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Amazon S3 vs Google Cloud Bigtable vs Hadoop: What are the differences?
Introduction
In this article, we will discuss the key differences between Amazon S3, Google Cloud Bigtable, and Hadoop. These three technologies are widely used for data storage and processing in the cloud computing domain. Understanding their differences will help users make a better choice for their respective use cases.
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Scalability and performance:
- Amazon S3: Amazon S3 provides virtually unlimited scalability for storing and retrieving data. It is highly durable and designed for 99.999999999% durability. However, it is not ideal for real-time or low-latency applications.
- Google Cloud Bigtable: Google Cloud Bigtable is a highly scalable NoSQL database ideal for handling high-velocity and high-volume data. It offers low-latency, high-throughput performance, making it suitable for real-time applications.
- Hadoop: Hadoop is a distributed processing framework that allows for parallel computation on large data sets. While it offers high scalability, it might not provide real-time processing capabilities as efficiently as Google Cloud Bigtable.
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Data model and query language:
- Amazon S3: Amazon S3 is an object storage service, meaning it stores data as objects in buckets. It does not provide a built-in query language, and access to data stored in S3 usually requires additional data processing tools like AWS Athena or AWS Glue.
- Google Cloud Bigtable: Google Cloud Bigtable is a wide-column NoSQL database that stores data in a schemaless fashion. It supports a subset of the SQL query language and provides efficient filtering and indexing capabilities.
- Hadoop: Hadoop is a data processing framework that supports various data models, including file-based storage like HDFS and key-value storage like HBase. It provides powerful query capabilities through tools like Hive and Pig.
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Cost structure and pricing:
- Amazon S3: Amazon S3 follows a pay-as-you-go pricing model, where users pay for the storage space they use, data transfers, and requests made. It offers a tiered pricing structure, with different costs for different storage classes and data transfer categories.
- Google Cloud Bigtable: Google Cloud Bigtable offers separate pricing for storage and operations like reads, writes, and deleting data. It provides on-demand autoscaling, reducing the cost of idle resources.
- Hadoop: Hadoop is open-source software and is generally free to use. However, users need to set up and manage their own Hadoop clusters, which involves infrastructure costs and administration overhead.
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Integration with other cloud services:
- Amazon S3: Amazon S3 integrates seamlessly with other AWS services like Amazon EC2, AWS Lambda, and Amazon Redshift. It acts as a common data storage layer for various applications.
- Google Cloud Bigtable: Google Cloud Bigtable integrates well with other Google Cloud Platform services like BigQuery, Dataflow, and Dataproc. It allows users to build end-to-end data processing pipelines using Google's ecosystem.
- Hadoop: Hadoop integrates with various cloud services through connectors and APIs. It provides interoperability with different storage systems like Amazon S3 and Google Cloud Storage, enabling users to perform data processing operations on cloud-based storage.
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Ease of use and management:
- Amazon S3: Amazon S3 is relatively easy to set up and use, with a simple web interface and comprehensive APIs. It also provides features for data lifecycle management, versioning, and cross-region replication.
- Google Cloud Bigtable: Google Cloud Bigtable offers a managed service, abstracting away the complexity of infrastructure management. It provides automated backups, monitoring, and performance tuning, making it easier to operate.
- Hadoop: Hadoop requires more expertise and manual configuration for setting up and managing clusters. It requires administrators to handle tasks like capacity planning, resource allocation, and cluster monitoring.
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Ecosystem and community support:
- Amazon S3: Amazon S3 has a large and mature ecosystem with a wide range of tools, libraries, and frameworks built on top of it. It has extensive documentation and a large user community for support.
- Google Cloud Bigtable: Google Cloud Bigtable, being part of Google Cloud Platform, leverages the rich ecosystem of Google's services. It also has good community support and resources for developers.
- Hadoop: Hadoop has a vibrant ecosystem with a vast range of tools like Spark, Kafka, and Hive. It benefits from a large open-source community and active development, ensuring continuous innovation and support.
In summary, Amazon S3 provides scalable storage with high durability and integrates well with other AWS services. Google Cloud Bigtable is ideal for real-time, high-throughput applications with a wide-column NoSQL data model. Hadoop offers a distributed processing framework with support for various data models, providing flexibility but requiring more manual management.
I have a lot of data that's currently sitting in a MariaDB database, a lot of tables that weigh 200gb with indexes. Most of the large tables have a date column which is always filtered, but there are usually 4-6 additional columns that are filtered and used for statistics. I'm trying to figure out the best tool for storing and analyzing large amounts of data. Preferably self-hosted or a cheap solution. The current problem I'm running into is speed. Even with pretty good indexes, if I'm trying to load a large dataset, it's pretty slow.
Druid Could be an amazing solution for your use case, My understanding, and the assumption is you are looking to export your data from MariaDB for Analytical workload. It can be used for time series database as well as a data warehouse and can be scaled horizontally once your data increases. It's pretty easy to set up on any environment (Cloud, Kubernetes, or Self-hosted nix system). Some important features which make it a perfect solution for your use case. 1. It can do streaming ingestion (Kafka, Kinesis) as well as batch ingestion (Files from Local & Cloud Storage or Databases like MySQL, Postgres). In your case MariaDB (which has the same drivers to MySQL) 2. Columnar Database, So you can query just the fields which are required, and that runs your query faster automatically. 3. Druid intelligently partitions data based on time and time-based queries are significantly faster than traditional databases. 4. Scale up or down by just adding or removing servers, and Druid automatically rebalances. Fault-tolerant architecture routes around server failures 5. Gives ana amazing centralized UI to manage data sources, query, tasks.
Hello! I have a mobile app with nearly 100k MAU, and I want to add a cloud file storage service to my app.
My app will allow users to store their image, video, and audio files and retrieve them to their device when necessary.
I have already decided to use PHP & Laravel as my backend, and I use Contabo VPS. Now, I need an object storage service for my app, and my options are:
Amazon S3 : It sounds to me like the best option but the most expensive. Closest to my users (MENA Region) for other services, I will have to go to Europe. Not sure how important this is?
DigitalOcean Spaces : Seems like my best option for price/service, but I am still not sure
Wasabi: the best price (6 USD/MONTH/TB) and free bandwidth, but I am not sure if it fits my needs as I want to allow my users to preview audio and video files. They don't recommend their service for streaming videos.
Backblaze B2 Cloud Storage: Good price but not sure about them.
There is also the self-hosted s3 compatible option, but I am not sure about that.
Any thoughts will be helpful. Also, if you think I should post in a different sub, please tell me.
If pricing is the issue i'd suggest you use digital ocean, but if its not use amazon was digital oceans API is s3 compatible
Hello Mohammad, I am using : Cloudways >> AWS >> Bahrain for last 2 years. This is best I consider out of my 10 year research on Laravel hosting.
Minio is a free and open source object storage system. It can be self-hosted and is S3 compatible. During the early stage it would save cost and allow us to move to a different object storage when we scale up. It is also fast and easy to set up. This is very useful during development since it can be run on localhost.
We offer our customer HIPAA compliant storage. After analyzing the market, we decided to go with Google Storage. The Nodejs API is ok, still not ES6 and can be very confusing to use. For each new customer, we created a different bucket so they can have individual data and not have to worry about data loss. After 1000+ customers we started seeing many problems with the creation of new buckets, with saving or retrieving a new file. Many false positive: the Promise returned ok, but in reality, it failed.
That's why we switched to S3 that just works.
Pros of Amazon S3
- Reliable590
- Scalable492
- Cheap456
- Simple & easy329
- Many sdks83
- Logical30
- Easy Setup13
- REST API11
- 1000+ POPs11
- Secure6
- Easy4
- Plug and play4
- Web UI for uploading files3
- Faster on response2
- Flexible2
- GDPR ready2
- Easy to use1
- Plug-gable1
- Easy integration with CloudFront1
Pros of Google Cloud Bigtable
- High performance11
- Fully managed9
- High scalability5
Pros of Hadoop
- Great ecosystem39
- One stack to rule them all11
- Great load balancer4
- Amazon aws1
- Java syntax1
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Cons of Amazon S3
- Permissions take some time to get right7
- Requires a credit card6
- Takes time/work to organize buckets & folders properly6
- Complex to set up3
Cons of Google Cloud Bigtable
Cons of Hadoop
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