October 2022 Summaries

The Amazon MWS is being retired in favor of the new Selling Partner API (SP-API), which will also provide interfaces for replicating Vendor Central data. CData Virtuality has developed a data connector for the SP-API, allowing users to replicate all desired Amazon data to their central data warehouse on a schedule. The Amazon MWS connector can provide information on various subjects such as orders, shipping, fulfillment by Amazon, inventory, products, payment, and more, including feeds API, finance API, Fulfillment API, Orders API, Products API, Recommendations API, and more. CData Virtuality is an official Software Partner and Service Partner of Amazon, enabling sellers and vendors to access the SP-API to load their data automatically into their data warehouse.

Batch replication is a type of data replication that appends all data from the source view/table to the target table, with the option to prevent duplicate records. It differs from other types of replication in that old rows in the target table can be kept even if they were deleted in the source table, and updates can be performed on current rows while inserted rows are added to the target table. This method is useful when you want to keep track of changes but don't need to track every single row, such as with customer information where only certain fields need updating. Batch replication can also be used with or without an identity field, with the latter providing a way to keep track of changes while avoiding duplicates. CData Virtuality offers batch replication as part of its data virtualization and replication capabilities, allowing for high-performance real-time data integration and master data management through advanced ETL.

CData Arc has introduced new connectors, sample flows, and API-driven features to enhance its B2B integration capabilities. The new connectors support industries such as healthcare, e-commerce, and manufacturing, providing a wide range of applications, data warehouses, and APIs for integrating into workflows. Industry-specific sample flows have been added to help users get started with customizing their B2B communications, covering logistics and supply chain, suppliers and manufacturers, retail, healthcare, and e-commerce use cases. The new Flow API enables the triggering of Arc workflows via custom API requests, pushing automation into the API-driven future. Additionally, enterprise enhancements include the introduction of the Arc Vault for storing and standardizing configuration values, and batch group processing improvements to reduce processing time for large volume transactions.

FHIR is a standard that determines how healthcare information is exchanged between different machines, providing SQL access to CData's connectivity solutions. Oracle SCM is a cloud-based supply chain management application with capabilities such as product innovation and integrated demand-supply planning, now supported by CData. Zoho Inventory is cloud-based inventory management software allowing businesses to integrate it with online sales channels and track inventory, also provided with SQL access through CData's connectivity solutions. These new data sources are now available in general availability, enabling easier access to enterprise data for all stakeholders.

CData Connect Cloud is a cloud-based technology solution that continuously evolves based on customer feedback, delivering key features and functionality to users, including derived views, single sign-on (SSO), user-defined credentials, and custom reports. The product's vision for the release focuses on addressing enterprise-level security needs and simplifying data access for line-of-business users. Mike Albritton, SVP of Cloud at CData, oversees cloud products and works with customer feedback to design and implement features that make Connect Cloud easier to use and bring value to users.

CData Connect Cloud has released several new features designed to improve the onboarding experience, provide customizable views into enterprise data, and give IT administrators more control over who is accessing data. The platform now includes Derived Views, Saved Queries, and Custom Reports, which allow users to create and share views into their data crafted from custom SQL queries built in the Data Explorer. These features provide a modernized onboarding experience, improved query federation capabilities, and enhanced security and data access control through User Defined Credentials and Single Sign-On (SSO) support. The platform also supports out-of-the-box connectors to over 125 data sources and continues to add new features to improve the user experience based on customer feedback.

CData is a company that enables self-service data connectivity with Snowflake, making it easier for developers and business users to access and utilize mass volumes of data stored in the platform. Organizations from various industries such as food processing, IT security, and veterinary software are leveraging CData's connectivity solutions to integrate their existing systems with Snowflake without disrupting operations or replacing existing infrastructure. With CData's JDBC Drivers and other specialized connectors, users can establish seamless connections to Snowflake, automate data integration pipelines, and unlock the full potential of their Snowflake data. By providing a range of tailored solutions, CData helps organizations overcome connectivity gaps and fill their data ecosystem needs.

The text discusses two types of data replication: Incremental Replication and Upsert Replication, which are often used together due to their similar mechanisms. Incremental Replication is a lightweight approach that focuses on transferring only newly added or updated data from the source table to the analytical storage. It's useful in scenarios where there are no deletes but only new entries in the source tables, or when all records need to be archived even after deletion from the source. On the other hand, Upsert Replication is used to update and insert certain information at the same time, making it suitable for situations like updating product prices based on specific conditions. Both replication methods require a reliable timestamp value to determine which rows are new or updated, and they have their own set of challenges and use cases.