A tech profile is a description of a technical role within the company. These profiles are described by their expertise in technologies and tools, their skills, background, knowledge, and previous experience. For example, a software developer’s tech profile will include expertise in programming languages, frameworks, and additional tools. A data scientist’s profile will be focused on machine learning
Machine learning (ML) is a subset of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. It involves the development of algorithms that can analyze and learn from data, making decisions or predictions based on this data.
Common misconceptions about machine learning
ML is the same as AI. In reality, ML is a subset of AI. While AI is the broader concept of machines being able to carry out tasks in a way that we would consider “smart,” ML is a specific application of AI where machines can learn from data.
ML can learn and adapt on its own. In reality, ML models do learn from data, but they don't adapt or evolve autonomously. They operate and make predictions within the boundaries of their programming and the data they are trained on. Human intervention is often required to update or tweak models.
ML eliminates the need for human workers. In reality, while ML can automate certain tasks, it works best when complementing human skills and decision-making. It's a tool to enhance productivity and efficiency, not a replacement for the human workforce.
ML is only about building algorithms. In reality, algorithm design is a part of ML, but it also involves data preparation, feature selection, model training and testing, and deployment. It's a multi-faceted process that goes beyond just algorithms.
ML is infallible and unbiased. In reality, ML models can inherit biases present in the training data, leading to biased or flawed outcomes. Ensuring data quality and diversity is critical to minimize bias.
ML works with any kind of data. In reality, ML requires quality data. Garbage in, garbage out – if the input data is poor, the model's predictions will be unreliable. Data preprocessing is a vital step in ML.
ML models are always transparent and explainable. In reality, some complex models, like deep learning networks, can be "black boxes," making it hard to understand exactly how they arrive at a decision.
ML can make its own decisions. In reality, ML models can provide predictions or classifications based on data, but they don't "decide" in the human sense. They follow programmed instructions and cannot exercise judgment or understanding.
ML is only for tech companies. In reality, ML has applications across various industries – healthcare, finance, retail, manufacturing, and more. It's not limited to tech companies.
ML is a recent development. In reality, while ML has gained prominence recently due to technological advancements, its foundations were laid decades ago. The field has been evolving over a significant period.
Building blocks of machine learning
We can state that machine learning consists of certain blocks, like algorithms and data. What is their role exactly?
Algorithms are the rules or instructions followed by ML models to learn from data. They can be as simple as linear regression or as complex as deep learning neural networks. Some of the popular algorithms include:
Linear regression – used for predicting a continuous value.
Logistic regression – used for binary classification tasks (e.g., spam detection).
Decision trees – A model that makes decisions based on branching rules.
Random forest – An ensemble of decision trees typically used for classification problems.
Support vector machines – Effective in high dimensional spaces, used for classification and regression tasks.
Neural networks – A set of algorithms modeled after the human brain, used in deep learning for complex tasks like image and speech recognition.
K-means clustering – An unsupervised algorithm used to group data into clusters.
Gradient boosting machines – Builds models in a stage-wise fashion; it's a powerful technique for building predictive models.
An ML model is what you get when you train an algorithm with data. It's the output that can make predictions or decisions based on new input data. Different types of models include decision trees, support vector machines, and neural networks.
What’s the role of data in machine learning?
Data collection. The process of gathering information relevant to the problem you're trying to solve. This data can come from various sources and needs to be relevant and substantial enough to train models effectively.
Data processing. This involves cleaning and transforming the collected data into a format suitable for training ML models. It includes handling missing values, normalizing or scaling data, and encoding categorical variables.
Data usage. The processed data is then used for training, testing, and validating the ML models. Data is crucial in every step – from understanding the problem to fine-tuning the model for better accuracy.
Tools and technologies commonly used in ML
Python and R are the most popular due to their robust libraries and frameworks specifically designed for ML (like Scikit-learn, TensorFlow, and PyTorch for Python).
Data Analysis Tools: Pandas, NumPy, and Matplotlib in Python are essential for data manipulation and visualization.
Machine Learning Frameworks: TensorFlow, PyTorch, and Keras are widely used for building and training complex models, especially in deep learning.
Cloud Platforms: AWS, Google Cloud, and Azure offer ML services that provide scalable computing power and storage, along with various ML tools and APIs.
Big Data Technologies: Tools like Apache Hadoop and Spark are crucial when dealing with large datasets that are typical in ML applications.
Automated Machine Learning (AutoML): Platforms like Google's AutoML provide tools to automate the process of applying machine learning to real-world problems, making it more accessible.
Three types of ML
Machine Learning (ML) can be broadly categorized into three main types: Supervised learning, Unsupervised learning, and Reinforcement learning. Let's explore them with examples
Supervised learning
In supervised learning, the algorithm learns from labeled training data, helping to predict outcomes or classify data into groups. For example:
Email spam filtering. Classifying emails as “spam” or “not spam” based on distinguishing features in the data.
Credit scoring. Assessing credit worthiness of applicants by training on historical data where the credit score outcomes are known.
Medical diagnosis. Using patient data to predict the presence or absence of a disease.
Unsupervised learning
Unsupervised learning involves training on data without labeled outcomes. The algorithm tries to identify patterns and structures in the data. Real-world examples:
Market basket analysis. Identifying patterns in consumer purchasing by grouping products frequently bought together.
Social network analysis. Detecting communities or groups within a social network based on interactions or connections.
Anomaly detection in network traffic. Identifying unusual patterns that could signify network breaches or cyberattacks.
Reinforcement learning
Reinforcement learning is about taking suitable actions to maximize reward in a particular situation. It is employed by various software and machines to find the best possible behavior or path in a specific context. These are some examples:
Autonomous vehicles. Cars learn to drive by themselves through trial and error, with sensors providing feedback.
Robotics in manufacturing. Robots learn to perform tasks like assembling with increasing efficiency and precision.
Game AI. Algorithms that learn to play and improve at games like chess or Go by playing numerous games against themselves or other opponents.
How do we use ML in real life?
Predictive analytics is used in sales forecasting, risk assessment, and customer segmentation.
Customer service. Chatbots and virtual assistants powered by ML can handle customer inquiries efficiently.
Fraud detection. ML algorithms can analyze transaction patterns to identify and prevent fraudulent activities.
Supply chain optimization. Predictive models can forecast inventory needs and optimize supply chains.
Personalization. In marketing, ML can be used for personalized recommendations and targeted advertising.
Human resources. Automating candidate screening and using predictive models to identify potential successful hires.
Predicting patient outcomes in healthcare
Researchers at Beth Israel Deaconess Medical Center used ML to predict the mortality risk of patients in intensive care units. By analyzing medical data like vital signs, lab results, and notes, the ML model could predict patient outcomes with high accuracy.
This application of ML aids doctors in making critical treatment decisions and allocating resources more effectively, potentially saving lives.
Fraud detection in finance and banking
JPMorgan Chase implemented an ML system to detect fraudulent transactions. The system analyzes patterns in large datasets of transactions to identify potentially fraudulent activities.
The ML model helps in reducing financial losses due to fraud and enhances the security of customer transactions.
Personalized shopping experiences in retail
Amazon uses ML algorithms for its recommendation system, which suggests products to customers based on their browsing and purchasing history.
This personalized shopping experience increases customer satisfaction and loyalty, and also boosts sales by suggesting relevant products that customers are more likely to purchase.
Predictive maintenance in manufacturing
Airbus implemented ML algorithms to predict failures in aircraft components. By analyzing data from various sensors on planes, they can predict when parts need maintenance before they fail.
This approach minimizes downtime, reduces maintenance costs, and improves safety.
Precision farming in agriculture
John Deere uses ML to provide farmers with insights about planting, crop care, and harvesting, using data from field sensors and satellite imagery.
This information helps farmers make better decisions, leading to increased crop yields and more efficient farming practices.
Autonomous driving in automotive
Tesla's Autopilot system uses ML to enable semi-autonomous driving. The system processes data from cameras, radar, and sensors to make real-time driving decisions.
While still in development, this technology has the potential to reduce accidents, ease traffic congestion, and revolutionize transportation.
, data visualization, and statistical analysis skills. A network engineer’s profile will highlight knowledge of network design, security protocols, and hardware systems.
In other words, tech profile expertise is a way to show what a tech specialist knows and what they can do in their area of work. They are needed to match the right people to the right jobs. Here are some of the popular tech roles and their tech profiles.
Software Developer
A software developer creates programs and applications that we use on computers, phones, and other devices. They write code in different languages to make these programs work, fix errors, and update them to improve their functionality. Developers work on many types of software, from mobile apps and websites to games and tools for businesses.
There are different types of software developers. Some work on front-end development, which is about designing what users see and interact with, like buttons, menus, or layouts on a website. Others focus on back-end development, which involves building the parts of a program that users don’t see, like databases and servers.
Depending on their level of expertise and ability to deal with complex tasks, there are junior, middle, and senior software developers. This is an example of an expertise a software developer might be required to have:
Programming languages: Python, Java, C++, JavaScript, Ruby, C#, etc.
Frameworks and libraries: React, Angular, Node.js, Spring, Django, Flask, etc.
Version control: Git, GitHub, or GitLab.
Databases: MySQL, PostgreSQL and NoSQL (MongoDB, Redis) databases.
DevOps
DevOps is a set of principles, practices, and tools that aims to bridge the gap between software development and IT operations. It promotes collaboration, automation, and continuous integration and delivery to streamline the software development and deployment lifecycle. Essentially, DevOps seeks to break down silos and foster a culture of collaboration between development and operations teams.
Why use DevOps?
Faster delivery – DevOps accelerates the software delivery process, allowing organizations to release updates, features, and bug fixes more rapidly.
Enhanced quality – By automating testing, code reviews, and deployment, DevOps reduces human error, leading to more reliable and higher-quality software.
Improved collaboration – DevOps promotes cross-functional collaboration, enabling development and operations teams to work together seamlessly.
Efficient resource utilization – DevOps practices optimize resource allocation, leading to cost savings and more efficient use of infrastructure and human resources.
What are the DevOps Tools?
DevOps relies on a wide array of tools to automate and manage various aspects of the software development lifecycle. Some popular DevOps tools include:
Version control: Git, SVN
Continuous integration: Jenkins, Travis CI, CircleCI
Configuration management: Ansible, Puppet, Chef
Containerization: Docker, Kubernetes
Monitoring and logging: Prometheus, ELK Stack (Elasticsearch, Logstash, Kibana)
Collaboration: Slack, Microsoft Teams
Cloud services: AWS, Azure, Google Cloud
What are the best DevOps practices?
Continuous Integration. Developers integrate code into a shared repository multiple times a day. Automated tests are run to catch integration issues early.
Continuous Delivery. Code changes that pass CI are automatically deployed to production or staging environments for testing.
Infrastructure as code (IaC). Infrastructure is defined and managed through code, allowing for consistent and reproducible environments.
Automated testing. Automated testing, including unit tests, integration tests, and end-to-end tests, ensures code quality and reliability.
Monitoring and feedback. Continuous monitoring of applications and infrastructure provides real-time feedback on performance and issues, allowing for rapid response.
Collaboration and communication. Open and transparent communication between development and operations teams is essential for successful DevOps practices.
What is the DevOps role in software development?
DevOps is rather a cultural shift that involves collaboration between various roles, including developers, system administrators, quality assurance engineers, and more. DevOps encourages shared responsibilities, automation, and continuous improvement across these roles. It fosters a mindset of accountability for the entire software development lifecycle, from code creation to deployment and beyond.
What are the alternatives to DevOps?
While DevOps has gained widespread adoption, there are alternative approaches to software development and delivery.
Waterfall is a traditional linear approach to software development that involves sequential phases of planning, design, development, testing, and deployment.
Agile methodologies, such as Scrum and Kanban, emphasize iterative and customer-focused development but may not provide the same level of automation and collaboration as DevOps.
NoOps is a concept where organizations automate operations to the extent that traditional operations roles become unnecessary. However, it may not be suitable for all organizations or situations.
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DevOps is a transformative approach to software development that prioritizes collaboration, automation, and continuous improvement. By adopting DevOps practices and tools, you can enhance your software delivery, improve quality, and stay competitive. Give us a call if you’re looking for a skilled DevOps engineer but fail to find them locally. basics: Knowledge of CI/CD pipelines and tools like Jenkins, Docker, or Kubernetes.
Testing and debugging: Writing unit tests and debugging software.
Soft skills: Collaboration, problem-solving, and agile development practices.
QA Engineer
Quality assurance engineers make sure that software works the way it should and has no problems. They test programs to find mistakes, bugs, or anything that could cause the software to fail. Their goal is to help developers fix issues before users experience them.
There are different types of QA engineers based on what they focus on. Some test software by using it like a regular user to find errors. These are manual QA engineers. Others use special tools to create automated tests that check the software quickly. These are automation QA engineers. Some focus on how well the software handles large amounts of users or data, like during busy times. These are performance QA engineers.
There are also different levels of seniority for QA engineers depending on their skills and seniority. As a rule, automation QA engineers make more money as they have to have more specific knowledge, like programming languages. Here’s an example of an automation QA engineer tech profile:
Testing techniques: Manual, automated, regression, and performance testing.
Automation tools: Selenium, Appium, Cypress, TestNG, or JUnit.
Bug tracking: Bug tracking systems like JIRA or Bugzilla.
Scripting: Python, Java, or JavaScript for test automation.
API
Imagine you're at a restaurant. You don't need to know how the kitchen operates or where the food comes from. You simply look at the menu (the API) and order what you want. The waiter (the API) takes your order, communicates it to the kitchen (the system), and brings you the food (the data).
In simpler terms, an API is a set of rules that allows different software programs to talk to each other. It's like a messenger that carries information between two applications. This makes it easier for developers to build new things without having to start from scratch.
For example, a weather app uses an API to get data from a weather service or a social media app uses an API to share content on other platforms. Essentially, APIs allow different software applications to work together seamlessly.
API definition
API (Application Programming Interface) is a set of protocols, routines, and tools for building software applications. It specifies how software components should interact. Essentially, an API acts as an intermediary, allowing different software applications to communicate and share data without requiring knowledge of each other's internal implementation.
How does API work?
An API is a mediator between two software applications, enabling them to communicate and exchange data. This interaction occurs through a request-response cycle.
Request. A client application (like a mobile app or website) sends a request to an API server. The request typically includes specific parameters or data.
Processing. The API server receives the request, processes it based on predefined rules, and accesses the necessary data or performs required actions.
Response. The API server sends a response back to the client, containing the requested data or a status indicating the outcome of the request.
What are the key components of an API?
An API consists of several key components that work together to facilitate communication between software applications. Here are some of them:
Endpoints. These are specific URLs that represent the resources or data accessible through the API. For example, https://api.example.com/users might be an endpoint for retrieving user information.
HTTP methods. These dictate the type of action to be performed on a resource. Common methods include:
GET: Retrieve data
POST: Create new data
PUT: Update existing data
DELETE: Delete existing data
Headers. Additional information sent with the request, such as authentication credentials, content type, and request parameters.
Request body. Data sent to the API server for processing, often in JSON or XML format.
Response. The data returned by the API server, typically in JSON or XML format, along with a status code indicating the success or failure of the request.
Documentation. Detailed information about the API's capabilities, endpoints, parameters, and expected responses.
How do you use API in practice?
Every modern application you use uses APIs. Weather apps use APIs to fetch weather data for different locations. An e-commerce website integrates payment gateways using their APIs to process transactions, and a mapping application incorporates maps and directions using Google Maps API.
Using an API typically involves several steps.
Finding a suitable API. Identify an API that offers the data or functionality you need. Popular platforms like Google, Twitter, and many others provide public APIs.
Understanding the API documentation. Carefully read the API documentation to learn about endpoints, parameters, request formats, and expected responses.
Obtaining necessary credentials. Some APIs require authentication, so you'll need to obtain API keys or tokens.
Making API calls. Use programming languages (like Python, JavaScript, or Java) to construct HTTP requests to the API's endpoints.
Parsing the response. Process the data returned by the API to extract the desired information.
Handling errors. Implement error handling mechanisms to gracefully handle unexpected responses or API failures.
Remember that most APIs have usage limits, so be mindful of your request frequency. Handle sensitive data securely, comply with relevant regulations, and be prepared for API changes and updates.
testing: Postman or SoapUI.
Performance testing: JMeter or LoadRunner for stress and load testing.
Agile and DevOps: Understanding workflows in agile environments and integration into CI/CD pipelines.
AI/ML Engineer
An AI/ML engineer creates systems that can learn from data and make their own decisions. They work with programs that recognize patterns, predict outcomes, or improve tasks like speech recognition, image processing, or recommendation systems. For example, they help build things like voice assistants, chatbots, or tools that suggest what you might like to watch or buy.
There are different kinds of AI/ML engineers. Some focus on training machine learning models by feeding them data and improving their accuracy. Others work on making sure these models can handle large amounts of data. Some specialize in natural language processing, which helps computers understand human language, while others focus on computer vision, which helps machines analyze images or videos.
AI/ML engineers are in high demand as of 2025, and their tech profile is in the process of formation. Here are some of the requirements for AI/ML engineers that employers pose:
Programming languages: Python, R, Java, or Julia, with a focus on libraries like TensorFlow, PyTorch, and scikit-learn.
Mathematics and statistics: Linear algebra, calculus, and probability.
Data preprocessing: Cleaning and preparing data for machine learning models.
Algorithms: Supervised, unsupervised, and reinforcement learning techniques.
Big data
Big data is a massive amount of information that is too large and complex for traditional data-processing application software to handle. Think of it as a constantly flowing firehose of data, and you need special tools to manage and understand it.
Big data definition in simple words
Big data encompasses structured, unstructured, and semi-structured data that grows exponentially over time. It can be analyzed to uncover valuable insights and inform strategic decision-making.
The term often describes data sets characterized by the "three Vs": Volume (large amounts of data), Velocity (rapidly generated data), and Variety (diverse data types).
How does big data work?
Big data is processed through a series of stages.
Data generation → Data is produced from sources, including social media, sensors, transactions, and more.
Data capture → This involves collecting data and storing it in raw format.
Data storage → Data is stored in specialized data warehouses or data lakes designed to handle massive volumes.
Data processing → Raw data is cleaned, transformed, and structured to make it suitable for analysis.
Data analysis → Advanced analytics tools and techniques, like machine learning and artificial intelligence, are applied to extract valuable insights and patterns.
Data visualization → Results are presented in visual formats like graphs, charts, and dashboards for easy interpretation.
What are the key technologies used in big data processing?
Big data processing relies on a combination of software and hardware technologies. Here are some of the most prominent ones.
Data storage
Hadoop Distributed File System (HDFS). Stores massive amounts of data across multiple nodes in a distributed cluster.
NoSQL databases. Designed for handling unstructured and semi-structured data, offering flexibility and scalability.
Data processing
Apache Hadoop. A framework for processing large datasets across clusters of computers using parallel processing.
Apache Spark. A fast and general-purpose cluster computing framework for big data processing.
MapReduce. A programming model for processing large data sets with parallel and distributed algorithms.
Data analysis
SQL and NoSQL databases. For structured and unstructured data querying and analysis.
Data mining tools. For discovering patterns and relationships within large data sets.
Machine learning and AI. For building predictive models and making data-driven decisions.
Business intelligence tools. For data visualization and reporting.
What is the practical use of big data?
Big data has revolutionized the way businesses operate and make decisions. In business, it helps with customer analytics, marketing optimization, fraud detection, supply chain management, and risk management. But that’s not all!
Big data in healthcare
Analyzing data helps identify potential disease outbreaks and develop prevention strategies. It became an important tool for virologists and immunologists, who use data to predict not only when and what kind of disease can outbreak, but also the exact stamm of a virus or an infection.
Big data helps create personalized medicine by tailoring treatments based on individual patient data. It also accelerates the drug development process by analyzing vast amounts of biomedical data.
Big data for the government
Big data can help create smart cities by optimizing urban planning, traffic management, and resource allocation. It can help the police to analyze crime patterns and improve policing strategies and response times. For disaster-prone regions, big data can help predict and respond to natural disasters.
Essentially, big data has the potential to transform any industry by providing insights that drive innovation, efficiency, and decision-making. That includes
finance (fraud detection, risk assessment, algorithmic trading),
manufacturing (predictive maintenance, quality control, supply chain optimization),
energy (smart grids, energy efficiency, demand forecasting), and even
agriculture (precision agriculture, crop yield prediction, and resource optimization).
What kinds of specialists work with big data?
The world of big data requires a diverse range of professionals to manage and extract value from complex datasets. Among the core roles are Data Engineers, Data Scientists, and Data Analysts. While these roles often intersect and collaborate, they have distinct responsibilities within big data.
Data engineers focus on building and maintaining the infrastructure that supports data processing and analysis. Their responsibilities include:
Designing and constructing data pipelines.
Developing and maintaining data warehouses and data lakes.
Ensuring data quality and consistency.
Optimizing data processing for performance and efficiency.
They usually need strong programming skills (Python, Java, Scala) and be able to work with database management, cloud computing (AWS, GCP, Azure), data warehousing, and big data tools (Hadoop, Spark).
A data analyst’s focus is on extracting insights from data to inform business decisions. Here’s exactly what they’re responsible for:
Collecting, cleaning, and preparing data for analysis.
Performing statistical analysis and data mining.
Creating visualizations and reports to communicate findings.
Collaborating with stakeholders to understand business needs.
Data analysts should be pros in SQL, data visualization tools (Tableau, Power BI), and statistical software (R, Python).
Data scientists apply advanced statistical and machine-learning techniques to solve complex business problems. They do so by:
Building predictive models and algorithms.
Developing machine learning pipelines.
Experimenting with new data sources and techniques.
Communicating findings to technical and non-technical audiences.
Data scientists need strong programming skills (Python, R), knowledge of statistics, machine learning, and data mining, and a deep understanding of business problems.
In essence, Data Engineers build the foundation for data analysis by creating and maintaining the data infrastructure. Data Analysts focus on exploring and understanding data to uncover insights, while Data Scientists build predictive models and algorithms to solve complex business problems. These roles often work collaboratively to extract maximum value from data.
Along with this trio, there are also other supporting roles. A Data Architect will design the overall architecture for big data solutions. A Database Administrator will manage and maintain databases. A Data Warehouse Architect will design and implement data warehouses. A Business Analyst will translate business needs into data requirements. These roles often overlap and require a combination of technical and business skills. As the field evolves, new roles and specializations are also emerging.
What is the future of big data?
The future of big data is marked by exponential growth and increasing sophistication. These are just some of the trends we should expect in 2024 and beyond.
Quantum computing promises to revolutionize big data processing by handling complex calculations at unprecedented speeds.
Processing data closer to its source will reduce latency and improve real-time insights.
AI and ML will become even more integrated into big data platforms, enabling more complex analysis and automation.
As data becomes more valuable, regulations like GDPR and CCPA will continue to shape how data is collected, stored, and used.
Responsible data practices, including bias detection and mitigation, will be crucial.
Turning data into revenue streams will become increasingly important.
The demand for skilled data scientists and analysts will continue to outpace supply.
Meanwhile, big data is not without its challenges. Ensuring its accuracy and consistency will remain a challenge and an opportunity for competitive advantage.
: Hadoop, Spark, or distributed systems for large datasets.
Cloud platforms: AWS, GCP, or Azure.
Natural language processing: Skills in working with text data, tools like NLTK, or Hugging Face transformers.
Business Analyst
A business analyst helps a company understand its needs and find the best solutions. They talk to different teams to learn about their goals and challenges, study data, and create plans to improve processes or solve problems. Their main job is to connect the business side with the technical side.
Some BA/BI specialists create reports and dashboards that show important numbers and trends, while others organize data from different sources. There are also specialists who focus on finding patterns in the data to predict future trends or risks. Here’s a brief tech profile of a business analyst:
Conducting interviews, workshops, and surveys to collect requirements.
UML diagrams, flowcharts, or BPMN for process mapping.
Creating business requirement documents and functional requirement documents.
Data analysis: Excel, SQL, or Power BI.
Tools: JIRA, Confluence, or Trello for project management.
Data Engineer
A data engineer builds systems that collect, store, and organize data. They make sure the data is clean, safe, and ready for others, like data scientists or analysts, to use.
There are different kinds of data specialists. Data engineers focus on creating and maintaining the systems that move and store data. Data scientists use this data to find patterns, make predictions, and solve problems. Data analysts study the data to answer questions and create reports that explain what it means. Database administrators manage databases to keep data organized and secure.
Here’s an example of a tech profile of a data specialist:
ETL processes: Building pipelines to extract, transform, and load data.
Big data tools: Hadoop, Apache Spark, Kafka, or Flink.
Database systems: SQL, NoSQL, and data warehousing (Snowflake, Redshift).
Programming: Python, Scala, or Java for data manipulation.
Cloud services: AWS Redshift, Azure Data Lake, or Google BigQuery.
Workflow automation: Apache Airflow or Prefect.
DevOps Engineer
DevOps engineers create systems to automate tasks so new features and fixes can be added without breaking the software. There are different kinds of DevOps specialists. Some focus on building and managing tools that automate processes, like testing or deployment. Others specialize in cloud platforms, setting up and managing servers in AWS or Azure. Some ensure the software is secure, while others focus on monitoring systems to fix issues if something goes wrong. Here is what expected of a DevOps engineer:
CI/CD pipelines: Build automated pipelines with Jenkins, GitLab CI/CD, or CircleCI.
Infrastructure as Code: Terraform or Ansible to manage infrastructure.
Cloud services: AWS, Azure, or Google Cloud.
Containerization: Docker and Kubernetes.
Monitoring: Prometheus, Grafana, or Splunk.
Scripting: Bash, Python, or Go.
Cybersecurity
What is cybersecurity? Cybersecurity encompasses the techniques and processes aimed at protecting computer systems, networks, and data from digital threats, unauthorized access, or damage. It involves deploying security measures, including firewalls, antivirus software, and intrusion detection systems, coupled with user education and stringent security policies.
With hybrid wars that include cyber attacks today, the importance of cybersecurity, common threats, and best practices for protection is essential.
What does cybersecurity do?
Protecting sensitive data. Cybersecurity shields personal and corporate data from theft, damage, or unauthorized modification. According to Verizon's 2023 report, data breaches have increased by 33% over the past year, emphasizing the need for robust data protection.
Preventing unauthorized access. Cybersecurity practices involve implementing measures like multi-factor authentication and access controls. A study by IBM found that unauthorized access was a primary cause of 43% of data breaches.
Maintaining privacy. This function of cybersecurity is essential for safeguarding user data against illicit tracking and collection. Privacy laws like GDPR in the EU have put a spotlight on the importance of privacy in cybersecurity.
Ensuring continuity of business operations. Cybersecurity prevents disruptions caused by cyberattacks. For example, the WannaCry ransomware attack of 2017 caused an estimated $4 billion in worldwide losses.
Legal and regulatory compliance. Non-compliance with laws like HIPAA can lead to heavy fines. For example, HIPAA violations can cost up to $1.5 million per incident.
Building trust. Effective cybersecurity practices enhance customer confidence. Surveys indicate that 85% of consumers value privacy and data protection when choosing companies to do business with.
Cybersecurity is integral to modern business operations, offering protection against a wide range of digital threats and ensuring compliance with legal standards. It safeguards data and plays a vital role in maintaining business continuity and building customer trust.
Common cybersecurity threats
These are some of the most common threats modern companies have to face.
Malware encompasses various forms of harmful software, including viruses that can replicate themselves, worms that spread across networks, trojans that disguise themselves as legitimate software, and ransomware that locks users out of their systems until a ransom is paid. The impact of malware can be severe: for example, the WannaCry ransomware attack we mentioned above affected more than 200,000 computers across 150 countries.
Phishing attacks involve deceptive emails or websites that trick individuals into revealing sensitive information like passwords or credit card numbers. The FBI’s Internet Crime Report noted that phishing was the most common type of cybercrime in 2020.
Man-in-the-middle attacks (MitM). This form of eavesdropping intercepts communication between two parties to steal or alter the data. A common example is a hacker intercepting data on an unsecured Wi-Fi network.
Denial of service (DoS) attacks flood systems, servers, or networks with traffic to exhaust resources and bandwidth, rendering the service unusable. One of the most notorious DoS attacks was against Dyn, a major DNS provider, in 2016, disrupting internet platforms and services.
SQL injection involves inserting malicious code into SQL-using databases via a vulnerable website, which can then be used to access and manipulate confidential data. For example, in 2019, a SQL injection attack exposed the data of over 1 million customers of an Australian telecommunications company.
Zero-day exploits target unknown vulnerabilities in software or hardware, making them particularly dangerous as they occur before the vendor becomes aware and fixes the issue. The Stuxnet worm, discovered in 2010, is one of the most famous examples of a zero-day exploit.
Best practices for IT and cyber protection
Here are some things you can do as an individual or as a business owner to protect your personal and sensitive data from the simplest attacks and cyber threats.
1/ Strong passwords and multi-factor authentication. Strong, unique passwords, coupled with MFA, significantly heighten security. According to Verizon's Data Breach Investigations Report, 81% of hacking-related breaches leveraged either stolen and/or weak passwords.
2/ Regular software updates. Consistently updating software and systems helps patch security vulnerabilities. Microsoft reported that updating systems could prevent 85% of targeted cyberattacks.
3/ Employee training and awareness. Training staff on cybersecurity risks is essential. IBM’s Cyber Security Intelligence Index found that 95% of cybersecurity breaches are due to human error.
4/ Firewalls and antivirus software. These tools are fundamental in safeguarding against various cyber threats. The use of antivirus software can detect and block nearly 100% of known malware.
5/ Data encryption. Encrypting sensitive data, both in transit and at rest, is critical. A study by the Ponemon Institute showed that encryption can significantly reduce the cost of a data breach.
6/ Regular backups. Backing up data ensures recovery in the event of an attack. Companies that regularly back up and encrypt their data can reduce the impact of data breaches significantly.
7/ Incident response plan. An effective response plan can reduce the cost of a data breach by as much as 40%, according to IBM’s Cost of a Data Breach report.
8/ Secure Wi-Fi networks. Securing wireless networks is vital. A survey by Symantec revealed that 60% of consumers believe their Wi-Fi networks are secure, but only 50% have taken steps to secure them.
9/ Vulnerability assessments and penetration testing. Regular testing and patching of vulnerabilities are key. Cisco’s Annual Cybersecurity Report highlighted that 42% of organizations faced public scrutiny after a security breach.
10/ Limiting user access. Implementing the principle of least privilege can significantly reduce risks. A study by Forrester found that 80% of security breaches involve privileged credentials.
What kind of specialists provide IT and cyber protection?
As cybersecurity is so complex and varied, it demands many skills from its providers. What used to be done by one person today is covered by five specialists and more. Here are some of the job positions you can find in cybersecurity and a short explanation of what these people do.
Cybersecurity Analyst monitors networks for security breaches, investigates violations, and implements protection solutions.
Network Security Engineer designs, implements, and maintains network security solutions to protect against cyber threats.
Information Security Manager oversees and coordinates the company’s information security policies and procedures.
Chief Information Security Officer (CISO) is a high-level executive responsible for the overall strategy and direction of information security in an organization.
Ethical Hacker/Penetration Tester simulates cyber attacks to identify and fix security vulnerabilities.
Security Software Developer develops security software, such as encryption technologies and firewall programs.
IT Security Consultant advises on best practices for protecting companies’ IT infrastructure and data.
Specialist
A cybersecurity specialist protects computers, networks, and data from hackers and other threats. They find weaknesses in systems and fix them. Their job is to stop attacks and keep sensitive information secure.
Some cybersecurity specialists protect networks by setting up firewalls and stopping unauthorized access. Others test systems to find and fix security flaws before hackers can exploit them. Some focus on responding to attacks, figuring out what happened, and preventing it from happening again. There are also specialists who protect sensitive data, like credit card details, or ensure companies follow laws about data security. Here’s a tech profile of a cybersecurity specialist:
Threat detection: Splunk, Wireshark, or SIEM platforms to identify threats.
Network security: Configure firewalls, VPNs, and intrusion detection/prevention systems.
Vulnerability management: Nessus or Qualys.
Compliance: GDPR, HIPAA, or ISO 27001.
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