Overview
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.
Hadoop: A Robust Big Data Platform
Great enterprise tool for handling large data
Good tool for unstructured data
Good solution for storing and processing large data
Apache Hadoop Can Save on the Headaches
Hadoop -- Great Value for What You Pay
Fault Tolerance and High Availablility Made Easy with Hadoop
Hadoop vs. Alternatives
Hadoop Review
Great Option for Unstructured Data
- Used for Massive data collection, storage, and analytics
- Used for MapReduce processes, Hive tables, Spark job input, and for backing up data
Hadoop is pretty Badass
Hadoop: Highly available, scalable and cost effective for big data storage and processing.
Hadoop for Justifying Business Decisions with Hard Data
Hadoop review 2346
Hadoop for Big Data
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Installation of Apache Hadoop 2.x or Cloudera CDH5 on Ubuntu | Hadoop Practical Demo
Big Data Complete Course and Hadoop Demo Step by Step | Big Data Tutorial for Beginners | Scaler
Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop Tutorial | Simplilearn
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What is Hadoop?
Hadoop Video
Hadoop Technical Details
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(270)Community Insights
- Business Problems Solved
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.
Attribute Ratings
Reviews
(1-25 of 36)Hadoop: A Robust Big Data Platform
Great enterprise tool for handling large data
Good tool for unstructured data
Apache Hadoop Can Save on the Headaches
Hadoop -- Great Value for What You Pay
Fault Tolerance and High Availablility Made Easy with Hadoop
Hadoop vs. Alternatives
Hadoop Review
Great Option for Unstructured Data
- Less appropriate for small data sets
- Works well for scenarios with bulk amount of data. They can surely go for Hadoop file system, having offline applications
- It's not an instant querying software like SQL; so if your application can wait on the crunching of data, then use it
- Not for real-time applications
Hadoop is pretty Badass
Hadoop should not be used directly for Real time Analytics. HDFS should be used to store data and we could use Hive to query the files.
Hadoop needs to be understood thoroughly even before attempting to use it for data warehousing needs. So you may need to take stock of what Hadoop provides, and read up on its accompanying tools to see what fits your needs.
Hadoop: Highly available, scalable and cost effective for big data storage and processing.
Hadoop for Justifying Business Decisions with Hard Data
Hadoop review 2346
Hadoop for Big Data
- To analyze a huge quantity of data at a low cost. It is definitely the future.
- Machine learning with Spark is also a good use case.
- You can also use AWS - EMR with S3 to store a lot of data with low cost.
A newbie's look at Hadoop
Experience with Hadoop by a novice user.
Hadoop quick review
Apache Hadoop is the best open source product I used.
Hadoop an awesome tool for large scale batch processing.
Hadoop - best data optimization for the Enterprise
A very generic Hadoop review
Hadoop - Effective tool for large scale distributed processing.
2. Do you require real-time analytical processing? If yes, Hadoop's map reduce may not be a great asset in that scenario.
3. Do you want to want to process data in a batch processing fashion and scale for TeraBytes size clusters? Hadoop is definitely a great fit for your use case.