Assemble a Data Stream once, then feed enriched social and web data into the products you build. One platform, one setup across all your sources, and no pipeline to maintain.
Used by market-leading platforms in brand intelligence, threat monitoring, and competitive analytics.
Assemble a Data Stream one time and Datastreamer handles the sourcing, enrichment, and delivery behind it. Add new sources or shift volume as your roadmap changes, without rebuilding anything or maintaining pipelines yourself.
Set your Data Stream up once and it runs the sourcing, enrichment, and delivery for all of your sources. Your team ships product features instead of building and maintaining data connectors one by one.
Add a new source, swap an enrichment, or change a destination without re-architecting what runs downstream. Your Data Stream adapts as your product and your data needs change.
Volume flexes with demand and pricing follows usage, so there is no over-provisioning and no surprise bills. Shift volume across your sources as needs change, all on the same setup.
Datastreamer's component-based builder lets you wire together sources, AI enrichments, transformations, and destinations without writing integration code. What used to take months of engineering takes an afternoon.
A full-stack data pipeline platform with no per-pipeline or per-user fees. Every feature below is included in your base platform subscription.
Assemble source, enrichment, transform, and destination components in any order or shape. No two products need the same architecture.
Sentiment (short and long form), emotion classification, named entity recognition, brand recognition, ESG scoring, intent classification, influence scoring, location inference, language detection, IPTC categorization, and more.
Store enriched documents in Datastreamer's managed search layer. Query via Lucene-powered Search, Count, and Aggregations APIs with pagination. No external Elasticsearch cluster required.
Programmatically create, update, list, and cancel data collection jobs via REST. Set document limits per job, schedule collection windows, and monitor DVU usage at the job level.
Write inline processing logic with pre-built recipes for spam detection, bot detection, PII extraction, keyword extraction, and readability scoring. Full JSON Schema Transformer with dot-notation mapping.
Per-pipeline and per-component metrics, document inspector, failed items viewer, component log viewer, volume health alerting, quick diagnostics, and pipeline versioning with full deployment history.
Usage-based DVU pricing, tag-level billing insights, per-job cost visibility, budget alerts at configurable thresholds, cost projections updated every 6 hours, committed usage discounts, and a cost estimation calculator.
Integrate your own sources, API keys, ML models, custom schemas, PII logic, and destinations. Datastreamer wraps around your existing stack rather than replacing it.
Datastreamer was built from the DNA of real-time threat intelligence. Today it powers the most demanding intelligence products across multiple verticals.
Monitor brand mentions, sentiment, and consumer conversations across social media, news, forums, and reviews in real time. Deliver structured, enriched brand signals directly to your analytics stack at a scale no manual process can reach.
Ingest and enrich data from dark web sources, forums, and surface web to surface emerging threats, track threat actors, and monitor for early warning signals. Datastreamer was designed from threat intelligence expertise from day one, not retrofitted for it.
Track competitor pricing, job postings, product launches, reviews, and market positioning across e-commerce, job boards, news, and social. Structured competitive signals delivered to your analytics stack without building a single connector.
Build or power a social listening product on top of Datastreamer's unified data layer. Aggregate, enrich, and normalize data from every major platform into a single consistent schema. Your product team builds features on top of clean, structured data from day one.
All data, regardless of provider, is normalized into Datastreamer's 493-field unified output schema. Your downstream tooling stays the same no matter how many sources you add.
Route your pipeline output to Datastreamer's built-in searchable storage layer and query it directly via REST using Lucene syntax. Full-text search, field filtering, aggregations, and pagination, all without managing a search cluster. Use it to power in-product search, dashboards, or analyst tooling.
Designed for teams operating in regulated industries, compliance-sensitive environments, and geographies with strict data residency requirements.
Deploy pipelines in specific geographic regions to meet data residency requirements. Keep data processing within your required jurisdiction: EU, US, or other supported regions.
Specific source connectors have compliance modes that restrict data collection to legally cleared subsets. Socialgist boards and news connectors include compliance-specific configuration for sensitive use cases.
Private AI PII Redaction runs inline in your pipeline, automatically detecting and redacting personally identifiable information before it reaches your destination. No data ever needs to leave your pipeline unredacted.
Full visibility into how your data moves through every component. Datastreamer provides pipeline-level transparency tooling, document inspection, and component-level logging, so you always know what happened to every document.
Prevent budget overruns with layered controls: document limits per job, volume health alerting per pipeline, budget alerts at the account level, tag-level billing insights, and DVU-level job cost API.
Datastreamer's operations team runs 24/7 monitoring on pipeline health and connector availability. Automatic job failure handling, recovery logic, and failover behavior ensure your pipelines keep flowing.
Every data connector, AI enrichment, and destination integration you build in-house is code your team has to write, test, maintain, and update every time an API changes. Here's what that actually costs.
Building, testing, and deploying even 10 to 15 production-grade social data connectors (with auth, rate limiting, schema normalization, and error handling) takes a senior data engineering team well over a year. Datastreamer ships hundreds on day one.
Based on typical data engineering velocity for production connector development.
APIs break. Rate limits change. Schemas shift. Social platforms deprecate endpoints without notice. Every connector you own is a maintenance liability. Datastreamer's operations team handles all of it so your engineers can focus on differentiated work.
Industry benchmark from data engineering teams at intelligence product companies.
With Datastreamer, adding a new source is a configuration change, not an engineering project. That means product roadmap flexibility your competitors who built in-house simply don't have. You can respond to market opportunities in days, not quarters.
Average time for Datastreamer customers to activate a new source connector post-onboarding.
Building your own social data pipeline platform means owning every layer: connector maintenance, infrastructure ops, AI model management, and everything in between.
| Capability | Build In-House | Datastreamer |
|---|---|---|
| Social & web data connectors | 6 to 18 months of engineering per connector set. Ongoing auth & API maintenance. Breaks every time a platform changes. | Hundreds of pre-built, production-grade connectors. Maintained by Datastreamer's ops team. New sources added regularly. |
| AI & NLP enrichments | Model research, training, and deployment per enrichment. Requires ML infrastructure and specialized expertise. | 30+ ready-to-use AI operations: sentiment, NER, language, brand recognition, ESG, emotion, intent & more. |
| Unified output schema | Custom normalization code per source. Schema drift and inconsistency as sources change. Hard to query across sources. | 493-field unified schema across all connectors. Consistent structure regardless of source. Always queryable together. |
| Audio & video analysis | Separate transcription pipeline, custom integration, storage requirements, manual orchestration. | Social Voice handles transcription, translation, tonality, toxicity, entities, and IAB categories in-pipeline with no extra infrastructure. |
| Auto-scaling infrastructure | DevOps team required to provision, monitor, and scale. Over-provisioning is expensive; under-provisioning breaks SLAs. | Fully managed auto-scaling within seconds. Usage-based billing means you pay for exactly what you consume. |
| Pipeline observability & debugging | Custom logging, metrics dashboards, alerting, and on-call rotation for pipeline failures. | Built-in pipeline analytics, failed items viewer, document inspector, component logs, and volume health alerting. |
| Compliance & PII controls | Legal review per data source, custom redaction logic, compliance-mode ETL handling. Ongoing legal and engineering cost with no clear end state. | Regional deployment, compliance-sensitive connector modes, and Private AI PII redaction, all configurable per pipeline. |
| Cost management | Unpredictable infrastructure costs. Separate tooling for budget tracking. No per-job cost visibility. | DVU-based usage pricing, per-job cost visibility, budget alerts, tag-level billing, committed discount tiers. |
Engineering teams and product leaders who evaluated the build-vs-buy decision and chose Datastreamer to accelerate their data pipeline work.
We estimated 12 to 18 months of engineering work to build what Datastreamer gave us on day one. The ROI case wasn't even close. Our team is shipping product features, not connector maintenance.
The unified schema alone is worth it. Before Datastreamer we had 6 different schemas across 6 sources and every new source was a 3-month project. Now it's a configuration change.
We went from "we need this data source" to data flowing into BigQuery in under a week. The pipeline builder is genuinely fast. The Social Voice features gave us capabilities we couldn't have built ourselves at any price.
No per-seat fees. No per-pipeline charges. No per-user limits. You pay for data volume, and earn lower rates by pre-committing to expected monthly usage.
Talk to our team. We'll help you design the right pipeline architecture for your use case, estimate your DVU costs, and get you running fast.
Used by market-leading intelligence platforms. Supported by a dedicated success team.