Relying on managed connectors, API pulls, and SFTP file transfers often causes silent processing failures and data drift due to mishandled timestamps. Reprocessing data typically requires painful workarounds like requesting manual client re-exports or dealing with stringent API rate limits. OpenSharing resolves these operational hurdles by using open protocols to access remote tables in place without requiring Databricks. It simplifies long-term maintenance by supplying a change data feed and enabling direct queries across full historical datasets.
Key points
Direct queries on provider tables eliminate the operational overhead of managing API rate…: Direct queries on provider tables eliminate the operational overhead of managing API rate limits and manual file re-exports.
Recipients gain immediate access to the freshest customer updates without waiting for fil…: Recipients gain immediate access to the freshest customer updates without waiting for file drops or batch API syncs.
Backfill operations can be handled smoothly by querying full historical tables directly r…: Backfill operations can be handled smoothly by querying full historical tables directly rather than requesting re-ingestion.
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Simplify how you ingest customer data, without the pipeline upkeep, backfill pain, and duplicate storage of traditional feeds
If you ingest data from your customers over SFTP or APIs, you have probably asked yourself at least one of these questions.
The answer is yes, but not with the patterns most teams still rely on.
Data engineering has come a long way, but customer ingestion has barely changed. Most teams still use SFTP file dumps, API pulls, or managed connectors. Each of these patterns works, but each also carries hidden costs that quietly add up as you take on more customers.
Ideally, you should be able to use a table's own version history to incrementally pull just the new data, and simply read the table your customer already maintains, with no SFTP to stand up and no per-customer connector to babysit. You would also get the freshest data, because as soon as your customer writes to the table, it becomes immediately available to you and every other recipient.
OpenSharing does exactly that. It is an open protocol for reading a table…
Software agents can now construct dynamic rich cards across messaging ecosystems by outputting a JSON specification mapped to a typed component catalog. Individual adapters convert these structured definitions into native layouts like Slack Block Kit and Microsoft Teams Adaptive Cards, saving developers from maintaining custom tooling for each platform. This framework also introduces automated validation to prevent content degradation caused by downstream messaging size limits.
Modern operational environments require a coordinated mix of specialized components to handle large deployments effectively. Systems rely on agents and automated tooling to coordinate with infrastructure-as-code frameworks. This operational structure provides the foundation necessary for maintaining consistency across enterprise environments.
Databricks-to-Databricks OpenSharing allows organizations to share live Delta tables directly with external recipients instead of relying on recurring file or API feeds. Under this architecture, data remains securely in the provider's storage without being copied, while the recipient accesses it through a Unity Catalog-enabled workspace. By sharing the table along with its history, the provider enables the recipient to query the data live or use it as an incremental Structured Streaming source.