Google Cloud Datastore vs Hadoop: What are the differences?
What is Google Cloud Datastore? A Fully Managed NoSQL Data Storage Service. Use a managed, NoSQL, schemaless database for storing non-relational data. Cloud Datastore automatically scales as you need it and supports transactions as well as robust, SQL-like queries.
What is 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.
Google Cloud Datastore belongs to "NoSQL Database as a Service" category of the tech stack, while Hadoop can be primarily classified under "Databases".
"High scalability" is the top reason why over 4 developers like Google Cloud Datastore, while over 34 developers mention "Great ecosystem" as the leading cause for choosing Hadoop.
Hadoop is an open source tool with 9.27K GitHub stars and 5.78K GitHub forks. Here's a link to Hadoop's open source repository on GitHub.
Airbnb, Uber Technologies, and Spotify are some of the popular companies that use Hadoop, whereas Google Cloud Datastore is used by Teleport, Policygenius, and Giftstarter. Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to Google Cloud Datastore, which is listed in 46 company stacks and 16 developer stacks.
What is Google Cloud Datastore?
What is Hadoop?
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What are the cons of using Google Cloud Datastore?
What are the cons of using Hadoop?
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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...
This is our primary database, though most of our actual data is stored in static storage. This database houses the metadata necessary for indexing and finding static data.
worked with a client that used datastore as their backend database. helped plan out their schema and architecture. loved the speed and simplicity.
Importing/Exporting data, interpreting results. Possible integration with SAS
TBD. Good to have I think. Analytics on loads of data, recommendations?