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Amazon AWS VS Apache Spark

Compare Amazon AWS VS Apache Spark and see what are their differences

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Amazon AWS logo Amazon AWS

Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.

Apache Spark logo Apache Spark

Apache Spark is an engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.
  • Amazon AWS Landing page
    Landing page //
    2022-01-29
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Amazon AWS features and specs

  • Scalability
    AWS offers highly scalable services, allowing businesses to easily adjust resources based on demand without significant upfront investment.
  • Comprehensive Service Offering
    AWS provides a wide range of services, from compute and storage to machine learning and analytics, catering to diverse business needs.
  • Global Reach
    With data centers located worldwide, AWS enables low-latency access and redundancy, supporting global operations.
  • Strong Security
    AWS has robust security measures, including compliance certifications, encryption, and physical security, ensuring data and infrastructure protection.
  • Pay-as-You-Go Pricing
    AWS offers a flexible pricing model, where users only pay for what they use, helping manage costs effectively.
  • Extensive Integration Options
    AWS integrates with a wide variety of third-party services and APIs, providing seamless integration capabilities for various applications.
  • Innovation
    AWS frequently releases new services and features, staying at the forefront of technology and providing users with cutting-edge tools.

Possible disadvantages of Amazon AWS

  • Cost Management Complexity
    While the pay-as-you-go model offers flexibility, it can be challenging to track and predict costs, especially for large-scale operations.
  • Learning Curve
    AWS has a comprehensive set of services and features, which can be overwhelming for new users to learn and manage effectively.
  • Potential Vendor Lock-In
    Relying heavily on AWS services may result in vendor lock-in, making it difficult to switch providers or migrate workloads in the future.
  • Service Limitations
    Certain AWS services might have limitations or restrictions, which could hinder specific use cases or require workarounds.
  • Support Costs
    AWS offers different support tiers, and premium support options can be expensive for businesses needing immediate and advanced technical assistance.
  • Performance Variability
    Performance can vary based on server load and geographic location, which may affect the consistency and reliability of certain services.
  • Complex Pricing Structure
    AWS's pricing structure can be complicated, with various pricing models and options making it hard to determine the most cost-efficient choice.

Apache Spark features and specs

  • Speed
    Apache Spark processes data in-memory, significantly increasing the processing speed of data tasks compared to traditional disk-based engines.
  • Ease of Use
    Spark offers high-level APIs in Java, Scala, Python, and R, making it accessible to a broad range of developers and data scientists.
  • Advanced Analytics
    Spark supports advanced analytics, including machine learning, graph processing, and real-time streaming, which can be executed in the same application.
  • Scalability
    Spark can handle both small- and large-scale data processing tasks, scaling seamlessly from a single machine to thousands of servers.
  • Support for Various Data Sources
    Spark can integrate with a wide variety of data sources, including HDFS, Apache HBase, Apache Hive, Cassandra, and many others.
  • Active Community
    Spark has a vibrant and active community, providing a wealth of extensions, tools, and support options.

Possible disadvantages of Apache Spark

  • Memory Consumption
    Spark's in-memory processing can be resource-intensive, requiring substantial amounts of RAM, which can drive up costs for large-scale deployments.
  • Complexity in Configuration
    To optimize performance, Spark requires careful configuration and tuning, which can be complex and time-consuming.
  • Learning Curve
    Despite its ease of use, mastering the full range of Spark's features and best practices can take considerable time and effort.
  • Latency for Small Data
    For smaller datasets or low-latency requirements, Spark might not be the most efficient choice, as other technologies could offer better performance.
  • Integration Overhead
    Though Spark integrates with many systems, incorporating it into an existing data infrastructure can introduce additional overhead and complexity.
  • Community Support Variability
    While the community is active, the support and quality of third-party libraries and tools can be inconsistent, leading to potential challenges in implementation.

Amazon AWS videos

+ Add

Amazon Web Services vs Google Cloud Platform - AWS vs GCP | Difference Between GCP and AWS

More videos:

  • Review - Are AWS Certifications worth it?
  • Review - AWS Certified Solutions Architect Associate Certification Will Get You Paid!

Apache Spark videos

+ Add

Weekly Apache Spark live Code Review -- look at StringIndexer multi-col (Scala) & Python testing

More videos:

  • Review - What's New in Apache Spark 3.0.0
  • Review - Apache Spark for Data Engineering and Analysis - Overview

Category Popularity

0-100% (relative to Amazon AWS and Apache Spark)

Amazon AWS

Apache Spark

Cloud Computing
100 100%
Cloud Computing
0% 0
Databases
0 0%
Databases
100% 100
Cloud Infrastructure
100 100%
Cloud Infrastructure
0% 0
Big Data
0 0%
Big Data
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Amazon AWS and Apache Spark

Amazon AWS Reviews

  1. macloughlin
    · AV engineer ·
    The best cloud platform out there

    You could say a lot of things about AWS, but among the cloud platforms (and I've used quite a few) AWS takes the cake. It is logically structured, you can get through its documentation relatively easily, you have a great variety of tools and services to choose from [from AWS itself and from third-party developers in their marketplace]. There is a learning curve, there is quite a lot of it, but it is still way easier than some other platforms. I've used and abused AWS and EC2 specifically and for me it is the best.

    👍 Pros:    Great documentation|Website structure visualization|You have control over everything|Flexibility
    👎 Cons:    Learning curve|A lot of dashboards for different things
Top 15 MuleSoft Competitors and Alternatives
API Gateway private endpoints allow AWS customers to use API endpoints inside their VPC. They can leverage Route 53 resolver endpoints and hybrid connectivity to access APIs and integrated backend services from on-premises clients.
Source: bstrategyhub.com
Best Dedicated Server Providers in India: A Comparative Analysis
Dedicated hosts on Amazon EC2 are physical servers that are completely dedicated to meeting corporate compliance standards. With AWS, you can create EC2 instances on a dedicated server. The flexibility offered by Amazon EC2 is definitely one of its biggest advantages, along with high scalability. Apart from that, it isn’t much better than dedicated servers.
Source: moralstory.org
Best Dedicated Server Providers for E-commerce Businesses in India
The dedicated server options from Amazon Web Services (AWS), a well-known brand in the tech industry, are equally excellent. AWS’s elastic infrastructure can smoothly adjust to your demands whether your e-commerce business encounters variable traffic or you expect quick development. AWS guarantees that the speed and performance of your website will always be unmatched thanks...
Source: pricealertbd.com
The Best Dedicated Server Operating System for UK-Based Business
Cloud computing behemoth AWS is renowned for its extensive infrastructure and scalability choices. You can make use of AWS’s numerous data centers, which are positioned strategically to offer low-latency services all across the UK.
Source: featurestic.com
The Best Dedicated Servers for Enterprise Businesses in India: Scalable and Reliable
The extensive selection of cloud-based solutions offered by AWS is one of its main advantages. AWS provides a wide range of cloud services, including computing power, storage choices, databases, machine learning, analytics tools, and dedicated servers. This adaptability enables businesses to create scalable, flexible, and affordable solutions customized to their needs.
Source: india07.in

Apache Spark Reviews

15 data science tools to consider using in 2021
Apache Spark is an open source data processing and analytics engine that can handle large amounts of data -- upward of several petabytes, according to proponents. Spark's ability to rapidly process data has fueled significant growth in the use of the platform since it was created in 2009, helping to make the Spark project one of the largest open source communities among big...
Source: searchbusinessanalytics.techtarget.com
Top 15 Kafka Alternatives Popular In 2021
Apache Spark is a well-known, general-purpose, open-source analytics engine for large-scale, core data processing. It is known for its high-performance quality for data processing – batch and streaming with the help of its DAG scheduler, query optimizer, and engine. Data streams are processed in real-time and hence it is quite fast and efficient. Its machine learning...
Source: www.spec-india.com
5 Best-Performing Tools that Build Real-Time Data Pipeline
Apache Spark is an open-source and flexible in-memory framework which serves as an alternative to map-reduce for handling batch, real-time analytics and data processing workloads. It provides native bindings for the Java, Scala, Python, and R programming languages, and supports SQL, streaming data, machine learning and graph processing. From its beginning in the AMPLab at...
Source: www.analyticsinsight.net

Social recommendations and mentions

Based on our record, Amazon AWS should be more popular than Apache Spark. It has been mentiond 444 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Amazon AWS mentions (444)

  • Step-by-Step Guide to Set Up a Cron Job to Run a Report
    Create an AWS Account: If you don’t already have one, sign up at aws.amazon.com. The free tier provides 750 hours per month of a t2.micro or t3.micro instance for 12 months. - Source: dev.to / 5 days ago
  • How to Host an Express App on AWS EC2 with NGINX (Free Tier Guide)
    Sign in to your AWS account. If you’re new to AWS, you can sign up for the free tier to get started without any upfront cost. - Source: dev.to / about 1 month ago
  • Understanding AWS Regions and Availability Zones: A Guide for Beginners
    Amazon Web Services (AWS) has completely changed the game for how we build and manage infrastructure. Gone are the days when spinning up a new service meant begging your sys team for hardware, waiting weeks, and spending hours in a cold data center plugging in cables. Now? A few clicks (or API calls), and yes — you've got an entire data center at your fingertips. - Source: dev.to / 25 days ago
  • AWS S3 Storage Classes Explained: Choosing the Right One
    Choosing the right AWS S3 storage class depends on how frequently you access your data and your cost constraints. - Source: dev.to / about 2 months ago
  • Deploy a Django Rest Api on AWS EC2 using Docker, NGINX, Gunicorn and GitHub Action.
    Let’s start by setting up an EC2 instance to deploy our application. To do this, and you’ll need to open an AWS account (if you don’t already have one). - Source: dev.to / 3 months ago
View more

Apache Spark mentions (70)

  • Every Database Will Support Iceberg — Here's Why
    Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 28 days ago
  • How to Reduce Big Data Analytics Costs by 90% with Karpenter and Spark
    Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 30 days ago
  • Unveiling the Apache License 2.0: A Deep Dive into Open Source Freedom
    One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
  • The Application of Java Programming In Data Analysis and Artificial Intelligence
    [1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 2 months ago
  • Automating Enhanced Due Diligence in Regulated Applications
    If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
View more

What are some alternatives?

When comparing Amazon AWS and Apache Spark, you can also consider the following products

DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.

Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.

Microsoft Azure - Windows Azure and SQL Azure enable you to build, host and scale applications in Microsoft datacenters.

Hadoop - Open-source software for reliable, scalable, distributed computing

Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.

Apache Storm - Apache Storm is a free and open source distributed realtime computation system.

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