When dealing with Hadoop, one question that often arises is why are we utilising both Sqoop and Flume to collect data from various sources and load it into HDFS? This question will be answered in this post, Apache Sqoop vs Flume. We'll start by learning about each tools' brief introductions. Following that, we'll compare Apache Flume and Sqoop to have a better understanding of each tool.
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.
If you would like to Enrich your career with a Apache Flume certified professional, then visit Mindmajix - A Global online training platform: “Apache Flume Certification Training” Course. This course will help you to achieve excellence in this domain.
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 systems increases with the data sources available. With diverse data sources and data from these data sources can be consistently produced on a large scale.
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.
In this Apache Sqoop vs Apache Flume article, we would be covering the following topics:
So, let us begin with the Sqoop definition first, which I am going to talk about in the section below.
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 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.
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.
The most important features of Apache Flume are provided as below, let us now take a look at the following features:
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.
Apache Flume has a very simple event-driven approach with very important roles like Source, Channel and Sink.
Apache Flume works on two major concepts as discussed below:
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.
----- Related Article: Streaming Big Data with Apache Spark -----
The most important features of Apache Flume are provided as below, let us now take a look at the following features:
----- Related Article: Spark Vs Hadoop -----
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. |
---- Related Article: Frequently Asked Apache Flume Interview Questions and Answers ----
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.
Explore Apache Flume Sample Resumes! Download & Edit, Get Noticed by Top Employers! Download Now!
Hadoop Administration | MapReduce |
Big Data On AWS | Informatica Big Data Integration |
Bigdata Greenplum DBA | Informatica Big Data Edition |
Hadoop Hive | Impala |
Hadoop Testing | Apache Mahout |
Our work-support plans provide precise options as per your project tasks. Whether you are a newbie or an experienced professional seeking assistance in completing project tasks, we are here with the following plans to meet your custom needs:
Name | Dates | |
---|---|---|
Apache Flume Training | Dec 24 to Jan 08 | View Details |
Apache Flume Training | Dec 28 to Jan 12 | View Details |
Apache Flume Training | Dec 31 to Jan 15 | View Details |
Apache Flume Training | Jan 04 to Jan 19 | View Details |
Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.