by Teknita Team | Jan 30, 2023 | Security
Increasing the efficiency of processes carried out within not only the IT industry but also the SEO industry requires companies to adopt new, unexpected measures. One of them is the introduction of special roles such as blackhat and whitehat.
Blackhat refers to unethical or illegal practices in the realm of computer security or hacking, usually with malicious intent. It involves unauthorized access to computer systems or networks for personal gain or to cause harm. Examples include hacking into computer systems without permission, distributing malware, and conducting online fraud or theft.
On the other hand, whitehat is a term used in the cybersecurity industry to describe ethical hacking practices. It involves the use of hacking techniques to identify security weaknesses in a computer system or network, with the goal of improving security. Whitehat hackers are often hired by organizations to test their defenses and help prevent unauthorized access or attacks.
Greyhat refers to hacking practices that fall between ethical (whitehat) and unethical (blackhat) behavior. Greyhat hackers may not have malicious intent, but they may engage in unauthorized access to computer systems or networks without permission. This behavior can range from harmless exploration to actions that may cause harm or violate laws. Greyhat activities blur the line between ethical and unethical behavior and can sometimes result in legal consequences.
Blackhat hacking should not be used at any time, as it involves unauthorized access to computer systems or networks, distribution of malware, and online fraud or theft. Engaging in these activities can result in serious legal consequences and harm to individuals and organizations.
Instead of blackhat hacking, organizations should use ethical hacking practices. Some common use cases include:
- Penetration testing: simulating a real-world attack on a system to identify vulnerabilities and assess the strength of security measures.
- Vulnerability assessments: regularly scanning systems for security weaknesses and vulnerabilities.
- Compliance testing: ensuring that systems and networks meet industry regulations and standards for security.
- Application security testing: evaluating the security of software applications before deployment.
These activities are performed with the owner’s permission and are designed to improve the overall security of a system or network.
The key difference between blackhat and whitehat hacking lies in the intention behind the actions and the methods used. Blackhat hacking is malicious and illegal, while whitehat hacking is ethical and done with the owner’s permission.
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by Teknita Team | Jan 26, 2023 | Process Automation
Autonomous Database is a cloud-based solution that uses machine learning to automate database optimization, security, backups, updates, and other routine management tasks traditionally performed by database administrators. Unlike a conventional database, an autonomous database performs all these and other tasks without human intervention.
The amount of data available to the enterprise is growing faster and faster. This increases the demand for efficient and secure database management that enhances data security, reduces downtime, improves performance, and is not prone to human error. An autonomous database can help you achieve these goals.
Types of data stored in databases
Information stored in a database management system can be highly structured (e.g., accounting records or customer information) or unstructured (e.g., digital images or spreadsheets). Data can be accessed by customers and employees directly or indirectly through enterprise software, websites or mobile applications. Additionally, many types of software—such as business analytics, customer relationship management, and supply chain applications—use information stored in databases.
Elements of an autonomous database
The standalone database consists of two key elements that are tailored to the types of workloads.
- The data warehouse performs numerous functions related to business analytics and uses data that has been previously prepared for analysis. The data warehouse environment also manages all database lifecycle operations and can scan millions of rows for queries. They can be scaled according to business needs and implemented almost on the spot.
- Transaction processing tools enable timely handling of transactional processes such as real-time data analytics, personalization and fraud detection. Transaction processing typically involves a very small number of records, relies on predefined operations, and allows simple application development and deployment.
How an autonomous database works
The autonomous database uses AI and machine learning to provide full, end-to-end automation for provisioning, security, updates, high availability, performance, change management, and error prevention.
In this regard, an autonomous database has specific characteristics.
- It’s automatic
All database and infrastructure management, monitoring and optimization processes are automated. DBAs can now focus on more important tasks, including data aggregation, modeling, data processing and management strategies, and helping developers take advantage of the features and functions available in the database with minimal changes to the application code.
- Protects itself automatically
Built-in security protects you from both external attacks and malicious internal users. This helps eliminate the fear of cyberattacks on unpatched or unencrypted databases.
- Self-repairs
This can prevent downtime, including unscheduled maintenance. A standalone database may require less than 2.5 minutes of downtime per month, including patching .
The benefits of an autonomous database
An autonomous database provides several benefits:
- Maximum database uptime, performance and security – including automatic patching
- Elimination of manual, error-prone management tasks as a result of automation
- Lower costs and increase productivity by automating routine tasks
An autonomous database also allows an enterprise to redeploy its database management staff to more responsible tasks that deliver greater business value to the enterprise, such as modeling data, helping developers define data architecture, and planning future resource requirements. In some cases, an autonomous database can help a company reduce costs by reducing the number of DBAs needed to manage databases or by adapting them to more strategic tasks.
You can read more about Autonomous Database here.
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by Teknita Team | Jan 25, 2023 | Artificial Intelligence - Machine Learning
DALL-E (stylized as DALL·E) and DALL-E 2 are deep learning models developed by OpenAI to generate digital images from natural language descriptions.
Like GPT-3, DALL·E is a transformer language model. It receives both the text and the image as a single stream of data containing up to 1280 tokens, and is trained using maximum likelihood to generate all of the tokens, one after another.This training procedure allows DALL·E to not only generate an image from scratch, but also to regenerate any rectangular region of an existing image that extends to the bottom-right corner, in a way that is consistent with the text prompt.
DALL-E can generate imagery in multiple styles, including photorealistic imagery, paintings, and emoji. It can “manipulate and rearrange” objects in its images, and can correctly place design elements in novel compositions without explicit instruction. DALL·E excels at following natural language descriptions so users can plainly describe what they want to see.
Developers can now integrate DALL·E directly into their apps and products through Open AI API. More than 3 million people are already using DALL·E to extend their creativity and speed up their workflows, generating over 4 million images a day. Developers can start building with this same technology in a matter of minutes.
Microsoft is bringing DALL·E to a new graphic design app called Designer, which helps users create professional quality social media posts, invitations, digital postcards, graphics, and more.
Company is also integrating DALL·E in Bing and Microsoft Edge with Image Creator, allowing users to create images if web results don’t return what they’re looking for.
You can read more about DALL-E here.
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by Teknita Team | Jan 23, 2023 | Uncategorized
Big Query is a advanced and multi-cloud data warehouse designed by Google, created with the aim of business flexibility.
What do you need to know about Big Query?
Big Query is a scalable data warehouse (cloud data warehouse) designed by Google, a global giant known by almost every internet user. What are the areas of application for this solution? It must be admitted that Big Query offers many possibilities.
Big Query allows for handling millions of queries and conducting advanced analysis of significant amounts of data in SQL language. Knowledge of this language is quite important here. At the same time, entities that use this solution do not have to worry about high costs associated with maintaining advanced technological infrastructure or scaling or balancing traffic. Google offers all new customers $300 of free funds that can be spent on Big Query. Additionally, all customers receive completely free 10 GB of storage space and even 1 TB of queries per month.
Data can be quickly uploaded or downloaded from Big Query and then analyzed in detail. We only pay for the data that we analyze, and only after exceeding the aforementioned 1 TB limit. To use Big Query, we do not have to invest in expensive equipment and tools and technologies, while the configuration is exceptionally simple.
What are the main advantages of Big Query data warehouse?
Big Query is one of the most popular data warehouses. This is mainly due to the fact that it offers a wide range of features, which are appreciated by thousands of data processing and analysis entities worldwide.
The most important advantages of the Big Query data warehouse are as follows:
- No need to invest in your own server – all data is stored in cloud technology.
- Data analysis using Big Query is fast and efficient. Big Query data warehouse stands out for its ability to analyze large amounts of data significantly faster than traditional databases. One petabyte is processed in about 3 minutes, and one terabyte in just a few seconds. This fast operation time ensures that, regardless of how much data we have to analyze, we will get results at a rapid pace. Data analysis is performed in real-time, and all changes can be observed in real-time.
- Full control over costs. In Big Query, we only pay when the number of analyzed data exceeds 1 TB per month. This billing model gives us full control over the expenses. If we do not use the tool at all or do not exceed the specified limit, we will not pay a penny.
- BigQuery offers a machine learning feature. Big Query ML feature enables creation and development of machine learning capabilities using classic SQL queries. This tool allows you to check trends, which helps to design a long-term strategy for the company in specific areas.
- The Big Query data warehouse can be invaluable in any industry. The need for fast and efficient information analysis is visible in many industries – finance, industry, marketing, logistics, etc. Therefore, any company that wants to gain significant competitive advantages should consider using it.
Why choose Big Query data warehouse?
Year after year, more and more entities analyzing significant amounts of data are choosing Google Big Query. This is because with this solution, we do not have to invest in modern equipment, manage infrastructure, perform configuration or software updates. Google engineers are responsible for ensuring proper tool operation. We can then focus on proper analysis and data collection.
To be able to use the possibilities offered by Big Query, we do not have to make major changes or rewrite the source code. This is because Big Query supports the ANSI SQL:2011 standard and also offers ODBC and JDBC programming interfaces for free.
In Big Query, we also do not have to worry about creating backups – the program performs backups on its own, which are later stored for 7 days. During this time, we can familiarize ourselves with the entire history of changes and, if necessary, restore one of the previous versions.
Big Query also has a very high level of security – the tool is known for its reliable security, management, and reliability mechanisms. All data stored in the program is encrypted by default. Google states on its website that it guarantees 99.99% uptime.
Limitations of Big Query data warehouse
Big Query data warehouse has certain limitations and limits in terms of information processing. The most important of these are as follows:
- Maximum number of exported bytes per day. The limit is 50 terabytes per day.
- Maximum number of exports per day. The limit is 100,000 exports per day.
- Number of daily queries. There are no limits on the number of bytes that can be processed in queries within a given project.
- Number of daily queries per user. There are no limits on the number of bytes that users can process in queries per day.
- Number of bytes of processed query data per hour. The limit is 1 terabyte per hour.
- Maximum size of a single table. The limit is 10 terabytes.
- Maximum size of a single partition. The limit is 4 terabytes.
- Maximum number of columns in a table. The limit is 10,000 columns.
- Maximum number of rows in a table. The limit is 1 trillion rows.
- Maximum number of partitions in a table. The limit is 10,000 partitions.
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by Teknita Team | Jan 20, 2023 | Uncategorized
The Java Virtual Machine (JVM) is an abstract computing machine that enables a computer to run Java programs. It is a virtual machine that provides a runtime environment for executing Java bytecode, which is a program compiled from Java source code. The JVM acts as a “layer” between the Java code and the underlying hardware and operating system, allowing Java programs to be run on any platform that has a JVM implementation. This makes Java a “platform-independent” programming language.
To use the Java Virtual Machine (JVM), you first need to have the Java Development Kit (JDK) installed on your computer. The JDK includes the JVM, as well as other tools necessary for developing and running Java programs.
Once you have the JDK installed, you can use the JVM by doing the following:
- Write your Java code using a text editor or an Integrated Development Environment (IDE) such as Eclipse or IntelliJ IDEA.
- Compile your Java code using the Java compiler (javac) that comes with the JDK. This will convert your source code into bytecode, which can be executed by the JVM.
- Run your bytecode using the Java interpreter (java) that comes with the JDK. This will start the JVM and execute your bytecode on the machine.
It’s important to note that the JVM is not just for running Java code, it is also used for running other JVM-based languages like Kotlin, Scala, and Groovy.
You should use the Java Virtual Machine (JVM) when you want to run Java or other JVM-based programming languages, such as Kotlin, Scala, or Groovy, on your computer. JVM provides a runtime environment for executing Java bytecode, which is a program compiled from Java source code.
Here are some specific scenarios where you might use the JVM:
- When you want to write cross-platform software that can run on any operating system, such as Windows, macOS, or Linux, that has a JVM implementation.
- When you want to write server-side applications, such as web servers or backend services, that need to handle multiple concurrent connections and perform complex computations.
- When you want to use the vast ecosystem of Java libraries and frameworks, such as Spring, Hibernate, or Apache Tomcat, that are available for various application domains.
- When you want to write code that can leverage the security, performance, and scalability features provided by the JVM, such as automatic memory management, built-in support for multithreading, and JIT compilation.
- When you want to use JVM based languages, such as Kotlin, Scala, or Groovy, that are built on top of JVM but offer features like functional programming, concise syntax, and improved type inference.
In summary, JVM is a powerful and versatile platform that allows you to develop and run a wide variety of software applications, from small scripts to enterprise-grade systems, on any platform that supports the JVM.
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