Apache Flume Use Cases – Future Scope in Flume

When we have to collect and transfer unstructured data from various sources to HDFS or HBase then we use Apache Flume. Apart from this, Apache Flume can be used for many other use cases. This article enlists some of the major use cases of Apache Flume.

We will see various different scenarios where we can use Apache Flume. Before learning the Apache Flume use cases, we will firstly see the short introduction to Apache Flume.

Introduction to Apache Flume

Apache Flume is an open-source tool that is used for collecting and transferring streaming data from the external sources to the terminal repository such as HBase, HDFS, etc. With Apache Flume we can transfer the real-time logs generated by web servers to the HDFS.

It is a reliable, scalable, and highly available service. It is designed for the purpose of collecting streaming data generated from various web servers to the Hadoop HDFS.

Consequently, let us now explore different use cases of Apache Flume

Different Use cases of Apache Flume

1. Apache Flume can be used in the situation when we want to collect data from the varieties of sources and store them on the Hadoop system.

2. We can use Flume whenever we need to handle high-volume and high-velocity data into a Hadoop system.

3. Apache Flume is a backbone for real-time event processing.

4. We use Apache Flume for reliable delivery of data from external sources to the destination.

5. Flume is a tool majorly for online analytics.

6. Flume proves out to be a scalable solution when the volume and velocity of data increases. When the data volume increases, Flume can be scaled easily by just more machines to it.

7. We can use Flume without being worried about configuring various flume components because flume dynamically configures the various components of its architecture without curing any downtime.

8. We can achieve a single point of contact with Apache Flume.

9. Apache Flume is the best option when we opt for real-time streaming of data.

10. It has a higher demand in e-commerce companies for analyzing the customer behavior of different regions.

11. We use Apache flume for effectively collecting log data from multiple servers and ingesting it into a centralized store such as HDFS, HBase.

12. With the help of Apache Flume, we can collect the data in real-time as well as in batch-mode from multiple servers.

13. Apache Flume helps us in importing and analyzing huge volumes of data generated in real-time by social media websites like Twitter, Facebook, and various e-commerce websites such as Flipkart, Amazon, etc.

14. With Flume it is possible to gather data from a wide range of sources and then transfer them to multiple destinations.

15. Flume supports Multi-hop flows, fan-out flows, fan-in flows, and contextual routing.

16. Moreover when we are having multiple web applications server running and generating logs and we have to move logs at a very fast speed to HDFS then in such case we can use Apache Flume.

17. Apache Flume is good for doing a sentiment analysis or when we have to download data from Twitter and then moving this data to HDFS.

18. We can process data in-flight by using interceptors in Apache Flume.

19. Flume is very useful for data masking or data filtering.

20. Lastly, Flume is the best option when we need to ingest textual log data into a Hadoop system.

Where to use Apache Flume?

1. Apache Flume is useful for Fraud Detection.
2. We can use it in IoT applications.
3. It helps in the aggregation of the machine and sensor-generated data.
4. It is useful in the alerting or SIEM.

Summary

So finally after reading this article, you are now aware of the situations or scenarios where we can use Apache Flume. The article enlisted almost all of the flume use cases. E-commerce companies such as Amazon, Flipkart, eBay, etc. are using Apache Flume to understand the customer’s buying behavior.

We can use Apache Flume mainly when we have to collect and move huge volumes of log data generated by web servers to the Hadoop HDFS. Apache Flume is useful for sentiment analysis.