Comparing Apache Sqoop, Flume, and Kafka
Apache Sqoop, Flume, and Kafka are tools used in data science. All three are open source, distributed platforms designed to move data and operate on unstructured data. Each also supports big data in the scale of petabytes and exabytes, and all are written in Java. (Kafka is written in Java and Scala.)
Type of data operation
Sqoop is used for bulk transfer of data between Hadoop and relational databases and supports both import and export of data.
Flume is used for collecting and transferring large quantities of data to a centralized data store. Though designed primarily for log data, Flume could be used for any kind of data sources, such as event data, network traffic data, and even email messages.
Kafka is used to build real-time streaming data pipelines that transfer data between systems or applications, transform data streams, or react to data streams. Kafka is similar to a messaging system, as it is used to publish and subscribe to streams of records.
Data sources, sinks and targets
As all these tools move data, each involves a data source and a data sink or target.
Sqoop supports import of data into the Hadoop Distributed File System (HDFS), Hive, HBase, and Accumolo. Multiple data file formats can be imported, but text file is the default. The source in a Sqoop import could be any relational database that provides a Sqoop connector.
Flume supports many data sources and data sinks, including custom sources and sinks.
Kafka is based on the publish/subscribe model and uses connectors to connect with systems that want to publish or subscribe to Kafka streams or messages. Some connectors are built in, such as command-line consoles and files, and others could be configured using the Connectors API.
Tools and components
Sqoop provides an import tool to import a table from a database to HDFS and an export tool to export an HDFS directory to a database table, but other tools could be used to import tables, list databases, and list tables.
A combination of source-channel-sink is configured as a Flume agent in a configuration file. Starting an agent starts the data collection or aggregation.
Kafka uses topics, producers, and consumers. A topic is a category or feed name to which messages are published and stored. A producer sends or publishes messages to a topic, and a consumer consumes the messages from the topic.
Run state
Sqoop import/export processes terminate after the data transfer. A Flume agent streams the data that is available when it is started and continues to run and stream new data as it becomes available. A Kafka producer/consumer also continues to run and stream messages in real time as they are published to a topic.
Support for multi-agents or subscribers
Sqoop does not support linking import or export processes to form a multi- import/export process. Flume does support a multi-flow agent in which output from one agent may be used as input by another agent.
Kafka is a multi-subscribe platform, which means multiple producers and consumers may subscribe to the same topic concurrently. Kafka uses partitions within a topic to parallelize among multiple producers and consumers.
