Flume and Kafka offers sub-second-latency event processing without the need for dedicated infrastructure.
In this previous post you learned some Apache Kafka basics and explored a scenario for using Kafka in an online application. This post takes you a step further and highlights the integration of Kafka with Apache Hadoop, demonstrating both a basic ingestion capability as well as how different open-source components can be easily combined to create a near-real time stream processing workflow using Kafka, Apache Flume, and Hadoop.
The Case for Flafka
When used in the right way and for the right use case, Kafka has unique attributes that make it a highly attractive option for data integration.
Congratulations to Hari Shreedharan, Cloudera software engineer and Apache Flume committer/PMC member, for the early release of his new O’Reilly Media book, Using Flume: Stream Data into HDFS and HBase. It’s the seventh Hadoop ecosystem book so far that was authored by a current or former Cloudera employee (but who’s counting?).
Why did you decide to write this book?
Why would any company be interested in searching through its vast trove of email? A better question is: Why wouldn’t everybody be interested?
Email has become the most widespread method of communication we have, so there is much value to be extracted by making all emails searchable and readily available for further analysis. Some common use cases that involve email analysis are fraud detection, customer sentiment and churn, lawsuit prevention, and that’s just the tip of the iceberg. Each and every company can extract tremendous value based on its own business needs.
Hue 2.2 , the open source web-based interface that makes Apache Hadoop easier to use, lets you interact with Hadoop services from within your browser without having to go to a command-line interface. It features different applications like an Apache Hive editor and Apache Oozie dashboard and workflow builder.
This post is based on our “Analyzing Twitter Data with Hadoop” sample app and details how the same results can be achieved through Hue in a simpler way. Moreover, all the code and examples of the previous series have been updated to the recent CDH4.2 release.
In this installment of “Meet the Engineer”, get to know Customer Operations Engineering Manager/Apache Sqoop committer Kathleen Ting (@kate_ting).
What do you do at Cloudera, and in what open-source projects are you involved?
I’m a support manager at Cloudera, and an Apache Sqoop committer and PMC member. I also contribute to the Apache Flume and Apache ZooKeeper mailing lists and organize and present at meetups, as well as speak at conferences, about those projects.
The post below was originally published via blogs.apache.org and is republished below for your reading pleasure.
This is Part 1 in a series of articles about tuning the performance of Apache Flume, a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of event data.
For several good reasons, 2013 is a Happy New Year for Apache Hadoop enthusiasts.
In 2012, we saw continued progress on developing the next generation of the MapReduce processing framework (MRv2), work that will bear fruit this year. HDFS experienced major progress toward becoming a lights-out, fully enterprise-ready distributed filesystem with the addition of high availability features and increased performance. And a hint of the future of the Hadoop platform was provided with the Beta release of Cloudera Impala, a real-time query engine for analytics across HDFS and Apache HBase data.
The following post was originally published via blog.apache.org; we are re-publishing it here.
Apache Flume was conceived as a fault-tolerant ingest system for the Apache Hadoop ecosystem. Flume comes packaged with an HDFS Sink which can be used to write events into HDFS, and two different implementations of HBase sinks to write events into Apache HBase. You can read about the basic architecture of Apache Flume 1.x in this blog post. You can also read about how Flume’s File Channel persists events and still provides extremely high performance in an earlier blog post. In this article, we will explore how to configure Flume to write events into HBase, and write custom serializers to write events into HBase in a format of the user’s choice.
This is the second article in a series about analyzing Twitter data using some of the components of the Hadoop ecosystem available in CDH, Cloudera’s open-source distribution of Apache Hadoop and related projects. In the first article, you learned how to pull CDH components together into a single cohesive application, but to really appreciate the flexibility of each of these components, we need to dive deeper.
Every story has a beginning, and every data pipeline has a source. So, to build Hadoop applications, we need to get data from a source into HDFS.
The post below was originally published via blogs.apache.org and is republished below for your reading pleasure.
This blog post is about Apache Flume’s File Channel. Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms. It uses a simple extensible data model that allows for online analytic application.
We’re getting really close to Strata Conference + Hadoop World 2012 (just over a month away), schedule planning-wise. So you may want to consider adding the tutorials, sessions, and keynotes below to your calendar! (Start times are always subject to change of course.)
The ones listed below are led or co-led by Clouderans, but there is certainly a wide range of attractive choices beyond what you see here. We just want to ensure that you put these particular ones high on your consideration list.
Social media has gained immense popularity with marketing teams, and Twitter is an effective tool for a company to get people excited about its products. Twitter makes it easy to engage users and communicate directly with them, and in turn, users can provide word-of-mouth marketing for companies by discussing the products. Given limited resources, and knowing we may not be able to talk to everyone we want to target directly, marketing departments can be more efficient by being selective about whom we reach out to.
In this post, we’ll learn how we can use Apache Flume, Apache HDFS, Apache Oozie, and Apache Hive to design an end-to-end data pipeline that will enable us to analyze Twitter data. This will be the first post in a series. The posts to follow to will describe, in more depth, how each component is involved and how the custom code operates. All the code and instructions necessary to reproduce this pipeline is available on the Cloudera Github.
Who is Influential?
We are happy to announce the general availability of CDH3 update 5. This update is a maintenance release of CDH3 platform and provides a considerable amount of bug-fixes and stability enhancements. Alongside these fixes, we have also included a few new features, most notable of which are the following:
Apache Flume is a scalable, reliable, fault-tolerant, distributed system designed to collect, transfer, and store massive amounts of event data into HDFS. Apache Flume recently graduated from the Apache Incubator as a Top Level Project at Apache. Flume is designed to send data over multiple hops from the initial source(s) to the final destination(s). Click here for details of the basic architecture of Flume. In this article, we will discuss in detail some new components in Flume 1.x (also known as Flume NG), which is currently on the trunk branch, techniques and components that can be be used to route the data, configuration validation, and finally support for serializing events.
In the past several months, contributors have been busy adding several new sources, sinks and channels to Flume. Flume now supports Syslog as a source, where sources have been added to support Syslog over TCP and UDP.
This blog was originally posted on the Apache Blog: https://blogs.apache.org/flume/entry/apache_flume_hackathon. Apache Flume is currently undergoing incubation at The Apache Software Foundation. More information on this project can be found at http://incubator.apache.org/flume.
This blog was originally posted on the Apache Blog: https://blogs.apache.org/flume/entry/flume_ng_architecture
Apache Flume is a distributed, reliable, and available system for efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store. Flume is currently undergoing incubation at The Apache Software Foundation. More information on this project can be found at http://incubator.apache.org/flume. Flume NG is work related to new major revision of Flume and is the subject of this post.
The Development track at Hadoop World is a technical deep dive dedicated to discussion about Apache Hadoop and application development for Apache Hadoop. You will hear committers, contributors and expert users from various Hadoop projects discuss the finer points of building applications with Hadoop and the related ecosystem. The sessions will touch on foundational topics such as HDFS, HBase, Pig, Hive, Flume and other related technologies. In addition, speakers will address key development areas including tools, performance, bringing the stack together and testing the stack. Sessions in this track are for developers of all levels who want to learn more about upcoming features and enhancements, new tools, advanced techniques and best practices.
On Monday, we held our second Flume Office Hours at Cloudera HQ in Palo Alto. The intent was to meet informally, to talk about what’s new, to answer questions, and to get feedback from the community to help prioritize features for future releases.
Below is the slide deck from Flume Office Hours:
Flume is a flexible, scalable, and reliable system for collecting streaming data. The Flume User Guide describes how to configure Flume, and the new Flume Cookbook contains instructions (called recipes) for common Flume use cases. In this post, we present a recipe that describes the common use case of using a Flume node collect Apache 2 web servers logs in order to deliver them to HDFS.
Using Flume Agents for Apache 2.x Web Server Logging
The past month has been exciting and productive for the community using and developing Cloudera’s Flume! This young system is a core part of Cloudera’s Distribution for Hadoop (CDH) that is responsible for streaming data ingest. There has been a great influx of interest and many contributions, and in this post we will provide a quick summary of this month’s new developments. First, we’re happy to announce the availability of Flume v0.9.1 and we will describe some of its updates. Second, we’ll talk about some of the exciting new integration features coming down the pipeline. Finally we will briefly mention some community growth statistics, as well as some recent and upcoming talks about Flume.