MapR Event Store vs. Apache Kafka – Whiteboard Walkthrough

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4 min read

In this week's Whiteboard Walkthrough, Jim Scott, Director of Enterprise Strategy and Architecture at MapR, discusses a business use case that leverages the power of MapR Event Store.

Here's the unedited transcription:

Hi – welcome to this Whiteboard Walkthrough. My name is Jim Scott, director of Enterprise Strategy & Architecture with MapR. Today we're going to talk about MapR Event Store (formerly called MapR Streams) versus Kafka.

Here at MapR, we love real time. Matter of fact, our customers demand it of us. We talked with a lot of different people in the industry to find out what were the pain points that they felt when dealing with real time.

A few of the things that they told us about that were the most important were number one, they wanted to reduce cluster sprawl. We decided to integrate a new streaming platform into our Hadoop in MapR database offering.

Next, they said, "We want to avoid data movement." What we did is we enabled direct access to the event data that comes through the platform and enabled them to perform analytics in place, preventing data movement.

The third thing they said is, "We are global, or we have aspirations to be global, so we need to handle global replication." Now, if this sounds like a lot, let's walk through a use case dealing with retail that can explain some of these for us.

Fortunately for me, I have this phenomenal grocery store. We're very successful. I'm global by the way, so I have a lot of retail stores. I also have headquarters. One of the use cases I have for my business is to improve my customer experience. From the time a customer enters the store through the times that they are removing products from the shelf, I have sensors in different places. Every one of these sensors triggers off events. These events flow into my streaming system.

I have the ability to then build software listening to these streams and react in real time. We can take all of this data that's coming in and we can do things like optimize when we have cashiers at the registers to make sure that customers don't wait in lines too long. We can also do things like making sure that if a product is moving quickly off the shelves when there are hundreds of people in the store that we can get someone to restock that product as fast as possible.

We have a strong focus in this use case on customer satisfaction. Now, as a global company, customer satisfaction is one facet. I want to get all of my data from my stores to global headquarters so I can do more with it. What I do is I stream the data, I replicate it between data centers, and I now have the ability to fix and optimize any issues with supply chain or shipping from global logistics management. In addition to that, I now have the ability to build solutions around targeted advertising for my customers.

Now, if this sounds very heavy on retail, well keep in mind that this is more than just retail. This use case fits nearly any different business that's out there. This works in manufacturing, this works in retail as we've covered, this works in any company that you have a need to deliver more real time solutions to your customers.

If you have any questions about this, please leave a comment below. Follow us on Twitter and have a great day.

This blog post was published December 15, 2015.

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