Real-time Analytics Vs Stream Processing – What Is The Difference?

One of the holy grails that many data teams seem to chase is real-time data analytics. After all, if you can have real-time analytics, you can make better decisions faster.

However, there often is a conflation between real-time data analytics and stream processing. 

These are two different concepts that are crucial to understanding how to implement them as well as what tools can help do so.

What is Real-time Analytics?

Before we talk about what real-time analytics is, I’d like to point out that real-time tends to mean very different things to everyone I have talked to.

According to some people, real-time analytics means that when data is created, it is automatically processed into a dashboard or fraud detection model.

Other people, however, translate it as “near” real-time analytics where the data might be delayed by a few seconds before being processed. When it comes to many implementations, most from an analytical standpoint are near real-time, depending on what tools and system set up you’re using. If you need to look into how to set up your real-time system, you can see a few examples here.

For an actual definition, TechTarget defines it as “Real-time analytics is the use of data and related resources for analysis as soon as it enters the system.” The adjective “real-time” refers to a level of computer responsiveness that a user senses as immediate or nearly immediate.

Some of this might be done just on the edge while the system processes and loads the data.

How Real-time Analytics Differs from Stream Processing

Now that we’ve covered the basis of real-time and near real-time analytics, let’s talk about stream processing and real-time analytics.

Here is how you can keep the two separated:

• Stream processing: I really liked the definition of stream processing from HarperDB which states, “Fundamentally, stream processing is about the continuous and immediate management of data. A notable distinction lies in its capability to handle multiple data streams, possibly originating from varied sources.” Stream processing in this way is more about the management of data in a stream and how to push it into systems that might include real-time analytics vs. the analytics themselves.
• Real-time analytics: As referenced above, real-time, and in many cases near-time, analytics involves systems that ingest data almost instantaneously and process it through some level of the analytical system. This could be to go to a dashboard, although more than likely the system processes the output into either an ML model or logic system that helps automate decisions rather than having a person on the other end try to understand data that is coming in faster than they could process it.

Real-life Use Cases Enabled by Real-time Analytics

There are plenty of use cases for real-time analytics. Below, I have outlined a few that I have either seen in real life or discussed with other data experts.

Industry-Specific Examples

• Supply Chain And Logistics – I worked on one project where we developed a real-time analytics system to help the team better understand order trends as well as how workflows would be impacted by the supply chain. Basically, whenever orders came in, the system would in real-time analyze the supply chain, current stock, and orders to assess how timelines for deliveries would be impacted.
• Retail And Ads: There are so many real-time analytics examples for retail. I am sure you’ve all gone to Amazon and gotten served ads. Much of this is done in real-time auctions. There are actually some great papers you should read here.
• Finance: If you’re building your first real-time data engineering project, I can almost guarantee you’ll end up picking stock data as a real-time source. There are a lot of real-time project examples out there and it’s where a lot of real-time analytics happens. After all, for stocks, being .001 milliseconds faster than the other companies ensures you’re millions of dollars ahead of them. 
• Telecommunications/ICPs: An often forgotten industry for real-time analytics is telecom. Some people are so focused on FAANGs that they miss the fact that all these FAANGs run on wires that many telecom companies and ICPs own. These systems manage all the data processing. In turn, they need real-time analytics. They have to ensure they are keeping their systems up and their customers happy.

Benefits and Challenges of Implementing Real-time Analytics

If your team has decided to go down the real-time analytics route, here are some pros and cons you’ll face as you’re working to implement it. 

• Benefits
  • Faster decision-making and response times – It goes without saying that real-time analytics makes it easier to make decisions in real time. With the right systems, machine learning models, and KPIs, you can get a good picture of what is going on in your business and make good decisions.
  • Enhanced customer experiences through personalized interactions – If your product is digital and honeslyt, these days even if it’s not. Having real-time analytics systems connected to machine learning models or logic systems can help you improve your customer experience. Perhaps you provide them with the right coupon at the right time or provide some level of extra service that you wouldn’t know to supply them without the data.
• Challenges
  • Technical challenges (data integration, processing power, latency issues) – When you’ve worked in data for a while, you’ll realize real-time can be expensive, which we’ll discuss below and its technically challenging. Now, if you’re going to go the route of implementing real-time yourself, then yes, it will be challenging and you’ll likely spend a lot of time managing your real-time systems. But there are some tools that can help make it easier.
  • Cost and resource implications – This is one of the biggest challenges that most people reference with real-time analytics, as pushing data real-time often requires more computation and finer tuning to ensure the data is correct, leading to more expensive systems. But  I would like to point you to this article if you’re looking to simplify your real-time analytics workflows.

Real-Time Analytics Tools

There are probably hundreds of tools, all geared towards making real-time analytics easier. After all, lots of companies want to implement real-time analytics.

We are only going to go over a few real-time analytics tools that you should know about.

Apache Kafka

Kafka framework is a distributed publish-subscribe messaging system that receives data streams from disparate source systems.

This software is written in Java and Scala. It’s used for real-time streams of big data that can be used to do real-time analysis. This system isn’t only scalable, fast, and durable but also fault-tolerant.

Owing to its higher reliability and throughput, Kafka is widely used for tracking service calls and IoT sensor data.

So who uses Kafka? Well, it originated with LinkedIn to provide a mechanism to load parallel data in Hadoop systems. Later, in 2011, it became an open-source project under Apache, and now LinkedIn is using it to track operational metrics and activity data.

Estuary 

Estuary is a real-time ETL/ELT and data integration platform. ETL—extract, transform, and load—is a critical component of data management and warehousing.

Estuary’s comprehensive feature set simplifies integration by way of “data flows.” This eliminates the hassle of consolidating data from multiple locations in various formats, providing a foundation for data accuracy. From there, the consolidated information is available for whatever your team needs it for, from building data-driven apps to enhanced analytics.

Estuary also offers Kafka API compatibility, which opens up it’s hundreds of real-time data source connectors to the entire Kafka ecosystem. Consuming real-time data is as easy as connecting to a Kafka broker.

Druid

Druid is a high-performance, real-time analytics database allowing companies to instantly gain insights from large volumes of event-driven data. Designed for sub-second queries, Druid has found its niche in scenarios where timely analytics is not just a luxury but a necessity.

It also has several unique features that allow it to be used as a real-time OLAP. Everything from its various nodes to its processes, each has unique functionality that lets it scale to the fact that the data is indexed to be pulled quickly and efficiently.

AWS Kinesis

Kinesis is a managed streaming service on AWS.

AWS Kinesis provides several advantages compared to some of the other tools on this list. It allows your team to spend less time managing infrastructure components and services and instead focuses more on development. Kinesis allows you to ingest everything including videos, IoT telemetry data, application logs, and just about any other data format live. This means you can run various processes and machine learning models on the data live as it flows through your system instead of having to go to a traditional database first.

AWS Kinesis also has clear support from companies like Netflix. They use Kinesis to process multiple terabytes of log data every day. This is made easier by the fact that Kinesis is a managed service.

But that’s enough about technology; let’s talk about use cases.

The Future of Real-time Analytics

Implementing real-time analytics systems can be challenging. But with the right tools and use cases, it can also be very much worth it. Your team can go from simply using batch processing or even manual data extraction to being able to answer key business questions in a timely manner.

All of this can ensure you make far better decisions that impact your bottom line. So if your team is looking for assistance in building better real-time systems, then feel free to reach out!

If you’re still hoping to learn more about this change and some of these skills, then you can check out these articles.

Explaining Data Lakes, Data Lakehouses, Table Formats and Catalogs.