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AWS IoT VS Apache Spark

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

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AWS IoT logo AWS IoT

Easily and securely connect devices to the cloud.

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.
  • AWS IoT Landing page
    Landing page //
    2023-04-28
  • Apache Spark Landing page
    Landing page //
    2021-12-31

AWS IoT features and specs

  • Scalability
    AWS IoT offers seamless scaling options to handle millions of devices and messages, allowing businesses to grow without worrying about infrastructure limitations.
  • Integration
    AWS IoT integrates effortlessly with other AWS services, such as AWS Lambda, Amazon S3, and Amazon DynamoDB, enabling a unified ecosystem for data processing and storage.
  • Security
    AWS IoT provides multiple layers of security, including device authentication and end-to-end encryption, to protect data and ensure secure communication between devices and the cloud.
  • Flexibility
    AWS IoT supports multiple communication protocols like MQTT, HTTP, and WebSockets, making it adaptable to a wide range of IoT devices and use cases.
  • Device Management
    AWS IoT includes features for managing and monitoring devices throughout their lifecycle, such as device registration, software updates, and diagnostics.
  • Analytics
    AWS IoT provides powerful analytics tools to process and analyze data from IoT devices, helping businesses gain valuable insights.

Possible disadvantages of AWS IoT

  • Complexity
    Setting up and managing an AWS IoT environment can be complex and may require a steep learning curve, especially for those new to IoT or AWS services.
  • Cost
    While AWS IoT offers a pay-as-you-go pricing model, costs can accumulate quickly, especially for large-scale deployments, making it potentially expensive for some businesses.
  • Internet Dependency
    AWS IoT relies heavily on stable internet connections for device communication, which can be a limitation in areas with poor connectivity.
  • Vendor Lock-In
    Using AWS IoT tightly integrates your IoT solutions with AWS infrastructure, which can make it difficult and costly to switch to other platforms or cloud providers later on.
  • Configuration Overhead
    The wide range of customization options and configurations can be overwhelming and may require dedicated resources to manage effectively.

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.

AWS IoT videos

+ Add

What is AWS IoT?

More videos:

  • Review - Introducción a AWS IoT
  • Review - AWS IoT in the Connected Home - AWS Online Tech Talks

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 AWS IoT and Apache Spark)

AWS IoT

Apache Spark

Data Dashboard
100 100%
Data Dashboard
0% 0
Databases
0 0%
Databases
100% 100
IoT Platform
100 100%
IoT Platform
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 AWS IoT and Apache Spark

AWS IoT Reviews

We have no reviews of AWS IoT yet.
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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, Apache Spark should be more popular than AWS IoT. It has been mentiond 70 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.

AWS IoT mentions (8)

  • Automatically Applying Configuration to IoT Devices with AWS IoT and AWS Step Functions - Part 1
    In this blog post series, we will look at a simple example of modeling an IoT device process as a workflow, using primarily AWS IoT and AWS Step Functions. Our example is a system where, when a device comes online, you need to get external settings based on the profile of the user the device belongs to and push that configuration to the device. The system that holds the external settings is often a third party... - Source: dev.to / about 2 years ago
  • Building a serverless talking doorbell
    Iot - MQTT broker to send messages to the Raspberry Pi. - Source: dev.to / over 3 years ago
  • GME NFT/blockchain is not to be a stock market...it's bigger
    " Amazon Web Services offers a broad set of global cloud-based products including compute, storage, databases, analytics, networking, mobile, developer tools, management tools, IoT, security and enterprise applications. These services help organizations move faster, lower IT costs, and scale. AWS is trusted by the largest enterprises and the hottest start-ups to power a wide variety of workloads including: web and... Source: over 3 years ago
  • What is AWS IoT Core and how do I use it?
    AWS IoT Core - message broker between all devices and AWS. - Source: dev.to / over 3 years ago
  • Which Cloud Suite is preferable when the focus is more towards IoT/IIoT as potential future job search keyword?
    If you have to ask, then you should be using AWS by default. They have plenty of IoT services for you to fiddle around with and get started. Source: almost 4 years 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 / about 1 month 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 / about 1 month 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 AWS IoT and Apache Spark, you can also consider the following products

Particle.io - Particle is an IoT platform enabling businesses to build, connect and manage their connected solutions.

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

ThingSpeak - Open source data platform for the Internet of Things. ThingSpeak Features

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

Blynk.io - We make internet of things simple

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

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