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Spektr API CDC with Data Vault

· 2 min read
Blair Murray
Blair Murray
Lead Software Engineer

If you need to push client data into Spektr, sending full data exports every time your pipeline runs quickly creates problems. Large updates slow down your pipelines, waste bandwidth, and can easily hit Spektr's API rate limits.

Here is a look at how we use Apache Hop to read client source data, filter out unchanged records, and send only the new updates to the Spektr API.

The Problem with Full Data Exports​

When connecting internal client systems (like an ERP, CRM, or SQL database) to Spektr's /v1/data-import endpoint, the simplest option is often to dump the whole table into the API payload.

In practice, that causes a few headaches:

  • API Throttling: Blasting thousands of duplicate records can trigger rate limits on your Spektr account.
  • Slow Runs: Uploading unchanged data over HTTP wastes time and network bandwidth.
  • Unnecessary Processing: Spektr has to work harder to parse data it already holds.

How We Build the Pipeline​

To keep syncs fast, we use Apache Hop to handle the extraction and delivery, with a change-check step in the middle:

Data Vault Spektr API CDC

  1. Extract with Apache Hop: Hop connects to the client database or system and prepares the data payload.
  2. Filter out unchanged records: Before making any API calls, we check the data against our storage layer. If a record hasn't changed since the last run, we drop it. (For a deeper look at how we store and track these changes, see our Data Vault Engine post.)
  3. Post to Spektr: Hop takes only the new or updated rows and posts them to Spektr's data import endpoint.

Example API Payload​

Hop formats the filtered records into Spektr’s expected JSON schema before posting:

{
  "data": [
    {
      "id": "CLIENT-98421",
      "attributes": {
        "status": "verified",
        "updatedAt": "2026-08-04T12:00:00Z"
      }
    }
  ]
}

Results​

  • Faster Pipelines: Runs finish in seconds because you are only sending tiny updates.
  • No Rate Limit Issues: Payload sizes stay small, keeping API calls well within Spektr's quotas.
  • Clean Audit Trail: You keep a clear record of when changes occurred without spamming the API.

If you are setting up integrations with Spektr or need help building Apache Hop pipelines, get in touch with our team at Millersoft.