In this post you will find the main differences between MongoDB and Hadoop. If you are not familiar with Big Data, you can download the e-book “De bit … a Big Data“clicking here.
When someone wonders how to select between MongoDB contra Hadoop to Big Data Project, In general, before they have had to solve other doubts such as what is the difference between Hadoop and MongoDB? OMongoDb and Hadoop can be used at the same time?
Differences between Mongo DB and Hadoop
Weather MongoDB, easier to use, uses a native C code technology ++, Hadoop uses Java and its use implies greater complexity. MongoDB often chosen to work with large volume systems and moderately sized data sets, while Hadoop gives an excellent result in Small map in relation to Big data and also in the report of the analysis of data.
Despite the limitations that, in itself, implies the lack of maturity of the first over the second, greater attention should be paid to its main drawback, what is that in your case, each node involves a single thread, question that needs many companies choose Hadoop, that does not have this disadvantage.
MongoDB vs Hadoop: who uses what
The dynamic scheme of MongoDB and its object-oriented structure make it a good choice for real-time analysis and dashboards. Some Deal who have been seduced by its advantages are:
– Idealista.com, you use it to save your message board messages.
– Craigslist, where this tool makes it possible to archive hundreds of millions of records.
– Forbes, that stores its posts and data about group companies with it.
Apache Hadoop is an open source software platform that works with the technology of Small map. The innovation brought about by his arrival and his vast experience working with Big data are some of the reasons that drive many institutions to choose them for their projects of prosecution, storage and analysis of large volumes of data. Some of them are:
- Amazons
- IBM
- Cloudera
- Essential
- DELL
MongoDB y Hadoop, Why select?
Why consider MongoDB contra Hadoop when both can fit nicely into a typical Big Data stack? Depending on the characteristics of the project to be carried out, the good news is that you should not select. The way to do it is using MongoDB as a real-time operational data warehouse and Hadoop for data processing and analysis. Some examples of implementations are:
– Batch aggregation: when complex data aggregation is needed MongoDB falls short with its aggregation functionality, that is not enough to complete the data analysis. In scenarios of this type, Hadoop provides a powerful framework that solves the situation thanks to its reach. To carry out this partnership, need to extract data from MongoDB (or other data sources, if you want to develop a multi-data source solution) to process them within Hadoop via MapReduce. The result can be sent back to MongoDB, ensuring its availability for subsequent consultations and analysis.
– Data warehouse– In a typical production scenario, data from one application can live in multiple data stores, each with its own query language and functionality. To reduce complexity in these scenarios, Hadoop can be used as a data warehouse and act as a centralized repository of data from various sources. In this situation, periodic MapReduce jobs could be performed for the Loading data from MongoDB into Hadoop. Once the MongoDB data, as well as data from other sources, are available from Hadoop, data analysts have the option of using MapReduce o cerdo to launch queries to the largest databases that incorporate data from MongoDB.
– ETL processes: Yes, Ok MongoDB It can be the operational data store of an application, it may happen that it has to coexist with other. In this stage, it is useful to achieve the ability to move data from one data warehouse to another, either from the application itself to another database or vice versa. The complexity of a ETL procedure exceeds that of the simple copy or transfer of data, so it can be use Hadoop as a complex ETL mechanism to migrate data in various ways using one or more MapReduce jobs to extract, transform and load data to the target. This approach can be used to move the data to or from MongoDB, according to the desired result.
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