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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Airbyte vs Xplenty

Airbyte vs Xplenty

OverviewComparisonAlternatives

Overview

Xplenty
Xplenty
Stacks12
Followers26
Votes2
Airbyte
Airbyte
Stacks105
Followers112
Votes5
GitHub Stars20.0K
Forks4.9K

Airbyte vs Xplenty: What are the differences?

Introduction:

In this analysis, we will compare Airbyte and Xplenty, two data integration platforms, and highlight their key differences in terms of functionality and features.

  1. Data Integration Approach: Airbyte enables real-time data integration and synchronization by providing a robust open-source platform that allows users to connect to a wide range of data sources and destinations easily. It follows an extract, load, and transform (ELT) approach, where data is extracted, loaded into a destination, and then transformed as needed. On the other hand, Xplenty primarily focuses on Extract, Transform, Load (ETL) process, where data is transformed during extraction and loaded into a destination.

  2. Deployment Options: Airbyte can be installed on-premises or in the cloud, giving organizations the flexibility to choose the deployment method that aligns with their security and infrastructure requirements. Xplenty, on the other hand, is a cloud-native platform, meaning it is fully hosted and managed in the cloud. This makes it easier to get started with Xplenty as no infrastructure setup is required.

  3. Connectivity and Integration: Airbyte boasts a wide range of connectors that enable seamless integration with various data sources like databases, APIs, cloud storage, and more. It also provides a developer-friendly framework that allows users to create custom connectors. Xplenty also offers a wide range of pre-built connectors, but it focuses mainly on integrations with popular cloud services such as Salesforce, Google Analytics, and Shopify.

  4. Transformation Capabilities: While both Airbyte and Xplenty offer data transformation capabilities, Airbyte provides a highly scalable and extensible data transformation framework. It allows users to apply complex transformations on their data using SQL, Python, or custom code. Xplenty, on the other hand, offers a visual drag-and-drop interface for designing data transformation workflows, making it easier for users without coding skills to perform transformations.

  5. Pricing Model: Airbyte follows an open-source model, providing its core functionality for free. Users can then choose to self-host or use Airbyte's managed service, which comes with additional features and support at a cost. Xplenty, on the other hand, offers a subscription-based pricing model, where the pricing is based on the volume of data processed and the number of integrations required.

  6. User Interface and User Experience: Airbyte offers a simple and intuitive user interface that focuses on ease of use, making it accessible for both technical and non-technical users. Xplenty provides a more comprehensive and feature-rich interface, allowing users to design complex data integration workflows visually.

In summary, Airbyte and Xplenty differ in their data integration approaches (ELT vs ETL), deployment options (on-premises vs cloud-native), connectivity and integration options, transformation capabilities, pricing models, and user interface experiences. The choice between these platforms depends on the specific requirements and preferences of an organization.

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Detailed Comparison

Xplenty
Xplenty
Airbyte
Airbyte

Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage. Once done processing, Xplenty allows you to connect with Amazon Redshift, SAP HANA and Google BigQuery. You can also store processed data back in your favorite relational database, cloud storage or key-value store.

It is an open-source data integration platform that syncs data from applications, APIs & databases to data warehouses lakes & DBs.

Xplenty provides you with an visual, intuitive interface to design your ETL data flows; Xplenty lets you integrate data from a variety of data stores, such as Amazon RDS, MySQL, PostgreSQL, Microsoft SQL Server and MongoDB.; Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage; Once done processing, Xplenty allows you to connect with Amazon Redshift, SAP HANA and Google BigQuery. You can also store processed data back in your favorite relational database, cloud storage or key-value store;Integrate semi-structured data with structured data. Our package designer makes it a snap for every data and BI user to write complex data flows for your flat files and JSON files on top of Hadoop without writing a single line of code.
Scheduled updates; Manual full refresh; Real-time monitoring; Debugging autonomy; Optional normalized schemas; Full control over the data; Benefit from the long tail of connectors, and adapt them to your needs; Build connectors in the language of your choice, as they run in Docker containers
Statistics
GitHub Stars
-
GitHub Stars
20.0K
GitHub Forks
-
GitHub Forks
4.9K
Stacks
12
Stacks
105
Followers
26
Followers
112
Votes
2
Votes
5
Pros & Cons
Pros
  • 2
    Simple, easy to integrate/process data without coding
Pros
  • 1
    Multiple capabilities
  • 1
    Free
  • 1
    Connect Multiple Sources
  • 1
    Change Data Capture
  • 1
    Easy to use
Integrations
Amazon S3
Amazon S3
Compose
Compose
Rackspace Cloud Files
Rackspace Cloud Files
MongoLab
MongoLab
MongoSoup
MongoSoup
Heroku
Heroku
Amazon Redshift
Amazon Redshift
Amazon RDS
Amazon RDS
Google Cloud SQL
Google Cloud SQL
ClearDB
ClearDB
Greenhouse
Greenhouse
Google Cloud Platform
Google Cloud Platform
Mixpanel
Mixpanel
Google Analytics
Google Analytics
PostgreSQL
PostgreSQL
MySQL
MySQL
Shopify
Shopify
Amazon EC2
Amazon EC2
Zendesk
Zendesk
Stripe
Stripe

What are some alternatives to Xplenty, Airbyte?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Stitch

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Dremio

Dremio

Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

Cloudera Enterprise

Cloudera Enterprise

Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.

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