No discussion on Big Data is complete without bringing up Hadoop and MongoDB, two of the most prominent software programs that are available today. Thanks to the plethora of information available on both programs, particularly their respective advantages and disadvantages, choosing the right one poses a challenge. Since both platforms have their uses, which is most useful for you and your organization? This article is a guide to help you make that crucial choice between the two qualified candidates.
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Hadoop is an open-source set of programs that you can use and modify for your big data processes. It is made up of 4 modules, each of which performs a specific task related to big data analytics.
These platforms include:
This is one of the two most crucial components of Hadoop. A distributed file system (or DFS for short) is important because:
MapReduce is the second of the two most crucial modules, and it’s what allows you to work with data within Hadoop. It performs two tasks:
Hadoop Common is a collection of tools (libraries and utilities) that support the other three Hadoop modules. It also contains the scripts and modules required to start Hadoop, as well as source code, documentation, and a Hadoop community contribution section.
It is the architectural framework that enables resource management and job scheduling. For Hadoop developers, YARN provides an efficient way for writing applications and manipulating large sets of data. Hadoop YARN makes possible simultaneous interactive, streaming, and batch processing.
Alright, so now that we know What Hadoop is, the next thing that needs to be explored is WHY Hadoop. Here for your consideration are six reasons why Hadoop may be the best fit for your company and its need to capitalize on big data.
As good as Hadoop is, it nevertheless has its own particular set of limitations. Among these drawbacks:
MongoDB is a highly flexible and scalable NoSQL database management platform that is document-based, can accommodate different data models, and stores data in key-value sets. It was developed as a solution for working with large volumes of distributed data that cannot be processed effectively in relational models, which typically accommodate rows and tables. Like Hadoop, MongoDB is free and open-source.
The storage engines include:
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Businesses today require quick and flexible access to their data to get meaningful insights and make better decisions. MongoDB's features are better suited to help in meeting these new data challenges. MongoDB’s case for being used boils down to the following reasons:
While MongoDB incorporates great features to deal with many of the challenges in big data, it comes with some limitations, such as:
In trying to answer this question, you could take a look and see which big companies use which platform and try to follow their example. For instance, eBay, SAP, Adobe, LinkedIn, McAfee, MetLife, and Foursquare use MongoDB. On the other hand, Microsoft, Cloudera, IBM, Intel, Teradata, Amazon, Map R Technologies are counted among notable Hadoop users.
Ultimately, both Hadoop and MongoDB are popular choices for handling big data. However, although they have many similarities (e.g., open-source, NoSQL, schema-free, and Map-reduce), their approach to data processing and storage is different. It is precisely the difference that finally helps us to determine the best choice between Hadoop vs. MongoDB.
No single software application can solve all your problems. The CAP theorem helps to visualize bottlenecks in applications by pointing out that distributed computing can only perform optimally on two out of three fronts, those being processing, partition tolerance, and availability. When choosing the big data application to use, you have to select the system that has the two most prevalent properties that you need.
Both Hadoop and MongoDB offer more advantages compared to the traditional relational database management systems (RDBMS), including parallel processing, scalability, ability to handle aggregated data in large volumes, MapReduce architecture, and cost-effectiveness due to being open source. More so, they process data across nodes or clusters, saving on hardware costs.
However, in the context of comparing them to RDBMS, each platform has some strengths over the other. We discuss them in detail below:
MongoDB is a flexible platform that can make a suitable replacement for RDBMS. Hadoop cannot replace RDBMS but rather supplements it by helping to archive data.
MongoDB is a C++ based database, which makes it better at memory handling. Hadoop is a Java-based collection of software that provides a framework for storage, retrieval, and processing. Hadoop optimizes space better than MongoDB.
Data in MongoDB is stored as JSON, BSON, or binary, and all fields can be queried, indexed, aggregated, or replicated at once. Additionally, data in MongoDB has to be in JSON or CSV formats to be imported. Hadoop accepts various formats of data, thus eliminating the need for data transformation during processing.
MongoDB was not built with big data in mind. On the other hand, Hadoop was built for that sole purpose. As such, the latter is great at batch processing and running long ETL jobs. Additionally, log files are best processed by Hadoop due to their large size and their tendency to accumulate quickly. Implementing MapReduce on Hadoop is more efficient than in MongoDB, again making it a better choice for analysis of large data sets.
MongoDB handles real-time data analysis better and is also a good option for client-side data delivery due to its readily available data. Additionally, MongoDB’s geospatial indexing makes it ideal for geospatial gathering and analyzing GPS or geographical data in real-time. On the other hand, Hadoop is not very good at real-time data handling, but if you run Hadoop SQL-like queries on Hive, you can make data queries with a lot more speed and with more effectiveness than JSON.
Now that you have all the information you need about MongoDB vs. Hadoop, your next step should be to get certification in the software that best fits your needs. You can go through the following courses:
Each company and individual comes with its own unique needs and challenges, so there’s no such thing as a one-size-fits-all solution. When determining something like Hadoop vs. MongoDB, you have to make your choice based on your unique situation. But once you make that choice, make sure that you and your associates are well-versed in the choice. The above training courses will go a long way towards giving you the familiarity you need in helping you get the maximum results from whichever choice you make.
Name | Date | Place | |
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MongoDB Developer and Administrator | 13 Mar -4 Apr 2021, Weekend batch | Your City | View Details |
MongoDB Developer and Administrator | 13 Mar -4 Apr 2021, Weekend batch | Chicago | View Details |
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