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Apache Spark VS StreamSets

Compare Apache Spark VS StreamSets 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.

StreamSets logo StreamSets

StreamSets provides Continuous Ingest technology for the next generation of big data applications.
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • StreamSets Landing page
    Landing page //
    2023-09-13

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.

StreamSets features and specs

  • User-Friendly Interface
    StreamSets provides an intuitive and visually appealing interface for designing and managing data pipelines, making it accessible even for users without extensive coding experience.
  • Real-Time Data Processing
    The platform excels at real-time data ingestion, transformation, and delivery, enabling timely insights and immediate actions on streaming data.
  • Comprehensive Connectors
    StreamSets supports a wide range of data sources and destinations out of the box, including cloud services, databases, and big data platforms, ensuring versatility in data integration tasks.
  • Data Drift Management
    It offers robust features for detecting and managing data drift, helping maintain data quality and consistency over time as source schemas evolve.
  • Scalability
    StreamSets is designed to scale effortlessly with increasing data volumes and can handle large-scale data pipelines efficiently.

Possible disadvantages of StreamSets

  • Cost
    The pricing model can be expensive, particularly for small to mid-sized enterprises, making it less accessible for organizations with limited budgets.
  • Learning Curve
    Although the interface is user-friendly, mastering the platform's advanced features and configurations may require a significant learning curve.
  • Resource Intensive
    Running StreamSets can be resource-intensive, requiring substantial computational and memory resources, which may lead to higher operational costs.
  • Limited Custom Scripting
    While StreamSets offers many in-built functionalities, it provides limited scope for custom scripting compared to other data pipeline tools, which may restrict flexibility for complex custom tasks.
  • Dependency on Internet Connectivity
    For cloud-based deployments, the performance and reliability of StreamSets can be heavily dependent on internet connectivity, which could be a concern for organizations with unstable connections.

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

StreamSets videos

+ Add

What is StreamSets Transformer?

More videos:

  • Review - Making Apache Kafka Dead Easy With StreamSets | DZone.com Webinar
  • Review - Power Your Delta Lake with Streaming Transactional Changes - Rupal Shah (StreamSets)

Category Popularity

0-100% (relative to Apache Spark and StreamSets)

Apache Spark

StreamSets

Databases
100 100%
Databases
0% 0
DevOps Tools
0 0%
DevOps Tools
100% 100
Big Data
100 100%
Big Data
0% 0
Continuous Integration And Delivery
0 0%
Continuous Integration And Delivery
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 StreamSets

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

StreamSets Reviews

We have no reviews of StreamSets 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 StreamSets. While we know about 70 links to Apache Spark, we've tracked only 2 mentions of StreamSets. 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 / 18 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 / 20 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 / 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 / 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

StreamSets mentions (2)

  • Best way to automate JSON to CSV/Relational Tables at scale? Anyone have used Flexter?
    If you would like to take a look at https://streamsets.com/ the Data Collector product can handle this for you as well as dynamically generate the target tables. It has a number of functions to handle your JSON no matter the complexity. However, given the dynamic nature it may benefit to touch base so please feel free to chat or message me. Source: almost 3 years ago
  • Data engineering in reality
    StreamSets offers a free tier and free option for training. You can build, run, and manage your pipelines in one place. Source: over 3 years ago

What are some alternatives?

When comparing Apache Spark and StreamSets, 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.

Terraform - Tool for building, changing, and versioning infrastructure safely and efficiently.

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

Packer - Packer is an open-source software for creating identical machine images from a single source configuration.

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

Puppet Enterprise - Get started with Puppet Enterprise, or upgrade or expand.

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