The business landscape is constantly changing, and with the rise of data streaming, things are starting to look a little different. Data streaming has made it easier than ever for businesses to access the information they need in real-time - no matter where they are. This has allowed companies to make better decisions, faster. In this blog post, we will explore how data streaming is changing the business landscape and how you can take advantage of it!

What is data processing?

Data processing is the practice of using data to extract insight, which can then be used in business decisions. Data streaming involves taking large volumes of raw inputs and organizing them into a format that makes sense for internal consumption by people or machines alike. It’s often called real-time because it allows organizations to access information as soon as they’re available, as opposed to waiting for the end of a data-collection cycle.

Data processing challenges

1. Batch processing issues

Batch processing refers to the idea of doing a set of tasks all at once, as opposed to one task after the next. So for example, you might batch-process your email by checking it all at once and then responding to it all at once. This can be helpful because it means you don't have to keep switching contexts, and it can save you time. The downside is that if you're batch-processing something that's error-prone, then the inevitable errors lead to tainting the whole batch. And if you're dealing with a complex or nuanced task, then trying to do it all at once can actually lead to poorer results. That's why it's important to be aware of the tradeoffs involved in batch processing - and to make sure that the benefits outweigh the costs. Additionally, batch processing is not online, and data is not processed in real time so data can be stale and not reflect what is happening as it happens. Also, you may miss some business opportunities or fail to recognize risks in time. Batch processing does not adapt the offer to the online user, nor does it respond immediately to an alarm.

2. Legacy approach to data processing

The legacy approach to data processing is the traditional way of handling data before the arrival of big data and the cloud. It involved setting up a data center with racks of servers, storage, and networking equipment. Companies would buy or lease this equipment and hire people to manage it. The legacy approach was very expensive and time-consuming. It required companies to plan for months in advance to order the right equipment, configure it, load their software onto it, and then test everything to make sure it worked. And if there was ever a problem with the system, they were stuck because they couldn't just spin up another server or rent more storage space on demand.

3. Customer products vs customer expectations

In the customer products area, the legacy approach for digital products was mainly purchase & download/buy (CD/DVD). However, this process is slow and not very efficient. It also requires a lot of manual work to keep track of inventory, orders, and customer information. Think alarms in a warehouse or fraudulent transactions - costs of not reacting to those immediately is big. This means that users do not receive their products and services real-time. Instead, they have to wait for a set amount of time to receive them. This can be frustrating for customers, and it can also lead to lost sales. Additionally, it can be difficult to track customer behavior when they're not interacting with your product or service in real time. As many challenges as there are with the legacy approach, it's still being used by many companies today. But there is quite a revolution coming. Stream-based, real-time models are forcing companies selling digital goods to change their operations in response to business competition.

Welcome to the streaming revolution

The extreme case of the streaming revolution is the shift from CD/DVD products to music and video streaming. With the advent of streaming services such as Netflix, Hulu, and Spotify, consumers have shifted away from buying CD's or DVD's to streaming content. This has had a major impact on the music and movie industry, with companies such as Blockbuster going out of business. It goes without saying that data streaming has revolutionized the business landscape, and has been a major contributing factor to the success of many technological companies. Additionally, data streaming has allowed many businesses to streamline their operations and reduce costs. Furthermore, the cost of transferring data is less expensive than ever: due to advances in fiber optics technology, bandwidth speeds have increased dramatically over the past few years; this allows companies that rely on large amounts of transferred data (such as Amazon) to pay a fraction of what they used to pay for bandwidth - this allows them to pass on the savings to their customers (think Amazon Prime).

High tech loves data streaming

Indeed, data streaming is a critical component of many high-tech industries. Smart devices, the Internet of Things (IoT), and smart cars all rely on data streaming in order to function properly. For instance, in order for a smart device to be able to "learn" and get better over time, it needs to be constantly streaming data. Likewise, in order for a car to be able to drive itself, it needs access to large amounts of streaming data. This is certainly true - data streaming is a critical component of many high-tech industries. Without it, these industries would not be able to function properly. For example, the video gaming industry would not be able to create the realistic graphics and gameplay that players enjoy without data streaming. Additionally, the music industry would not be able to distribute its music quickly and efficiently without data streaming. In fact, the music industry would probably not be what it is today without data streaming. Another industry that heavily relies on data streaming is the wearable technology industry. In order for a wearable device to track your activity and health data, it needs to constantly stream data. This is why many fitness trackers require you to have an active internet connection in order to function properly. As you can see, data streaming has had a major impact on the business landscape, and is critical for the success of many high-tech industries. It's safe to say that the streaming revolution is here to stay and will continue to play a major role in the business landscape - so make sure you're ready for it.

Moving from batch processing to data streaming

In the past, most data processing was done in batches. For example, if you wanted to process a large amount of data you would have to wait for it all to come through and then run your analysis on that batch of data. This is similar to how people used to watch television shows - they had no choice but to wait until the show aired at a specific time and day; otherwise they missed out completely (unless they taped it). However, with advances in technology and more powerful computers available today than ever before, companies are now able to stream their data as it comes through instead of waiting for everything at once. They can do this by using a real-time stream processing engine which allows them to process data as it comes in. This is a big shift from the way things used to be done and has many benefits. For one, it allows companies to react more quickly to changes in their data. Additionally, it allows companies to get insights into that data that they would not have been able to obtain otherwise. Finally, it allows companies to run analytics on live data which can give them a competitive edge over other companies. Businesses are now moving from batch processing to streaming data. The first reason is that customers are increasingly demanding real-time experiences. They want to be able to interact with companies in real time, and they want companies to be able to react quickly to their needs. The second reason is that businesses are facing more rivals than ever before. In order to stay ahead of the competition, they need to be able to process data as it comes in and make decisions quickly. Streaming data allows them to do just that.

Data streaming with Scramjet Cloud Platform

If you're looking for a solution here, then we'd recommend checking out Scramjet Cloud Platform. Scramjet is a real-time stream processing engine that allows you to process data as it comes in. Many companies are looking for a lightweight stream processing engine that is easy to use and doesn't require any installation or configuration. No need to maintain heavy clusters for stream processing. Scramjet Cloud Platform is the perfect solution for processing data in motion. It has a low latency, efficient memory management and fault tolerance. This makes it the perfect and affordable stream processing solution for both small and big companies who want to get started with data streaming quickly and easily.

Scramjet can both help in:

How about you?

Have you started using data streaming in your business yet? If not, what's holding you back from doing so today or tomorrow? Give us a shout and we'd be happy to help you get started.

Over to you

Data streaming allows you to process data in near-real time, which means that your data will always be up-to-date. Additionally, by using a stream processing system, you can avoid many of the common problems associated with batch processing. However, data streaming is still a relatively new technology and not many companies are using it yet. This means that there is a lot of opportunity for those who are willing to make the switch. So, if you're still on the fence about whether or not to start using data streaming in your business, we'd say go for it! You won't regret it, and you'll never look back.

Join our BETA PROGRAM and get limited possibility to test our next generation 3-in-1 data processing platform!

Be sure to join our Discord server here and say hi to our community!