What is Hadoop?
Hadoop is a Java-based, open source programming system that allows users to store and process Big Data sets in a computing environment. It’s created by the Apache project that’s powered by a company named Apache Software Foundation.
With Hadoop, you’re able to create, run, and test applications on the system that contain thousands of hardware nodes, and can terabytes of Big data at a time. It has a file distribution system that allows the system to keep working if one of the nodes fails. This reduces the risk of Big Data loss and major system failure, in the event a multitude of nodes are inoperative.
Also,in Big data Hadoop has emerged as a system to help Big data processing tasks, such as sales and business planning, scientific analysis, and creating IoT sensors.
When it comes to cybersecurity, Hadoop makes it easier for you to save your Big data and keep everyone alerted when there is another Big Data breach that happens. We’ll use this guide to explain how cybersecurity is accomplished via Hadoop and how you can store your Big Data with it to keep your company safe for the long-term.
Hadoop and Cybersecurity
When it comes to cybersecurity, Hadoop allows you to obtain all of the Big Data your company generates. It gives you complete access to information that’s created by your users, Internet of Things, Big data and endpoints – which is needed to provide an accurate analysis on anomalies, suspicious behaviors, and other threats.
With Hadoop, you can leverage flexible applications and machine learning from both open source and proprietary markets, and you have a solution that can meet emerging and current challenges.
However, cybersecurity wasn’t always this developed. A decade ago, we used to think that SIEM and other products were sufficient. We believed that it would provide the information needed to help our networks complete cybersecurity challenges.
But as Mobile, Cloud, and IoT applications were made, we learned that SIEM systems aren’t robust enough to handle the variant Big data or scale properly with it. SIEM wasn’t made to handle large Big data volumes and don’t have the analytic answers to help detect issues that are underlying within the network.
When security professionals couldn’t use SIEM to perform advanced analysis or protect their Big data, what was done to protect their networks seemed to be constrained. These capabilities left people in charge of finding a limited form of attacks, moderately advanced and known attacks.
When you use Hadoop, there is a larger set of security use cases to help you. Companies can access their user-behavior analytics to mitigate and identify insider threats, share threat intelligence, and spot suspicious outsider activity within your network.
Cybersecurity revolves around three themes enhancing incident response, better incident detection, and how these scenarios impact your business. When you use Hadoop, all three of these themes are possible because it’s designed to provide access to analytics, contextual understanding, and information.
The security community isn’t limited by one application’s insights on risk. Hadoop has flexibility allows your team to get answers to these questions instead of being limited to what they know on different security applications and the systems capable of notifying them.
Also, Hadoop integrates both open source and proprietary technologies to make a full cybersecurity defense. For instance, with its open source security, its Open Network insights (ONI) network is the first to make an advanced threat detection solution to its platform via big data analysis and open data models.
Hadoop’s Cybersecurity Features
Here are the most common cybersecurity features that Hadoop provides:
- Comprehensive: Hadoop gives a single view of all the alert summaries, relevant Big Data, and advanced search options. This eliminates information overload and helps with conflict analysis and resolution.
- High-Speed Ingestion: Big data is constantly generated. This data needs to be collected, stored and normalized at extremely fast speeds to make it accessible for advanced analytics and computation.
- Realistic Processing: Hadoop offers real-time engagement to help stream Big data feeds with important information such as geolocation, threat intelligence, and its DNS makes meta-data that’s necessary for each data breach investigation
Efficient: Your company will need cost-effective Big Data storage to help log data, and its telemetry can be analyzed and mined with long-term visibility. With Hadoop, it helps users understand what was the cause of the threat, which data was exposed, and where the data was sent to.
How Does it Store Big Data?
Hadoop stores big data in a distributed manner. For example, if you have about 5G of data and you change Hadoop’s settings to create 1 GB of Big data blocks. Hadoop will divide the Big Data into 5 blocks since 5/1 = 5, and it will be placed across multiple DataNodes. Also, it duplicates the Big Data blocks on different nodes. Now, since we’re using commodity hardware, storing isn’t as difficult.
Also, Hadoop solves the scaling issue. Unlike most systems which use vertical scaling, it uses horizontal scaling instead. You can add extra Big Data nodes to Hadoop’s cluster when it’s needed. Basically, if you don’t need a 1 TB system in order to store 1 TB of data. You can use multiple 128GB systems to ensure that you don’t waste too much space.
How Does its Data Analytics Work?
Data analytics using Hadoop is rather simple. In fact, you can store multiple types of data whether it’s unstructured, semi-structured, or structured. Since in Hadoop, there’s no pre-dumping schema validation. Also, it follows the read many and write once model. Because of this, you can write out the data one time and be able to read it multiple times in order to find insights.
How Can I Analyze and Process Data Faster?
People who do Big Data analytics using Hadoop know that processing and accessing data can be difficult. To solve it, we bring the processing to the data and not placing data into a process. This means that instead of moving your data to the master node and processing it afterward.
With Hadoop, you can use MapReduce in order to process data faster. The processing logic is delivered to multiple slave nodes and the Big Data becomes processed through these slave nodes. After that, the processed data is given to the master node where the response is given to the client.
In Hadoop’s YARN architecture, it has a NodeManager and a ResourceManager. The ResourceManager which can be configured to be used on the NameMode’s machine. However, NodeManagers are required to be on the same machine wherever the Data Nodes are located.
Data analytics using Hadoop is a great way for users to analyze data without having to worry about it being compromised. When it comes to big data, you can store, process, and analyze it without having it take up too much space on your network.
That’s why most large networks tend to use Hadoop as a reliable source of Big Data protection. Not only can it store your Big Data, but its advanced cybersecurity principles make it easier for them to save it as well.
Once your team is reminded and trained on how to use it, you’ll find it easier to operate your network while being more alert on threats and underlying issues that can occur. Thus, give Hadoop a look if you’re serious about improving your Big Data and how you manage it.