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Apache Spark VS Google Cloud Pub/Sub

Compare Apache Spark VS Google Cloud Pub/Sub and see what are their differences

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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.

Google Cloud Pub/Sub logo Google Cloud Pub/Sub

Cloud Pub/Sub is a flexible, reliable, real-time messaging service for independent applications to publish & subscribe to asynchronous events.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Google Cloud Pub/Sub Landing page
    Landing page //
    2023-03-23

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.

Google Cloud Pub/Sub features and specs

  • Scalability
    Google Cloud Pub/Sub is designed to handle large volumes of messages, allowing it to scale effortlessly to accommodate varying workloads.
  • Global Availability
    The service is globally distributed, ensuring low-latency access and reliability wherever your application is hosted.
  • Asynchronous Communication
    Supports asynchronous communication between services, decoupling the producer and consumer, leading to better fault tolerance and resource utilization.
  • Integration
    It integrates smoothly with other Google Cloud services and supports many third-party tools, enhancing its utility in diverse environments.
  • Security
    Offers robust security features including encryption of messages both at rest and in transit.
  • Managed Service
    Being a fully managed service, it reduces the operational overhead associated with maintaining messaging infrastructure.

Possible disadvantages of Google Cloud Pub/Sub

  • Cost Structure
    Depending on usage patterns, costs can increase significantly, making it difficult to predict expenses in high-throughput scenarios.
  • Complexity
    For beginners, setting up Pub/Sub and managing topics and subscriptions can be complex and require a learning curve.
  • Latency Variability
    While generally low, message delivery latency can sometimes vary, especially under peak loads.
  • Dependency on Network
    As a cloud-based service, its performance is heavily dependent on network reliability, which might not be suitable for extremely sensitive real-time applications.
  • Limited Message Retention
    By default, messages are retained for a limited period, which may not be suitable for applications needing long-term message storage.

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 Google Cloud Pub/Sub

Overall verdict

  • Google Cloud Pub/Sub is a powerful and reliable messaging service that is highly regarded for its scalability, integration capabilities, and security features. It is a strong choice for businesses looking for a robust cloud-based messaging solution.

Why this product is good

  • Scalability: Google Cloud Pub/Sub is built to handle huge amounts of data, making it ideal for large-scale applications.
  • Reliability: It provides strong reliability and consistent performance due to its distributed nature across multiple data centers.
  • Integration: Pub/Sub integrates well with other Google Cloud services, enhancing its functionality and making it easier to create comprehensive cloud solutions.
  • Security: Offers robust security features including encryption at rest and in transit, aligning with Google Cloud's overall focus on security.
  • Ease of Use: It provides a user-friendly interface and comprehensive documentation, making it accessible even for those new to cloud services.

Recommended for

  • Organizations needing to process and analyze large volumes of messages in real-time.
  • Developers building cloud-native applications requiring scalable messaging services.
  • Businesses already leveraging the Google Cloud ecosystem, as Pub/Sub integrates seamlessly with other services.
  • Teams looking for a secure and reliable messaging solution with global availability.

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

Google Cloud Pub/Sub videos

No Google Cloud Pub/Sub videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to Apache Spark and Google Cloud Pub/Sub)

Apache Spark

Google Cloud Pub/Sub

Databases
100 100%
Databases
0% 0
Stream Processing
34 34%
Stream Processing
66% 66
Big Data
100 100%
Big Data
0% 0
Data Integration
0 0%
Data Integration
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 Apache Spark and Google Cloud Pub/Sub

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

Google Cloud Pub/Sub Reviews

We have no reviews of Google Cloud Pub/Sub yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Apache Spark should be more popular than Google Cloud Pub/Sub. 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 — 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

Google Cloud Pub/Sub mentions (15)

  • Event-Driven Architecture 101
    Secondly, Go is incredibly easy to learn and in my opinion, maintain. This means that if you're a growing company and expect to onboard new teams and team members, having Go as a basis for your systems should mean that new engineers can get up to speed quickly.  Below is a small sample application that can connect to Google PubSub, subscribe to a topic, send an event and then clean up. In total, its 82 lines of... - Source: dev.to / over 1 year ago
  • Top 6 message queues for distributed architectures
    Google Cloud Pub/Sub is a fully-managed, globally scalable and secure queue provided by Google Cloud for asynchronous processing messages. Cloud Pub/Sub has many of the same advantages and disadvantages as SQS due to also being cloud hosted. It has a free and paid tier. - Source: dev.to / about 2 years ago
  • Job Scheduling on Google Cloud Platform
    Cloud Pub/Sub: A global messaging service for event-driven architectures. - Source: dev.to / about 2 years ago
  • Effortlessly Scale Your Applications with FaaS: Learn How Functions as a Service Can Help You Grow and Thrive
    Google Cloud Functions is a FaaS offering from Google Cloud Platform (GCP). It allows developers to run their code in response to events, such as changes in a database or the arrival of a message in a Pub/Sub topic. Like AWS Lambda, Google Cloud Functions can be used to build a variety of applications, including serverless websites, data processing pipelines, and real-time data streams. - Source: dev.to / over 2 years ago
  • Mixing GCloud and F#
    That gets triggered when a Pub/Sub topic is fired (from the webhook function). - Source: dev.to / over 2 years ago
View more

What are some alternatives?

When comparing Apache Spark and Google Cloud Pub/Sub, 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.

Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.

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

Amazon Kinesis - Amazon Kinesis services make it easy to work with real-time streaming data in the AWS cloud.

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

RabbitMQ - RabbitMQ is an open source message broker software.

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