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Explaining Volume of Data Storage – HDFS v/s S3

 

While considering volume data the storage options is equally relevant and the business strategies needs to be planned in a manner that can somehow accommodate the storage planning in order to manage the big data. The term volume is often equivalent for the storage medium itself. Fortunately, we have come a long way from room-sized computers and storage banks to options like HDFS and S3. 

The constantly increasing volume of data and adoption of big data tools is a spur to revenue growth, considering a 2.5 quintillion bytes of data is generated every day, but it also is an immediate challenge to conventional IT structures. It calls for strategic business planning along with a scalable data storage like physical storage (HDFS)or cloud-based storage (S3) depending on the requirement of the business, companies and organizations.

Hadoop Distributed File system – is the storage component of Hadoop which is used to distribute the volume of data to multiple machines. The files are stored in blocks, the default size of each block is 64 Megabytes. These files are quite extensive as compared to 1k or 4k on Windows.

Volume Data

 

 Unlike HDFS Amazon Simple Storage Service is yet another platform that is designed to handle and manage volume data in a very analytics friendly manner.

 


 

The difference between both the software is stated below for detailed understanding


Hadoop Distributed File System (HDFS)

Amazon Simple Storage System (S3)

HDFS is designed around physical storage, so volume data storage is possible. As the storage is physical additional hard drives or machines are to be added in order to accommodate access data. This can be costly for business.

S3 is cloud-based system all the volume data is on Amazon’s servers. Besides the businesses pay per month without having to worry about the voluminous data storage. Also, the content can be accessed anywhere.

HDFS by default stores data at multiple locations. Unless the data is backed up off site its stores it in one physical location.

 

The chances of losing data in HDFS is small on a large cluster which stores a volume of data but somewhat bigger when you are storing a smaller amount of data in a larger cluster

S3 has a data loss rate of one in 10,000 objects once every 10,000,000 years. This probably works out to over 99.9999% durability.

 

S3 can effectively manage this record is because Amazon not only duplicates the volume data, but it also preserves these duplicate data on different servers at different physical locations.

HDFS comprises a combination of the price of hardware and the cost of maintenance. While it might seem cheaper to just buy storage, it is believed that while the raw drive price might be calculated in pennies per gigabyte, the actual cost of volume data storage of a business or company would be much higher.

S3 being a cloud based system is simpler and cheaper, the cost is based on the data stored per month. Its approximately $.02 per gigabyte per month.

 

Share your comments on the technology used for storage of big data in your company/ organization.

Similar content that might fascinate please click on the link to review. https://digitalcharms.blogspot.com/2021/02/big-data-abstract-linkage-to.html

 

Written by Prajakta Jadhav

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Keyword – HDFS, S3, Volume of Data, Volume, Data storage, Companies, Business planning, Businesses

 

 

ltd, R. and M. (no date) Global Big Data Analytics Market Size, Market Share, Application Analysis, Regional Outlook, Growth Trends, Key Players, Competitive Strategies and Forecasts, 2019 To 2027. Available at: https://www.researchandmarkets.com/reports/4992328/global-big-data-analytics-market-size-market (Accessed: 23 February 2021).

adt_admin (2017) ‘Five Things to Know About Big Data Storage’, Absolutdata, 14 September. Available at: https://www.absolutdata.com/blog/five-things-to-know-about-big-data-storage/ (Accessed: 25 February 2021).

Using Apache Spark for Massively Parallel NLP | TripAdvisor Engineering and Product Blog (no date). Available at: https://www.tripadvisor.com/engineering/using-apache-spark-for-massively-parallel-nlp/ (Accessed: 23 February 2021).

 

 

 

 

 

 

Comments

  1. Divya Sherigar2 March 2021 at 03:31

    Very informative blog. Thank you for sharing your knowledge :)

    ReplyDelete
    Replies
    1. Digital Charms2 March 2021 at 03:42

      Thank you, Divya, I am glad you found it informative

      Delete
  2. Anita K2 March 2021 at 03:42

    Data storage today has become stimulating task, especially with the immense data that is generated every day. This post gives an idea about the possibilities to venture the data storage.
    Good article

    ReplyDelete
  3. Carlos Saez M.4 March 2021 at 06:01

    We are increasingly connected to the 2.0 world, which is why we generate more and more data. For some companies, being in the digital world is mandatory, so the amount of data generated is even more tremendous. For example, a company that sells its products only through an online channel, it would be convenient for it to implement Big Data technology to process all the information that its website collects by tracking all the actions carried out by the client; know where to click the most times, how many times we have passed through the shopping cart, which are the most viewed products, the most visited pages, so on.
    Not all companies will opt for the same methodology regarding developing and creating their capabilities with Big Data technologies. However, in all sectors, there is the possibility of using these technologies such as HDFS and S3, and analytics to improve decision-making and performance, both internally and in the market.
    Incredible article. Thanks

    ReplyDelete
  4. Mandar Butkar4 March 2021 at 14:17

    Also the most immediate challenge to traditional IT systems is posed by this volume. It calls for flexible storage and a distributed query approach. Many organizations do have a vast volume of stored data, maybe in the form of records, but not the capacity to process it. Assuming data volumes are greater than what traditional relational database infrastructures can handle, computing options fall into two categories: massively parallel processing architectures (data centers or databases like Greenplum) and Apache Hadoop-based solutions. The degree to which one of the other "Vs" — variety — comes into play also influences this decision. Predetermined schemas are typically used in data warehousing methods, which are best suited to a standard and slowly changing dataset. The nature of the data that Apache Hadoop can handle, on the other hand, is unrestricted.

    Comment by - Mandar Butkar.

    ReplyDelete
  5. amar7 March 2021 at 04:54

    HDFS is a distributed file system designed to store big data. It runs on physical machines that can run something else. S3 is the storage of AWS objects, it has nothing to do with storing files, all data in S3 is stored as Object Entities to which the key (document name), value (object content) and VersionID are associated. There is nothing else you can do in S3 because it is not a file system. S3 has “ presumably” unlimited storage in the cloud, but HDFS does not. S3 performs deletion or modification of the records in a eventually consistent way.
    There are many other criteria like cost, SLA, durability, elasticity (you can create a custom lifecycle and version control over objects). But let’s not think about it, S3 wins there anyway.
    Hadoop and HDFS have made it cheap to store and distribute large amounts of data. But now that everyone is moving to cloud architectures, the benefits of HDFS are minimal and not worth the complexity that it brings. That’s why now and in the future organizations will use S3 as a backend for their data storage solutions.

    Comment by : Amarbant Singh

    ReplyDelete
  6. Akshata20 April 2021 at 02:34

    very informative article. keep it up guys!

    ReplyDelete
  7. Shiva Shakthi25 October 2021 at 02:26

    Great Post!!! thanks for sharing.
    Big Data Career
    How to Learn Big Data?

    ReplyDelete

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