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Apache Spark VS Azure Event Hubs

Compare Apache Spark VS Azure Event Hubs and see what are their differences

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Contents:
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  2. » Videos
  3. » Reviews
  4. » Alternatives

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.

Azure Event Hubs logo Azure Event Hubs

Learn about Azure Event Hubs, a managed service that can ingest and process massive data streams from websites, apps, or devices.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Azure Event Hubs Landing page
    Landing page //
    2023-03-27

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.

Azure Event Hubs features and specs

  • Scalability
    Azure Event Hubs can handle millions of events per second, making it highly scalable for large-scale data ingestion solutions.
  • Fully Managed
    As a fully managed service, it reduces the overhead associated with managing infrastructure, allowing teams to focus on application development.
  • Integration
    Seamlessly integrates with other Azure services like Azure Stream Analytics, Azure Functions, and more, making it a versatile solution within the Azure ecosystem.
  • Data Retention
    Supports event retention of up to seven days, allowing applications to replay streams and facilitating debugging or application state recovery.
  • Security
    Offers comprehensive security features, including encryption at rest and in transit, VNet service endpoints, and Shared Access Signatures (SAS) for access control.

Possible disadvantages of Azure Event Hubs

  • Complexity in Setup
    The initial setup and configuration can be complex for new users, especially those unfamiliar with Azure services.
  • Cost
    Costs can accumulate quickly, particularly with high-throughput or extensive data retention requirements, potentially impacting budget-conscious projects.
  • Limited On-premises Integration
    Primarily designed for cloud environments, making it less suitable for on-premises scenarios without additional integration layers.
  • Latency
    Although generally low, latency can become noticeable in high-load scenarios, which might affect applications requiring real-time processing.
  • Partition Management
    Dynamic partition scaling is not available. Once set, partition counts cannot be changed without creating a new event hub, which requires thoughtful upfront planning.

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

Azure Event Hubs videos

+ Add

Messaging with Azure Event Hubs

Category Popularity

0-100% (relative to Apache Spark and Azure Event Hubs)

Apache Spark

Azure Event Hubs

Databases
100 100%
Databases
0% 0
Stream Processing
61 61%
Stream Processing
39% 39
Big Data
90 90%
Big Data
10% 10
Data Management
0 0%
Data Management
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 Azure Event Hubs

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

Azure Event Hubs Reviews

We have no reviews of Azure Event Hubs yet.
Be the first one to post

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Azure Event Hubs. While we know about 70 links to Apache Spark, we've tracked only 4 mentions of Azure Event Hubs. 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 / 14 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 / 15 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 / about 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 / about 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

Azure Event Hubs mentions (4)

  • Anyone routing firewall logs to Microsoft Event Hubs?
    We're looking into some sort of cloud-based solution to route our Palo Alto firewall logs to across our customer base. I'm with an MSP that manages over a hundred PA firewalls. I was intrigued by the Event Hubs (https://azure.microsoft.com/en-us/products/event-hubs/) solution as a way to push logs to it and then ingest them from there into our SIEM, without having to deal with challenges of multi-tenancy and... Source: over 2 years ago
  • Microsoft Releases Stream Analytics No-Code Editor into General Availability
    Microsoft released Azure Stream Analytics no-code editor, a drag-and-drop canvas for developing jobs for stream processing scenarios such as streaming ETL, ingestion, and materializing data to data into general availability. The no-code editor is hosted in the company’s big-data streaming platform and event ingestion service, Azure Event Hubs. Interestingly, the offering follows up after Confluent's recent release... Source: over 2 years ago
  • Infrastructure as code (IaC) for Java-based apps on Azure
    Sometimes you don’t need an entire Java-based microservice. You can build serverless APIs with the help of Azure Functions. For example, Azure functions have a bunch of built-in connectors like Azure Event Hubs to process event-driven Java code and send the data to Azure Cosmos DB in real-time. FedEx and UBS projects are great examples of real-time, event-driven Java. I also recommend you to go through 👉 Code,... - Source: dev.to / over 2 years ago
  • Setting up demos in Azure - Part 1: ARM templates
    For event infrastructure, we have a bunch of options, like Azure Service Bus, Azure Event Grid and Azure Event Hubs. Like the databases, they aren't mutually exclusive and I could use all, depending on the circumstance, but to keep things simple, I'll pick one and move on. Right now I'm more inclined towards Event Hubs, as it works similarly to Apache Kafka, which is a good fit for the presentation context. - Source: dev.to / about 4 years ago

What are some alternatives?

When comparing Apache Spark and Azure Event Hubs, 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.

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

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

Azure Stream Analytics - Azure Stream Analytics offers real-time stream processing in the cloud.

Apache Hive - Apache Hive data warehouse software facilitates querying and managing large datasets residing in distributed storage.

Amazon Elasticsearch Service - Amazon Elasticsearch Service is a managed service that makes it easy to deploy, operate, and scale Elasticsearch in the AWS Cloud.

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