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Xplenty VS Apache Spark

Compare Xplenty VS Apache Spark and see what are their differences

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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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Xplenty logo 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.

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.
  • Xplenty Landing page
    Landing page //
    2023-09-18

Xplenty is a cloud-based ETL (extract, transform, load), ELT (extract, load, transform), and Reverse ETL data integration platform that easily unites multiple data sources. The Xplenty platform offers a simple, intuitive visual interface for building data pipelines between a large number of sources and destinations. Contact us for a free 14 day trial on the platform.

  • Apache Spark Landing page
    Landing page //
    2021-12-31

Xplenty

Website
xplenty.com
$ Details
Free Trial
Platforms
Cloud Salesforce REST API
Release Date
2012 January
Startup details
Country
Israel
City
Tel Aviv
Employees
10 - 19
Edit details

Apache Spark

Website
spark.apache.org
$ Details
Platforms
-
Release Date
-
Edit details

Xplenty features and specs

  • Ease of Use
    Xplenty offers a user-friendly interface with a drag-and-drop feature that simplifies the process of data integration and transformation, making it accessible even for users with limited technical expertise.
  • Scalability
    Xplenty can handle large volumes of data and can scale according to your needs, ensuring performance remains consistent even as your data grows.
  • Integrations
    The platform supports a wide range of data sources and destinations, making it versatile for various data ecosystems. It seamlessly integrates with popular databases, cloud services, and data warehouses.
  • Support and Documentation
    Xplenty provides extensive documentation and customer support, including tutorials, webinars, and a responsive support team to assist you with any issues.
  • Customization
    Offers advanced features for custom transformations and workflows through scripting, allowing for greater flexibility in handling complex data integration tasks.

Possible disadvantages of Xplenty

  • Cost
    Xplenty can be expensive, particularly for small to mid-sized businesses. The pricing model is based on the number of connectors and data volume, which can add up quickly.
  • Learning Curve
    Although the interface is user-friendly, there may be a learning curve for new users to fully leverage the platform’s more advanced features and capabilities.
  • Performance
    Some users have reported performance issues, especially with large datasets, which can result in slower processing times compared to other ETL tools.
  • Limited Real-time Processing
    Xplenty is optimized for batch processing rather than real-time data integration, which may not be suitable for use cases requiring real-time data processing.
  • Dependence on Internet Connection
    As a cloud-based platform, Xplenty requires a stable internet connection. Any disruptions in connectivity can affect the ability to access and use the service.

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.

Analysis of Xplenty

Overall verdict

  • Xplenty is a good option for businesses looking for a reliable and user-friendly data integration platform, especially if they require extensive support for cloud-based data sources and flexibility in integration processes. However, as with any tool, the effectiveness of Xplenty depends on the specific needs and resources of the organization.

Why this product is good

  • Xplenty is a cloud-based data integration platform designed to simplify the complex processes of data preparation, transformation, and integration. It offers a user-friendly interface and a wide range of pre-built connectors, making it accessible for users without deep technical expertise. The platform supports ETL (Extract, Transform, Load) processes and can handle large volumes of data efficiently.

Recommended for

    Small to medium-sized businesses, teams without extensive technical expertise in data engineering, organizations needing to integrate data from multiple sources quickly, and those looking for a scalable cloud-based ETL solution.

Analysis of Apache Spark

Overall verdict

  • Yes, Apache Spark is generally considered good, especially for organizations and individuals that require efficient and fast data processing capabilities. It is well-supported, frequently updated, and widely adopted in the industry, making it a reliable choice for big data solutions.

Why this product is good

  • Apache Spark is highly valued because it provides a fast and general-purpose cluster-computing framework for big data processing. It offers extensive libraries for SQL, streaming, machine learning, and graph processing, making it versatile for various data processing needs. Its in-memory computing capability boosts the processing speed significantly compared to traditional disk-based processing. Additionally, Spark integrates well with Hadoop and other big data tools, providing a seamless ecosystem for large-scale data analysis.

Recommended for

  • Data scientists and engineers working with large datasets.
  • Organizations leveraging machine learning and analytics for decision-making.
  • Businesses needing real-time data processing capabilities.
  • Developers looking to integrate with Hadoop ecosystems.
  • Teams requiring robust support for multiple data sources and formats.

Xplenty videos

+ Add

Xplenty - The Leading Data Integration Platform

More videos:

  • Demo - Create a Customer 360 View with Xplenty & Salesforce
  • Review - Xplenty Customer Story - CloudFactory

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 Xplenty and Apache Spark)

Xplenty

Apache Spark

Data Integration
100 100%
Data Integration
0% 0
Databases
0 0%
Databases
100% 100
ETL
100 100%
ETL
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 Xplenty and Apache Spark

Xplenty Reviews

Top 7 ETL Tools for 2021
Scalability, security, and excellent customer support are a few more advantages of Xplenty. For example, Xplenty has a new feature called Field Level Encryption, which allows users to encrypt and decrypt data fields using their own encryption key. Xplenty also makes sure to maintain regulatory compliance to laws like HIPPA, GDPR, and CCPA.
Source: www.xplenty.com
The 11 Best Low-Code Development Platforms
Xplenty is a low-code and no-code ETL (extract, transfer and load) data integration platform. It is made for both small, non-technical businesses and for deeply technical developers and engineers. It allows users to easily build data pipelines to and from over 100 data sources and destinations. Xplenty provides versatility, customization, and pre-built integrations to...
Source: www.xplenty.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
Customer Story Keith connected multiple data sources with Amazon Redshift to transform, organize and analyze their customer data. Amazon Redshift Keith Slater Senior Developer at Creative Anvil Before we started with Xplenty, we were trying to move data from many different data sources into Redshift. Xplenty has helped us do that quickly and easily. The best feature of the...
Source: www.xplenty.com

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 more popular. It has been mentiond 70 times since March 2021. 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.

Xplenty mentions (0)

We have not tracked any mentions of Xplenty yet. Tracking of Xplenty recommendations started around Mar 2021.

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 / 3 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 / 3 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 / 4 months ago
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What are some alternatives?

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

Talend Data Integration - Talend offers open source middleware solutions that address big data integration, data management and application integration needs for businesses of all sizes.

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

Matillion - Matillion is a cloud-based data integration software.

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

Talend Data Services Platform - Talend Data Services Platform is a single solution for data and application integration to deliver projects faster at a lower cost.

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

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