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?
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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.
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(1-4 of 4)Hadoop the solution to big data problems
- Processing huge data sets.
- Concurrent processing.
- Performance increases with distribution of data across multiple machines.
- Better handling of unstructured data.
- Data nodes and processing nodes
- Make Haadop lighweight.
- Installation is very difficult. Make it more user friendly.
- Introduce a feature that works with continuous integration.
- Process container metrics
- Products that are dependent on data
- real time stream processing
- Parallel processing of metrics
- Map reduce increases the performance
- Distribution of data on multiple nodes
- In data centers to manage machines
- Standalone architecture
- Seamless integration
Advantage Hadoopo
- Processes big volume of data using parallelism in faster manner.
- No schema required. Hadoop can process any type of data.
- Hadoop is horizontally scalable.
- Hadoop is free.
- Development tools are not that friendly.
- Hard to find hadoop resources.
- None
- You dont need to pay a heavy licensing fee for Hadoop. You save money.
- It is open source technology so some times you need to purchase support from Cloudera or Hortonworks.
- Implemented in-house
Hadoop for better economy and efficiency
- Hadoop stores and processes unstructured data such as web access logs or logs of data processing very well
- Hadoop can be effectively used for archiving; providing a very economic, fast, flexible, scalable and reliable way to store data
- Hadoop can be used to store and process a very large amount of data very fast
- Security is a piece that's missing from Hadoop - you have to supplement security using Kerberos etc.
- Hadoop is not easy to learn - there are various modules with little or no documentation
- Hadoop being open-source, testing, quality control and version control are very difficult
- We had a large ROI due to improved performance and expedited reporting - our clients were happier and business improved
- Our storage costs reduced
- Our infrastructure costs reduced - we used old hardware for our Hadoop cluster
- Use of HDFS / Hive for storage / analysis of data processing logs
- Use of HDFS / Hive for storage / analysis of historical financial data
- Use of HDFS for Archival
- Archival
- Reporting
- ETL
- Data transfer
- Staging area
- Historical reporting
- Price
- Product Features
- Product Usability
Benefits of using Hadoop
- Definitely speed up data processing efforts
- I think certain design patterns should be more recommended than others.
- faster data processing