What is Hadoop?
Hadoop is an open source framework that is ideally suitable for developing software apps and reliable, distributed, scalable computing. The Hadoop distributed computing is required to support the growth of voluminous data. It is also perfect for analyzing, storing, and processing big data files running over the clusters. The big data ecosystem techniques are placed at the heart of the Hadoop. They are generally needed to support advanced big data analytics that includes predictive analytics, machine learning, or data mining etc.
List of Hadoop Frameworks
The major components of the Hadoop framework include – HDFS (Hadoop Distributed File System), YARN (Yet another Resource Navigator), or MapReduce jobs etc.
- HDFS is utilized by the top Companies like IBM, or EMC to support the high throughput access for the application data. This is based on the objective to distribute large datasets uniformly across clusters of low-budget computer systems.
- In the same way, the MapReduce jobs allow users to distribute voluminous data across computer series for parallel processing of large datasets.
- In the end, Hadoop YARN is the framework to manage the job scheduling and cluster resources too.
How is Hadoop connected with the Cloud?
Cloud computing is a new buzz in the industry where apps or programs are installed over the centralized server and they can be accessed from anywhere anytime over the network. Since cloud technology is just a power solution to deliver the needed computation power to the big data apps, merging the Hadoop framework and the cloud together will ease the processing of parallel datasets magically.
The power of computing large data set is recommendable and it can be scaled based on your requirements. When Hadoop will run over the cloud, it can provide the users with distributed computing, data mining, data analytics and cloud infrastructures too.
Top six reason why the association of Cloud and Hadoop makes sense?
The relation of cloud and Hadoop has become a trendy topic these days. Further, in this blog, we will discuss the top six reasons why the association of two popular techniques makes sense and their value is constantly increasing in the IT marketplace.
- Innovation Costs are Reduced
- Resources are procured faster
- Batch Workloads are managed efficiently
- Variable Resources managed perfectly
- Runs Closer to the Data
- Hadoop Operations are Simplified
1). Innovation Costs are Reduced
When Hadoop runs over the cloud, the extra investments can be lower down instantly. Every time when big data computation is needed for a project, the cloud makes a sense in this context. The concept was introduced by the leading technology experts when benefits of association were realized by Companies.
2). Resources are procured faster
Quick resources procurement is the biggest need for organizations. Hadoop needs heavy storage drives to storage and computes the large data sets. For small-scale Companies, this is not possible to procure all resources quickly, the best solution is to run Hadoop on the cloud where heavy or expensive resources can be procured automatically whenever needed. Also, you can release the resources again, once the objective is complete.
As soon as data analytics demand grew by organizations, there was an emergency need of expanding Hadoop cluster nodes too. Here, the cloud platform itself witnessed handling linear scaling especially when it comes to innovation and growth. With the cloud computing, shifting hardware to Hadoop becomes much easier that was not possible earlier. When it comes to resource procurement, it was usually taking weeks or months by Companies but cloud solved this problem dramatically where resources are hired within minutes.
3). Batch Workloads are managed efficiently
The main objective of the Hadoop framework includes job scheduling and processing data on a fixed basis. Companies usually collect data from different sources that should be analyzed wisely to derive meaningful insights from the same. For this purpose, workloads are reduced to batches and they are managed more efficiently when computed over the cloud.
Cloud helps to analyze user patterns effectively and even clusters can be divided into suitable sizes at the right time when needed. On the above of data analysis, this is possible for Companies to schedule cloud-based clusters for a specific time period when data should be crunched.
4). Variable Resources managed perfectly
The association of cloud and the Hadoop is one of the trendy choices these days. Not all MapReduce jobs in Hadoop are created equal. A few of them demand more computing resources and investment when compared to others. In this case, this is necessary to manage the diversity in Hadoop jobs.
One of the amazing solutions is running Hadoop on the cloud. It will suggest the proper scheduling techniques and make you avail with necessary computing resources. Intuitively, the cloud is the more adaptable solution to handle the variable resources requirements when compared to other IT techniques.
5). Runs Closer to the Data
Every time when businesses are planning to move their data to the cloud, they had to follow the standard process for a successful migration. At the same time, there is a need for proper data analytics techniques where large data sets can be managed efficiently and the overall time for migration can be reduced.
Here, running the clusters of Hadoop in the cloud environment is just the wonderful choice to solve the problem. Combine cloud benefits with data locality principle of Hadoop at the Macro level.
6). Hadoop Operations are Simplified
Once clusters are consolidated by the organization, one thing remains the same that is resources isolation by multiple sets of users. Users need to bunch the MapReduce jobs in a shared cluster and this is the job of administrators here to handle the multi-tenancy issues like interference of jobs with one another and managing security constraints too.
The one and most typical solution to the problem is to enforce data policies at the cluster level that will avoid users to perform any harmful activities with other user jobs. With this approach, the secured use cases will also be safe. This activity is common for the administrators to protect the data from harm and unwanted access. Also, Companies need to spend heavy amount to manage the resources in a clustered manner.
With the help of a cloud, this is possible for users to configure clusters with varied characteristics and features. Each cluster is suitable for a particular set of jobs and this is possible to manage to complicated cluster policies without any multi-tenancy issues. In brief, the right configuration is available for multiple jobs.
This is clear from the discussion that the relation of cloud and Hadoop is just a trendy choice these days. The association of both the framework will result in wonderful solutions for big data analytics without any potential problems. To know more on the Hadoop framework, join the big data certification program at JanBask Training right away.