What is Hadoop?
Industry Specialties: Serves all industries
Apache Hadoop is an open source framework for dealing with large quantities of data. It’s considered a landmark group of products in the business intelligence and data analytics space, and is comprised of several different components. It functions on basic analytics principles like distributed computing, large data processing, machine learning and more.
Hadoop is part of a growing family of free, open source software (FOSS) projects from the Apache Foundation, and works well in conjunction with other third-party products.
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Benefits and Insights
Why use Hadoop?
Key differentiators & advantages of Hadoop
- Cost Savings: With highly efficient data storage and retrieval, you can save money on operating costs and cut costs related to storing data as a whole.
- Faster Data Analysis: Analyzing data is a slow process that Hadoop can help speed up with efficient data retrieval and sophisticated algorithms. And with in-memory processing, it’s even easier to parse large volumes of information.
- Label-Based Scheduling: You can accurately place data on highly efficient clusters versus spreading out work on under-performing nodes.
- Ease of Use: Using industry-standard tools and third party adaptations, you can operate Hadoop even if you’re not a highly technical user.
Apache Software has been providing stable, open source software products since 1999, and Hadoop is no exception. The application is considered the industry benchmark in big data processing and analytics, so it’s no surprise that the IT, healthcare and manufacturing industries use it. Some major users of Hadoop include Marks and Spencer, Royal Mail, Expedia, Royal Bank of Scotland and British Airways.
- Distributed Computing: Also known as the Hadoop Distributed File System (HDFS), this feature can easily spread computing tasks across multiple nodes, providing faster processing and data redundancy in the event that there’s a critical failure. Hadoop is the industry standard for big data analytics.
- Fault Tolerance: Data is replicated across nodes, so even in the event of one node failing, the data is left intact and retrievable.
- Scalability: The app is able to run on less robust hardware or scale up to industrial data processing servers with ease.
- Integration With Existing Systems: Because Hadoop is so central to so many big data analytics applications, it integrates easily into a number of commercial platforms like Google Analytics and Oracle Big Data SQL or with other Apache software like YARN and MapR.
- In-Memory Processing: Hadoop, in conjunction with Apache Spark, is able to quickly parse and process large quantities of data by storing it in-memory.
- Hadoop MapR: MapR is a component of Hadoop that combines a number of features like redundancy, POSIX compliance and more into a single, enterprise grade component that looks like a standard file server.
Apache Hadoop Suite SupportBecause the Apache Software Foundation builds free, open source software, its support options are limited to community help forums and a library of documentation.
mail_outlineEmail: According to Apache, the company cannot provide email support at this time.
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schoolTraining: Apache provides an extensive library of support documentation and training videos. You can also access their community support options as well. They offer a “mentoring” program where users teach other users and walk them through various processes.
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