Hadoop was created back in 2006 as a means of storing and processing large data volumes. It is a combination of software utilities with an open-source nature that the Apache Software Foundation created. Hadoop has been used mainly by various institutions and research centers for a long time. Still, the advent of AI in recent years brought much attention to Hadoop’s capability to handle large data pools.
The main goal of Hadoop is to deliver a highly scalable data storage infrastructure with fault tolerance and high availability. It is possible to separate Hadoop into four essential elements, with each element being responsible for a specific subset of features.
ZooKeeper is the leading assurance of data consistency across different elements of Hadoop storage. It is a distributed information coordination service that can offer a single unified registry for data configuration, naming, and synchronization. ZooKeeper can monitor every node in the Hadoop cluster at all times, ensuring that the overall system state is consistent at all times.
YARN, or Yet Another Resource Negotiator, is a framework for resource management across different Hadoop nodes. YARN makes the scheduling and resource allocation process much easier by acting as an overseer for storage, memory, and CPU management to specific Hadoop apps within the cluster. Two of the most significant advantages of YARN are simplified scalability and improved app management.
HDFS, or Hadoop Distributed File System, is a self-explanatory FS made explicitly for handling large data volumes in Hadoop’s infrastructure consisting of nodes and clusters. It can ensure fault tolerance by replicating data blocks across multiple nodes while also providing a convenient data management system.
MapReduce is the primary programming model for Hadoop; it enables the ability to process Hadoop’s large data sets stored using distributed clusters as a single entity. MapReduce makes it possible to process Hadoop’s data using many cluster nodes after the data in question has been separated into small chunks. The final output for this kind of processing action is the combination of multiple processed data chunks.
Hadoop is a very compelling option for businesses that operate with large data volumes on a regular basis. It can process and analyze massive data sets for a variety of purposes. Here are some of the most prominent examples of Hadoop use cases:
- Collection and analysis for large data log volumes (from servers, apps, or websites).
- Creation of data warehouses for massive data volumes to be stored in.
- Data lake generation (storage locations for large volumes of unprocessed data).
- There are plenty of opportunities for data analysis, allowing for the possibility of detecting patterns, trends, and insights from said data.
- More accessible training for various machine learning models.
This last use case is one of the biggest reasons for the recent popularity surge of Hadoop. All kinds of newly-introduced Large Language Models (LLMs) and machine learning algorithms have to be trained beforehand using massive data pools – something that Hadoop excels at.
At the same time, the process of securing these kinds of massive data volumes has always been a significant problem for the industry. The main reason is apparent – the sheer size of an average Hadoop data set makes it extremely expensive to replicate multiple times. Another notable roadblock for the Hadoop backup and recovery process is its extreme scalability, making it very difficult for most backup solutions to keep up with.
There is also the issue of false sense of security that some Hadoop users have – due to the fact that there is a built-in data replication capability in the Hadoop framework. Data replication in Hadoop was never meant to serve as a security measure in the first place – the data in question is copied three times for the sake of redundancy and nothing else. The fact that all of these replicated copies are stored alongside the original data source makes it even less helpful as a data security measure.
All of these issues make it clear that proper backup measures are necessary for Hadoop-based environments. As such, setting up a proper backup strategy is a good idea in this case. However, finding the correct solution for this task can prove a bit of a challenge since a proper backup solution for Hadoop environments has to be able to offer many different features and advantages at once.
Balancing between comprehensive data backup features and custom scripting is the first requirement – many of Hadoop’s backup measures rely on built-in backup methods that often require a decent level of configuration beforehand. There is also a lot of demand for general ease of use for the backup itself and its handling of Hadoop backups.
A proper Hadoop backup offering has to offer a competent level of scaling, the capability to work with large volumes of data, and the capability to meet various compliance requirements regarding RTOs and RPOs. The demand for comprehensive data security capabilities is also present, ensuring that the data integrity is not broken due to a malicious attack or a natural disaster.
Other requirements for Hadoop backup solutions include an efficient recovery process, multiple data copies in different locations, the capability to integrate with cloud storage, and at least some automation capabilities to simplify time-consuming and menial tasks during the backup process.
All in all, there are plenty of features that a backup solution has to have in order to be suitable for Hadoop backup and recovery tasks. However, these features and requests are entirely justified due to the importance of large data pools in a modern environment, among other reasons.
