Did you know that any network’s nightmare is security? No matter how safe they are calling themselves corrupt internet ghosts, they will find one way or another to break into networks and cause a lot of damage, from data breaches to total server crashes.
As more and more companies start taking advantage of big data, these crooked internet criminals, or as they are sometimes referred to as cybercriminals, are being given more opportunities. But what’s wrong with big data? Let’s look at the answers to these questions together.
What is Big Data Security?
As we have seen in previous articles, the data analysis process involves a number of steps from data collection to reporting the data obtained to a reputable authority. Big data security refers to the process of improving the security of our data. This includes various security measures taken, use of security tools and all other measures that protect data from being stolen by a named person.
If you regularly study Edus data online, you may have read another great article where we talk about the popular big data analysis tool. As we can see, almost all the tools are free open source projects and are community-supported.
These tools are not designed with security in mind, which means that Hadoop is only expected to perform well in a reliable environment. There is no one to blame, because at first confidentiality and security were not prioritized. But if the big data these companies collect over the years isn’t strictly protected, it creates huge problems that sometimes can’t be fixed.
However, before we understand the key challenges for big data organizations, you need to have a basic understanding of big data analytics or have a deeper understanding of how Hadoop works.
Hadoop consists of two main pieces of software, HDFS (Hadoop Distributed File System) and MapReduce, each of which fulfills two basic computing needs, namely storage and processing.
What’s special about Hadoop is the distributed storage system implemented using HDFS technology. As we all know, it is very difficult to process a lot of data at once because it takes longer. To reduce computing time, HDFS divides large amounts of data into smaller data blocks and distributes them across multiple systems (called nodes) and these systems are connected to form clusters.
Now that we have solved one of the problems with distributed storage systems, it is time to tackle the next serious problem, processing time. And each node processes data individually, which greatly reduces the load on a single node and the time it takes to process it. This is done through parallel processing with MapReduce technology.
Suppose you have 10 GigaBytes of unstructured data and need to analyze that data with Hadoop (at least 128MB, but breaking the data into such small sizes results in the use of more nodes, but smaller sizes are faster to process). Every 1 GB of data is stored and processed in a single node, so the time it takes to process up to 10 GB of data is the same as the time it takes to process 1 GB of data from each node.
This will greatly reduce the time required for processing, but increase the computational cost, but in today’s fast-paced world, it should be noted that time is more important than money.
The importance of big data security
Data security is undeniable as we all know that data is the most important asset for any company. If left unprotected, many problems like identity theft, financial loss, legal issues, etc. can appear. Some high-priority details, such as customer medical data, need to be kept highly secure without falling into the hands of hackers. Types of attacks range from DDoS, SQL injection, and ransomware, with each of these attacks causing different levels of system damage.
Big data security
Attacks by cybercriminals will give the organization a bad reputation, we have seen real cases like that in the past. According to a survey by BI-Survey.com, nearly 90% of participants said that big data security plays an important role in organizations. Many companies believe that big data security issues are long term problems and can be solved in the long term, but the reality is that they can have short term effects as well. We may have read about data breaches happening at companies, all of which happened because they didn’t follow strict big data security measures.
Protection often requires 100% participation of the user base. So, if many users follow weak security practices, the whole system can be compromised. Data protection often involves protecting all aspects of your environment, including physical security. A common tactic is to trick authorized users into granting access. Even if you feel you have proper control, there is always room for human error, especially in complex environments.
Big Data V/S cybersecurity
The main ideologies of these two concepts contradict each other. The main goal of data science is to extract valuable information into specialized and more structured data sets by processing large amounts of data. While cybersecurity protects and protects large groups of data and networks from unauthorized access.
There is controversy and many analysts argue that big data and cybersecurity cannot coexist. Let’s take a look at some further findings on this case.
Cybersecurity is the protection of electronic data systems from criminal or unauthorized behavior. Working in cybersecurity is suitable for curious people who have a strong desire to learn and enjoy creative problem solving.
On the other hand, the data scientist has a more abstract role, as his work does not focus solely on analysis or engineering, but is a multidisciplinary position that involves a combination of collecting, extracting and analyzing large amounts of data from multiple sources. . This field requires an understanding of artificial intelligence and machine learning techniques such as: B. vector machine support, regression, cluster analysis and neural networks.
Cybersecurity sometimes uses big data and data science to spot future threat opportunities. Together, data science and cybersecurity will enhance consumer security and data protection in the future.
How can you implement big data protection in your company?
There are 5 simple steps you can take now to keep big data safe in your business
Secure Distributed Computing Framework Use improved authentication methods to build trust between different decentralized nodes. De-identification should be introduced to ensure compliance with confidentiality restrictions. The company then needs to verify access to the files and ensure that sensitive data is not leaked in any way.
Secure data storage Data must be stored securely to increase the security of big data. SUNDR (Secure Untrusted Data Repository) should be used to monitor unauthorized changes by third party agents.
Protect your data To protect your data, organizations must use firewalls, intrusion detection and prevention tools, scanning tools, and require validation for all data access.
Audits Not to be Missed The organization must conduct a full audit to ensure that operations are functioning properly. Tools like Apache Oozie can be used to identify potential serious threats to data security.
Hardware and software configurations must be protected. Loss of data in an organization can occur in many ways. This could be due to a server failure, data breach, or software bug. The most common are hardware and software failures, so it is very important to have a backup of all data so that no data is lost.
In Hadoop, as we have seen, all nodes are used to store data. You might think that corrupting a node can lead to data loss, but Hadoop uses a replication system to store the same data on at least 3 nodes, so if one node fails, the data can be retrieved by another node that has a stored copy of the data loss.
If you follow these 5 steps, the security of big data in your company will be at great risk from market demand.