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    It can send out large data amounts.Databricks gives you the flexibility of using several programming languages independently or in combination to build models.
  2. The way we can replicate information and send it to several subscribers is most valuable. It can be used for any kind of business where you've got multiple users who need information. Any company, such as LinkedIn, with a huge number of subscribers and any business, such as publishing, supermarket, airline, or shipping can use it.
  3. Find out what your peers are saying about Databricks, Solace, Amazon and others in Streaming Analytics. Updated: March 2021.
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  4. The solution works well in rather sizable environments. The feature that I've found most valuable is the replay. That is one of the most valuable in our business. We are business-to-business so replay was an important feature - being able to replay for 24 hours. That's an important feature.
  5. The solution has a lot of functionality that can be pushed out to companies. I like the IoT part. We have mostly used Azure Stream Analytics services for it
  6. The setup was not too difficult. This is truly a real-time solution.
  7. There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure.The most valuable feature is real-time streaming.
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  9. MSK has a private network that's an out-of-box feature.

Streaming Analytics Articles

Matthew Shoffner
IT Central Station

Living in a digital world where every second matters and every delay in receiving or processing data could mean a lost sale, a poor user experience, or a compromised security system. The best real-time analytics software is providing great benefit in moving our data evolution closer to the edge of our systems where the data is being generated then streaming and processing data to more quickly make smart decisions. There are obvious advantages of real-time analytics that have been realized across a variety of use cases where streaming analytics is integrated with other tools.

Here are the most common and impactful use cases:


Networks, systems, and data stacks are constantly being threatened by malicious attacks that could slow or grind your business to a halt. Using streaming analytics can save you valuable time in recognizing and resolving issues before becoming a serious issue.

  • Security Information and Event Management (SIEM). Even when the best SIEM tools pull data in from various security elements - Intrusion Detection and Prevention Systems, EDR tools, etc - to create alerts, there’s a time lag between an incident occurring and when an alert is sent out. Streaming data can help with this lag by processing data more expeditiously, shortening the time between incident and response.
  • User Behavior Analysis. There are engaging with your system all the time. UEBA tools monitor any suspicious activity that could signal either an insider threat or an account that has been compromised in some way. Adding the ability to stream data can give you a closer look when activities start to slide toward suspicious.
  • Network Analysis and Monitoring. Network monitoring software looks at a network’s uptime, availability, and response time to configure the system for optimal performance. The use of real-time data enhances the optimization of the system to near time as well, which can allocate resources to more critical systems.

Internet of Things (IoT)

Financial services, banking, manufacturing, and municipalities use systems that are interconnected through a system of networks. From monitoring stocks and investments to managing spikes in electricity consumption, data flows through these systems and is analyzed in real-time using streaming analytics. Instead of missing an opportunity to sell a stock or having to black out a city block, adjustments can be made in real-time to avoid issues.

Processing Customer Behavior

eCommerce uses streaming analytics to make real-time adjustments to enhance the customer’s journey through a digital buying cycle. As a consumer browses, searches, and clicks, a streaming analytics platform can ingest data while the consumer is in the process of engaging in the experience. Using the real-time data, they can make changes to what the consumer is shown and even alter the customer journey.

Marketing and Advertising

Streamline analytics can ingest data across numerous marketing and advertising campaigns taking into account hundreds, if not thousands, of variables. A consumer’s engagement only lasts for a limited time and marketing needs to strike while the iron is hot. A user’s engagement on a website can give insight into their interest that marketing can use to show related products or services. Targeting a consumer and serving a relevant ad based on real-time data can drastically improve the ad’s effectiveness and improve campaign performance.

Wearable Devices

Watches used to track exercise, monitor heart rate, tell you the weather, or maybe even complete work assignments for you, can stay up-to-date and provide you that information through streaming real-time processes. Imagine tracking your heart rate when exercising but not receiving the information until the next day. Data is ingrained in our lives and we want to know what we want to know the second we want to know it.

As you can see, there are a lot of use cases for data streaming and a lot of advantages. The top streaming analytics companies continue to improve integrations and applications, so use cases for streaming analytics is bound to broaden beyond what’s listed.

Matthew Shoffner
IT Central Station

Event Stream Processing (ESP) is when you have a series of data points related to an event that is coming from a system that is constantly generating data, and you take action on that set of data points. In this case, talking about an “event” is referencing each actual data point, and the “stream” is referring to delivering all of those events. So you will be streaming or delivering the data points or events to produce accurate data very quickly. The best real-time analytics software can include event stream processing, real-time processing, and batch stream processing.

When it comes to stream processing, you want to compare batch processing vs stream processing. Typically, this means that batch processing is looking at grouped data points over a predetermined period of time, whereas with stream processing you are looking for real-time streaming analytics platforms that analyze data continuously – within seconds of the data being delivered.

You might choose batch processing when you are trying to analyze different groups of data within a timeframe. If this is more important than churning high volume data within seconds, you would want to use batch processing. However, if you are looking for that high volume instantaneous processing, you need to consider stream processing instead. In some cases, you might want to do both types of processing.

There are some data streaming use cases that are great examples of event stream processing. Take a look at any good weatherman. Predicting the weather involves stream processing data in real-time. Without using event stream processing, any data points that are reported would be wrong, and we know that weathermen are never wrong.

How Does Event Stream Processing Work?

Event stream processing is not too complicated of a process, and there are a few different key components to learn about. Overall, remember that stream processing is basically responsible for technology being as fast and efficient as it is.

Event Streaming Platform

When you are talking about this type of platform, think of it like event streaming that is allowing organizations to easily interpret and analyze data which directly correlates to a specific event. Then, using an event streaming platform they can respond to that specific event in real-time using real-time analytics.

This is more important than ever before as customers expect instant responses from companies in this day and age with so much technology available. A platform like this allows companies to analyze data in real-time and provide quick and accurate responses to clients.

Event Streaming Architecture

When you think of stream processing recognize that it is a type of data stream architecture. When you are working with event-driven architectures, you are talking about when a component performs a type of work when other components should be interested in that same effort. When the component, or producer, creates an event that then serves as an official record the action was performed. Then, other producers are able to do their own work as a result of that action being completed by the first producer.

Data stream architecture is essentially a computer system that is the best example of teamwork in a supply chain. The first person performs a task, and then other “people” can perform their related tasks. Without that first action being completed, the whole chain might falter.

Event Streaming Database

Databases can be the secret ingredient to any good computer structure. Without databases, the information could get lost. An event streaming database is just a type of database that works to help the user-built apps designed for stream processing. There are a lot of components out there in event streaming architecture and the database works to consolidate into a more cohesive, usable format.

Use Cases and Examples

Now that you know what event stream processing is, let’s take a look at some real-world examples to help you see how you can incorporate it into your organization.

  • Business and bank deposits – when you make a deposit in a bank, in most cases, your account is instantly updated. Nowadays, you can cash a check by taking a picture of it and uploading it into an app on your phone. Within seconds of uploading the picture, you will likely see that the balance of the money in your account now includes the amount of that check. That is event stream processing. That real-time analytics allows the bank to instantly analyze data and provide you with an update in real-time.
  • Social media - we have all spent time in the vortex of social media. Like posts, posting pictures, or just scrolling. Believe it or not, that is event stream processing. When you post a picture and the Likes start rolling in, that is event stream processing. Data points are being analyzed and updated. As soon as someone else pushes the thumbs up, your phone shows you that your post got a like. That is real-time analytics that is processing data instantaneously.
  • GPS and other location data - GPS is probably the best example of stream processing. You could not use a GPS to guide your car without it. Every time your car moves forward whether it be 1MPH or 60MPH real-time analytics and event stream processing is updating the data on your app so that you know how far ahead to turn right. Stream processing allows for that instant analyzing of new data. When you make a wrong turn and you hear that dreadful “re-calculating” coming from the GPS that is a sign that more data is being processed so that you can accurately interpret it in time to make the right turn that will get you back on track toward your destination.

We use real-time analytics and event stream processing every day of our lives. Now that technology has taken off, we can make the argument that every app on our phone uses stream processing. When you get real-time notifications of emails and alerts that is because data was churned and outputted back to you as soon as it was reported. Stream processing is the reason technology is so much further along than decades ago. As the processing has improved, so has technology.

Matthew Shoffner
IT Central Station

There are a lot of advantages of using the best real-time analytics software with real-time information. Overall, consider that with these types of analytics, your team will be able to get more accurate data than ever before, and faster too. Gone are the days of waiting for hours to process data as it comes in. With stream processing, you can analyze data instantly, mere seconds after it was processed.

Take a look here at some more of the advantages of streaming analytics platforms:

  • Identify and make changes in real-time – any changes in the market could end up affecting the demand for a product. With stream processing and real-time analytics, you can predict and make any necessary changes on the fly. Since you will be seeing the data in real-time it is easy to know if there are errors or if anything needs to change going forward.
  • Improved speed – one review remarks of the advantages of the speed of using real-time analytics, “fast data loading processes and data storage capabilities are great.” Consider processing data like when ESPN churns out sports scores; those scores are always up to date within seconds thanks to real-time analytics
  • Combine different streams of data – data streaming use cases have proven that combining data by using analytics is the best way to keep track of any and all information about a single source.
  • Reducing the number of mistakes and fixing them quickly – one user remarked, “Provides operational efficiency and better mean time to resolution for incidents.”
  • Best for accurate information – never worry about information being stale, with real-time analytics everything is current and accurate. When you are processing data within seconds of its production, there is no chance that the data you process will be inaccurate thanks to real-time analysis of the data.

Real-time analytics offers a lot of benefits. From proving to be a reliable, accurate source of information to helping you process mistakes quickly and efficiently, there are very few reasons to get your information in any other method than with real-time analytics.

The truth is, once you start using real-time analytics, you will wonder how you lived without it.

Disadvantages of Real-Time Processing

Like anything in the world, there are just a couple of disadvantages of using real-time information. While the pros far outweigh the cons, you want to at least be aware of any negative effects before signing up to use real-time analysis.

To make sure you know all the angles of what you are signing up for, it is worth considering these disadvantages of using a real-time system:

  • Cost – Because of the intricacies of real-time analytics, it could cost a little more upfront to get set up. And, down the road, it could be costly to maintain the systems. That said, if you can afford it, there is no better option than real-time analytics. This could be a case where the extra money is absolutely worth it, as the benefits you get will make up for the cost in no time at all.
  • Overly Complex – The information you get is going to have a lot of layers to it. While this helps you get the best information possible, it means you will have to spend some time wading through it. Real-time processing is not the best if you are just looking for a little bit of parsed information.
  • Difficult to Organize – With a continuous stream of data being delivered to you, organizing this data into useable information can become difficult. You will need to figure out a system to actually make use of this massive amount of data your organization will constantly be receiving. However, there are data management methods designed specifically for utilizing real-time analytics.
  • Time-Consuming – Constant data requires constant action if you want to make use of that data. At first, it can seem overbearing if your organization is used to only receiving data sets once a week or month in regular reports. So, getting used to receiving new data every second can take some time to adjust.

Which is More Important for Your Data Analysis - Speed or Quantity?

Whether you need quick data or need to analyze a lot of different data, real-time analytics can be performed for both. Batch streaming is great for high volumes of data that are grouped into pre-determined periods of time. And, stream processing can help get you continuous real-time access to data as it changes within seconds of the change.

Your organization can also choose to perform both types of processing, and employ them each depending on different circumstances. The possibilities of real-time data analysis are seemingly endless. One thing is for sure, if your organization is not doing this analysis now, the sooner you start the better the future of your company will be.

Matthew Shoffner
IT Central Station

The great debate between batch and stream processing can be easily simplified. There are clear-cut differences between these two methods of processing data, both of which can be great options depending on your specific use-case. A batch means you want to be able to collect data that is already in a group and also within an established time frame. Stream processing is going to give you a real-time continuous data stream while converting big data into fast data.

Some of the best real-time analytics software provide both batch streaming and real-time stream processing.

Batch processing can be used when the data is already organized and it was collected within a window of time. A real-time stream processing system is more about instantaneous data. You can constantly analyze data, so if time is important, you want to stream that data in real-time.

What is Batch Processing?

Batch processing is when you process or analyze data that has stacked up over time, which is usually an hour, a few days, or even longer. Batch streaming is the act of streaming the data to a database where the data will be processed. Usually, batch processing is done on a schedule that is set up ahead of time, or it can be triggered to run after the data stored up hits a certain point.

In the case of one product, Hadoop batch processing involves using a framework to work to process a lot of data in different batches before performing batch analytics. The best time for something like this is when you do not actually need real-time analysis. In this case, you might prefer processing high volumes of data, which will give you more detailed insight instead of fast analytics with fewer details. You are choosing quantity over the speed with Hadoop batch processing.

Batch Processing Use Cases

You usually want to use batch processing when you have a high volume of data to analyze or when the data sources are older, legacy systems that have not been upgraded to be able to deliver data in continuous streams. Most data that is created on mainframes is a great example of data that is always processed in batch processing.

Most organizations utilize batch processing for their payroll, billing, and for orders from customers. This type of data makes sense to process in batches as organizations do not need live-streamed processing of these data sets. However, some eCommerce sites have begun utilizing stream processing for orders from customers so they can show current visitors that others are buying the same products they’re looking at. They’ll then still utilize batch processing to actually process the order so they can ship more efficiently.

Reviewers love that batch processing gives the “ability to work collaboratively without having to worry about the infrastructure.” Sometimes the ability to analyze data that was on legacy systems is important, and the machines are not equipped to do it without using batch processing. In that case, this type of processing can be the difference in making your data usable or not.

Micro-Batch Processing

When batch processes are run on small amounts of data that is considered micro-batch processing. Usually, this is data that accumulates just in a few seconds or up to a minute’s worth of time. This is the key to making the data available in real time, which is the best way to review data.

Don’t underestimate micro-batch processing. While the data might be small in size that doesn’t make it any less important. Sometimes that one piece of data you find could be the difference in your organization’s success.

Differences Between Batch Processing and Stream Processing

While you should be aware of a few key technical differences between the two types of processing, there is actually not a huge difference between batching and stream processing. A lot of times the two terms are used interchangeably when talking about in data architecture descriptions. That said, let’s look at a few times when you might use one over the other.

Reasons To Choose Batch Processing

Great for high volumes of data, use batch processing instead of stream processing when you need to gather data that already exists in a group that was defined by a certain time period. So if you want to analyze just a window of a few months, choose batch processing.

Reasons To Choose Stream Processing

You should consider choosing stream processing when you want speed. Having real-time analytics with data that is churned over in a matter of seconds is the main benefit of using stream processing. If time is of the essence, stream processing is the way to go.

Matthew Shoffner
IT Central Station

Real-time data analysis software gives you the ability to stream data and perform real-time analytics, so you are never dealing with stale data. Whether you use batch processing vs stream processing, you will quickly see the benefits of real-time data analysis. Each type of processing has its benefits, and your company can benefit from either as long as the right architecture is put in place.

As one reviewer puts it, data streaming architecture offers “good performance and support for big data.” Having the right data streaming architecture to fit your needs can be a vital component to the success of your business. Without it, you will struggle to process data quickly and efficiently potentially leaving you lagging behind your customers. The right processes can help you manage large volumes of data within seconds, giving you instant access to any type of information that you need.

Streaming Analytics Platforms

With these platforms, you can easily extract high volumes of information to churn through when you need it. You will be collecting information, analyzing it, and correlating information from a variety of different sources when you use streaming analytics platforms. If your company has incredibly large quantities of data, you absolutely need to use streaming analytics.

Nowhere other than real-time data analysis can you so easily put together a complete package of information. How else could you churn through such a high volume of information and make it into anything worthwhile that your organization can actually use?

Components of Streaming Architecture

Think of streaming data as data that is constantly being generated with no break in the process. And, it typically is streamed in high volumes and very quickly. Because of the speed and size that the data is streamed, there are a few different types of streaming architecture for you to consider, and each type comes with its own set of pros and cons.

Here’s a quick look at some of the pros and cons of different components of streaming architectures.

Stream Processor

With a stream processor, you are taking the data from a source and putting it into a standard message that is easy to process.


  • Works to make data easier to interpret and understand
  • Usually have a high capacity to move data quickly
  • Makes data easy to understand and someone who is unfamiliar with the data can use it and grasp the concepts.


  • Can be confusing to interpret the data without a little background knowledge

Batch Tools

These tools combine data from more than one source to make a more cohesive data package for your team.


  • Easy to process data from multiple sources into one cohesive batch
  • Can store high volumes of data
  • Best used when a window of time has been identified


  • Does not work well with just one source of data, and it is better when you have multiple data sources

Data Analytics

Streaming data is analyzed and turned into information you can use.


  • Great way to process real-time analytical data, so you can get the instant answers you need in your organization
  • Can analyze multiple streams of data
  • Makes data easy to interpret, so people who are not as familiar with your numbers can still use and interpret them


  • Almost too many different tools to choose from, all of which interpret data just a little bit differently


You can store your own streaming data since most technology for storage is relatively inexpensive.


  • Store data personalized for your organization, and keep proprietary information safe and secure
  • Relatively inexpensive


  • Can be technologically challenging to build the right kind of storage system

Any of your streaming architecture options are good ones, especially if you are not using any of them now. If you do not prefer one option, you can always use another. No matter what, using any of them are better than using none of them, and you should see improvements in your organization. Even when there are cons, they typically do not outweigh the pro, so you should confidently move forward with any of the options.

Matthew Shoffner
IT Central Station

Stream processing is a data processing methodology that provides a continuous data directly from the source without the interference of other systems so you get real-time data. When evaluating batch processing vs stream processing, you’re going to find that real-time data analytics improves the responsiveness of your operations. It’s very similar to event stream processing, which also looks at continuous data sets, but does so in a slightly different way.

The best real-time data analysis software allows you to act and respond in the present. It’s not meant to build reports for managers and executives. It’s capability resides in making business decisions in the here and now. With real-time data processing, you will end up receiving outputs almost instantly, making this method of data processing quick and efficient. When the data is inputted, processing happens immediately. Therefore, the continuous stream of that input data turns around and provides constant output.

As one reviewer put it, stream processing is “great for dealing with huge amounts of data, and it is easy to connect to different sources of data.” Anytime you are looking for quick data, stream processing is the solution. If processing and analyzing data quickly has been a big problem for your organization, you need to consider starting stream processing immediately.

Real-Time Stream Processing

Any time you take action on data immediately when that data is first created to measure that data in small bites of time like microseconds (as opposed to hours or even days) that is considered real-time stream processing.

The key component of that definition is the time period definition. Real-time is microseconds. Don’t get caught up thinking that processing data in hours constitutes “real-time.” For data to be streamed and processed in real-time it can only be done so in a matter of seconds. Anything longer and the data is no longer considered to be delivered in real-time.

Stream processing is the best way to get real-time data. Think about computer programs that do things like track financial movement with stocks or analyze the usage on websites. That data should be able to be analyzed instantly, so you can track it and react accordingly. With stream processing, you can do just that.

Once you have data streaming real-time, you’ll need to process it. AI and ML tools can be used to take the data and process it in an instant to deliver actionable insights or alerts. This is a huge benefit to any business relying on small or rapid changes, as seen in the case of stocks.

Stream Processing Framework

Stream processing involves computing data that is in motion. This requires that streamed data to be computed as soon as it is created and received by the framework. Think about the stock market or the ticker of sports scores going across the bottom of your TV screen. This information needs to be computed and churned constantly, as soon as it is received. It is the only way to track the fluctuating stock prices or get the updated score from your favorite team.

Your data streaming architecture is a key to being able to process the mass amounts of data being generated, which can be at the scale of petabytes or terabytes. The unique characteristics of your data need to be planned for when building your framework. From ingestion to processing to storage all need to be built to handle all cases.

Take into consideration the capabilities of the framework:

  • Near real-time vs real-timeSome are only near real-time, while others are actual real-time (i.e. microseconds).
  • Hybrid framework. Some have the capability to process data both in batches as well as real-time, which is a hybrid framework.
  • Supported coding languages. Frameworks can come in a wide variety of supported languages, which can include Java, Scala, Python, R, and SQL.

Java Stream Framework

There are many Java stream processing frameworks that can work to complete these tasks, which means your team will not have to start from scratch when creating a stream processing application. This could be very important if your IT infrastructure is older, as you can use this java stream processing to successfully analyze data on a legacy machine. Don’t let an older computer slow down the progress of your team. Instead, use a Java stream processing framework to facilitate your data processing needs without requiring an expensive upgrade.

You will find that the Java stream processing framework will help simplify the need to develop streaming applications. There are a lot of functions that are already built, saving your IT department both time and effort when implementing stream processing.

Find out what your peers are saying about Databricks, Solace, Amazon and others in Streaming Analytics. Updated: March 2021.
474,038 professionals have used our research since 2012.