Hadoop vs PostGIS: What are the differences?
Hadoop: Open-source software for reliable, scalable, distributed computing. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage; PostGIS: Open source spatial database. PostGIS is a spatial database extender for PostgreSQL object-relational database. It adds support for geographic objects allowing location queries to be run in SQL.
Hadoop and PostGIS are primarily classified as "Databases" and "Database" tools respectively.
"Great ecosystem" is the primary reason why developers consider Hadoop over the competitors, whereas "De facto GIS in SQL" was stated as the key factor in picking PostGIS.
Hadoop and PostGIS are both open source tools. It seems that Hadoop with 9.18K GitHub stars and 5.74K forks on GitHub has more adoption than PostGIS with 636 GitHub stars and 242 GitHub forks.
According to the StackShare community, Hadoop has a broader approval, being mentioned in 237 company stacks & 116 developers stacks; compared to PostGIS, which is listed in 53 company stacks and 14 developer stacks.
What is Hadoop?
What is PostGIS?
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What are the cons of using Hadoop?
What are the cons of using PostGIS?
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Since the beginning, Cal Henderson has been the CTO of Slack. Earlier this year, he commented on a Quora question summarizing their current stack.Apps
- Desktop: And Electron to ship it as a desktop application.
- Android: a mix of Java and Kotlin.
- iOS: written in a mix of Objective C and Swift.
- The core application and the API written in PHP/Hack that runs on HHVM.
- The data is stored in MySQL using Vitess.
- Caching is done using Memcached and MCRouter.
- The search service takes help from SolrCloud, with various Java services.
- The messaging system uses WebSockets with many services in Java and Go.
- Load balancing is done using HAproxy with Consul for configuration.
- Most services talk to each other over gRPC,
- Some Thrift and JSON-over-HTTP
- Voice and video calling service was built in Elixir.
- Built using open source tools including Presto, Spark, Airflow, Hadoop and Kafka.
The MapReduce workflow starts to process experiment data nightly when data of the previous day is copied over from Kafka. At this time, all the raw log requests are transformed into meaningful experiment results and in-depth analysis. To populate experiment data for the dashboard, we have around 50 jobs running to do all the calculations and transforms of data.
in 2009 we open sourced mrjob, which allows any engineer to write a MapReduce job without contending for resources. We’re only limited by the amount of machines in an Amazon data center (which is an issue we’ve rarely encountered).
The massive volume of discovery data that powers Pinterest and enables people to save Pins, create boards and follow other users, is generated through daily Hadoop jobs...
PostGIS makes it easy (and fast) to do geographic queries, such as nearest-neighbor and bounding box queries.
Backend for weather forecast data that Geoserver queries to build updated weather maps
Importing/Exporting data, interpreting results. Possible integration with SAS
TBD. Good to have I think. Analytics on loads of data, recommendations?