Connector Principals & Functionalities

#Feature coverage

The V1 of the Akeneo BigCommerce Connector covers the following features and attributes:

#Features

Feature V1 Scope
Basic Product Attributes Yes
Image Attribute Yes
File Attribute No
SIMPLE PRODUCTS Management Yes
MANUAL LOGS Management Yes
SINGLE STORE Management Yes
MULTI-STORE Management No
Categories Management Yes
Enable/Disable Product State No
1 variation axes for Product Model Management Coming soon
2 variation axes for Product Model Management Coming soon
Product Association Type Management No
ASSET MANAGER No
API Import filters Coming soon
Attributes deletion Coming soon
Reference entities No
FULL/DIFF synchronization Yes
Event API management No

Please be aware that the attributes and features scope will constantly be evolving. We aim to update you with new features regularly to provide you with a seamless user experience.

#Attributes coverage

SIMPLE PRODUCT & PRODUCT MODELS

  • BASIC INFORMATION
    • Product Name
    • Default Price
    • Product Type
    • Weight
  • DESCRIPTION
    • Description
  • IMAGE & VIDEO
    • Images
  • PRODUCT IDENTIFIER
    • SKU
    • Product UPC/EAN
  • DIMENSIONS & WEIGHT
    • Height
    • Width
    • Depth

COMING SOON

PRODUCT VARIANTS

  • Variant images
  • SKU
  • Default Price
  • Weight
  • Width
  • Height
  • Depth
  • UPC/EAN

#Our recommendations

This page aims at sharing some best practices regarding our Akeneo Connector for BigCommerce implementation and some basic guidelines to keep in mind.

Akeneo Connector is a bridge between Akeneo PIM (in its core version) and BigCommerce(SaaS version). Why is it important to notice?

Because our Akeneo Connector is an agnostic connector independent of customers' specificities, our goal is to provide a solid technical basis to plug Akeneo PIM into a BigCommerce instance. If some particularities are required, please consider the development of the complementary Connector that will act as the additional data source. Please note that we aim to nourish the Connector and provide you frequently with new features. However, according to our SaaS approach, none of those features will be specific to your project.

Another important notion here is to consider that Akeneo Connector uses API to API integration strategy. It means that we will not integrate any feature not covered by the API (both from PIM and BigCommerce side).

#Talking about performance

We are often asked about the metrics and performance of Akeneo Connectors. Unfortunately, this is not that easy. In general, please note that two main criteria impact the performance of the Connector:

  • The catalog volume: even if that's the first one that comes to mind, it is not the only one to consider.
  • The catalog complexity: indeed, importing 500 000 products can be an easy task if the Product is described by "just" a name, a description, and an image. However, if the Product is very complex (high number of attributes, plenty of variations, etc.), importing 2 000 products can be a very painful and lengthy process.

#So what can I do to preserve my Connector's performances?

Always prefer direct data mapping between Akeneo PIM and BigCommerce. This way, you don't need to rely on BigCommerce to convert the PIM data into the required format. By doing so, you will help your system to be more efficient.

#Full import or partial import?

There are two ways to import product data:

  • Full import: this is the most extended import process because everything, including images, will be synchronized between Akeneo PIM and BigCommerce. However, this full import happens once for the first synchronization used to build your catalog on the BigCommerce side. Then, you will only run a differential import to update your BigCommerce catalog most of the time.
  • Differential import: Depending on the last successful import date, the system can sync only new products when necessary. However, to reconstruct some data on the Bigcommerce side, some tasks have to be run as a full import anyway (e.g., categories are fully imported even in a differential import or if you applied any changes to your mapping, make sure to use full import instead of differential one). However, this differential import process will allow you to decrease the needed import time. This type of import is used daily to resync data that has changed in the days. This one is often a manual operation.

You will have to define your data update policy to adjust the full import and differential import threshold.

#Can I customize my BigCommerce Connector?

As Akeneo BigCommerce Connector is an in-house SaaS product hosted by Akeneo, you cannot personalize it nor add any additional code.

The V1 of the Connector is compatible with PIM's Growth Edition. We are working on including other features and attributes, but we cannot promise you any particular timeline for a moment.

So what can I do if I need some development that is not available?

  • You can build a complementary Connector that covers specific features you need and will act as the supplementary data getaway to retrieve the additional data from your PIM. Still, please be aware that this Connector needs to be hosted and maintained by your team. and it has to take into account the evolution of our SaaS Connector.
  • We will be providing you incrementally with additional features. However, please keep in mind that to maintain our SaaS strategy, all additional features will be generic and not project-related.
  • You can ask your Akeneo's contact for any update of the Akeneo BigCommerce Connector roadmap.
  • Also, if you have a specific feature request that you would like to share with us, please do not hesitate to contact your Akeneo's contact and share your (feedback](https://akeneo.atlassian.net/servicedesk/customer/portal/8/group/28))

#How to ensure the success of my integration?

This is pretty easy: you should test the Connector as early as possible in the integration process. By doing so, you will challenge the data model and ensure the efficiency of the mapping.

Please make sure that you are aware of our feature coverage before using our Connector. Always keep in mind that the earlier you start testing, the more confident you are in the connector compatibility and its adoption.

You should ask yourself:

  1. Are Akeneo PIM and/or the e-commerce platform customized in any way? If they are, that should raise a warning.
  2. Are all the project actors aware of the SaaS approach we are proposing? With both pros and cons of this solution.
  3. Does the system integrator (or the IT team in charge of the integration) know that in case if you do decide to use our connector and develop a complementary connector to fit your needs, this complimentary connect will need to be maintained by your team (or your integrator) Akeneo will provide you with the Support of our Connector only. We do not provide Support (level 1 to 3) neither maintenance of your complementary Connector.
  4. The bigger the catalog, the sooner the Connector has to be tested, especially in terms of performance and synchro data policy definition.
  5. The more complex the catalog, the sooner the connector mapping has to be considered in order to confirm the compatibility between both systems.
  6. Be brave, don't worry about trying out new things. Benefit from this opportunity to challenge your existing data model and eliminate all the complexity inherited from a legacy solution. Our advice here is that this is better to modify an existing data model slightly on the e-commerce side to efficiently map and smoothly import your data rather than recreating a legacy complexity in the PIM.
  7. An Akeneo Connector will do what it is created for: plug any standard PIM into BigCommerce store by maintaining the highest performance and scalability for all its users. If Akeneo Connectors tests are not up to your performance standards, then there are two options for you:
    • Not using the Akeneo Connector at all and creating yours from scratch (and we are totally fine with that)
    • Develop a complementary Connector to use with Akeneo BigCommerce connector to fill the gaps between our generic data import and your specific use cases.