This blog post will discuss what is web-scale, why we need web-scale systems as opposed to traditional approaches, and how various technologies allow us to build web-scale systems at a fraction of the cost.
When it comes to building an application, many people think that bigger is always better. That is only partially true. When it comes to something like a blog, for example, bigger images are almost always better. However, when you are dealing with something like a web-scale application that needs to process massive amounts of data and queries, small is definitely beautiful. With the rise of cloud computing and the ability to create virtual machines in the blink of an eye, we now have access to virtually limitless processing power.
The problem is that most developers aren’t taking advantage of this new level of technology in their applications. As such, we are seeing more and more companies build their applications on-premises in order to take advantage of all this new technology has to offer.
What Is Web-Scale?
What is Web-Scale? Well, to start, you need to understand what the term “Big Data” actually refers to. According to Gartner, Big Data refers to any data that is too large or complex to process using standard data processing tools and techniques. Big Data is one of the most hyped terms in IT, and many people incorrectly use it to refer to anything that is larger than they can handle.
Web-Scale is almost synonymous with Big Data. Although the two terms don’t necessarily have to go hand in hand, many people find it easier to understand what Web-Scale is by first understanding Big Data. To put it simply, Web-Scale is a type of architecture that was designed from the ground up to handle massive amounts of data and traffic. It also takes into account a variety of different factors, such as cost and resource availability. A Web-Scale architecture is not for everyone, but it’s an approach that is ideal for companies that have massive data processing needs.
Why do we need Web-Scale?
Due to the sheer volume of information that the Internet generates, companies realized they would need a whole new level of architecture to handle it all. Web Scale was created to handle the massive amount of data and traffic companies deal with on a daily basis.
It is no longer sufficient to have a system in place that can handle the typical load of a few hundred users. To handle the massive load, we now have to look at completely different architecture. In order to understand the need for Web-Scale, one must understand Big Data and the growing need for real-time analytics. Big Data is a term used to describe the massive amounts of data that are generated every day. According to Gartner, more than 90% of the data in existence today has been created in the last two years alone.
With so much data being created, companies need a way to analyze it in real-time. Traditionally, this meant having a team of data scientists who would process the data and then store it in a database, where it could be accessed as needed. This worked well when the data users were generating was relatively low. However, this method just can’t keep up with the volume of data being created now.
Real-time analytics require constant access to the data, which means that this method just can’t keep up with the level of data that we are now dealing with. Web-Scale architectures were created to handle this massive amount of data.
Apache Hadoop — The Big Boy of Web Scaling
Hadoop is an open-source software framework for processing and analyzing large amounts of data. It was originally developed by Google, and it has become the foundation for Web-Scale computing. In fact, Hadoop is the Big Boy of Web Scaling. It houses and processes large amounts of data and is flexible enough to work with just about any type of data.
Beyond data processing, Hadoop can also be used for other things, such as asset management, security analysis, and click fraud detection. When it comes to Web-Scale architectures, Hadoop is definitely the gold standard. There are a few key elements of Hadoop that make it ideal for Web-Scale environments. First, Hadoop runs on inexpensive hardware.
This is important because most companies using Web-Scale architecture don’t have the budget to use the latest and greatest hardware. They need something that is cost-effective and easy to obtain. Hadoop is definitely cost-effective. It also offers a high level of scalability. This means that you can add more hardware to increase your processing capabilities as your business grows. This is important because most Web-Scale architectures are designed to handle massive amounts of data. Using Hadoop, you can scale your architecture to support this massive amount of data.
Apache Kafka — The Event Hub
Kafka is a distributed streaming platform that Somebody designed to handle massive amounts of data. Because it is designed to stream data to multiple consumers, people often refer to it as a "publishing platform." Kafka is often used as an event hub that feeds data to a variety of different applications. Kafka is a distributed system, which means that each Kafka cluster is distributed across multiple servers.
Using Kafka as an event hub has a few advantages. First, you can stream data to multiple consumers at once. This means that you can send your data to a variety of different applications with a single stream. This means that you don’t have to handle multiple feeds and feeds are typically a source of data problems.
Second, you can scale your Kafka cluster to handle massive amounts of data. Kafka uses a publish-subscribe model, which allows you to add and remove consumers without interrupting the flow of data. This is especially important when handling massive amounts of data. Kafka was designed for Web-Scale architecture and can easily be used as an event hub for large amounts of data.
Cost Factor in Web-Scale
The biggest advantage that Web Scale systems have over traditional data architectures is cost. Traditional data architectures are typically designed for specific data types and applications. For example, if you need to process text data, you would use a relational database. If you need to process images, you would use a NoSQL database or an object store.
For example, Facebook uses a Relational Database Management System (RDBMS) for users’ profiles and photos, while Instagram uses a Graph Database for its social media feed. This means that in Facebook’s case, they have both the users’ data and the photos they post in the same database.
Similarly, in the case of Instagram, they have the users’ photos in their Graph Database, but they use a SQL database for the photos they share on the site. This means that they have a single database that tracks both users and photos.
Web Scale isn’t for everyone, and that’s OK. Web Scale architecture is ideal for organizations that have massive data processing needs. It is ideal when you need to process a huge amount of data and send it to multiple different applications. It also helps when you need real-time analytics capabilities. Most companies that are building products are using this architecture, and you will almost always see it within organizations with more than 1,000 employees. If you are part of a small business, don't worry. You can still benefit from Web-Scale architecture. The key is to focus on the right scaling factors. The correct scaling factors will result in a scalable, real-time architecture that can easily handle all of your data processing needs.