Software Alternatives, Accelerators & Startups

Apache Spark VS Ably

Compare Apache Spark VS Ably and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

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.

Ably logo Ably

The definitive realtime experience platform. Built for scale.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Ably Ably homepage
    Ably homepage //
    2024-08-05

Ably is the definitive realtime experience infrastructure of the internet; serving more WebSocket connections than any other pub/sub platform on earth. Businesses like HubSpot, NASCAR and Split trust us to power their critical applications - reliably, securely and at a serious scale.

Our range of application building blocks and integrations enable developers to create the realtime experiences that users and businesses demand. From live chat to data broadcast, collaborative UXs and notifications our SDKs unlock innovation - with no infrastructure to build.

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.

Ably features and specs

  • Scalability
    Ably is designed to handle a massive number of concurrent connections and messages, making it scalable for both small and large applications.
  • Real-time Messaging
    Ably provides robust real-time messaging capabilities with low latency, supporting use-cases like live chat, gaming, and real-time data streaming.
  • Global Presence
    Ably has a globally distributed network, ensuring low latency and high availability for users around the world.
  • Advanced Features
    It offers advanced features such as message history, presence, and push notifications, which can enhance the functionality of real-time applications.
  • Security
    Ably includes built-in security features such as TLS encryption, token-based authentication, and private channels to ensure secure communication.
  • Extensive Documentation
    The service provides extensive and well-organized documentation, making it easier for developers to integrate and utilize its features.
  • SDKs and Libraries
    Ably offers a wide range of SDKs and libraries for different programming languages and platforms, easing the development process.
  • Reliable Delivery
    It ensures reliable message delivery with features like message queuing and message de-duplication.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Analysis of Ably

Overall verdict

  • Ably is a reliable and efficient real-time messaging platform, well-suited for applications requiring high performance and scalability. It is generally well-regarded within the developer community for its ease of use and robust feature set.

Why this product is good

  • Features
    Ably provides a comprehensive suite of features such as pub/sub messaging, presence, and history.
  • Security
    It implements robust security practices including TLS encryption, token-based authentication, and private channels.
  • Integration
    Supports numerous protocols and provides SDKs for various programming languages, making it easy to integrate with different systems.
  • Reliability
    Ably offers high availability and low latency, ensuring messages are delivered in real-time.
  • Scalability
    The platform can handle millions of active connections and is designed for horizontal scaling.

Recommended for

  • developers building real-time applications like chat apps, dashboards, or multiplayer games
  • businesses in need of a scalable solution for live updates and notifications
  • teams looking for a platform with strong security and compliance credentials
  • organizations requiring integration with existing development workflows and tools

Apache Spark videos

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

Ably videos

How to build a collaborative environment around your product

More videos:

  • Demo - How to build an avatar stack with Ably Spaces
  • Demo - Ably 101: Serverless WebSockets at scale

Category Popularity

0-100% (relative to Apache Spark and Ably)
Databases
100 100%
0% 0
Mobile Push Messaging
0 0%
100% 100
Big Data
100 100%
0% 0
Developer Tools
0 0%
100% 100

User comments

Share your experience with using Apache Spark and Ably. For example, how are they different and which one is better?
Log in or Post with

Reviews

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

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...
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...
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...

Ably Reviews

We have no reviews of Ably yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Ably. 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.

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 â€” can read and/or write Iceberg data directly. - Source: dev.to / about 2 months 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 2 months 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 / 3 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 / 3 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 / 4 months ago
View more

Ably mentions (23)

  • Real-Time Authorization in a Chat Application with Permit.io and WebSockets
    For our web socket-based application, we’ll be using Next.js and Ably, a service that allows us to easily integrate and manage real-time capabilities in our apps powered by web socket. - Source: dev.to / 5 months ago
  • Curiosity: Using Ably.io Realtime Messaging as a Lightweight Database
    Ably offers various solutions for developers, with their most popular being realtime messaging based on the pub/sub model. When you publish a message to a channel, all devices connected to that channel receive it instantly. - Source: dev.to / 5 months ago
  • Chat API pricing: Comparing MAU and per-minute consumption models
    A per-minute consumption model goes beyond traditional consumption-based pricing by billing customers based on their actual usage of service resources—connection time, channels, and messages. This approach directly addresses the inefficiencies inherent in MAU pricing models. This isn’t a common model in the industry, but we’ve adopted it here at Ably to meet the usage needs of our customers at scale. - Source: dev.to / 6 months ago
  • Essential guide to WebSocket authentication
    Of course, if this all sounds like a headache, you might consider Ably. Apart from solving the authentication problem, Ably provides additional features you’d need to implement on top of WebSockets like Presence and message queues, and provides production guarantees that will be time-consuming or costly to achieve on your own like 99.999% uptime guarantee, exactly-once delivery, and guaranteed message ordering. - Source: dev.to / over 1 year ago
  • The top real-time notification services for building in-app notifications
    Ably is a robust real-time data delivery platform based on WebSockets with features like message ordering, presence, and connection recovery. Customers include Toyota, HubSpot, and Verizon. - Source: dev.to / over 1 year ago
View more

What are some alternatives?

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

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

Pusher - Pusher is a hosted API for quickly, easily and securely adding scalable realtime functionality via WebSockets to web and mobile apps.

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

Trophy.so - Ship gamification in hours, not months.

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

Mambo.io - Use the Mambo gamification platform to engage your teams, measure activities, set goals and increase the overall performance of your business.