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Apache Sqoop vs Apache Flume

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Difference between Sqoop and Flume

Introduction:

Big Data is unquestionably synonymous with Apache Hadoop because of its cost-effectiveness and also for its virtues like scalability to process humongous loads of data. To get your data that needs to be analyzed on the Hadoop clusters is one of the most critical activities that can be done in any Big Data deployments. Data ingestion is the most critical activity as we just spoke about it, as it is required to load humongous loads of data in the orders of petabytes and exabytes.

Apache Sqoop and Apache Flume are two different technologies from the Hadoop ecosystem which can be put to use to gather data from various kinds of data sources and finally load that data into a traditional HDFS system. Apache Sqoop in Hadoop is used to fetch structured data from RDBMS systems like Teradata, Oracle, MySQL, MSSQL, PostgreSQL and on the other hand Apache Flume is used to fetch data that is stored on various sources as like the log files on a Web Server or an Application Server.

Big Data systems, in general, are very popular and are known to be able to process huge amounts of unstructured and structured data from various kinds of data sources. The complexity of big data system increases with the data sources available. With diverse data sources and data from these data sources can be consistently produced on a large scale.

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What is Sqoop in Hadoop?

Apache Sqoop, which can be comfortably referred to as SQL to Hadoop is a lifesaver for any individual who experiences difficulties in moving data from data warehouses to the orthodox Hadoop environments. It is a very efficient and an effective Hadoop tool that can be used to import data from the traditional RDBMS onto HBase, Hive or HDFS. Apache Sqoop can also be used for the reverse use cases as well, that is to import data from a traditional HDFS to an orthodox RDBMS system too.

How does Apache Sqoop work?

Apache Sqoop is an effective Hadoop related tool for all non-programmers to look at the RDBMS that needs to be imported into HDFS systems. Once the input is identified by Apache Sqoop, metadata on the table can be read and a specific class definition is created for the input requirements. Apache Sqoop can also be brutally forced to obtain the details of columns that are required before input instead of importing the whole input and saves a great amount of time in the process of it.

Features of Apache Sqoop:

The most important features of Apache Flume are provided as below, let us now take a look at the following features:

  • Apache Sqoop supports bulk import
  • Sqoop allows parallel data transfers for optimal utilization of system resources and also to ensure faster performances
  • Sqoop is made to increase the data analysis efficiency by a great deal
  • Sqoop helps in mitigating excessive loads on external systems
  • Sqoop provides interaction with the data programmatically by generating Java classes

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What is Flume in Hadoop?

Apache Flume can be explained as a service that is designed specifically to stream logs into Hadoop’s environment. Apache Flume is a distributed and a reliable source to collect, aggregate larger amounts of log data. Apache Flume’s architecture is specifically based on streaming data flows which is quite simple and makes it easier to use. Apache Flume provides many tunable reliability mechanisms, recovery and failover mechanisms that come to our rescue at the right time.

How does Apache Flume work?

Apache Flume has a very simple event-driven approach with very important roles like Source, Channel and Sink.

  • A Source is defined as the point from where the data comes (eg. Message queue or a file)
  • A Sink is defined as the point of data pipelined from various sources
  • A Channel is defined as the pipes that establish connections between Sources and Sinks

Apache Flume works on two major concepts as discussed below:

  • Master acts as a reliable configuration service that is used by nodes to retrieve their specific configurations
  • Change in the configuration for a particular node on the Master is dynamically updated by the Master itself.

A Node is generally an event pipe in Apache Hadoop Flume that reads from a Source and writes to a Sink. The characteristics and the roles of an Apache Flume node can be determined by the behavior of Sources and Sinks. Apache Flume was developed in such a manner as if the various options of Sources and Sinks do not match the requirements, then custom Sources and Sinks can be written to answer the needs.

Features of Apache Flume

The most important features of Apache Flume are provided as below, let us now take a look at the following features:

  • Apache Flume is a flexible tool that enables scalability in the environments
  • Flume provides very high throughput and at a very low latency
  • Flume has a nice way of declarative configuration and alongside with it the ease of extensibility
  • Flume in Hadoop is known to be fault tolerant, linearly scalable and also stream-oriented

Difference between Sqoop and Flume:

With the understanding that we have gained through the sections earlier explaining each of the technologies that we wanted to learn in this article, it is a good opportunity for us to discuss further upon the differences between them. This will not only provide greater understanding on the products that you’ve known until now but also gives you an edge in making the necessary decisions, deciding upon which one to use in what situation. Let us take a closer look at the differences between Sqoop and Flume, shall we?

Apache Sqoop

Apache Flume

Apache Sqoop is basically designed to work with any type of Relational database system (RDBMS) which has the basic JDBC connectivity. Apache Sqoop can import data from NoSQL databases like MongoDB, Cassandra and along with it also allow data transfer to Apache Hive or HDFS. Apache Flume works pretty well in Streaming data sources that are generated continuously in Hadoop environments, such as log files
Apache Sqoop load is not driven by events Apache Flume data loading is completely event-driven
Apache Sqoop will be considered an ideal fit if the data is being available in Teradata, Oracle, MySQL, PostgreSQL or any other JDBC compatible database Apache Flume is considered the best choice when we are talking about moving bulk streaming data from sources likes JMS or Spooling directories
HDFS is the destination for importing data in Apache Sqoop Data is said to flow to HDFS through channels in Apache Flume
Apache Sqoop has a connector based architecture, which means the connectors know a great deal in connecting with the various data sources and also to fetch data correspondingly Apache Flume has agent-based architecture, that means code written in Flume is known as an agent that will be held responsible for fetching the data
Apache Sqoop connectors are designed specifically to work with structured data sources and to fetch data from them alone. Apache Flume is specifically designed to fetch streaming data like tweets from Twitter or log files from Web servers or Application servers etc.
Apache Sqoop is specifically used for Parallel data transfers, data imports as it copies the data pretty quick Apache Flume is specifically used for collecting and aggregating data because of its distributed, reliable nature, and also because of its highly available backup routes.

 

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Conclusion:

In this article, we have learned about Apache Sqoop and Apache Flume. We have discussed each of these technologies in great detail and have provided enough details about each of these individually. Alongside that, we also have understood the differences between each of these to understand the most basic point that these two technologies are designed for various specific needs and are in no comparison to each other with their functionalities as such.

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