Who should use the Real-time Analytics Workflow Blueprint workflow?
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Data
Real task-to-tool workflow for "Real-time Analytics" built from live mapping data.
Deliverable outcome
Data-driven decisions executed in near real-time, improving outcomes while the data is still fresh.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
Data-driven decisions executed in near real-time, improving outcomes while the data is still fresh.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use Adverity to a clear set of real-time metrics and a live data ingestion pipeline ready for processing. Then, you pass the output to DataNectar to a running pipeline that continuously processes raw events into clean, aggregated data streams. Then, you pass the output to InfluxDB to a low-latency data store that can serve real-time queries on the processed streams. Then, you pass the output to Sigma Computing to live visual monitoring and automated alerts that enable immediate response to changing conditions. Finally, SAP Emarsys Customer Engagement is used to data-driven decisions executed in near real-time, improving outcomes while the data is still fresh.
Define Metrics & Data Sources
A clear set of real-time metrics and a live data ingestion pipeline ready for processing.
Build Real-time Data Pipeline
A running pipeline that continuously processes raw events into clean, aggregated data streams.
Implement Real-time Storage & Indexing
A low-latency data store that can serve real-time queries on the processed streams.
Create Real-time Dashboards & Alerts
Live visual monitoring and automated alerts that enable immediate response to changing conditions.
Optimize & Iterate on Campaigns (Optional)
Data-driven decisions executed in near real-time, improving outcomes while the data is still fresh.
Identify the key performance indicators (KPIs) and business events that need real-time tracking. Connect to live data streams (e.g., web traffic, sensor feeds, transaction logs) and configure data ingestion endpoints.
Why Adverity: Adverity provides multi-channel data aggregation and automated reporting, which directly supports defining metrics and integrating streaming data sources like Kafka or Kinesis.
Design a stream processing pipeline that ingests, cleans, and transforms raw events into structured, queryable data. Use a stream processor to handle windowed aggregations, filtering, and enrichment in near real-time.
Why DataNectar: DataNectar specializes in ETL/ELT pipeline construction, which aligns with building a real-time data pipeline using stream processing engines.
Store the processed data in a real-time database or data store optimized for low-latency queries and time-series data. Configure indexes and retention policies to balance performance and cost.
Why InfluxDB: InfluxDB is a dedicated time-series database, directly meeting the need for real-time storage and indexing of time-series data.
Build live dashboards that refresh automatically with the latest data from the storage layer. Set up threshold-based alerts to notify stakeholders when metrics deviate from expected ranges.
Why Sigma Computing: Sigma Computing enables building interactive dashboards and reports directly on cloud data warehouses, fitting the need for real-time dashboards and alerts.
Use the real-time insights to adjust marketing campaigns, resource allocation, or system configurations on the fly. Run A/B tests or manual tweaks based on live metric trends.
Why SAP Emarsys Customer Engagement: SAP Emarsys Customer Engagement offers predictive lifecycle management and omnichannel workflow automation, directly supporting campaign optimization.
§ Before you start
Teams or solo builders working on data tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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