Traditional ETL and BI workflows often mean dashboards are minutes or hours stale, and critical signals get buried in the delay.
With Timeplus, streaming ingestion, transformation, aggregation, and alerting happen in one unified engine. That means click‑once dashboards and SQL triggers firing in milliseconds.
Trading
Trading platforms digest huge volumes of data, such as order books, trade feeds, price ticks, which all need sub‑second context.
Why Timeplus?
Streaming SQL handles multi‑stream JOINs, ASOF joins for historical context, VWAP, risk metrics, and P&L monitoring. Alerts on arbitrage patterns or price swings can be routed via Slack, PagerDuty, or streaming sinks.
E-Commerce
Need to monitor cart behavior, conversion flow, checkout issues, and also detect fraud or pricing anomalies in real time, not after customers bounce.
Why Timeplus?
SQL‑based aggregation and windowing over checkout streams. Real‑time dashboards showing drop‑off rates and conversion funnels. Alert rules detect sudden spikes in failure events or unusual user activity.
Gaming
While tracking player sessions, purchases, and match events in real-time, sudden spikes, bots, or load issues can throw everything off balance.
Why Timeplus?
Immediate joins and analytics over live game streams (such as in‑game purchases or match outcomes). Low‑latency dashboard updates and triggered alerts when anomalies (such as bot attacks, surges in usage) appear.
Trusted by industry leaders with mission-critical workloads:
Trusted by industry leaders with mission-critical workloads:
Discover End-to-End Capabilities
Unified Streaming + Historical SQL Processing
Timeplus combines a write‐ahead log (for ultra‑fast ingestion) with a historical store for efficient queries. Run analytics over the latest stream and past data in one engine, with no Kafka and warehouse stitching.
Ultra‑Low Latency + High Throughput
Timeplus achieves sub-second end‑to‑end latency and the ability to handle millions of events per second on commodity hardware. That’s critical when you need alerts or dashboards that mirror live activity.
Streaming SQL with JOINs, Windowing, UDFs
Use enriched SQL for complex tasks like hop/tumble/windowed aggregations, multi‑stream joins (including ASOF joins), filtering, downsampling, and enrichment. Plus, leverage Python or JavaScript UDFs when your logic goes beyond SQL.
Multi‑Stream and ASOF Joins for Context
Correlate event streams with historical baselines or contextual tables using ASOF joins, enabling real‑time trend detection, fraud scoring, anomaly detection, or conversion funnel tracking with context.
Append-Only + Mutable Streams
Configure streams per your use case. Append‑only streams are columnar optimized, ideal for high‑velocity aggregations. Meanwhile, mutable streams are row-based with UPSERTs, when records need continuous updating.
Native Data Ingestion + Routing
Ingest from Kafka, Redpanda, CSV, file, REST, SDKs, WebSocket, JDBC/ODBC, and route outputs via SQL to sinks like Kafka, ClickHouse, downstream systems, tables, and more. No extra connector services needed.
Auto-Scaling Materialized Views
Materialized views handle continuous transformations. Additionally, Timeplus supports auto-scaling compute nodes and tiered storage that offloads older data to Iceberg-compatible object storage.
Real‑Time Visualization + Alerts
Dashboards stream updates via SSE (Server-Sent Events), so graphs auto-refresh without manual reloads. SQL‑based triggers can fire alerts to Slack, PagerDuty, Kafka, or downstream sinks, based on conditions, thresholds, or pattern matches.
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