Transcript

00:01Earlier this morning, we saw an architecture to perform analysis on social media streams. Real-time analysis on social media.

00:09Now we're going to look at another big data architecture and how to efficiently analyze more than 3.5 billion records.

00:18So please welcome Mansour Raad.

00:21Thank you John. Working with big data is challenging because of the volume of the data, the velocity of the data...

00:31...and the variety of the data. I'd like to share with you today two things that you can do with the ArcGIS tools...

00:37...that you already have. One, spatially analyzing big data, and two, spatially querying billions of records.

00:48Let's start with the big data analysis. ArcGIS Online processes 1.7 billion records every month. What I'm showing you here...

01:00...is a density map for all the street map requests in the month of February. What we see is we see of course...

01:06...a lot of activity around the urban areas. There was a lot of activity in Germany, but we can see that there's a hot spot...

01:15...in the Southeast Asia region. And actually, there's a reason behind that. It's because we've been updating the streets...

01:22...for that region recently. Lot of activities in Japan; lot of activities in Thailand. And again, one of the many reasons...

01:31...because of that, is because the streets are now annotated in the local language. Let's go now to Europe.

01:40We see a lot of activities in Germany and in London, but something very interesting happens if I switch to analyzing...

01:48...all the topo map requests. We see a lot of activity again in the Netherlands and in London, and there is a reason behind this.

01:58And that is because the local agencies are producing high-quality topo maps. In England, it is the Ordnance Survey...

02:08...and in the Netherlands, it's the Dutch cadastral agency. Basically, if you produce high-quality maps and you share it...

02:18...if you build it, they will come and get it. Now, we've been analyzing big data. I'd like to share something with you...

02:26...that I'm very passionate about. And that is querying billions of records. If you remember a couple of weeks ago...

02:35...a meteor blasted through the atmosphere in Russia. I'd like to do a spatial analysis over the area for all the map requests...

02:45...in the area and the following couple of days to see if there's any trends. So I'm going to be looking for the sixteenth...

02:54...the seventeenth, and the eighteenth. Let me run this. While it is running, let me share with you the back-end architecture.

03:05I have a Hadoop cluster of 18 data nodes on commodity machines running MapReduce, Hive, Pig, and Impala.

03:15Using an ArcGIS Python geoprocessing script, I'm instructing Hadoop to perform a spatial and temporal distributed analysis...

03:26...over 3.5 billion records. When the result comes back, I'm telling ArcGIS to perform a density analysis in such a way...

03:35...I can see hot spots on the map. What used to take hours to run now will take minutes, and the reason behind this is that because...

03:47...we're taking the program and we're sending it to the data rather than taking the data and bringing it to the program.

03:55You can see we've returned 1.1 million records in 48 seconds. Pretty impressive. And now we're doing the density analysis.

04:05And we can see the trends on the map. Nothing unusual over Moscow. Lots of map requests over Moscow.

04:15But if you look to the east of it, we can see a lot of map requests. Why? Because that was the area that was affected...

04:23...with the meteor. So to recap. Two things that you can do today with the tools that you already have.

04:30You can spatially analyze big data, and you can spatially query billions of records. Again with the ArcGIS tools...

04:37...that you already have. Back to you, John.

04:45Thanks, Mansour. You sound like my college professor. Sounds really, really smart, talking about things...

04:51...that I don't totally understand, but I think I definitely understand what used to take me hours and hours and days to do...

04:58...you just did in a matter of seconds by using all those acronyms and all those technologies and putting them all together.

05:04Is that right? Yes, and they are real, as I keep saying. You know, I geeked out, I know, because I am very passionate about it...

05:10...you know, and I wanted to share this with all of you, but it is real. Okay, thanks Mansour.

Copyright 2014 Esri
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Big Data in ArcGIS

Mansour Raad demonstrates the ArcGIS tools for analyzing and querying big data and billions of records.

  • Recorded: Feb 25th, 2013
  • Runtime: 05:17
  • Views: 726
  • Published: Mar 5th, 2013
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