CodeIsGo.com

As part of our ongoing series on cost management for observability data in Google Cloud, we’re going to share four steps for getting the most out of your logs while on a budget. While we’ll focus on optimizing your costs within Google Cloud, we’ve found that this works with customers with infrastructure and logs on prem and in other clouds as well. By Afrina M, Mary Koes.

The article focuses on:

• Analyze your current spending on logging tools
• Eliminate waste — don’t pay for logs you don’t need
• Optimize costs over the lifecycle of your logs
• Setup alerts to avoid surprise bills

One final way to stretch your observability budget is to use more Cloud Operations. We’re always working to bring our customers the most value possible for their budget such as our latest feature, Log Analytics, which adds querying capabilities but also makes the same data available for analytics, reducing the need for data silos. Good read!

Big data technology is transforming nearly every industry quickly by providing actionable business information. Because of its effectiveness, even business leaders who are generally slow to adopt new technology are curious about implementing big data analytics and using its insights. By Jennifer Dublino.

The article then coverse:

• What is big data?
• What is the importance of big data for businesses?
• How can small businesses use big data?
• How can small businesses get started with big data?
• What is the future of big data?
• Big data and data analytics FAQs

Big data is defined by the three V’s: bigger variety of data, a larger volume of data (minimum of 1 terabyte) and a higher velocity of data. In other words, with big data, many different types of information are coming in fast. Another two V’s ― value and veracity ― describe big data that is truly useful and accurate.

Tracking sales data, customer retention statistics and gross revenue is a good start. However, in addition to tracking these metrics, you must ask insightful questions like the following: Which vendors offer the most value? Which product lines need the most improvement? Then, review the granular details that reveal the consequences of your current operational practices. These insights can yield crucial improvements. Nice one!

One of the powerful features provided by Next.js is that we can write server components. This allows us to use static rendering, which means that all the HTML pages will be pre-rendered at build time. The front-facing site will not send any requests to the backend (in this case, Contentful). The content API will be queried only when we run the next build command and generate the static files. By Antonio Cosentino.

Next.js is a React framework that supports static site generation, server-side rendering, and incremental static regeneration. It allows you to create fast and SEO-friendly websites with dynamic data.

The article describes then following:

• Static rendering
• Tailwind CSS
• Project setup
• Set up content in Contentful
• API keys
• Install the Contentful client
• Outputting the content

The article also provides links to the source code and a live demo of the blog. You can follow the detailed instructions and screenshots to build your own blog or use the code as a reference for your own projects. The code can be deployed from a repository on GitHub and using vercel. Good read!

Optimizing Nginx performance is essential to boosting the speed of your web applications. As one of the central elements in your web hosting control panel, the open-source web server, NGINX, delivers high-speed performance out of the box. You can significantly boost NGINX performance by making tweaks to its configurations. Check out the guide with practical techniques to help you achieve just that. By Nikita S.

The article pays attention to:

• NGINX performance tuning tips
• Understanding NGINX configuration and worker processes
• Connection management and load balancing
• Optimizing static content delivery and compression
• Buffering and logging techniques
• FAQs: NGINX performance

Optimizing your NGINX worker processes is essential for maximizing performance. The key is to set the number of worker processes based on the number of CPU cores available in your server. This will ensure that your server resources are utilized efficiently. Good read!

PostgreSQL, a popular open-source relational database system can be used as a hybrid database to handle both structured and semi-structured data. PostgreSQL’s support for JSON and JSONB object types allows for flexible data modelling and can solve many of the same problems that NoSQL databases are made to address. By Leslie S. Gyamfi.

The rise of digital technology in this age has made data a pivotal component for businesses, leading to the need for efficient database management systems.

The main parts of this article:

• SQL vs. NoSQL
• Specific features of PostgreSQL also applicable to NoSQL
• JSON and JSONB object types can help bridge the NoSQL
• JSON and JSONB: What are the differences?
• Working with JSON data in PostgreSQL
• Querying JSON data
• Querying JSON arrays
• Working with JSONB data in PostgreSQL
• Indexing JSONB data using the ‘GIN’ index
• Cases where PostgreSQL’s NoSQL features can be particularly useful
• A comparison of JSON in Postgres and NoSQL

While NoSQL databases like MongoDB are known for their flexibility and ability to handle unstructured data, some relational databases like PostgreSQL offer support for JSON and JSONB data types along with its relational database management system features, thus providing a practical and fully-fledged alternative to NoSQL databases. With its SQL-based querying capabilities and ACID compliance, PostgreSQL is an attractive option for developers who need NoSQL scalability, but still require the power of SQL. Nice one!

In the realm of technology, quantum computing represents the next frontier. This revolutionary technology promises to dramatically increase computing power, potentially solving problems that are currently beyond the reach of even the most advanced classical computers. By defianceetfs.com.

Supercomputers, which have been at the forefront of computational power for decades, are now being challenged by the emergence of quantum computers. While supercomputers use classical bits to perform calculations, quantum computers use quantum bits, or qubits, which can represent both 0 and 1 at the same time. This allows quantum computers to perform multiple calculations simultaneously, giving them the potential to solve complex problems much faster than supercomputers.

Further in the article you will find:

• Quantum supremacy: Google’s Sycamore
• Microsoft’s Quantum Leap
• Intel’s Aurora supercomputer
• IBM’s Eagle Quantum computer
• Quantum computing market: A promising investment theme

The advancements in quantum computing by Google, Microsoft, Intel, and IBM are pushing the boundaries of computing. Quantum computers are demonstrating capabilities far beyond those of classical supercomputers, which could lead to significant changes in various industries, from medicine and drug discovery to cryptography and climate science. Good read!

What do a rusty iron nail, a tarnished sterling silver ring, and a copper statue turned minty green all have in common? They are all products of a chemical process known as corrosion. For the researchers and engineers at a global aerospace manufacturer like The Boeing Company, however, corrosion is a multibillion-dollar problem — and quantum computers may be able to help. By Robert Davis.

We were using the quantum devices to compute energy at a very fundamental level. For the most part, when chemists do these types of calculations today, they use tools like density functional theory (DFT), which require a lot of approximation so they can run on classical computing hardware. DFT is really a workhorse of the field, including in industry, but we’ve known for a long time that it is deficient in some areas — including accuracy in predicting chemical kinetics.

In this paper, we actually showed that if we take this very fundamental equation involving water, and we recompute the energies with quantum hardware, we get energies that are more accurate than DFT. The DFT method has been used to study this same reaction in hundreds of other papers. So to me, that was very significant because it shows that you do need that quantum description to be able to study this reaction very precisely.

Very interesting article. Recommended for anybody interested in computer science and quantum computing!

Last year researcher, well-known within the industry as mr.d0x, raised the issue of almost uncompromised faith in URLs only to bring it down describing a relatively new attack. Baptized as a newborn by the media, a browser in the browser attack (BITB) became a creepy story of its kind. By Marcus Jackson.

People who are exposed to website security risks are the majority of daily Google search users or Starbucks customers. That’s why it is so crucial to find at least a good solid web browser that will care about their right to live in peace. Luckily enough, plenty of personal browsers emerged in the last decade and decided to make users and their safety the main priority.

The information in the article includes:

• Contemporary a®ttack: Types of cybersecurity threats
  • Malicious plugins
  • Browser in the browser (BITB)
  • DNS poisoning
  • SQL injection
  • Cross site scripting (XSS)
• Private special: Browsing security
• Mighty knights?

Modern-day is full of surprises, so the evolution of the Internet inevitably leads to newer and wiser cyber threats emerging. However, the situation isn’t hopeless, though can be dark from time to time. While computer security threats and solutions are liable to differ, help from private browsers is more universal. Nice read!

These days we widely use Scala in Data Science and Machine Learning fields. Scala is a small, fast, and efficient multi-paradigm programming language built on a compiler. The JVM(Java Virtual Machine) is Scala’s main advantage . Scala code is first compiled by a Scala compiler, which generates bytecode, which is then transported to the JVM for output generation.

The guide then delves into:

• Why we learn Scala for data science
• Scala: A quick guide
• Scala as a data science tool
• Data types in Scala
• Expressions in Scala
• Functions and methods in Scala
• Classes and objects in Scala
• Packages and imports
• Parallel collection In Scala:
• Scala’s benefits
• Scala’s drawbacks

Scala was built to implement scalable solutions to crunch big data in order to produce actionable insights. Scala’s static types help complicated applications avoid problems, and its JVM and JavaScript runtimes allow you to construct high-performance systems with simple access to a vast library ecosystem. Scala is a multi-stream wonder of the twentieth century. It has experienced phenomenal growth since its inception, and it is without a doubt one of the most in-demand programming languages. Good read.

Java Code refactoring is the practice of restructuring and improving existing code by making small incremental changes that do not impact the external behavior of the code. Code refactoring involves changing the internal structure of the code to improve readability, performance, maintainability, and efficiency without breaking the code’s functionality. By Digma.

Authors also consider:

• Java code refactoring techniques
• Renaming variables and methods
• Extract method
• Remove magic numbers and strings
• Code de-duplication
• Simplify methods
• Red-green refactoring
• 13 code refactoring best practices

Choosing meaningful variable and method names is key to making your code more readable. Code readability is arguably one of the most important aspects of any good codebase. Readable code clearly communicates the intent to the reader, while less readable code increases the likelihood of bugs during refactoring. Nice one!