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Spark Streaming VS Azure Data Factory

Compare Spark Streaming VS Azure Data Factory and see what are their differences

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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.

Azure Data Factory logo Azure Data Factory

Learn more about Azure Data Factory, the easiest cloud-based hybrid data integration solution at an enterprise scale. Build data factories without the need to code.
  • Spark Streaming Landing page
    Landing page //
    2022-01-10
  • Azure Data Factory Landing page
    Landing page //
    2023-01-12

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.

Azure Data Factory features and specs

  • Scalability
    Azure Data Factory can handle significant data volumes and allows for scaling up or down as needed, making it suitable for both small and complex data integration projects.
  • Integration
    It provides native integration with various Azure services and a wide array of connectors for different data sources, facilitating seamless data flow across platforms.
  • Cost-effective
    The pay-as-you-go pricing model enables cost management by aligning expenses with actual usage patterns, which can be beneficial for budget-conscious projects.
  • Ease of Use
    Offers a user-friendly interface with drag-and-drop features, making it accessible even for users with limited coding experience.
  • Security
    Azure Data Factory includes robust security features like network isolation, access management, and encryption both in-transit and at-rest, ensuring data protection.

Possible disadvantages of Azure Data Factory

  • Complexity
    Managing large and complex data pipelines may require a steep learning curve and expertise in Azure services, which could be a hindrance for non-technical users.
  • Debugging Challenges
    Debugging tasks and identifying error sources in complex ETL processes can be cumbersome, requiring detailed monitoring and analysis.
  • Limited On-Premise Integration
    While ADF offers numerous connectors, integration with certain on-premise data stores might still require additional configuration and setup.
  • Latency Issues
    Data transfer latency can occur when dealing with extremely large datasets or when integrating multiple cloud and on-premise sources.
  • Dependency on Cloud
    As a cloud-based service, performance can be impacted by internet connectivity issues, and consistent access to the cloud is necessary for operations.

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

Azure Data Factory videos

+ Add

Azure Data Factory Tutorial | Introduction to ETL in Azure

More videos:

  • Review - Use Azure Data Factory to copy and transform data
  • Review - Pass summit 2019: Head to Head, SSIS Versus Azure Data Factory

Category Popularity

0-100% (relative to Spark Streaming and Azure Data Factory)

Spark Streaming

Azure Data Factory

Stream Processing
100 100%
Stream Processing
0% 0
Data Integration
0 0%
Data Integration
100% 100
Data Management
100 100%
Data Management
0% 0
ETL
0 0%
ETL
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 Spark Streaming and Azure Data Factory

Spark Streaming Reviews

We have no reviews of Spark Streaming yet.
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Azure Data Factory Reviews

Best ETL Tools: A Curated List
Azure Data Factory uses a pay-as-you-go pricing model based on several factors, including the number of activities performed, the duration of integration runtime hours, and data movement volumes. This flexible pricing allows for scaling based on workload but can lead to complex cost structures for larger or more complex data integration projects.
Source: estuary.dev
15+ Best Cloud ETL Tools
Azure Data Factory is a fully managed, serverless data integration service by Azure Cloud. You can easily connect to more than 90 built-in data sources without any added cost, allowing for efficient data integration at an enterprise level. Azure's visual platform lets you create ETL and ELT processes without having to write any code.
Source: estuary.dev
Top 8 Apache Airflow Alternatives in 2024
While Apache Airflow focuses on creating tasks and building dependencies between them for workflow automation, Azure Data Factory is suitable for integration tasks. It would be a perfect fit for the construction of the ETL and ELT pipelines for data migration and integration across platforms.
Source: blog.skyvia.com
A List of The 16 Best ETL Tools And Why To Choose Them
Azure Data Factory is a cloud-based ETL service offered by Microsoft used to create workflows that move and transform data at scale.
Source: www.datacamp.com
Top Big Data Tools For 2021
Azure Data Factory is a cloud solution that enables you to integrate data between multiple relational and non-relational sources, transforming it according to your objectives and requirements.
Source: blog.bismart.com

Social recommendations and mentions

Spark Streaming might be a bit more popular than Azure Data Factory. We know about 5 links to it since March 2021 and only 4 links to Azure Data Factory. 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 / about 2 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 / 10 months 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 / over 2 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 / over 3 years ago

Azure Data Factory mentions (4)

  • Choosing the right, real-time, Postgres CDC platform
    The major infrastructure providers offer CDC products that work within their ecosystem. Tools like AWS DMS, GCP Datastream, and Azure Data Factory can be configured to stream changes from Postgres to other infrastructure. - Source: dev.to / 6 months ago
  • (Recommend) Fun Open Source Tool for Pushing Data Around
    You might want to look at Azure Data Factory https://azure.microsoft.com/en-us/services/data-factory/ to extend SSIS EDIT: Yes, I missed the "open source" part :). Source: about 3 years ago
  • Deploying Azure Data Factory using Bicep
    I'm also planning to do more content with Azure Data Factory, so I'd thought it be good to make a video combining the two. - Source: dev.to / about 4 years ago
  • Class construction help
    Or, if oyu are using azure then azure data factory https://azure.microsoft.com/en-us/services/data-factory/. Source: about 4 years ago

What are some alternatives?

When comparing Spark Streaming and Azure Data Factory, you can also consider the following products

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

Workato - Experts agree - we're the leader. Forrester Research names Workato a Leader in iPaaS for Dynamic Integration. Get the report. Gartner recognizes Workato as a “Cool Vendor in Social Software and Collaboration”.

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

DataTap - Adverity is the best data intelligence software for data-driven decision making. Connect to all your sources and harmonize the data across all channels.

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

Xplenty - Xplenty is the #1 SecurETL - allowing you to build low-code data pipelines on the most secure and flexible data transformation platform. No longer worry about manual data transformations. Start your free 14-day trial now.

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