FOR Data Storage providers
Unstructured data pipelines for ETLs and database providers
Datastreamer is used by engineering teams to ingest and enrich unstructured data sources into databases.
Built for converting unstructured data into structured streams
How Datastreamer supports database and indexing platforms
- Unstructured data transformation
- Metadata enrichment
- Real-time and flexible
Deliver processed data to your database, ETL, or indexing platforms via APIs, webhooks, or direct database connections.
- Maintenance-free pipelines
Reduce operational overhead with managed pipelines that require minimal maintenance.
Built for teams that need more than scrapers & scripts
Ready-to-use components allow complex multi-source data pipelines to be assembled in minutes. Deployment happens instantly and is fully managed, allowing low-latency, high volume processing.
Common use cases
Social -> Datalakes
Continuous data collection and structuring of varied social data sources, melded to existing schemas.
Files -> JSON
Structuring of scanned PDFs and other files into enriched JSON, accelerating due diligence.
Streams -> Storage
Handling streaming real-time data sources by batching in pipelines and sorting to lower input rates.
Proserv -> Automation
Reducing custom integration work using pre-built library of connectors.
AI application -> Searchability
Applying vectors, LLM prompts to the data pre-ingestion to increase search performance and volume.
Storage free -> Processing
With storage-free processing by default, meet adherence to compliance and sub-processing requirements.
Why product and engineering teams use Datastreamer
6 weeks
reduced build time per new source or enrichment connection
$285k
average annual benefit per Datastreamer customer
6,373
average annual “people hours” of pipeline work saved
80M
average pieces of web content consumed monthly per pipeline
7+
average datasources or enrichments used per pipeline
38,000+
ready-to-deploy capabilities available in the Datastreamer registry
Data pipelines for database platforms
Using Datastreamer, your engineering teams can rapidly deliver the data pipelines for complex use cases, reducing speed-to-launch of new features by 85%
FAQ’s
Anything from social platforms, review sites, forums, other SI platforms, SERP data, trend tools, or any other inputs. Datastreamer handles structured and unstructured inputs.
With over 38,000 ready-to-use capabilities, it's very easy to meet every ask.
Yes. Datastreamer has a full registry of ready-to-use NLP models. You can also use LLMs in pipelines or plug in your own logic and systems through APIs.
We abstract each source into a common schema, so your downstream systems don’t have to handle structural drift, missing fields, or format mismatches.
While compliance requirements can be quite broad and use-case specific, Datastreamer has a wealth of data processing, PII detection, redaction, hashing, and other capabilities that you can use in your pipelines.
Faster time to market, no ongoing maintenance, and a much lower risk surface for managing content ingestion and normalization.
It’s fully managed. There is no infrastructure to deploy or maintain. We handle scaling, updates, and availability so your team can stay focused on building product.
Yes, we partner with many providers for ETL, database, and similar offerings.
ElasticSearch, Google Cloud, Fivetran, and more are listed partners of Datastreamer.
