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All the Apache Streaming Projects: An Exploratory Guide | @BigDataExpo #Apache #BigData #OpenSource
The speed at which data is generated, consumed, processed, and analyzed is increasing at an unbelievably rapid pace
By: Janakiram MSV
Aug. 8, 2016 12:15 PM
The speed at which data is generated, consumed, processed, and analyzed is increasing at an unbelievably rapid pace. Social media, the Internet of Things, ad tech, and gaming verticals are struggling to deal with the disproportionate size of data sets. These industries demand data processing and analysis in near real-time. Traditional Big Data-styled frameworks such as Apache Hadoop are not well-suited for these use cases.
As a result, multiple open source projects have been started in the last few years to deal with the streaming data. All were designed to process a never-ending sequence of records originating from more than one source. From Kafka to Beam, there are over a dozen Apache projects in various stages of completion.
With a high overlap, the current Apache streaming projects address similar scenarios. Users often find it confusing to choose the right open source stack for implementing a real-time stream processing solution. This article attempts to help customers navigate the complex maze of Apache streaming projects by calling out the key differentiators for each. We will discuss the use cases and key scenarios addressed by Apache Kafka, Apache Storm, Apache Spark, Apache Samza, Apache Beam and related projects.
Flume’s configuration includes a source, channel, and sink. The source can be anything from a Syslog to the Twitter stream to an Avro endpoint. The channel defines how the stream is delivered to the destination. The valid options include Memory, JDBC, Kafka, File among others. The sink determines the destination where the stream gets delivered. Flume supports many sinks such as HDFS, Hive, HBase, ElasticSearch, Kafka and others.
Apache Flume is ideal for scenarios where the client infrastructure supports installing agents. The most popular use case is to stream logs from multiple sources to a central, persistent data store for further processing analysis.
Sample Use Case: Streaming logs from multiple sources capable of running JVM.
Read the article at The New Stack.
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