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Apache Spark VS Cyclr

Compare Apache Spark VS Cyclr 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.

Cyclr logo 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.
Visit Website
  • Apache Spark Landing page
    Landing page //
    2021-12-31
  • Cyclr Cyclr Website
    Cyclr Website //
    2024-08-06

Cyclr is a SaaS integration toolkit for SaaS platforms and app developers, providing a complete solution to serve your customers integration needs -- all from within your application. Cyclr enables you to deliver integrations to 100s of popular apps and services with low-code and low engineering overhead. Cyclr also handle all the updates, cutting development teams integration maintenance overhead.

Integrations are created using a drag and drop designer, enabling members of your wider teams (customer success, sales and support) to build and publish new integrations and workflows in minutes.

Integrations can then be published directly into your application so your users can self-serve. This can be achieved by building your own UI on top of Cyclr's fully featured API, or through deploying their white-labelled and completely customisable embedded marketplace.

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.

Cyclr features and specs

  • Pre-built Connectors
    +400
  • Embedded Integration Marketplace
  • Low Code Integration Builder
  • SOC 2 Type 2 Accreditation
  • Private Cloud Deployments
  • Proxy API

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

Cyclr videos

No Cyclr videos yet. You could help us improve this page by suggesting one.

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Category Popularity

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

Apache Spark

Cyclr

Databases
100 100%
Databases
0% 0
Monitoring Tools
0 0%
Monitoring Tools
100% 100
Big Data
100 100%
Big Data
0% 0
Integrations Platform As A Service
0 0%
Integrations Platform As A Service
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 Cyclr

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

Cyclr Reviews

  1. Moshe Strugano | Moshe Strugano and Co Law Firm
    Best platform to use

    This is the best platform to use. You can rely on this platform for different kind of work. Highly recommended

7 Best Zapier Alternatives to Meet Your Integration Needs
With Cyclr, you can use the APIs and SDKs made available by the apps in your techstack that you want to integrate — so you don't have to create your own from scratch. Cyclr offers a variety of capabilities ranging from authentication and authorization to storage and processing so you can design your desired integration flows.
Source: www.linkedin.com
The 7 Best Embedded iPaaS Solutions to Consider for 2024
Description: Cyclr is a UK-based provider of embedded Integration Platform as a Service (iPaaS) solutions. The vendor offers a white-labeled, low-code approach to offering in-app integrations for end-users. Cyclr touts a global user base and helps its customers enhance their native connectivity suites while simplifying the creation and deployment method. Flexible deployment...
Source: solutionsreview.com
13 data integration tools: a comparative analysis of the top solutions
Cyclr is an embedded integration technology ideal for enhancing SaaS platforms and applications. It improves the speed and satisfaction of customer integration needs directly from the application.
Source: blog.n8n.io
Best iPaaS Softwares
Cyclr is an embedded integration toolkit (iPaaS) empowering SaaS applications to rapidly expand their in-app integration capabilities. Create powerful integration workflows and automations using our low-code toolkit, including a drag and drop interface for building integrations between your platform and hundreds of third-party applications.
Source: iotbyhvm.ooo

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Cyclr. While we know about 70 links to Apache Spark, we've tracked only 1 mention of Cyclr. 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 / 26 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 / 28 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

Cyclr mentions (1)

  • Automating update of contacts via FTP
    Other good solutions with similar features would be PieSync, Automate.io, Zapier, Cyclr, Workato. All of these app integrations allow you to connect your Mailchimp account with your SaaS app (in your case with your database). Source: about 4 years ago

What are some alternatives?

When comparing Apache Spark and Cyclr, 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.

Zapier - Connect the apps you use everyday to automate your work and be more productive. 1000+ apps and easy integrations - get started in minutes.

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

Boomi - The #1 Integration Cloud - Build Integrations anytime, anywhere with no coding required using Dell Boomi's industry leading iPaaS platform.

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

ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.

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