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Hadoop has been widely adopted by organizations for various use cases. One of its key use cases is in storing and analyzing log data, …
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What is Hadoop?
Hadoop is an open source software from Apache, supporting distributed processing and data storage. Hadoop is popular for its scalability, reliability, and functionality available across commoditized hardware.
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Hadoop is an open source software from Apache, supporting distributed processing and data storage. Hadoop is popular for its scalability, reliability, and functionality available across commoditized hardware.
Reviewers rate Data Sources highest, with a score of 8.7.
The most common users of Hadoop are from Enterprises (1,001+ employees).
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TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!
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Hadoop has been widely adopted by organizations for various use cases. One of its key use cases is in storing and analyzing log data, financial data from systems like JD Edwards, and retail catalog and session data for an omnichannel experience. Users have found that Hadoop's distributed processing capabilities allow for efficient and cost-effective storage and analysis of large amounts of data. It has been particularly helpful in reducing storage costs and improving performance when dealing with massive data sets. Furthermore, Hadoop enables the creation of a consistent data store that can be integrated across platforms, making it easier for different departments within organizations to collect, store, and analyze data. Users have also leveraged Hadoop to gain insights into business data, analyze patterns, and solve big data modeling problems. The user-friendly nature of Hadoop has made it accessible to users who are not necessarily experts in big data technologies. Additionally, Hadoop is utilized for ETL processing, data streaming, transformation, and querying data using Hive. Its ability to serve as a large volume ETL platform and crunching engine for analytical and statistical models has attracted users who were previously reliant on MySQL data warehouses. They have observed faster query performance with Hadoop compared to traditional solutions. Another significant use case for Hadoop is secure storage without high costs. Hadoop efficiently stores and processes large amounts of data, addressing the problem of secure storage without breaking the bank. Moreover, Hadoop enables parallel processing on large datasets, making it a popular choice for data storage, backup, and machine learning analytics. Organizations have found that it helps maintain and process huge amounts of data efficiently while providing high availability, scalability, and cost efficiency. Hadoop's versatility extends beyond commercial applications—it is also used in research computing clusters to complete tasks faster using the MapReduce framework. Finally, the Systems and IT department relies on Hadoop to create data pipelines and consult on potential projects involving Hadoop. Overall, the use cases of Hadoop span across industries and departments, providing valuable solutions for data collection, storage, and analysis.
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August 17, 2021
Great enterprise tool for handling large data
Apache Hadoop is one of the most effective and efficient software which has been storing and processing an extremely colossal amount of data in my company for a long time now. The software Hadoop is primarily used for data collection of large amounts, storage as well as for analytics. From my experience, I have to say that Hadoop is extremely useful and has a reliable plus valid purpose.
- The various modules sometimes are pretty challenging to learn but at the same time, it has made Hadoop easy to implement and perform.
- Hadoop comprises a thoughtful file system which is called as Hadoop Distributed File System that beautifully processes all components and programs.
- Hadoop is also very easy to install so this is also a great aspect of Hadoop as sometimes the installation process is so tricky that the user loses interest.
- Customer support is quick.
- As much as I really appreciate Hadoop there are certain cons attached to it as well. I personally think that Hadoop should work attentively towards their interactive querying platforms which in my opinion is quite slow as compared to other players available in the market.
- Apart from that, a con that I have noticed is that there are many modules that exist in Hadoop so due to the higher number of modules it becomes difficult and time-consuming to learn and ace all of them.
- Data distribution.
- Machine scaling.
- Cloud processing.
- Data management.
- There are many advantages of Hadoop as first it has made the management and processing of extremely colossal data very easy and has simplified the lives of so many people including me.
- Hadoop is quite interesting due to its new and improved features plus innovative functions.
Different departments of my organization have been getting the benefit from Apache Hadoop as it serves the purpose of saving lives when large amounts of data is unable to be converted and processed in a timely manner from a node or a simple computer. Hadoop also has an easier process of configuration in a clustered environment. Additionally, from my experience, I have noticed that Hadoop provides great scalability and redundancy. Also, it provides enterprise-level support from a variety of vendors. Lastly, I think that a great positive fact of Hadoop is its horizontal scaling.
July 21, 2021
Good tool for unstructured data
Apache Hadoop is an open-source software library that is designed for the collection, storage, and analysis of large amounts of data sets. Apache Hadoop’s architecture comprises components that include a distributed file system. This is mostly used for massive data collection, analytics, and storage. Also, having consistent data can be integrated across other platforms and have one single source of truth.
- Apache Hadoop has made managing large amounts of data quite easy.
- The system contains a file system known as HDFS (Hadoop Distributed File System) which processes components and programs.
- The parallel processing tool of this software is also a good aspect of Apache Hadoop.
- It keeps interesting and reliable features and functions.
- Apache Hadoop also has a store of very big data files in machines with high levels of availability.
- I personally feel that Apache Hadoop is slower as compared to other interactive querying platforms. Queries can take up to hours sometimes which can be frustrating and discouraging sometimes.
- Also, there are so many modules of Apache Hadoop so it takes so much more time to learn all of them. Other than that, optimization is somewhat a challenge in Apache Hadoop.
- Data sourcing is excellent.
- Efficient customer support.
- Reliable customization of functionalities.
- Spark integration.
- Workload processing.
- Apache Hadoop can handle even large amounts of data as well for business-level purposes.
- HDFS also keeps data files across the machines by distinguishing them into larger blocks and then distributing them across nodes.
- It is keeping a great role in the growth of our organization.
I feel that this is a highly reliable and scalable solution computing technology that is highly capable of processing large data sets across multiple servers and thousands of machines in a well-defined and distributed manner. Apache Hadoop can automatically scale up the number of servers and machines that are needed to process, store, and analyze data sets. It also handles explosions in data with big data technology. Apache Hadoop is good at handling all node failures as well.
We use Apache Hadoop to store and process large amounts of data (petabytes per day) across thousands of data pipelines. Hadoop works reliably for this purpose. Data scientists at the company also use it for interactive querying for analytics and modeling purposes.
- Storing large amounts of data
- Processing large amounts of data via a familiar SQL interface
- Slower than other interactive querying engines. Queries take minutes at least and up to hours sometimes
- Tuning the settings to be able to run certain queries can require a lot of domain knowledge
- Large scale data storage
- Large scale data processing
- Large scale interactive data querying
- Makes all of the company's data easily accessible via a SQL interface
- Allows affordable data storage
Spark is a good alternative to Hadoop that can have faster querying and processing performance and can offer more flexibility in terms of applications that it can support.
Google BigQuery has also been a great alternative and is especially great in terms of ease of use. The capacity to process data and the speed are great without having to do any settings tuning or optimization. It also doesn't require any on-site hosting, making it a great hands off solution.
Google BigQuery has also been a great alternative and is especially great in terms of ease of use. The capacity to process data and the speed are great without having to do any settings tuning or optimization. It also doesn't require any on-site hosting, making it a great hands off solution.