What Is Big Data With Hadoop?
There are two major technologies in play when discussing what is big data: Hadoop and Map/Record. The former is a framework for managing data, while the latter refers to an online datastore designed for this purpose. The former is an open source project based on the Java/Scala platform, while the latter is Apache Hadoop, a commercial solution from Yahoo! and Facebook. While both of them have their own strengths and limitations, they are strongly dependent upon one another.
Most of the discussion about what is big data hoop revolves around applications and the use of large amounts of storage space. While this is certainly a requirement, the amount of available memory for large scale applications is quickly depleting, especially with the introduction of large numbers of servers, and the need for storage management systems becomes more critical. Map/Record tries to deal with these issues by providing a database management system (DBMS) that is both elastic in nature and schema-free. Being schema-free, it allows for easy creation and modification of values within the database. As an example, you can easily insert values or map keys within an existing RDBMS.
In its efforts to provide a better database management system for big data, Map/Recorder attempts to adopt some of the key features of Hadoop. Hadoop was co-founded by the well-known Yahoo! programmer, Ray Jardine, who also happens to be the founder of MapDota, an open source project aimed at providing Map/UI components. MapDota is in essence a map/ui framework that provides tools for managing large volumes of data. One of the key characteristics of Map/Recorder is the ability to manage the metadata of large files, and as a result the developers at MapDota have been able to extend the framework to support image processing, video recording, and other image manipulation applications. Image processing is one area where Hadoop has a lot of potential since the storage and processing of images is a critical part of web design.
A key feature that makes Map/Recorder more interesting compared to Hadoop is the fact that it supports the usage of multiple languages. This is especially important for those who would like to integrate MapDota with JAVA, and the developers at MapDota have made this possible through the incorporation of the MapReduce framework. The MapReduce framework, together with MapDota, provides developers with the ability to write programs that can efficiently use large numbers of streamed data, greatly reducing the time required for processing.
Although Hadoop and MapDota are two of the most commonly used frameworks when discussing big data, they differ in a number of different ways. For example, while Hadoop uses the Map/Recorder framework, MapDota adds a number of additional capabilities, such as the ability to create a fully featured cluster, which allows users to manage different nodes independently from other users in the same system. Additionally, Hadoop focuses on a single data model, while MapDota supports a number of different data models, including both vertical and horizontal data flows, in order to support more complex data analysis scenarios. Furthermore, Hadoop is known for its high level of reliability and performance, whereas MapDota is much less reliable but comes with a wide range of guarantees.
In terms of programming language, both Hadoop and MapDota come with an expressive DSL (domain-specific language) that allows programmers to build large applications without having to learn a new programming syntax. In Hadoop, the programming language is strongly typed, requiring programmers to explicitly specify the type of each piece of data that will be stored. On the other hand, MapDota programs are less dependent on type conversions and do not require a programmer to understand a new programming syntax. In addition, both frameworks support multiple interface patterns, allowing a user to seamlessly swap between MapDota and Hadoop.
Similar to other large scale database frameworks, Hadoop supports several different modes for managing data. These include naive, deep, and optimized maps. An optimizer is a piece of software that takes an already measured set of inputs and produces a map by the weights of the results. The naive mapper is similar to an execution-blocking handler, as it simply uses the most efficient current operation to produce the map. A deep mapper, meanwhile, makes heavy use of batching to reduce the total amount of time spent on generating the map.
Overall, Hadoop and MapDota are complementary projects that can provide the needed functionality for managing large amounts of data. While Hadoop provides a general purpose framework and map building capabilities, MapDota takes care of the complexity of implementing a better data management system. The two frameworks also share a few core methods for managing the underlying data, with MapDota using an SQL-like language for configuration data and the former using its own proprietary format for key value storage and map creation. Both frameworks, however, strongly suggest that you employ an expert to manage your deployment.