Not everything in the universe is constant. We adopt the changes, and that is how the world keeps developing. The same thing applies to technology as well. Latest trends in every technology help in making the world a better place to live. Here, we have discussed some of those top trending technologies to learn in 2019.
Data Integration (ETL)
What is Data Integration?
Data integration is the process of combining data located in different sources to give a unified view to the users. However, data integration varies from application to application. In a commercial application, two organizations can merge their databases. In a scientific application such as in a bioinformatics project, the research results from various repositories can be combined into a single unit.
What is ETL?
ETL refers to three processes – Extract, Transform and Load. Simply defined, ETL enables the collection of data from various sources into one data store, ready for analysis.
ETL can be implemented with scripts (custom DIY code) or with a dedicated ETL tool. ETL performs several important functions including:
- Parsing/Cleansing – Data generated by applications is created in various formats like JSON, XML or CSV. During the parsing stage, data is mapped into a table format with headers, columns, and rows, and the specified fields are extracted.
- Data Enrichment – To prepare data for analytics, certain enrichment steps are usually required, including tweaking, injecting expert knowledge, geo modifications, matching between sources and correcting bugs.
- Setting Velocity – Velocity refers to the frequency of data loading, whether new data should be inserted, or if existing data needs to be updated.
- Data Validation – There are cases where data is empty, corrupted, missing crucial elements, too thin or too bloated. ETL finds these occurrences and determines whether to stop the entire process, skip it or set it aside for inspection while alerting the relevant administrators.
What is the difference between Data Integration and ETL?
The main difference between data integration and ETL is that the data integration is the process of combining data in different sources to provide a unified view to the users while ETL is the process of extracting, transforming and loading data in a data warehouse environment.
Data integration refers to combining data from disparate sources into meaningful and valuable information. Therefore, a complete data integration solution delivers trusted data from different sources. It is an important process when merging multiple systems and consolidating applications to provide a unified view of the data.
Why do I need a Data Integration Layer?
The short answer is that ETL saves significant time on data extraction and preparation – time that could be better spent on extracting insights.
Each of the 3 main components in the ETL saves time and development effort by doing it once in a dedicated data flow:
Extract – The extract stage determines different data sources, refresh rate (velocity) of each source, and priorities (extract order) between them – all of which heavily impact time-to-insights.
Transform – After extracting the data into an ETL environment, transformations bring clarity and order to the initial data swamp.
Implementing ETL in a DWH:
When an ETL process is used to load a database into a Data Warehouse (DWH), each phase is represented by a physical layer:
- Mirror/Raw layer – This layer is a copy of the source files or tables, with no logic or enrichment. Source data is copied and added to the target mirror tables, which then hold historical raw data that is ready to be transformed.
- Staging layer – Once the raw data from the mirror tables are transformed, all transformations are stored in staging tables. These tables hold the final form of the data for the incremental part of the ETL cycle in progress.
- Aggregating layer – In some cases, it is beneficial to aggregate data to a daily or store level from the full dataset. This can improve report performance, enable the addition of business logic to calculated measures and make it easier for report developers to understand the data.
ETL Tool Implementation:
Building your data transformation tool (usually a set of shell scripts) is the preferred approach for a small number of data sources that reside in the storage of the same type. The reason for that is the effort to implement the necessary transformation is little due to similar data structure and common system architecture. If the transformations become more sophisticated during the time or there is a need to integrate other systems, the complexity of such an ETL system grows but the manageability drops significantly. Similarly, the implementation of your tool often resembles re-inventing the wheel.
There are many ready-to-use ETL tools on the market. The main benefit of using off-the-shelf ETL tools is the fact that they are optimized for the ETL process by providing connectors to common data sources like databases, flat files, mainframe systems, XML, etc. This includes filtering, reformatting, sorting, joining, merging, aggregation and other operations ready to use. The tools also support transformation scheduling, version control, monitoring, and unified metadata management. Some of the ETL tools are even integrated with BI tools.
Some of the well-known ETL Tools are:
The most well-known commercial tools are Ab Initio, IBM InfoSphere DataStage, Informatica, Oracle Data Integrator and SAP Data Integrator.
There are several open-source ETL tools, among others Apatar, Clover ETL, Pentaho and Talend.
Who is a Data Engineer?
Data Engineers are the ones who develop, construct, tests and maintain the complete architecture of the large-scale processing system.
Let’s further drill down the Job Role of a Data Engineer. The crucial tasks included are:
- Designing, developing, constructing, installing, testing and maintaining the complete data management & processing systems.
- Building highly scalable, robust & fault-tolerant systems.
- Taking care of the complete ETL (Extract, Transform & Load) process.
- Ensuring architecture is planned in such a way that it meets all the business requirements.
- Discovering various opportunities for data acquisitions and exploring new ways of using existing data.
- Data Engineers are the ones who develop, construct, tests and maintain.
- Creating a complete solution by integrating a variety of programming languages & tools.
- Creating data models to reduce system complexity and hence increase efficiency & reduce cost.
- Deploying Disaster Recovery Techniques
- Introducing new data management tools & technologies into the existing system to make it more efficient.
What skill do Data Engineers need?
- Need to know Linux and should be comfortable using the command line.
- Should have experience in programming Python or Scala/Java.
- Need to know SQL.
- Need to have some understanding of Distributed Systems in general and how they are different from traditional storage and processing systems.
- Need a deep understanding of the ecosystem, including Ingestion (e.g. Kafka, Kinesis), Processing Frameworks (e.g. Spark, Flink) and Storage Engines (e.g. S3, HDFS, HBase, Kudu).
- Need to know how to Access and Process Data.
Data Engineering Roles:
Although data engineers need to have the skills listed above, the day to day of a data engineer will vary depending on the type of company they work for. Broadly, you can classify data engineers into a few categories:
Let’s go through each one of these categories.
- Generalist: A generalist data engineer typically works on a small team. Without a data engineer, data analysts and scientists don’t have anything to analyze, making a data engineer a critical first member of a data science team.
- Pipeline-centric: Pipeline-centric data engineers tend to be necessary in mid-sized companies that have complex data science needs. A pipeline-centric data engineer will work with teams of data scientists to transform data into a useful format for analysis. This entails in-depth knowledge of distributed systems and computer science.
- Database-centric: A database-centric data engineer is focused on setting up and populating analytics databases. This involves some work with pipelines, but more work with tuning databases for fast analysis and creating table schemas. This involves ETL work to get data into warehouses. This type of data engineer is usually found at larger companies with many data analysts that have their data distributed across databases.
Data Analytics & Data Visualization
What does Data Analytics mean?
Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioural data and patterns, and techniques vary according to organizational requirements.
Data analytics is also known as data analysis.
What is Data and Why it is Important?
Data is simply collected facts and statistics of business operations. Data can be used to measure and record a wide range of external and internal business activities. Although some of the data collected may not be informative, it is used as the basis for all reporting, planning, strategizing and crucial business decision making at all business levels. Its importance cannot be understated as it provides the basis for daily business operations.
The Process involved in Data Analysis:
- The first step is to determine the data requirements or how the data is grouped.
- The second step is the process of collecting it.
- Once the data is collected, it must be organized so it can be analyzed. The organization may take place on a spreadsheet or other form of software that can take statistical data.
- The data is then cleaned up before analysis. Although some of the data collected may not be informative, it is used as the basis for all reporting, planning,. This step helps correct any errors before it goes on to a data analyst to be analyzed.
Types of Data Analytics:
- Descriptive Analytics: The objective of descriptive models is to analyze historical trends and figure out relevant patterns to gain insights on the population behaviour. It is the most commonly used form of analytics by organizations for their day to day functioning and is generally the least complex.
Descriptive models use basic statistical and mathematical techniques to derive key performance indicators that highlight historical trends. The primary purpose of the model is not to estimate a value, but gain insight on the underlying behaviour. Common tools used for running descriptive analysis include MS Excel, SPSS, and STATA.
- Diagnostic Analytics: Diagnostic analytics is a form of advance analytics that examines data or content to answer the question “why did it happen?”, Descriptive analytics involves finding answers to ‘what has happened?’ Diagnostic analytics takes a deeper look at data to attempt to understand the causes of events and behaviours.
- Predictive Analytics uses statistical modelling to determine the probability of a future outcome or a situation occurring. It involves finding answers to ‘what could happen?’. Predictive models build upon descriptive models as they move beyond using historical data as the principal basis for decision making, often using structured and unstructured data from various sources. They enable decision-makers to make informed decisions by providing a compressive account of an event’s likelihood to occur in the future. They encompass various advanced statistical models and sophisticated math concepts like Random Forests, GBM, SVM, GLM, game theory, etc.
A predictive model builds on a descriptive model to predict future behaviour. However, unlike a descriptive model that only profiles the population, a predictive model focuses on predicting single customer behaviour.
Tools used to run predictive models vary by the nature of the model’s complexity, however, some of the commonly used tools are RapidMiner, R, Python, SAS, Matlab, Dataiku DSS, amongst many others.
- Prescriptive Analytics is the most sophisticated type of analytics that uses stochastic optimization and simulation to explore a set of possible options and recommend the best possible action for a given situation. It involves finding answers to ‘what should be done?’.
Prescriptive models go beyond descriptive models that only address what is going on, and beyond predictive models that can only tell what will happen, as they go on to advise what actually should be done in the predicted future. They quantify the effect of future actions on key business metrics and suggest the most optimal action.
Prescriptive models synthesize Big Data and business rules using complex algorithms to compare the likely outcomes several actions and choose the most optimum action to drive business objectives. Most advanced prescriptive models follow a simulation process where the model continuously and automatically learns from the current data to improve its intelligence.
That said, technical advancements such as supercomputers, Cloud Computing, Hadoop HDFS, Spark, in-database processing, MPP architecture, etc. have made deployment of complex prescriptive models using structured and unstructured data much easier. Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context.
What does Data Visualization mean?
Tools used to run prescriptive models are mostly the same as predictive models however, require advanced data infrastructure capabilitiesPatterns, trends, and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software.
Importance of Data Visualization:
Data visualization has become the de facto standard for modern Business Intelligence (BI). The success of the two leading vendors in the BI space is Tableau and Qlik. Virtually all BI software has strong data visualization functionality.
Data visualization tools have been important in democratizing data and analytics and making data-driven insights available to workers throughout an organization. When a Data Scientist is writing advanced predictive analytics or Machine Learning algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.
How does Data Visualization work?
Most of today’s data visualization toolsbut, increasingly, the software automates this step. Some tools automatically interpret the shape of the data and detect correlations between certain variables and then place . The visualization software pulls in data from these sources and applies a graphic type to the data.
Data visualization software allows the user to select the best way of presenting the data, tools come with connectors to popular data sources, including the most common relational databases, Hadoop and a variety of cloud storage platforms. these discoveries into the chart type that the software determines is optimal.
Difference between Data Visualization and Data Analytics?
Data visualization represents data in a visual context by making explicit the trends and patterns inherent in the data. Such pattern and trends may not be explicit in text-based data. Most tools allow the application of filters to manipulate the data as per user requirements. The traditional forms of visualization, in the form of charts, tables, line graphs, column charts, and many other forms, have of late been supplanted by highly insightful 3D visualizations.
Data analytics go a step deeper, identifying or discovering the, need not give the complete picture. Visualizations are only as effective as the data used to prepare the visualization in the first place. Feeding visualization engine with incomplete data will render half-baked, obsolete, trends and patterns inherent in the data. Data visualizations, while allowing users to make sense of the data.
Skills required in the field of Data Analytics:
- Statistics: Should be capable of working with tools like statistical tests, distributions, and maximum likelihood estimators.
- Programming & Software Skills:
- R: This programming language is used for statistical analysis, data visualization, and predictive modelling.
- Python: Python is the most common coding language required in data science roles, along with Java, Perl, SAS, SCALA or C/C++. Because of its versatility, you can use Python for almost all the steps involved in data science processes. It can take various formats of data and you can easily import SQL tables into your code.
- Tableau: Tableau offers a suite of products that complement data science standbys such as R and Python. Tableau may not be the best tool for cleaning or reshaping data, and its relational model doesn’t allow for procedural computations or offline algorithms, but it is great for data exploration and interactive analysis. Tableau provides a high-level interface for exploring and visualizing data in friendly and dynamic dashboards.
- Hadoop: Hadoop is an open-source software framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models.
- There are multiple implementations of the same general syntax, including MySQL, SQLite, and PostgreSQL
- SQL: SQL, is used for managing data held in relational database management systems. Hadoop offers computing power, flexibility, fault tolerance, and scalability.
- Apache Spark: Similar to Hadoop, Spark is a cluster computing framework that enables clusters of computers to process data in parallel. Spark is faster at many tasks than Hadoop due to its focus on enabling faster data access by storing data in RAM. It replaces Hadoop’s MapReduce implementation but still relies on the Hadoop Distributed File System.
- Critical Thinking: A data scientist should be able to abstract the essence of the problem and ignore irrelevant details.
- Knowledge of Machine Learning, Deep Learning, and AI: Machine Learning is a subset of Artificial Intelligence that uses statistical methods to make computers capable of learning with data. Deep Learning is a part of a family of machine learning methods. It is based on learning data representations; learning can be unsupervised, semi-supervised, or supervised.
- Mathematics: A data scientist should be able to develop complex financial or operational models that are statistically relevant and can help shape key business strategies.
- Data Wrangling: A lot of data you will be working on will be messy. Values could be missing, there could be inconsistent formatting with dates and strings. Hence, cleaning and wrangling of the data is required.
- Ability to work with Unstructured Data.
Job Roles in the field of Data Analytics:
- Data Analyst: Data analysts are responsible for a variety of tasks including visualization, munging, and processing of massive amounts of data.
- Data Engineers: Data engineers build and test scalable Big Data ecosystems for the businesses so that the data scientists can run their algorithms on the data systems that are stable and highly optimized.
- Database Administrators: They are responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on their requirements.
- Machine Learning Engineer: Apart from having in-depth knowledge in some of the most powerful technologies such as SQL, REST APIs, etc. machine learning engineers are also expected to perform A/B testing, build data pipelines, and implement common machine learning algorithms such as classification, clustering, etc.
- Data Scientist: Data scientists have to understand the challenges of business and offer the best solutions using data analysis and data processing. For instance, they are expected to perform predictive analysis and run a fine-toothed comb through an “unstructured/disorganized” data to offer actionable insights.
- Data Architect: A data architect creates the blueprints for data management so that the databases can be easily integrated, centralized, and protected with the best security measures.
- Statistician: Should have a sound understanding of statistical theories and data organization.
- Business Analyst: They should have a good understanding of how data-oriented technologies work and should also know how to handle large volumes of data. They also separate the high-value data from the low-value data. In other words, they identify how Big Data can be linked to actionable business insights for business growth.
Virtual Reality is defined as “the use of computer technology to create a simulated environment.”
When you view VR, you are viewing a completely different reality than the one in front of you. Virtual reality may be artificial, such as an animated scene, or an actual place that has been photographed and included in a virtual reality app. With virtual reality, you can move around and look in every direction — up, down, sideways and behind you, as if you were physically there.
You can view virtual reality through a special VR viewer, such as the Oculus Rift. Other virtual reality viewers use your phone and VR apps, such as Google Cardboard or Daydream View.
Augmented reality is defined as “an enhanced version of reality created by the use of technology to add digital information on an image of something.”
AR is used in apps for smartphones and tablets. AR apps use your phone’s camera to show you a view of the real world in front of you, then put a layer of information, including text and/or images, on top of that view. Apps can use AR for fun, such as the game Pokémon GO, or information, such as the app Layar.
The Layar app can show you interesting information about the places you visit, using augmented reality. Open the app when you are visiting a site and read the information that appears in a layer over your view.
Virtual Reality Technical Breakdown?
Technically VR induces targeted behaviour in an organism by using artificial sensory stimulation, while the organism has little or no awareness of the interference.
This allows us to break the functional elements down into four main components associated with VR:
- Targeted Behaviour: The organism has some “experience” which is designed by virtual reality developers. Example: Walking, flying, space exploration, doing lab experiments and interacting with other organisms.
- Organism: Organism refers to the VR user, to include other life forms. Example: Human beings, animals and chatbots.
- Artificial Sensory Stimulation: With the integration of modern techniques of engineering, various sensory experiences of organisms can be replicated and the sensory inputs are replaced by artificial stimulation.
- Awareness: With effective virtual reality experiences, the organism experiences a smooth interaction and there is no friction between the user and the experience of the interface to the simulated world, thereby easily “misleading” the user into really feeling present in the virtual world.
Augmented Reality Technical Breakdown?
Augmented Reality (AR) is regarded as a variation of VR. For this reason, AR is often listed in combination with VR, as ‘AR/VR’, and sometimes ‘VR/AR’, and also ‘AR/VR/MR’, where MR stands for Mixed Reality.
Mixed Reality is a useful term because it encapsulates the fact that there are various configurations or hybrid AR/VR systems.
Augmented Reality Systems have the following characteristics:
- The mix of the real world and virtual objects in a real environment.
- Synchronize real and virtual objects with each other.
- Highly interactive and runs in 3D in real-time.
Technologies for Virtual Reality?
About selecting VR devices, the main importance is to select a device that is user-friendly, i.e. comfortable to wear, flexible in operations, and the viewing depth and visual experience have to provide a good dynamic VR experience for the user.
- Stereoscopic Imagery: A binocular Head-Mounted Display HMD can display slightly different viewing angles for each eye, creating a binocular overlap, which gives the viewer the illusion of stereoscopic depth and a sort of realistic 3D viewing experience.
- Interpupillary Distance (IPD): The distance between the eyes, measured at the pupils.
- Field of View (FOV): The natural FOV of human beings is about 180 degree, but so far HMDs are not capable of creating this. Current HMDs offer a field of view of between 60-150 degree.
- HMD Resolution: For an effective visual experience, a resolution of 1920*1080 (960*1080 per eye) is required. HMD specifications are usually described by the total number of pixels or the number of pixels per degree, also called ‘pixel density’.
- On-Board Processing and Operating System: Wireless HMDs, also known as ‘smart goggles’, have on-board operating systems such as Android, allowing applications to run locally on the HMD.
Technologies for Augmented Reality?
AR is enabled by new technical features such as computer vision, object recognition, miniature accelerometers, a Global Positing System (GPS) and the solid-state compass. The AR display technologies that are essential to creating the AR experience are briefly described below:
- Marker-based AR: It relies on the image recognition technique that takes help of a camera and certain types of visual markers like QR/2D code, that gives result out only when sensed by a reader.
- Marker-less AR (also known as location-based, position-based, or GPS-based AR): It uses miniature versions of a GPS, a digital compass, and a velocity meter, or accelerometer, which are embedded in the device, to provide data based on the exact location and orientation of the device.
- Projection-based AR: It works by projecting artificial light onto real-world surfaces.
- Superimposition-based AR: This type partially or fully replaces the original view of the object with an augmented view of that object.
How do you become an AR/VR Developer?
The most important skills that you need are in the 3D area. Because AR and VR are about creating immersive worlds or environments that can be interacted with in three dimensions, like in real life. So depending on how deep you want to go, you may have to learn about 3D modelling and/or scanning, 3D games engines, 360° photos and videos, maybe a little bit of math and geometry, programming languages like C/C++/C# and Software Development Kits (SDKs), and how to design experiences for users in 3D.
Augmented/Virtual reality (and 3D development in general) demands high-end hardware. If we take a look at the requirements of the two most popular VR platforms (HTC Vive and Oculus Rift), we’ll see they are the same.
These are the recommended hardware specifications:
- Processor: Intel Core i5-4590 or AMD FX 8350
- Graphics: NVIDIA GeForce GTX 1060 or AMD Radeon RX 480
- Memory:8 GB RAM
- Ports: 3x USB 3.0.
- Operating System: Windows 7 or superior
The above are the recommended specifications to have a smooth experience, so for some projects, maybe a little less would do the work, and for others, maybe you’ll have to compensate for the lack of power in one area with an increase of power in another one (for example, by pairing an i3 processor with a GTX 1070).
Virtual Reality Devices
In this area, we have many options. Let’s categorize the most popular by their Degree of Freedom (DOF), which refers to how an object can move. We have two options: three and six DOF.
Three DOF means that you will be able to interact with the virtual world in three dimensions (in an X, Y, Z coordinate system) by moving your head using an HMD, however, you wouldn’t be able to move forward or backward. With six DOF, you can move forward/back, up/down, and left/right, so you now have three more types of movements, hence the name. You can learn more about the degrees of freedom in this article.
Devices supporting 3 DOF: Google Cardboard. Google Daydream. Samsung Gear VR
Devices supporting 6 DOF: HTC Vive. Oculus Rift
Of course, each device uses different SDKs, programming languages, and has different constraints, but you’ll find that they have some things in common:
- The principles for designing a Virtual Reality experience are the same
- Most of them are compatible with motion controllers to interact with the virtual world
- Three DOF devices use smartphones as head-mounted displays
- Six DOF devices use desktop headsets
3D Game Engines and Programming Languages
C# and C/C++ are the most used programming languages for AR/VR development, and the most popular game engines you’ll need to learn to use them are:
- Unity, which uses C# as its primary programming language
- Unreal Engine, which uses C++ and a node-based language called Blueprints Visual Scripting
The good news is that all VR devices have SDKs available for both engines so you can use only one of them to develop AR/VR applications and target more than one device. The bad news is that the learning curve is relatively steep for both. When in doubt, most people recommend Unity because it’s easier to learn and more resources are available. However, Unreal can offer you better graphics and more power.
The recommendation is to try both of these engines to see which one suits you best. Also, it’s worth mentioning that Google provides SDKs for Android (in Java) and iOS (in Objective-C) to develop for Daydream and Cardboard devices.
Make or Find Assets?
The first things you’ll need for AR/VR development are art assets, especially 3D models. You have two options here: make them yourself or use models made by someone else. Making 3D models by yourself is the most difficult option, but in the long run, it may be the best (and most cost-effective).
A technology that can help you create your models is 3D scanning. Things captured by a 3D scanner in the real world become a virtual 3D model. They may not be perfect yet, but they can help you get started, and there are a lot of options with a wide range of price points. Some of these are DAVID SLS2. Da Vinci 1.0. AiO Structure Sense. Otherwise, you’ll want to get 3D models from places like TurboSquid. Free3D. CGTrader Sketchfab.
From Web Development to Virtual Reality
- A-frame: A framework for building virtual reality experiences with HTML and an Entity-Component-System approach. It was developed by the Mozilla VR team and provides one of the most powerful ways to develop WebVR content.
- React VR: A new library developed by Facebook based on React and React Native. It allows you to build VR websites and interactive 360 experiences using the same declarative component approach than React.
Now, from a development standpoint, VR and AR are pretty similar. You can use Unity and Unreal (with the help of some plugins) to develop AR content. For example, a simple AR app will recognize an object and present a 3D model that you could manipulate as if it were real, so the skills needed for VR apply to AR also.
One of the most popular tools for developing AR is Vuforia, which is available for Unity, Android, and iOS, provides a lot of features, and support for many devices.
User Interface (UI) / User Experience (UX)
The AR/VR industry is new and therefore there are not many best practices yet for developing this kind of experiences, but we should take for granted that they are different from traditional 2D apps. For example:
How do you handle input?
A keyboard in a virtual world may not be the best choice in some situations. On the other hand, one of the biggest problems with VR is simulator/motion sickness. People can get sick either by lagging, unnatural movements, and mismatches between physical and visual motion cues, among others.
Kinds of Jobs in the AR/VR Career Path?
- AR/VR Engineer/Developer
- Mixed Reality Developer
- IoT Software Engineer/Developer/Analyst
To summarize the steps to become an AR/VR Developer are:
- Defining the Platform: Decide which devices to target, which platforms (mobile, desktop, web), and which game engine/SDK/framework to use.
- Learning the Skills: Learn about the terminology, 3D modelling, the language of that engine/SDK/framework, UI/UX for AR/VR.
- Implementing something Small: Although a great number of AR/VR apps are games, there are a lot of areas that can be targeted, like education, data visualization, 360° experiences.
“It is only when they go wrong, that machines remind you how POWERFUL they are!” ~ Clive James.
What is Cybersecurity?
Cybersecurity refers to the use of network architecture, software, and other technologies to protect organizations and individuals from cyber-attacks. The objective of cybersecurity is to prevent or mitigate harm to, or destruction of computer networks, applications, devices, and data.
Cybersecurity may also be referred to as Information Technology Security.
Information and communications technology (ICT)?
It is an extensional term for Information Technology (IT) that stresses the role of unified communications and the integration of telecommunications (telephone lines and wireless signals) and computers, as well as necessary enterprise software, middleware, storage, and audio-visual systems, that enable users to access, store, transmit, and manipulate information.
Difference between Cybersecurity and ICT Security?
The two words “Cyber Security” and “Information Security” are generally used as synonyms in the security terminology.
While cybersecurity is about securing things that are vulnerable through ICT. It also considers that, where the data is stored, and, the technologies used to secure the data. Part of cybersecurity is about the protection of information and communications technologies – i.e. hardware and software, collectively known as the ICT security.
Notice that cybersecurity (right set) includes everything and everyone that can be accessed through cyberspace. So, one could argue that everything in this world is vulnerable through ICT. However, going by the definition of cybersecurity, we should protect which is to be protected, because of the security challenges posed by the use of ICT.
The Importance of Cybersecurity?
Organizations transmit sensitive data across networks and to other devices in the course of doing businesses, and cybersecurity describes the discipline dedicated to protecting that information and the systems used to process or store it.
Challenges in Cybersecurity?
For cybersecurity strategy to succeed, it must continually evolve to keep pace with the shifting strategies and technologies used by hackers. More importantly, it requires a multi-pronged effort that includes:
- Security Management for better monitoring and visibility.
- Cloud Protection for all environments.
- Mobile Security that follows wherever the business leads.
- Threat Prevention.
- Anti-Ransomware Technology.
- Security Appliances that grow with business needs to current and future cybersecurity needs.
Required… NOT Optional!
Cybercriminals constantly hone their skills, advancing their tools and tactics. At the same time, the technologies and applications we rely on daily are also changing and sometimes that means ushering in new vulnerabilities. While we can apply patches and updates, use firewalls and anti-malware programs, true cybersecurity requires an evolving, holistic approach—and one that focuses on prevention, not detection.
Technical Skills required for a Cyber Security Specialist?
For starters, tech pros should understand the:
- Management of Operating Systems (various Linux distros, Windows, etc.)
- Virtualization Software
In other words, things like firewalls and network load balancers. That’s in addition to general programming/software development concepts and software analytics skills.
Now to venture into an In-Depth Skill(s) Requirements/Analysis:
- Security Incident Handling & Response: A security practitioner must be able to handle any imminent threat of current violation of an organization’s security policies or standard security practices. These security incidents could include Malware, Ransomware, Phishing, Advanced Persistent Threats, Distributed Denial of Service (DDoS) attacks, and more.
- SIEM Management: Must be able to manage and analyse the Security Information and Event Management (SIEM) tools and services. Should be able to create automation with the SIEM and take the real-time analysis produced from alerts and translate that into incident response plans.
- Audit & Compliance: Must be able to conduct a thorough review of the organization’s adherence to regulatory guidelines, such as HIPAA, FISMA, SOX, PCI DSS, GDPR, ISO 27001 and 20000, and COBIT. Security audit and Compliance Knowledge are very important because any missed area of regulatory compliance could lead to significant fines and penalties for the organization.
- Analytics & Intelligence: Must be able to leverage analytics and intelligence gathering to identify and detect attacks as quickly as possible. Using analytics and intelligence allows the security practitioner to aggregate network and application data to prevent attacks from occurring in the future.
- Firewall/IDS/IPS Skills: Must be able to leverage a firewall to filter network traffic and prevent unauthorized access onto the network. Besides, the security expert must know about the Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) and know how they relate to the firewall.
- Intrusion Detection: Must be able to operate the IDS and then identify any suspicious traffic on the network as well as any security policy violations.
- Application Security Development: Must be able to improve the security of any application by finding, fixing, and preventing its vulnerabilities. Also, the expert must test and validate during the Software Development Lifecycle (SDLC) so that vulnerabilities are addressed before an application is deployed.
- Advanced Malware Prevention: Must be able to leverage advanced threat protection software to prevent, detect, and identify Advanced Persistent Threats (APTs) that might circumvent traditional security solutions like anti-virus, firewalls, and IPS/IDS.
- Mobile Device Management: Must be able to work with the IT department to secure and deploy smartphones, tablets, and laptops as well as understand data loss prevention strategies.
- Data Management Protection: Must be able to handle, analyse, and securely store all types of data.
- Digital Forensics: Should understand forensic tools and investigative methods used to find data, anomalies, and malicious activity on the network, in files, or other areas of the business.
- Identity & Access Management: A security practitioner needs to understand the best practices for Identity and Access Management (IAM) and ensure that the security policy demonstrates an acceptable use for various roles and responsibilities within the organization.
There’s also the need to understand the more common Programming languages:
- Assembly language
- Scripting languages (PHP, Python, Perl, or Shell)
Many employers demand certifications as a prerequisite for employment. In a recent survey, the International Information System Security Certification Consortium (ISC)2 noted that degrees and certifications were often a major factor in hiring.
Potentially important certifications include the following:
- GIAC Security Expert (GSE) – the most prestigious credential in the information security industry.
- GIAC Security Leadership Certification (GSLC) – intended for security professionals with managerial or supervisory responsibilities.
- Certified Information Systems Security Professional (CISSP) – regarded as another elite credential in the information security industry.
- CompTIA Security+ – globally recognized certification known as a benchmark for best practices in information security.
- CompTIA Advanced Security Practitioner (CSAP) Exam – for IT security professionals with at least five years of experience to validate advanced IT security.
- CompTIA Cybersecurity Analyst+ (CSA+) – for cybersecurity analysts that apply behavioral analytics to improve overall IT security.
- EC-Council Certified Ethical Hacker (CEH) – For cybersecurity professionals who want to understand how to identify weaknesses and vulnerabilities in systems.
- Mile2 Certified Penetration Testing Engineer and Digital Forensics – a vendor-neutral certification designed to train practitioners on forensics, digital discovery, and advanced investigation techniques.
Any good cybersecurity pro knows how to examine a company’s security setup from a holistic view, including threat modelling, specifications, implementation, testing, and vulnerability assessment. They also understand the security issues associated with operating systems, networking, and virtualization software.
But it’s not just about understanding; it’s also about implementation. They study the architecture of systems and networks, then use that information to identify the security controls in place and how they are used. Same with weaknesses in databases and app deployment.
That’s in addition to the aforementioned skills. Security professionals often need to communicate complicated subjects to people who might not have much of a technical background. With that in mind, mastering the following is usually a prerequisite for climbing to more advanced positions on the cybersecurity ladder:
- Excellent presentation and communications skills to effectively communicate with management and customers.
- Ability to articulate complex concepts (both written and verbally).
- Ability, understanding, and usage of active listening skills (especially with customers!).
From a cybersecurity perspective, soft skills will also allow you to identify examples of, and explain, social engineering, which is a pervasive issue within the security community.
Job Titles/Description in Cybersecurity
- Security Analyst: Analyses and assesses vulnerabilities in the infrastructure (software, hardware, networks), investigate available tools and countermeasures to remedy the detected vulnerabilities, and recommends solutions and best practices. Analyses and assesses damage to the data/infrastructure as a result of security incidents, examines available recovery tools and processes and recommends solutions. Tests for compliance with security policies and procedures. May assist in the creation, implementation, and/or management of security solutions.
- Security Engineer: Performs security monitoring, security and data/logs analysis, and forensic analysis, to detect security incidents and mounts an incident response. Investigates and utilizes new technologies and processes, to enhance security capabilities and implement improvements.
- Security Architect: Designs a security system or major components of a security system, and maybe the head of a security design team building a new security system.
- Security Administrator: Cyber security or information technology security are the techniques of protecting computers, networks, programs and data from unauthorized access or attacks that are aimed for exploitation..
- Security Software Developer: A Security Software Developer (a.k.a. programming guru) does one of two things: 1. Develops security software. 2. Integrates security into applications software during the course of design and development. In the first job, you will be creating new tools & systems for virus/spyware/malware detection, intrusion detection, traffic analysis, etc. In the second job, you will be ensuring that security measures are “baked-in” to any software your organization produces.
- Cryptographer/Cryptologist: Uses encryption to secure information or to build secure software. Also works as a researcher to develop stronger encryption algorithms.
- Cryptanalyst: Analyses encrypted information to break the code/cipher or to determine the purpose of malicious software.
- Chief Information Security Officer: A high-level management position responsible for the entire information security division/staff. The position may include hands-on technical work.
Very Specialized Roles
- Intrusion Detection Specialist: Monitors networks, computers, and applications in large organizations looking for events and traffic indicators that signal intrusion. Determines the damage caused by detected intrusions, identifies how an intrusion occurred and recommends safeguards against similar intrusions. Also does penetration testing to identify vulnerabilities and recommend safeguards as pre-emptive measures.
- Computer Security Incident Responder: A member of the team that prepares for and mounts a rapid response to security threats and attacks such as viruses and denial-of-service attacks.
- Source Code Auditor: Reviews software source code to identify potential security issues and vulnerabilities that could be exploited by hackers to gain unauthorized access to data and system resources.
- Virus Technician: Analyses newly discovered computer viruses, and designs and develops software to defend against them.
- Penetration Tester (also known as Ethical Hacker or Assurance Validator): Not only scans for and identifies vulnerabilities but exploits them to provide hard evidence that there are vulnerabilities. When penetration-testing, large infrastructures such as power grids, utility systems, and nuclear facilities, large teams of penetration testers, called Red Teams, are employed.
- Vulnerability Assessor: Scans for, identifies and assesses vulnerabilities in IT systems including computers, networks, software systems, information systems, and applications software.
- Technology and Internet companies
- Security software companies
- Defence companies
- Many government departments and defence/intelligence agencies
- Many IT companies, and IT divisions of companies in many industry sectors
- The E-Commerce sectors
Internet of Things (IoT)
Understanding – Simply made-easy
Internet of Things is an amazing new paradigm shift that’s going on in computers and networking and technology and something that you should know about and understand.
So, whenever you’re thinking about where you should put your career in technology and where to make money in technology, you should understand that in this business we go through what is called paradigm shifts where we start focusing on specific types of technology.
The basic concept of the Internet of Things is that everything or, almost everything will be able to be connected in an Internet-like fashion.
So, what does that mean?
What that means is, as we have gone from computers being able to communicate with a network, to like smartphones being able to communicate with a network, to tablets and all those things that still like look like computer devices. But going forward what we’re going to be dealing with is devices that don’t look so computer, like a Fitbit.
A Fitbit, is actually this device you put in your running shoe and it tracks how far you run so basically that little device goes in your running shoe, it tracks how far you’ve run, it sends that information up to your cellphone, that cellphone then looks at it, and looks at the data that’s been sent to it and figures out things like how many calories you’ve burnt over an amount of time.
So, basically what we’re looking at with the Internet of Things is thinking about how everything can be tagged up with some kind of computer-readable information and how that data can be sent so it can be as simple as an RFID tag on individual cartons of eggs, or it can be something like you can see what the weather forecast is or will be.
How can we put this computer network type device onto almost anything and then what can we do with it?
Let’s break down the IoT concept, Structurally and Technically.
IoT − Key Features
- AI: IoT essentially makes virtually anything “smart”, meaning it enhances every aspect of life with the power of data collection, artificial intelligence algorithms, and networks.
- Connectivity: New enabling technologies for networking, and specifically IoT networking mean networks are no longer exclusively tied to major providers. Networks can exist on a much smaller and cheaper scale while still being practical. IoT creates these small networks between its system devices.
- Sensors: IoT loses its distinction without sensors. They act as defining instruments which transform IoT from a standard passive network of devices into an active system capable of real-world integration.
- Active Engagement: Much of today’s interaction with connected technology happens through passive engagement. IoT introduces a new paradigm for active content, product, or service engagement.
- Small Devices: Devices, as predicted, have become smaller, cheaper, and more powerful over time. IoT exploits purpose-built small devices to deliver its precision, scalability, and versatility.
- Sensors: The most important hardware in IoT might be its sensors. These devices consist of energy modules, power management modules, RF modules, and sensing modules. RF modules manage communications through their signal processing, WiFi, ZigBee, Bluetooth, radio transceiver, duplexer, and BAW. The sensing module manages to sense through assorted active and passive measurement devices.
- Wearable Electronics: Smartwatches. Fitness bands. Smart glasses.
- Standard Devices: Desktop. Cellphone. Tablet. Routers. Switches.
- Data Collection: This software manages sensing, measurements, light data filtering, light data security, and aggregation of data. It uses certain protocols to aid sensors in connecting with real-time, machine-to-machine networks. Then it collects data from multiple devices and distributes it per settings. It also works in reverse by distributing data over devices. The system eventually transmits all collected data to a central server.
- Device Integration: Software supporting integration binds (dependent relationships) all system devices to create the body of the IoT system. It ensures the necessary cooperation and stable networking between devices. These applications are the defining software technology of the IoT network because, without them, it is not an IoT system. They manage the various applications, protocols, and limitations of each device to allow communication.
- Real-Time Analytics: These applications take data or input from various devices and convert them into viable actions or clear patterns for human analysis. They analyze information based on various settings and designs to perform automation-related tasks or provide the data required by industry.
- Application and Process Extension: These applications extend the reach of existing systems and software to allow a wider, more effective system. They integrate predefined devices for specific purposes such as allowing certain mobile devices or engineering instruments access. It supports improved productivity and more accurate data collection.
IoT Technology & Protocols
IoT primarily exploits standard protocols and networking technologies. However, the major enabling technologies and protocols of IoT are RFID, NFC, low-energy Bluetooth, low-energy wireless, low-energy radio protocols, LTE-A, and WiFi-Direct. These technologies support the specific networking functionality needed in an IoT system in contrast to a standard uniform network of common systems.
- NFC and RFID: RFID (radio-frequency identification) and NFC (near-field communication) provide simple, low energy, and versatile options for identity and access tokens, connection bootstrapping, and payments.
- RFID technology employs 2-way radio transmitter-receivers to identify and track tags associated with objects.
- NFC consists of communication protocols for electronic devices, typically a mobile device and a standard device.
- Low-Energy Bluetooth: This technology supports the low-power, long-use need for IoT function while exploiting a standard technology with native support across systems.
- Low-Energy Wireless: This technology replaces the most power-hungry aspect of an IoT system. Though sensors and other elements can power down over long periods, communication links (i.e., wireless) must remain in listening mode. Low-energy wireless not only reduces consumption but also extends the life of the device through less use.
- Radio Protocols: ZigBee, Z-Wave, and Thread are radio protocols for creating low-rate private area networks. These technologies are low power but offer high throughput, unlike many similar options. This increases the power of small local device networks without the typical costs.
- LTE-A: LTE-A, or LTE Advanced, delivers an important upgrade to LTE technology by increasing not only its coverage but also reducing its latency and raising its throughput. It gives IoT a tremendous power through expanding its range, with its most significant applications being a vehicle, UAV, and similar communication.
- WiFi-Direct: It eliminates the need for an access point. It allows P2P (peer-to-peer) connections with the speed of WiFi, but with lower latency. WiFi-Direct eliminates an element of a network that often bogs it down, and it does not compromise on speed or throughput.
Top must-have skills for an IoT Developer
- Communicative Chips.
- Communication Gateways.
- Cloud Management.
- Security solutions that cut across the IoT stack.
- Mobile development.
- UI/UX Design.
- Big Data.
- Machine Learning.
- Embedded System.
- Programming Skills: C, C++.
IoT job roles in the Market
- IoT Product Manager.
- IoT Architect.
- IoT Developer.
- IoT Cloud Engineer.
- Data Scientist.
- Industrial Engineer.
- Industrial UI/UX Designer.
Now that we know what all technologies you should learn here is the list of training provided by IIHT:
- Cloud Management.
- Mobile development.
- UI/UX Design.
- Big Data.
- Machine Learning.
- Embedded System.
- Programming Skills: C, C++.
To know more about these courses, please visit our website https://www.iiht.com/.