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Hadoop

Hadoop

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.

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Recent Reviews

TrustRadius Insights

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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Hadoop Review

7 out of 10
May 16, 2018
Incentivized
It is massively being used in our organization for data storage, data backup, and machine learning analytics. Managing vast amounts of …
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Product Demos

Installation of Apache Hadoop 2.x or Cloudera CDH5 on Ubuntu | Hadoop Practical Demo

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Big Data Complete Course and Hadoop Demo Step by Step | Big Data Tutorial for Beginners | Scaler

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Hadoop Tutorial For Beginners | Apache Hadoop Tutorial For Beginners | Hadoop Tutorial | Simplilearn

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Product Details

What is Hadoop?

Hadoop Video

What is Hadoop?

Hadoop Technical Details

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Frequently Asked Questions

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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Comparisons

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Reviews and Ratings

(270)

Community Insights

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!

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

(1-2 of 2)
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Michael Reynolds | TrustRadius Reviewer
Score 10 out of 10
Vetted Review
Verified User
Hadoop is slowly taking the place of the company-wide MySQL data warehouse. Sqoop is being used to import the data from MySQL. Impala is gradually being used as the new data source for all queries. Eventually, MySQL will be phased out, and all data will go directly into Hadoop. Tests have shown that the queries run from Impala are much faster than those from MySQL
  • The built-in data block redundancy helps ensure that the data is safe. Hadoop also distributes the storage, processing, and memory, to work with large amounts of data in a shorter period of time, compared to a typical database system.
  • There are numerous ways to get at the data. The basic way is via the Java-based API, by submitting MapReduce jobs in Java. Hive works well for quick queries, using SQL, which are automatically submitted as MapReduce Jobs.
  • The web-based interface is great for monitoring and administering the cluster, because it can potentially be done from anywhere.
  • Impala is a very fast alternative to Hive. Unlike Hive, which submits queries as MapReduce jobs, Impala provides immediate access to the data.
  • If you are not familiar with Java and the operating system Hadoop rides on, such as Linux, and have trouble with submitted MapReduce jobs, the error messages can seem cryptic, and it can be challenging to track down the source of the problem.
Hadoop is designed for huge data sets, which can save a lot of time with reading and processing data. However, the NameNode, which allocates the data blocks, is a single point of failure. Without a proper backup, or another NameNode ready to kick in, the file system can be become instantly useless. There are typically two ways to ensure the integrity of the NameNode.

One way is to have a Secondary NameNode, which periodically creates a copy of the file system image file. The process is called a "checkpoint". In the event of a failure of the Primary NameNode, the Secondary NameNode can be manually configured as the Primary NameNode. The need for manual intervention can cause delays and potentially other problems.

The second method is with a Standby NameNode. In this scenario, the same checkpoints are performed, however, in the event of a Primary NameNode failure, the Standby NameNode will immediately take the place of the Primary, preventing a disruption in service. This method requires additional services to be installed for it to operate.
Hadoop is still young and evolving. There is a lot of potential and undiscovered uses for it.
  • Because Hadoop is open source, the cost is basically limited to the hardware. However, organizations with large clusters might want to invest in support services from companies like Cloudera or Hortonworks.
Bhushan Lakhe | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Hadoop is used for storing and analyzing log data (logs from warehouse loads or other data processing) as well as storing and retrieving financial data from JD Edwards. It's also planned to be used for archival. Hadoop is used by several departments within our organization. Currently, we are paying a lot of money for hosting historical data and we plan to move that to Hadoop; reducing our storage costs. Also, we got a much better performance out of our Hadoop cluster for processing a large amount of financial data. So, in that senese, Hadoop addressed multiple business problems for us.
  • 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
Hadoop is best suited for warehouse or OLAP processing. It's not suitable for OLTP or small transaction processing
  • 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
not applicable - I have not evaluated any other products
50
Various - IT, business users, vendors
3
Hadoop Administrator, Java Developer, Hive deveoper
  • 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
Hadoop is organization-independent and can be used for various purposes ranging from archiving to reporting and can make use of economic, commodity hardware. There is also a lot of saving in terms of licensing costs - since most of the Hadoop ecosystem is available as open-source and is free
Yes
We replaced 5 Windows based servers by a 10 node CentOS based desktops. Saved a lot on hardware and Windows server licenses
  • Price
  • Product Features
  • Product Usability
Price. We saved a lot of money
I will evaluate the ROI more closely
Hadoop is a complex topic and best suited for classrom training. Online training are a waste of time and money.
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