In data streaming, data flows in continuously. That means that data is processed instantly and in real or near real-time. This is an advantage over batch processing where it’s required for data to be downloaded in batches first. It’s an obvious benefit for decision-making. Here, decision-makers get data at the time of the event and not after the facts. So imagine having multiple data sources, from electronic devices, IoT devices, mobile phones, cloud services, databases, sensor devices, and so on. That’s a lot of data coming in from everywhere. Now imagine if important data from these sources came too late. Your reactions to changes and disruptions are also too late. You have missed important facts that could’ve helped you stay on top of things.
What does data streaming do for you?
Data streaming is based on dynamic data intake, meaning its processing is done on instantaneously consumed data. While it ingests and collects data streams from multiple sources, it simultaneously stores and aggregates the data for processing. That’s the basic concept of Kafka, a streaming technology that has risen in popularity. We have covered Kafka in one of our blog posts since it’s the main part of our retail business intelligence platform.
Companies whose products or services depend on quick responses and correct information have implemented streaming technologies as their core business value. They have built streaming architecture to support not only their daily operations but customer engagement and experience as well. For data consumption in streaming, there are a couple of stages. Stream ingestion, stream processing, stream analytics, and data presentation in selected data topics.
Basically, streaming technologies ingest data from various streams or sources, process it through aggregation and transformation of data, turn it into actionable values or insights, and present data topics to users. Why is this valuable? Well, real or near real-time information and data that is processed at such a speed mean less time spent analyzing static data which in some cases loses its value. For example, if you look at data regarding financial markets, you need updated information since it can change instantly. What happened yesterday doesn’t have to be the same for today, and high-stakes decisions rely on real-time data.
Each event-streaming application has these basic functions or purposes: stream processing and data integration. In stream processing, it has to have the ability to process or transform consumed data. For data integration, it has to feed these events to other data systems like data warehouses, lakes, etc.
Why Kafka and how does it work?
Apache Kafka is a widely used technology and it’s extremely popular for event storing and streaming. What Kafka does is it receives and stores messages from producers to a server that’s called a broker. Those records are arranged into topics. Multiple brokers compromise a cluster. The aforementioned topics are served to consumers who subscribe to the ones they want.
So, in short, we have event producers, topics where these events are organized, and consumers.
Kafka is a fast and flexible tool, and it decouples data producers from processors with better latency and scalability. That’s why it’s the first choice for data streaming in most cases. The one very cool thing Kafka offers us is scalability. Furthermore, it does it smoothly because we don’t have to worry about rebalancing after adding a new consumer – Kafka automatically takes care of it.
Kafka’s main purpose is to produce and consume messages, without putting emphasis on data processing. On the other hand, Spark is a well-known, easy-to-use, distributed processing engine used mainly with big data. The core idea of Spark is to allow us to efficiently transform and process large amounts of data in real time.
Product or service vs business operations
Data streaming can be used for business process optimizations, decision-making, and for creating products or services. When we talk about decision-making it is obvious where data plays its role. Users can make decisions and perform actions instantly if they get instant access to real-time data. If data streaming is based on data from business operations, users can spot errors, discrepancies, or opportunities in the processes themselves.
But when we talk about products or services, this is where data streaming really comes into play. What matters in business is customer satisfaction and experience. They expect a great service or product every time, and data-based products and services have to be executed perfectly.
For example, think about e-commerce and online shopping. One area where data streaming comes into play is stock information. Buyers need information on whether an item is available. Also, data streaming focuses on bringing recommendations to customers based on their searches and purchases so they get offered products up to their tastes. This stimulates consumers to fill their cart and ultimately finish the purchase.
Or let’s look at taxi apps. When a user gets on the app, they need information on when the pick-up will be, what’s the price and what’s the estimated time of arrival based on traffic information. Data streaming is what streams that information to form a service. And users or customers depend highly on the accuracy of those pieces of information. Even content streaming services use data streaming technologies to track user activity and present the best offers and recommendations to the end users. The benefit of data streaming as a base for a product or service is the key to increasing customer value and to creating impeccable user experiences.
What is the value of streaming technologies?
You get multiple benefits by integrating streaming technologies into your data processing operations. They can strengthen your business, accelerate decision-making and anticipate upcoming disruptions or issues.
Respond in real-time
Streaming data directly at the moment of generation is key. Reducing the time between when an event is recorded and processed presents an opportunity to minimize response lag time. Users get more confident in making decisions, especially if they operate in fast-paced environments and such industries. Time is of the essence and staying behind is not an option.
Track your activity
Whether it’s your sales data, user activity, or anything that has continuous data generation, you want to track it. Being in the loop is important and you want to have an overview of your whole operations. With streaming, you get information on current affairs and not old news.
Get access to continuous business insights, metrics, and KPIs
The right decisions must come at the right time. In today’s world, we cannot imagine running a business based on old data, because it leads to mistakes and obsoleteness. If a company’s products or services depend on providing its customer or users with instant information, it needs data streaming to achieve that.
Data agility
Data flows faster. Data agility is how fast can you react to bigger amounts of data and adapt your actions to the received information. Streaming allows users to access and react to that data by constructing actions in real-time.
Increase user or customer satisfaction
If you use data streaming as a building block for a product or service, it can increase customer satisfaction by bringing relevant information on time and upfront. User satisfaction is achieved when all their needs are met. And with data streaming, those needs manifest through services that need to provide enough information or create an offer that aligns with users’ wants.
Increase ROI
Stimulating positive results and return on investment is one of the primary goals of any business. By using data streaming technologies, services and products can be optimized to higher efficiency which means bigger customer satisfaction and a greater number of finished purchases. With instantaneous data accessibility, businesses can act on the spot to lower costs and raise profits.
Reduce negative effects and losses
With data streaming, users can get a better understanding of data relevant to business processes. Since it focuses on bringing information in real or near real-time, users can act on them much faster. If data shows market disruptions or errors, reactions to them can be instant thus reducing negative impact. In business, minimizing negative effects and loss is integral to achieving more stable operations and revenue maximization.
Analyze data for patterns and trends
The difference between dynamic and static data is that dynamic takes in the current state of events. This allows for trends and patterns to be recognized more quickly and accurately. With continuous data intake, there is more of it for analysis, and the scalability of data streaming technologies ensures that larger amounts of data can be ingested faster.
So, why streaming technologies?
In such a fast-paced world, offering simple and often outdated solutions is not profitable in any sense. Great amounts of data generated in any business operation or through a product or service, means that certain tools have to be in place to utilize it properly. When the market moves so fast, streaming technologies like Kafka offer data streaming to access any important information instantaneously. Getting either insights and KPIs or alerts to changes is vital to enhancing business performance or customer experience.
To create the best user experience, companies need to streamline events or messages in the right direction at the right time. The constant flow of information has to be handled as it’s happening and convey insights to push businesses to a higher operational level. Working in the moment is the way to cohesive business strategies and success.
