Apache Flume vs Sqoop

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Apache Flume

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Sqoop

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Apache Flume vs Sqoop: What are the differences?

What is Apache Flume? A service for collecting, aggregating, and moving large amounts of log data. It 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.

What is Sqoop? A tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores. It is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases of The Apache Software Foundation.

Apache Flume can be classified as a tool in the "Log Management" category, while Sqoop is grouped under "Database Tools".

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What is Apache Flume?

It 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.

What is Sqoop?

It is a tool designed for efficiently transferring bulk data between Apache Hadoop and structured datastores such as relational databases of The Apache Software Foundation

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What are some alternatives to Apache Flume and Sqoop?
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Logstash
Logstash is a tool for managing events and logs. You can use it to collect logs, parse them, and store them for later use (like, for searching). If you store them in Elasticsearch, you can view and analyze them with Kibana.
Apache Storm
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm has many use cases: realtime analytics, online machine learning, continuous computation, distributed RPC, ETL, and more. Storm is fast: a benchmark clocked it at over a million tuples processed per second per node. It is scalable, fault-tolerant, guarantees your data will be processed, and is easy to set up and operate.
Kafka
Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
Apache Flink
Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
See all alternatives