Automate the flow from real-time event streams to your analytics warehouse. Stop writing custom connectors, managing Kafka Connect clusters, or maintaining brittle ETL scripts that break when schemas evolve.
Redbird gives your team ready-to-run workflows — just connect your accounts and go.
Continuously load user behavior events, page views, and interaction data from Kafka into Redshift for historical analysis. Redbird handles schema mapping, batching, and deduplication so your analytics tables stay current without custom pipeline code.
Persist application logs, audit trails, and transaction events from high-throughput Kafka topics into Redshift for long-term storage. Automatically organize by date partitions and compress for cost-efficient archival that meets regulatory requirements.
Aggregate temperature, location, and telemetry readings from device streams into structured Redshift tables. Redbird intelligently batches micro-events and handles late-arriving data so your dashboards show accurate historical trends.
Look up customer segments, product catalogs, or pricing tiers from Redshift to augment real-time Kafka events. Redbird caches frequently accessed dimension data and injects it into event payloads for smarter routing and personalization.
Run scheduled SQL queries in Redshift to identify unusual patterns—like revenue drops or spike in errors—and publish alerts to Kafka topics. Downstream consumers react in real-time while maintaining a single source of analytical truth.
Export pre-computed user profiles, spend totals, or churn scores from Redshift into Kafka topics that power recommendation engines and ML models. Keep low-latency applications fed with fresh analytical features without building dual infrastructure.
No engineers, no pipelines to maintain. Redbird handles the connectivity — you focus on the outcome.
Authorize Kafka and Redshift with OAuth or API credentials. Redbird never stores your data — it just passes through.
Tell Redbird what to do in plain language — no SQL, no code, no configuration files required.
Redbird shows you exactly what it will do before running anything. Approve the workflow, set a schedule, and switch it on.
Workflows run on your schedule or on triggers. Every run is logged. Adjust with natural language at any time.
Redbird understands both Kafka's streaming semantics and Redshift's columnar storage model, automatically bridging the gap between real-time events and batch analytics.
Redbird's AI reads your Kafka message formats—Avro, JSON, Protobuf—and maps them to Redshift table structures without manual configuration. It detects schema evolution, handles nested arrays and objects, and chooses optimal distribution keys for query performance. When your event payloads change, Redbird adapts table schemas and data types automatically, eliminating the brittle mapping files that break production pipelines.
faster than building Kafka Connect pipelines with custom transformers
Redbird can pull from Kafka and Redshift simultaneously, merge the results, and format a polished report — sent on a schedule or on demand.
Set conditions in natural language. Get notified in Slack or email the moment a threshold is crossed in either Kafka or Redshift.
SOC 2 Type II certified. Data flows encrypted in transit and at rest. Fine-grained permission controls with full audit logs.
Push data from Kafka into Redshift, or from Redshift back into Kafka. Resolve conflicts with configurable merge rules.
Every workflow run is logged — what ran, what changed, and why. Replay or revert any individual step at any time.
Start automations from new Kafka messages or Redshift query results—Redbird handles offset management, batching, and connection pooling.
Trigger when Kafka receives events matching partition, key pattern, or header filters.
Detect when message processing falls behind by monitoring consumer group offsets.
React when Kafka reassigns partitions across consumer group members.
Write events to Kafka with custom keys, headers, and partition routing.
Provision new Kafka topics with retention policies, partition counts, and replication factors.
Manually reset offsets to replay historical events or skip corrupted messages.
Run SQL queries on intervals and trigger when result sets meet defined conditions.
Detect when daily load volumes spike or drop beyond expected variance.
React when Redshift Spectrum detects new files in connected S3 prefixes.
Execute optimized COPY commands with automatic compression and distribution.
Run analytical queries, aggregations, or maintenance operations on demand.
Export transformed datasets to Parquet or CSV for downstream consumption.
Join data teams who've replaced fragile Kafka Connect configurations with AI-powered automation. Sync Kafka event streams to Redshift in minutes, not weeks.