Database Storage has become an integral part of Databases, Data Warehouses, and BI(Business Intelligence) software. Depending on different applications, storing data in different file formats can optimize the ETL (Extract, Transform, and Load) process associated with multiple data sources, allowing you efficiently unify and analyze data to gain valuable insights from your crucial business data. When it comes to the field of Data Storage, the Hadoop S3 Comparison can be a relatively tough one.
HDFS or the Hadoop Distributed File System is a distributed file system that handles a large number of big data sets across multiple hardware devices. It does this by scaling a single Apache Hadoop cluster to almost 100 nodes. Amazon S3 or Amazon Simple Storage Service is a service offered by Amazon Web Services that stores your data in the form of objects and allows you to access them through a web interface. Both HDFS and S3 have unique features that make them a good choice. The main aim of this article is to resolve the Hadoop S3 Comparison question so that you can choose the best storage option for your organization.
This article provides you with a comprehensive analysis of both storage techniques and highlights the major differences between them to help you make the Hadoop S3 comparison easily. It also provides you with a brief overview of both storage technologies along with their features. Finally, it highlights a few challenges you might face when you use these database storage techniques. Find out how you can choose the right database storage technique for your organization.
What is HDFS (Hadoop Distributed File System)?
HDFS or the Hadoop Distributed File System is a database storage technique that houses the distributed file system design. It runs on commodity hardware, is highly fault-tolerant, and is designed using low-cost hardware. HDFS stores large amounts of data in multiple machines in order to simplify access for its users. All files that use HDFS are stored in a redundant manner to reduce data losses and improve their parallel processing capabilities.
Architecture of HDFS
The architecture of HDFS is shown below.
Hadoop S3- HDFS Architecture
Key Features of HDFS
HDFS houses a variety of features that make it a good alternative to other database storage solutions. Some of those features are:
- HDFS is suitable for distributed storage and processing.
- It provides a command-line interface for user interactions.
- As HDFS makes use of names and data nodes, the status of the clusters can be checked with ease.
- Data can be streamed efficiently using HDFS.
- HDFS provides file permissions and authentication.
Introduction to Amazon S3
Amazon S3 or Simple Storage Service is a scalable, low-cost, high-speed data storage web service provided by Amazon. Amazon S3 is designed for online backup and archiving of data and application programs. Amazon S3 stores data in the form of objects. Each object consists of a file with an associated ID and metadata. These files work like records and directories to store data within your AWS region.
Amazon S3 allows you to upload, store, and download any type of file up to 5 TB in size. All of its subscribers can access the same storage capabilities that Amazon uses on its website too. Amazon S3 is designed to give the subscriber total control over the accessibility of data.
Architecture of Amazon S3
The architecture of Amazon S3 is shown below.
Hadoop S3 Comparison: Amazon S3 Architecture
Key Features of Amazon S3
Amazon S3 combines many unique features and traits that make it one of a kind database storage technique. Some of those features are:
- Users that leverage Amazon S3 can store large amounts of data at very low charges.
- Amazon S3 supports data transfer over SSL and the data gets encrypted automatically once it is uploaded.
- It is highly scalable and can store almost any amount of data in a single process.
- It offers high performance as it is integrated with Amazon CloudFront, which distributes data among all users with a low latency rate.
- It can easily integrate with other Amazon services like Amazon CloudFront, Amazon CloudWatch, Amazon Kinesis, Amazon RDS, Amazon Route 53, Amazon VPC, AWS Lambda, Amazon EBS, and many more.
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Factors that Drive the Hadoop S3 Comparison
Now that you have a basic idea of both technologies, let us attempt to answer the Hadoop S3 Comparison question. There is no one-size-fits-all answer here and the decision has to be taken based on the business requirements, budget, and parameters listed below. The following are the key factors that drive the Hadoop S3 Comparison decision:
1) Hadoop S3 Comparison: Scalability
Scalability refers to the processing power maintained by the application as the number of users and objects increases, linearly or exponentially. Having high scalability ensures that your application can work with almost any amount of data.
HDFS relies on a local storage technique and follows a horizontal scalability pattern. In case you want to increase your storage space, you will either have to incorporate larger hard drives to existing nodes or add more machines to the cluster. This is possible but can turn out to be more expensive.
Amazon S3 scales vertically and requires almost minimal-to-no interaction from the user. The process is completely automatic and as Amazon does not put any limitations on storage, you have almost infinite space for your data. This proves that Amazon S3 is highly scalable.
2) Hadoop S3 Comparison: Durability
Durability refers to the ability to keep data in its best form on the Cloud for a long period of time.
According to a statistical study, the durability of HDFS is low. This is because the probability of losing a block of data (64 megabytes by default) on a large 4,000 node cluster (16 petabytes total storage, 250,736,598 block replicas) is 0.00000057 in 1 day and 0.00021 (2.1 x 10^-4) in 1 year.
Amazon S3, on the other hand, provides the durability of 99.99%. This means that a single object could be lost per 10,000,000 objects once every 10,000 years. This proves that Amazon S3 is highly durable.
3) Hadoop S3 Comparison: Persistence
Persistence plays an important role in database storage. It refers to the quality of data after the original process that created it gets completed.
Data does not persist in HDFS but you can persist data by using Amazon EBS Volumes on EC2.
Data is always persistent in Amazon S3. This proves that Amazon S3 is highly persistent.
4) Hadoop S3 Comparison: Performance
Performance refers to the efficiency of the application in completing its tasks and for its users.
HDFS provides excellent performance and high throughput as data is stored and processed on the same machines, access and processing speed are lightning-fast. HDFS deploys chains of Map-Reduce jobs where intermediate data is stored on a common local disk system. Hence, the throughput of HDFS will be the total throughput of the local disk. This proves that HDFS has a higher performance.
Amazon S3, on the other hand, does not have a high performance due to its lower data throughput. This is because data is read from and written to Amazon S3 on local disks and so the throughput is also calculated in a similar manner.
5) Hadoop S3 Comparison: Security
Security is an important concept for any application. No matter how efficient an application is, if it cannot protect confidential data, it is of no benefit to the users.
HDFS provides user authentication via Kerberos and authorization via file system permissions. Hadoop YARN improves this fundamental procedure by securing clusters through a process called Federations. In this process, a cluster is divided into several namespaces so that users are restricted to only access data they are authorized to. Data can also be uploaded to Amazon securely via an SSL(Secure Sockets Layer) connection.
Amazon S3 has built-in security. To control data access, it supports user authentication. When you configure your bucket for the first time, only the bucket and object owners have access to the data. Further permissions can be given to users and groups by ACLs (Access Control Lists) and bucket policies. This proves that both HDFS and Amazon S3 have robust security measures built in them.
6) Hadoop S3 Comparison: Pricing
Pricing plays a major role in deciding which data storage technique to use. The choice of technology a company decides depends on the budget and financial stature of the company.
HDFS stores 3 copies of each data block by default. This means that HDFS requires triple the amount of storage space for your data and thus, triples the cost for storing it. In some cases, even if you store one copy of your data, there is a chance that it can get lost.
Amazon S3, on the other hand, performs data backups itself. So, you only pay for the storage you need. Amazon S3 also supports compressed files and so reduces the cost even more. This proves that Amazon S3 has an affordable and consistent pricing model.
7) Hadoop S3 Comparison: Data Integrity & Elasticity
Data Integrity is the process of preventing data modification as it is being processed. Elasticity is the process of estimating the number of resources required by an organization at any given time.
HDFS relies on an atomic rename feature to support atomic writes. This means that the output of a job is dependent on an “all or nothing” answer. Either all the jobs execute or else none of the jobs execute. This is important because when a job fails, no partial data should be written to corrupt the data. This proves that HDFS has high data integrity. HDFS does not have any model for elasticity.
Amazon S3, on the other hand, lacks an atomic directory and so is difficult to guarantee data integrity. Amazon S3 uses a pay-as-you-go pricing model and all the resources are determined automatically by the S3 bucket. This proves that Amazon S3 is highly elastic.
What are the challenges of HDFS (Hadoop Distributed File System)?
Now that you have a good idea about HDFS, it is now important to understand some of the challenges you might encounter while working with HDFS. The challenges of HDFS are:
- Although HDFS can store files of any size, it has many documentation issues when it comes to storing small files. Such files must be unified to the Hadoop Archives.
- Data saved on a particular cluster in HDFS can only be accessed by the machines available in that cluster and not by other machines outside that cluster.
- HDFS lacks support from multiple data sources and quality.
- It also lacks application deployment support and cannot manage resources in a flexible manner.
What are the challenges of Amazon S3?
Now that you have a good idea about Amazon S3, it is now important to understand some of its challenges. The challenges of Amazon S3 are:
- The maximum file size that can be stored on an S3 bucket is only 5 GB and additional Hadoop storage formats, like Parquet or ORC, cannot be used on S3.
- Users need to be highly skilled in AWS in order to use Amazon S3 efficiently.
- In case you use Amazon S3 regularly, the technical support expenses can be very high.
- Performance and Data Integrity are lower in Amazon S3 as it uses smaller buckets and lacks atomicity.
Conclusion
This article gave a comprehensive analysis of the 2 popular Database storage technologies in the market today: HDFS and Amazon S3. It talks about both database storage techniques, their features, and their challenges. It also gave the parameters to judge each of the technologies. Overall, the Hadoop S3 Comparison solely depends on the goal of the company and the resources it has.
HDFS can be a good choice for smaller companies because of its high performance and robust data integrity. It paves a strong foundation for these companies and offers robust security policies as well. Amazon S3 is a good option for middle to large-sized companies as it allows companies to store large amounts of data due to its highly elastic, scalable, durable, persistent nature along with its low costs for storing any volume of data. Furthermore, it can easily integrate with other Amazon technologies seamlessly.
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Aakash is a research enthusiast who was involved with multiple teaming bootcamps including Web Application Pen Testing, Network and OS Forensics, Threat Intelligence, Cyber Range and Malware Analysis/Reverse Engineering. His passion to the field drives him to create in-depth technical articles related to data industry. He holds a Undergraduate Degree from Vellore Institute of Technology in Computer Science & Engineering with a Specialization in Information Security and is keen to help data practitioners with his expertise in the related topics.