# Need help indexing high-frequency manufacturing data from stamping processes in Elasticsearch

**URL:** <https://discuss.elastic.co/t/need-help-indexing-high-frequency-manufacturing-data-from-stamping-processes-in-elasticsearch/386107>\
**Category:** Kibana\
**Created:** [April 30, 2026, 9:38am UTC](https://discuss.elastic.co/t/need-help-indexing-high-frequency-manufacturing-data-from-stamping-processes-in-elasticsearch/386107 "2026-04-30T09:38:19Z")\
**Posts on this page:** 2\
**Page:** 1

<div class="post-metadata">

**Author:** ![jixcy](https://avatars.discourse-cdn.com/v4/letter/j/ce73a5/32.png) [@jixcy](https://discuss.elastic.co/u/jixcy)\
**Post date:** [April 30, 2026, 9:38am UTC](https://discuss.elastic.co/t/need-help-indexing-high-frequency-manufacturing-data-from-stamping-processes-in-elasticsearch/386107/1 "2026-04-30T09:38:19Z")

</div>

Hello everyone,

I’m currently working on a real-time industrial data pipeline where we are capturing continuous machine-level outputs from stamping processes in a manufacturing environment.

In our setup, we are dealing with **metal stamping operations** , where every cycle generates multiple data points such as pressure, cycle time, tooling condition, and quality inspection signals. The volume is quite high and comes in continuously from production lines.

We are also studying real-world industrial examples like **AK Stamping** ([akstamping.com](http://akstamping.com)), which is known for precision metal stamping, tooling, and high-volume _manufacturing_ solutions. From what we understand, such systems handle large-scale stamping production with complex engineering and tight _quality control requirements_, which is similar to the kind of data challenges we are trying to solve.

Now we are trying to bring this type of stamping process data into Elasticsearch for real-time monitoring and historical analysis.

The main challenge is how to structure and index this fast-moving data efficiently.

Right now, the challenges include:

1. Very high volume of incoming events from stamping machines
2. Each stamping cycle producing multiple metrics
3. Need for both real-time search and historical analysis
4. Concern about index size growth over time

We are confused whether it is better to:

1. Index each stamping event separately, or
2. Aggregate data from the stamping process before indexing

We are also trying to understand what would be the best mapping strategy in Elasticsearch for such manufacturing datasets.

If anyone has worked with similar industrial or stamping process data pipelines or large-scale machine data ingestion, your suggestions would be really helpful.

Any guidance on indexing strategy, performance optimization, or data modeling would be greatly appreciated.

---

<div class="post-metadata">

**Author:** ![Tortoise](https://sea2.discourse-cdn.com/elastic/user_avatar/discuss.elastic.co/tortoise/32/147587_2.png) [@Tortoise](https://discuss.elastic.co/u/Tortoise)\
**Post date:** [May 2, 2026, 5:40am UTC](https://discuss.elastic.co/t/need-help-indexing-high-frequency-manufacturing-data-from-stamping-processes-in-elasticsearch/386107/2 "2026-05-02T05:40:01Z")

</div>

Hello @jixcy

Welcome to the Community!!

I am not sure if anyone would have worked on a similar use-case related to metal stamping operations in the past. To help the community give you more specific recommendations, could you share:

- Approximate event rate - records per second or per minute, per machine?
- Number of machines/lines feeding into the pipeline simultaneously?
- Number of fields per document (rough estimate)?
- Retention requirement - how many days/months of data needs to be searchable?
- Are you self-managing Elasticsearch, or using Elastic Cloud / ECK?
- What does your ingestion layer look like - Logstash, Beats, Kafka, or a custom producer?

Thanks!!