The amount of data being produced across the world is currently doubling in size once in every two years. By the year 2020, the data available is expected to reach 44 zettabytes (44 trillion gigabytes). The processing of these large amounts of data is known as Big Data. The concept of big data has been around for a decade now, though this term has only gained popularity in recent years.
For addressing this explosion of data growth, many big data platforms have been developed to manage and structure this data. There are currently 150 non-relational database-driven, No-SQL solutions or platforms that are associated with big data, though not all are considered as a big data solution. Out of these various platforms, there are two that have become popular choices: Hadoop and MongoDB. Both these platforms are open-source, schema-less, MapReduce, and NoSQL. But they differ in terms of processing and data storage. In this article, we will be analyzing both Hadoop and MongoDB.
What is Hadoop?
Hadoop is an open-source platform that allows storage and processing of huge volumes of data. It is a Java-based application that contains resource management, data processing, distributed file system, and other components for an interface. Hadoop was created by Doug Cutting, and it originally stemmed from Nutch, an open-source web crawler project created in 2002. In 2003, Nutch released its distributed file system called NDFS. After Google introduced the MapReduce concept in 2004, Nutch also announced the adoption of the MapReduce architecture in 2005. Hadoop was officially released in 2007. More than acting as a replacement for transactional RDBMS systems, Hadoop is a supplement to them.
What is MongoDB?
It is mainly designed for data storage and retrieval. It also performs data processing and scalability. It belongs to the NoSQL family and is based on C++. Instead of relying on creating relational tables, it stores its records as documents. It was originally developed in 2007 as a cloud-based engine to run assorted software and services. Babble and MongoDB were the two main components that were developed. But since the idea didn’t take off, it was released as open-source software that garnered support from a growing community. Though Mongo DB can be considered as a big data solution, it is primarily a general-purpose platform designed to enhance or replace existing RDBMS systems.
MongoDB and Hadoop- How They Work?
Traditional Relational Database Management Systems (RDBMS) are modelled around tables and schemas to structure and organize data in columns and rows. Mongo DB, a document-oriented database management system, stores data in collections where different data fields can be queried once, versus multiple queries needed by RDBMS systems that allocate data across tables in columns and rows. Data is stored in binary JSON (BSON) and is available for indexing, replication, ad-hoc queries, and MapReduced aggregation. Database sharding can be done to allow distribution across multiple systems for horizontal scalability. MongoDB is written in C++. While MongoDB is actually a database, Hadoop is a collection of various software components that create a data processing framework.
Hadoop comprises of a software ecosystem. Its primary components are MapReduce and Hadoop Distributed File System (HDFS) written in Java. Some of the secondary components are Pig, Hive, a collection of Apache products, HBase, Oozie, Flume, and Sqoop. Hadoop’s HBase database attains horizontal scalability through database sharding. Hadoop runs on clusters of commodity hardware and has the ability to consume data in any format.
Vertical scaling refers to enhancements done to server hardware such as RAM, CPU, or switching to Solid State Drives. Horizontal scaling refers to adding more system nodes.
Platform Popularity- MongoDB Vs. Hadoop
MongoDB is the most popular NoSQL platform that seems all set to overtake PostgreSQL as the 4th most popular database. Considering the various applications and websites powered by MongoDB, it is apparent that it is being used as a real-time data solution. This includes serving up content and web pages, real-time analytics, and data availability for users on production websites.
Hadoop has lower adoption rates than MongoDB. It is meant to operate in an ecosystem of databases and platforms, and connectors. While MongoDB is popular for real-time data needs, Hadoop is the preferred choice when it comes to long-running operations/analytics and batch jobs.
Big Data Use Cases- Platform Strengths
While both are good in terms of parallel processing, scalability, handling large amounts of aggregated data, and MapReduce architecture, MongoDB’s greatest strength is its robust and flexible nature that includes a potential replacement of the existing RDBMS. It is better at handling real-time data analytics. Its geospatial indexing abilities, make it the best fit for real-time geospatial analysis.
Hadoop excels at batch processing and long-running ETL jobs and analysis. Hadoop’s biggest strength is that it was built for big data, while MongoDB started becoming more of an option. Hadoop helps in processing log files. Though MongoDB is better at handling real-time data, ad hoc SQL-like queries can be run using Hive. Hadoop’s Map Reduce implementation is suitable for analyzing large amounts of data.
Big Data Use Cases- Platform Weaknesses
Some of the similar weaknesses of these two platforms are security issues, data quality concerns, and potential fault tolerance issues. Fault tolerance issues result in data loss. Complaints against MongoDB are data aggregation issues and poor integration with RDBMS. It can only consume CSV or JSON formats, and this may require additional data transformation.
Hadoop’s primary issue is the NameNode. It is a single point of failure for HDFS clusters. Hadoop is unpredictable with regard to the amount of time it takes to complete data processing jobs and the inability to administer job priority.
It is important to do a lot of research and consider many points before deciding whether MongoDB or Hadoop would be suitable for your organization. If you are looking for an encompassing solution for processing low-latency real-time data, MongoDB is an appropriate choice. Based on the volume and velocity of your data, Hadoop can be considered for features such as scalability and expandability.
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