Datastreamer is an end-to-end data streaming platform that assembles pipelines combining social media and web sources with AI enrichments, delivering the enriched output to analytics warehouses. Over 30 built-in AI/NLP enrichments run automatically as data flows through the pipeline: sentiment analysis, named entity recognition, emotion classification, brand recognition, ESG scoring, and intent classification, plus Social Voice transcription, translation, and tonality analysis for video content.
Applying enrichment automatically inside the pipeline – rather than dumping raw data into a warehouse and running separate analysis jobs afterward – is the practical advantage: the data arrives already tagged with sentiment, entities, and classification, ready for analysis rather than requiring a second processing stage. Private AI PII Redaction doing inline detection and redaction as data streams through also handles compliance at the pipeline level instead of as an afterthought bolted onto storage.
Datastreamer connects to 284 pre-built source connectors (Twitter/X, Reddit, TikTok, LinkedIn, news, and more) and delivers to destinations including BigQuery, Snowflake, Databricks, and S3, normalizing everything into a 493-field unified output schema. It also supports bringing your own ML models or OpenAI completions for custom enrichment. Pricing runs on usage-based Data Volume Units with committed-usage discounts, no per-seat or per-pipeline fees.
