Hadoop vs Oracle: 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; Oracle: An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism. Oracle Database is an RDBMS. An RDBMS that implements object-oriented features such as user-defined types, inheritance, and polymorphism is called an object-relational database management system (ORDBMS). Oracle Database has extended the relational model to an object-relational model, making it possible to store complex business models in a relational database.
Hadoop and Oracle can be primarily classified as "Databases" tools.
"Great ecosystem" is the top reason why over 34 developers like Hadoop, while over 36 developers mention "Reliable" as the leading cause for choosing Oracle.
Hadoop is an open source tool with 9.26K 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 Oracle is used by Netflix, ebay, and LinkedIn. Hadoop has a broader approval, being mentioned in 237 company stacks & 127 developers stacks; compared to Oracle, which is listed in 106 company stacks and 92 developer stacks.
What is Hadoop?
What is Oracle?
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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...
Gerenciamento de banco de dados utilizados por odos os serviços/aplicações criados
Importing/Exporting data, interpreting results. Possible integration with SAS
recommended solution at school, also used to try out alternatives to MySQL
TBD. Good to have I think. Analytics on loads of data, recommendations?