Streams…. the Apache Kafka one….

Streams, streams, streams…. when will it ever end? It’s hard to keep up with all the messaging systems these days. GCP PubSub, AWS SQS, RabbitMQ, blah blah. Of course there is Kafka, hard to miss that name floating around in the interwebs. Since pretty much every system designed these days is a conglomerate of services… it’s probably a good idea to poke at things under the cover. Of course Apache Kafka is probably at the top of list of those open source streaming services. Today I’m going to attempt to install a Kafka cluster and push some messages around.

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So what’s up with Apache Hive? It’s been around a long time…but all the sudden it seems like it’s requirement in every other job posting these days. “It’s not you… it’s me.” That’s what I would tell Hive if it suddenly materialized as Mr. Smith via the Matrix that I’m pretty sure is the new reality these days. I’ve been around Hadoop and Spark for awhile now and I feel like Hive is that weird 2nd cousin who shows up at Thanksgiving. You know you should like and be nice to him, but you’re not sure why. It seems like Hive sits in a strange world. It’s not a RDBMS, although it does ACID, but it’s touted as a Data Warehousing tool. Time to dig in.

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Apache Spark and RasterFrames the big data geospatial processing juggernaut.

Yikes, distributed geospatial data processing at scale. That has fun written all over it… not. There isn’t that many people doing it so StackOverflow isn’t that useful. Anyone who has been around geospatial data knows the tools like GDAL are notoriously hard to use and buggy… and that one’s probably the “best.” What to do when you want to process and explore large satellite datasets like Landsat and Modis? Terrabytes/petabytes of data, what are going to do, download it? The power of distributed processing with Apache Spark. The simplicity of using SQL to work on geospatial data. Put them together… rasterframes. What a beast.

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