Building a Real-Time Data Pipeline for Enhanced Analytics

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Technology & Software

Building a Real-Time Data Pipeline for Enhanced Analytics

Real-time data pipeline integrating data ingestion, processing, and analytics layers.

Focus Areas

Real-Time Data Engineering

Scalable Architecture

Analytics and Observability

Real-time data processing, transformation, and enrichment before analytics consumption.

Business Problem

A fast-growing e-commerce technology company faced growing latency and inefficiencies in its analytics workflows. As user activity increased across mobile and web platforms, batch ETL pipelines were no longer sufficient to support real-time decision-making. Marketing, operations, and product teams lacked access to fresh insights for personalization, fraud detection, and inventory forecasting. The company needed a scalable, low-latency data pipeline to collect, process, and serve analytics-ready data in real time.

Key challenges:

  • Data Latency: Analytics lagged by several hours due to batch processing windows.

  • Pipeline Fragility: ETL jobs often failed due to schema drift, missing data, or unmonitored dependencies

  • Scalability Limits: Existing data infrastructure could not keep up with streaming ingestion rates.

The Approach

Curate partnered with the organization to architect and deploy a real-time data pipeline capable of ingesting, transforming, and delivering high-throughput event data with low latency. The solution was designed to scale with business growth while improving data reliability, observability, and downstream analytics readiness.

Key components of the solution:

Discovery and Requirements Gathering:

  • Current State Assessment: Reviewed legacy ETL processes, data warehouse schema, and ingestion patterns.

  • Business Use Case Mapping: Aligned data requirements with key functions such as product analytics, fraud detection, and supply chain forecasting.

  • Volume and Velocity Profiling: Benchmarked peak loads, message sizes, and SLAs.

  • Stakeholder Alignment: Engaged data engineering, analytics, and business teams to define SLAs and target architecture.

Solution Design and Implementation:

Streaming Ingestion Architecture

  • Deployed Apache Kafka and AWS Kinesis for high-throughput, fault-tolerant data ingestion.

  • Partitioned event streams by type (e.g., user activity, transactions, error logs) for parallel processing.

  • Introduced schema registry (Confluent Schema Registry) to enforce consistency and evolve schemas safely.

Real-Time Processing Layer

  • Implemented Apache Flink and Spark Structured Streaming to process and enrich events on the fly.

  • Applied windowing, deduplication, and joins to support near-instant analytics and alerting.

  • Used Airflow for orchestration and state management for time-sensitive workflows.

Analytics-Ready Storage

  • Routed processed data to AWS Redshift and Snowflake for interactive querying and BI use cases.

  • Used S3 as a raw data lake layer, with metadata managed via AWS Glue Catalog and partitioned by time/event type.

  • Ensured ACID guarantees using Delta Lake for time travel and schema enforcement.

Monitoring and Observability

  • Integrated Grafana and Prometheus for pipeline metrics (e.g., lag, throughput, failure rates).

  • Set up log tracing using ELK stack and Flink-native dashboards.

  • Defined SLA dashboards for business teams to track freshness, completeness, and delivery status.

Data Quality and Governance

  • Applied data contracts and expectations with tools like Great Expectations and Monte Carlo.

  • Built alerting for schema mismatches, null value spikes, and outlier detection.

  • Created lineage and catalog views with OpenMetadata for visibility and audit readiness.

Business Outcomes

Real-Time Insights for Decision-Making


Data consumers accessed fresh data within seconds, enabling dynamic pricing, real-time personalization, and live performance monitoring.

Scalable and Resilient Infrastructure


The new architecture processed millions of events per hour with built-in fault tolerance and retry mechanisms.

Improved Data Quality and Trust


Schema enforcement, monitoring, and data contracts significantly reduced bad data incidents.

Enhanced Collaboration Between Teams


Analytics, engineering, and business teams gained a shared understanding of data flows and SLAs.

Sample KPIs

Here’s a quick summary of the kinds of KPI’s and goals teams were working towards**:

Metric Before After Improvement
Data Refresh rate 3 hours 10 seconds 99% faster
Pipeline failure rates 8/month 1/month 88% reduction
Analytics availability SLA 75% 99.5% 33% increase
Data quality incident rate Weekly Quarterly 90% fewer issues
Time to onboard new data source 10 days 1 day 90% faster
**Disclaimer: The set of KPI’s are for illustration only and do not reference any specific client data or actual results – they have been modified and anonymized to protect confidentiality and avoid disclosing client data.

Customer Value

Faster Insights


Delivered real-time access to behavioral and operational data, driving proactive decision-making.

Scalable Architecture


Designed for horizontal scalability to support future data growth and use cases.

Sample Skills of Resources

  • Data Engineers: Built real-time ingestion and processing pipelines using Kafka, Flink, and Spark.

  • Streaming Architects: Designed event-driven architecture and performance-optimized stream topology.

  • DevOps Engineers: Deployed and monitored scalable infrastructure with Kubernetes and Terraform.

  • Data Quality Analysts: Defined and enforced quality checks, lineage, and observability.

  • BI & Analytics Engineers: Enabled fast, reliable access to processed data for downstream use.

Tools & Technologies

  • Streaming & Ingestion: Apache Kafka, AWS Kinesis, Confluent Schema Registry

  • Stream Processing: Apache Flink, Spark Streaming, Apache Beam

  • Storage & Warehousing: S3, AWS Redshift, Snowflake, Delta Lake

  • Orchestration & Monitoring: Airflow, Grafana, Prometheus, ELK

  • Quality & Governance: Great Expectations, Monte Carlo, OpenMetadata, Glue Catalog

Analytics dashboard displaying live business metrics and insights powered by the real-time pipeline.

Conclusion

Curate helped the e-commerce firm build a robust real-time data pipeline that empowered teams with fresh, reliable insights at scale. By modernizing its architecture and automating quality and observability, the organization gained a competitive edge through faster, data-driven decisions and streamlined analytics operations.

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