Software Alternatives, Accelerators & Startups
Register | Login

Spark Streaming VS Google Cloud Pub/Sub

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

Cyclr
Powerful SaaS integration toolkit for SaaS developers - create, amplify, manage and publish native integrations from within your app with Cyclr's flexible Embedded iPaaS. featured
Contents:
  1. » Base Details
  2. » Videos
  3. » Reviews
  4. » Alternatives

Spark Streaming logo Spark Streaming

Spark Streaming makes it easy to build scalable and fault-tolerant streaming applications.

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.
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • Google Cloud Pub/Sub Landing page
    Landing page //
    2023-03-23

Spark Streaming features and specs

  • Scalability
    Spark Streaming is highly scalable and can handle large volumes of data by distributing the workload across a cluster of machines. It leverages Apache Spark's capabilities to scale out easily and efficiently.
  • Integration
    It integrates seamlessly with other components of the Spark ecosystem, such as Spark SQL, MLlib, and GraphX, allowing for comprehensive data processing pipelines.
  • Fault Tolerance
    Spark Streaming provides fault tolerance by using Spark's micro-batching approach, which allows the system to recover data in case of a failure.
  • Ease of Use
    Spark Streaming provides high-level APIs in Java, Scala, and Python, making it relatively easy to develop and deploy streaming applications quickly.
  • Unified Platform
    It provides a unified platform for both batch and streaming data processing, allowing reuse of code and resources across different types of workloads.

Possible disadvantages of Spark Streaming

  • Latency
    Spark Streaming operates on a micro-batch processing model, which introduces latency compared to real-time processing. This may not be suitable for applications requiring immediate responses.
  • Complexity
    While it integrates well with other Spark components, building complex streaming applications can still be challenging and may require expertise in distributed systems and stream processing concepts.
  • Resource Management
    Efficiently managing cluster resources and tuning the system can be difficult, especially when dealing with variable workload and ensuring optimal performance.
  • Backpressure Handling
    Handling backpressure effectively can be a challenge in Spark Streaming, requiring careful management to prevent resource saturation or data loss.
  • Limited Windowing Support
    Compared to some stream processing frameworks, Spark Streaming has more limited options for complex windowing operations, which can restrict some advanced use cases.

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 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.

Spark Streaming videos

+ Add

Spark Streaming Vs Kafka Streams || Which is The Best for Stream Processing?

More videos:

  • Tutorial - Spark Streaming Vs Structured Streaming Comparison | Big Data Hadoop Tutorial

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 Spark Streaming and Google Cloud Pub/Sub)

Spark Streaming

Google Cloud Pub/Sub

Stream Processing
38 38%
Stream Processing
62% 62
Data Management
69 69%
Data Management
31% 31
Data Integration
0 0%
Data Integration
100% 100
Big Data
100 100%
Big Data
0% 0

User comments

Share your experience with using Spark Streaming and Google Cloud Pub/Sub. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, Google Cloud Pub/Sub should be more popular than Spark Streaming. It has been mentiond 15 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.

Spark Streaming mentions (5)

  • RisingWave Turns Four: Our Journey Beyond Democratizing Stream Processing
    The last decade saw the rise of open-source frameworks like Apache Flink, Spark Streaming, and Apache Samza. These offered more flexibility but still demanded significant engineering muscle to run effectively at scale. Companies using them often needed specialized stream processing engineers just to manage internal state, tune performance, and handle the day-to-day operational challenges. The barrier to entry... - Source: dev.to / 6 months ago
  • Streaming Data Alchemy: Apache Kafka Streams Meet Spring Boot
    Apache Spark Streaming: Offers micro-batch processing, suitable for high-throughput scenarios that can tolerate slightly higher latency. https://spark.apache.org/streaming/. - Source: dev.to / about 1 year ago
  • Choosing Between a Streaming Database and a Stream Processing Framework in Python
    Other stream processing engines (such as Flink and Spark Streaming) provide SQL interfaces too, but the key difference is a streaming database has its storage. Stream processing engines require a dedicated database to store input and output data. On the other hand, streaming databases utilize cloud-native storage to maintain materialized views and states, allowing data replication and independent storage scaling. - Source: dev.to / over 1 year ago
  • Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
    Spark Streaming: The component for real-time data processing and analytics. - Source: dev.to / almost 3 years ago
  • Spark for beginners - and you
    Is a big data framework and currently one of the most popular tools for big data analytics. It contains libraries for data analysis, machine learning, graph analysis and streaming live data. In general Spark is faster than Hadoop, as it does not write intermediate results to disk. It is not a data storage system. We can use Spark on top of HDFS or read data from other sources like Amazon S3. It is the designed... - Source: dev.to / almost 4 years ago

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 / about 2 years 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 / over 2 years ago
  • Job Scheduling on Google Cloud Platform
    Cloud Pub/Sub: A global messaging service for event-driven architectures. - Source: dev.to / over 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 / almost 3 years ago
View more

What are some alternatives?

When comparing Spark Streaming and Google Cloud Pub/Sub, you can also consider the following products

Confluent - Confluent offers a real-time data platform built around Apache Kafka.

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

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

RabbitMQ - RabbitMQ is an open source message broker software.

Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.

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

Loading...