Cloudera Blog
In an era where artificial intelligence (AI) is reshaping enterprises across the globe—be it in healthcare, finance, or manufacturing—it’s hard to overstate the transformation that AI has had on businesses, regardless of industry or size. At Cloudera, we recognize the urgent need for bold steps to harness this potential and dramatically accelerate the time to […] Read blog post
Inside Santander’s Near Real-Time Data Ingest Architecture - Cloudera Blog
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 fe
Architectural Patterns for Near Real-Time Data Processing with Apache Hadoop - Cloudera Blog
Architectural Patterns for Near Real-Time Data Processing with Apache Hadoop 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 F
Flafka: Apache Flume Meets Apache Kafka for Event Processing - Cloudera Blog
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 capabilit
Feb 2013 HUG: Large Scale Data Ingest Using Apache Flume
Apache Flume is a highly scalable, distributed, fault tolerant data collection framework for Apache Hadoop and Apache HBase. Flume is designed to transfer massive volumes of event data in a highly scalable way into HDFS or HBase. Flume is declarative and easy to configure and can easily be deployed to a large number of machines using configuration management systems like Puppet or Cloudera Manager
Cloudera Blog
Riding the wave of the generative AI revolution, third party large language model (LLM) services like ChatGPT and Bard have swiftly emerged as the talk of the town, converting AI skeptics to evangelists and transforming the way we interact with technology. For proof of this megatrend look no further than the instant success of ChatGPT, […] Read blog post
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GitHub - sharethrough/flume-kinesis: Flume source and sink for Amazon Kinesis
[FLUME-2498] Implement Taildir Source - ASF JIRA
GitHub - gilt/logback-flume-appender: Logback appender to forward log messages to a Flume agent
[FLUME-2055] Flume leaves .tmp files in HDFS (unclosed?) after NameNode goes down - ASF JIRA
GitHub - cloudera/cdh-twitter-example: Example application for analyzing Twitter data using CDH - Flume, Oozie, Hive
Apache Flume
https://blogs.oracle.com/datawarehousing/post/entry/flume_and_hive_for_log
When Hadoop isn’t fast enough: The Argument for Storm « Nodeable Blog
[FLUME-1286] Storm Sink - ASF JIRA
Impalaの挙動及びそのトラブルシューティングについて - Google グループ
A Quick HBase Primer from a SQLBI Perspective
An Introduction to Streaming Log Processing for Hadoop
[FLUME-247] Add Efficient HBase Sink whith Flexible Event's Attributes Writing - Cloudera Open Source
FlumeBase - Home