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Skyvia VS Apache Spark

Compare Skyvia VS Apache Spark 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
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Skyvia logo Skyvia

Free cloud data platform for data integration, backup & management

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.
  • Skyvia Landing page
    Landing page //
    2023-09-12
  • Apache Spark Landing page
    Landing page //
    2021-12-31

Skyvia features and specs

  • Ease of Use
    Skyvia provides a user-friendly interface that makes it easy for non-technical users to set up and manage data integration, backup, and other tasks without requiring coding skills.
  • Versatile Data Integration
    Supports a wide range of data sources including cloud apps, databases, and CSV files, allowing for flexible data integration scenarios.
  • Pricing Model
    Offers a freemium model that allows users to start with basic features for free and scale up with more advanced features as needed.
  • Cloud-Based
    As a cloud-based tool, Skyvia eliminates the need for on-premises installation and maintenance, reducing IT overhead.
  • Comprehensive Features
    Provides a suite of tools for data integration, backup, management, and data visualization, making it a one-stop solution for many data-related tasks.
  • API Integration
    Allows for integration via APIs, enabling automated and scheduled data operations.

Possible disadvantages of Skyvia

  • Learning Curve
    Despite its user-friendly interface, some users may still face a learning curve when it comes to understanding and utilizing all of its features effectively.
  • Dependency on Internet
    As a cloud-based solution, users are dependent on a stable internet connection to use the platform, which could be a limitation in areas with unreliable connectivity.
  • Customization Limitations
    While Skyvia offers a range of features, highly specialized customization might require additional development work outside the platform.
  • Data Latency
    For very large data sets, there could be a noticeable delay in data synchronization or backup processes.
  • Price for Premium Features
    Advanced features and higher usage limits can become costly, which might be a concern for small businesses or startups operating on tight budgets.
  • Limited Offline Access
    Because it's a cloud-based service, Skyvia offers limited functionality when offline.

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.

Skyvia videos

+ Add

Skyvia Data integration review 2020 | Best Data integration 2019

More videos:

  • Review - Skyvia Data Integration
  • Review - Jira to Google BigQuery Data Integration with Skyvia - Build vs Buy

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

Category Popularity

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

Skyvia

Apache Spark

Data Integration
100 100%
Data Integration
0% 0
Databases
0 0%
Databases
100% 100
Web Service Automation
100 100%
Web Service Automation
0% 0
Big Data
0 0%
Big Data
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 Skyvia and Apache Spark

Skyvia Reviews

15+ Best Cloud ETL Tools
Skyvia, a product of Devart, is a no-code cloud data integration platform for data integration, backup, management, and connectivity. It supports numerous data integration scenarios, like ETL, ELT, Reverse ETL, data migration, one-way and bi-directional data sync, and workflow automation.
Source: estuary.dev
Top 8 Apache Airflow Alternatives in 2024
Skyvia offers the ETL, ELT, and Reverse ETL functionality for any data integration process. Set up source and destination data platforms or apps for indicating the data path. Then, determine how data needs to be transformed and mapped. Also, Skyvia allows scheduling data integration processes so that new or updated data is transferred regularly. While simple scenarios are...
Source: blog.skyvia.com
13 data integration tools: a comparative analysis of the top solutions
Skyvia is a user-friendly data integration tool that excels in creating simple relationships and executing straightforward tasks. It's ideal for organizations looking to automate data backups with robust security, thanks to its hosting in the secure Azure data cloud.
Source: blog.n8n.io
15 Best ETL Tools in 2022 (A Complete Updated List)
Skyvia is a cloud data platform for no-coding data integration, backup, management and access, developed by Devart. Devart company is a well-known and trusted provider of data access solutions, database tools, development tools, and other software products with over 40 000 grateful customers in two R&D departments.
Source: www.softwaretestinghelp.com
Best iPaaS Softwares
Skyvia is a universal SaaS (Software as a Service) data platform for quick and easy solving a wide set of data-related tasks with no coding: data integration, automating workflows, cloud data backup, building reports and dashboards, data management with SQL, CSV import/export, creating OData services, etc. It supports a number of cloud applications and databases, and...
Source: iotbyhvm.ooo

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

Social recommendations and mentions

Based on our record, Apache Spark seems to be a lot more popular than Skyvia. While we know about 70 links to Apache Spark, we've tracked only 2 mentions of Skyvia. 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.

Skyvia mentions (2)

  • Drop Salesforce Report into SFTP folder
    You can try https://skyvia.com/, they seem to have a free product to do this, but it's pretty limited in the free version. Dataloader.io also can do this, but it's $300 a month to unlock SFTP exports. Source: over 3 years ago
  • Free for dev - list of software (SaaS, PaaS, IaaS, etc.)
    Skyvia.com — Cloud Data Platform, offers free tier and all plans are completely free while in beta. - Source: dev.to / almost 4 years ago

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 / about 1 month 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 / about 1 month 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
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What are some alternatives?

When comparing Skyvia and Apache Spark, you can also consider the following products

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

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

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.

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

AWS Glue - Fully managed extract, transform, and load (ETL) service

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

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