In today's data-driven world, data security is more important than ever. As companies increasingly rely on data to make critical business decisions, they need to ensure its proper protection against external and internal threats. While data security is often associated with cybersecurity professionals, data engineers also have a significant part in the matter.
So, what important role do they play in data security?
What skills and knowledge are required for the job, what are the challenges they face, and what are the best practices they can implement to ensure data integrity, confidentiality, and availability?
Who is a data engineer?
Let’s start with the basics. Who actually is a data engineer, and what are their responsibilities?
A data engineer is a professional responsible for designing, building, and maintaining an infrastructure that supports data-intensive applications. They work with large amounts of data, ensuring they are accurate, reliable, and accessible to users when they need them.
Data engineers work closely with data scientists, analysts, and other professionals on proper data collection, storage, processing, and analysis. They may also work with an IT department to ensure that data infrastructure is integrated with other systems and properly maintained or updated.
Some of the key skills and knowledge required to become a data engineer include
- proficiency in programming languages such as Python, Java, and SQL
- knowledge of database design and management
- expertise in data modeling and schema design
- understanding of cloud computing and distributed systems
- strong understanding of data security best practices
Read more: Who Is a Data Engineer?
Data engineers in data security
One of the primary responsibilities of data engineers in data security is ensuring the integrity and confidentiality of the data, which involves implementing specific mechanisms to prevent data from being modified, corrupted, or destroyed by unauthorized users.
They also play a critical role in ensuring the availability of data. They need to design and implement systems that can handle high volumes of data and traffic and quickly recover from system failures or outages.
Techniques and tools used by data engineers in data security
Data masking protect sensitive data by hiding or replacing it with non-sensitive data. Its goal is to prevent unauthorized access to sensitive data while providing a realistic-looking dataset for testing, development, or other purposes.
The process of data masking involves replacing sensitive data with random, fictitious, or similar-looking data that retains the original data's format, structure, and relationships. For example, a Social Security number may be replaced with a randomly generated one following the same format.
There are various techniques used in data masking, including substitution, shuffling, and encryption. Substitution involves replacing sensitive data with similar-looking data, while shuffling involves changing the order of data elements. Encryption involves transforming sensitive data into an unreadable format using a cryptographic algorithm.
Data masking is commonly used in industries that deal with sensitive data, such as healthcare, finance, and government. By using this technique, companies can comply with data privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR), while still being able to use the data for testing or other purposes.
Data encryption transforms plain, readable data into a secure, unreadable format using an encryption algorithm. This way, it protects sensitive data from unauthorized access or theft by making it unreadable to anyone who doesn’t have the key to decrypt it.
The plaintext data is transformed into ciphertext using a key and an encryption algorithm in data encryption. The key is a secret value used to transform the plaintext into ciphertext, while the encryption algorithm defines the transformation process. The ciphertext can only be transformed back into plaintext using the correct key.
There are various algorithms used in data encryption, including symmetric-key encryption, which uses the same key for both encryption and decryption, asymmetric-key encryption, which uses a pair of keys - a public key for encryption and a private key for decryption, and hashing being a one-way encryption process that transforms plaintext into a fixed-size hash value that cannot be decrypted.
Access control is a security mechanism that limits access to data or information systems only to authorized users with policies, procedures, and technologies.
Such systems can be classified into two types: physical access control and logical access control. Physical access control is used to control physical access to the building or facility where the data is stored. In contrast, logical access control is used to control access to data within the information system.
Logical access control is typically implemented using:
- authentication, which is the process of verifying the identity of a user, typically by requiring a username and password or other biometric factors like fingerprint or facial recognition
- authorization which is the process of granting or denying access to a user based on their identity and their level of access rights
- accounting mechanisms which is the process of tracking and monitoring user activity within the system
Data backup and recovery
Data backup and recovery is the process of creating and storing copies of important data in case the original data is lost, corrupted, or destroyed. Its goal is to ensure that data can be recovered during a disaster or system failure.
Data backup involves creating a copy of important data and storing it in a separate location, such as an external hard drive, cloud storage, or tape backup. The backup can be done manually or automatically using backup software that schedules and manages the backup process.
Data recovery, on the other hand, involves retrieving the data from the backup storage in an unwanted event. Depending on the backup and recovery system, the process can be initiated manually or automatically. It can be time-consuming as the speed of recovery depends on the size of the data and the speed of the backup storage and recovery system.
Different types of backup and recovery systems include:
- full backup, which involves creating a complete backup of all data
- incremental backup that backs up data that has changed since the last backup
- differential backup, which backs up all data that has changed since the last full backup
Replication refers to the process of creating copies of data and storing them in multiple locations to ensure that data is available and accessible in any unwanted event. Possible locations involve remote data centers or cloud storage, and the data is synchronized between the primary and the replicated locations to ensure that both copies are up-to-date.
Data replication can be synchronous or asynchronous. In synchronous replication, changes made to the primary copy are immediately reflected in the replicated copy, making both copies identical. In asynchronous replication, changes made to the primary copy are delayed and reflected in the replicated copy with a delay.
Load balancing is distributing workloads across multiple computing resources, such as servers or storage systems, to optimize performance, increase availability, and improve reliability.
Incoming traffic or workloads are distributed evenly across multiple computing resources to ensure no single resource is overwhelmed. It can be done at the application, network, or transport layers.
Load balancing can improve performance, guaranteeing that computing resources are used efficiently and that workloads are processed quickly. It can also increase availability and reliability.
Load balancing is commonly used in industries that require high data availability and performance, such as healthcare, finance, and e-commerce. This way, organizations can be sure that critical data and applications are always available and accessible, even during periods of high traffic or demand. It can also improve data security by reducing the risk of denial-of-service (DoS) attacks and other security threats that can overload computing resources.
With the increasing importance of data in today's digital world, the role of data engineers in data security is more important than ever before. By working closely with other stakeholders, such as data scientists, analysts, and IT professionals, they help protect data from loss, corruption, and unauthorized access and ensure that critical data is available and accessible in any unpredictable event.
At Sunscrapers, we specialize in providing cutting-edge data engineering and security services to businesses like yours. Our team of experts has a proven track record of delivering innovative solutions that help our clients unlock their data's full potential while ensuring that their sensitive information is protected against cyber threats.
If you're looking for a partner, you can trust to help you harness the power of your data and protect it against potential threats - contact us.