The processing power of application servers continues to increase at an exponential rate. This causes databases to lag behind because of their speed limitations and overall limited capacity. However, today’s applications aren’t only running application servers but also generating big data for processing.
Developers and big data experts needed a way to “makeover” these databases to account for the new amounts of data, which is where Hadoop comes into play. Apache Hadoop is a Java-based, open-source software platform designed for data processing management and big data applications storage.
It’s worth noting that the big data of today’s technology isn’t actually the original cause for the development of Hadoop. Apache Hadoop originates from the need to process growing volumes of data and also deliver faster web results via new search engines such as Google.
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Hadoop’s flexible nature means that companies have the ability to modify their data systems by utilizing less expensive, more readily available information technology resources or vendors. Today, it’s one of the most widely used systems for providing data storage and processing data across commodity hardware.
Hadoop can link together relatively more inexpensive off-the-shelf systems as opposed to requiring expensive custom-made systems to get the job done. Many of the Fortune 500 companies of the 2010s utilized or continue to utilize Hadoop to this day. However, it’s worth noting that many have moved on to newer solutions as of 2021.
While Hadoop’s popularity isn’t what it was a decade ago, it’s still widely used.
Why is Hadoop Important and Useful?
- Hadoop offers developers and big data scientists the ability to store and process enormous amounts of any type of data at a rapid pace. With the size and scale of data volumes and varieties continually increasing, it’s a key consideration with special emphasis on the growth of the Internet of Things and social media networks.
- Hadoop’s distributed computing model processes big data very quickly thanks to its power. The more computing nodes used by a developer, the more processing power is available to make the systems move faster.
- It features a fault tolerance that protects data and application processing against hardware failures. Should nodes go down, the system automatically redirects jobs to other nodes to ensure that the distributed computing doesn’t fail. The system also automatically stores multiple copies of all data.
- Hadoop is an open-source framework, which means it’s free and uses commodity hardware to store huge amounts of data.
- Unlike more traditional relational databases, Hadoop doesn’t require the preprocessing of data before storage, which makes it more flexible compared to the competition. Developers and engineers have the ability to store as much data as they want and can decide how to use it at a later date. This data may include unstructured data such as images, videos, and text.
- By simply adding nodes, developers can easily grow their Hadoop systems to handle more data and scale as needed while only requiring a few administrative actions.
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What is the HDFS in Hadoop?
The HDFS is the Hadoop Distributed File System, which is a central part of the Hadoop collection of software. The Hadoop Distributed File System helps to abstract away the complexities typically involved in distributed file systems. These complexities include high availability, hardware diversity, and replication.
Two of the biggest components of the Hadoop Distributed File System are NameNode and the DataNodes sets. NameNode exposes the filesystem API as well as persists metadata and assists with replication among DataNodes. The MapReduce component helps to natively make use of the data locality API within Hadoop to dispatch MapReduce tasks to run in the data locations.
What are the 3 modes that Hadoop runs in?
- Hadoop’s fully-distributed mode uses separate nodes for running the different Hadoop services.
- The pseudo-distributed mode utilizes a single-node deployment for the execution of all services.
- Standalone mode is the default mode of Hadoop and uses the local FileSystem along with a single Java process for running the Hadoop services.
Explain Hadoop’s “small files problem.”
The registry of all of the metadata within the Hadoop Distributed File system is NameNode. Although journaled on disk, the system serves the metadata from memory and, as a result, must often deal with the limitations involved with the runtime. As a Java application, NameNode runs using the Java Virtual machine runtime and can’t operate at its maximum efficiency with larger heap allocations.
How does rack awareness work within the HDFS?
Rack awareness refers to the knowledge of different DataNodes and how they’re distributed across the Hadoop Cluster racks. By default, the system replicates each data block 3 times on various DataNodes across different racks and 2 identical blocks aren’t placed on the same DataNode. When clusters are rackware, all block replicas can’t live on the same rack. Should a DataNode crash, devs have the ability to retrieve the data block from a different DataNode.
Job Description
We’re searching for a skilled Hadoop developer to build storage software and big data infrastructure at our company. The right candidate’s primary responsibilities include designing, building, and maintaining Hadoop infrastructure while evaluating existing data solutions, developing documentation, and training staff members.
Responsibilities
- Assess the company’s existing big data infrastructure
- Design and code Hadoop applications for the analyzing of data collections
- Building data processing frameworks
- Isolating data clusters and extracting data
- Maintaining the security of data
- Training staff on application usage
Requirements
- Bachelor’s degree in Computer Science, Software Engineering, or equal work experience
- 3+ years experience as a Hadoop developer or big data engineer
- Advanced knowledge of the Hadoop framework and its necessary components
- Experience with back-end programming languages such as JavaScript and Node.js
- Experience with big data and data management
