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Realtime Data Pipeline Development Services

Logiciel builds real-time data pipelines that capture, process, transform, and deliver data with sub-second latency. From streaming architectures to event-driven systems, pub/sub pipelines, CDC frameworks, and real-time analytics, we engineer data infrastructure that powers modern AI systems, dashboards, and customer-facing applications.

See Logiciel in Action

Why Real-Time Pipelines Matter

Modern businesses operate in real time. Batch processing slows you down:

  • Analytics & decision-making

  • Anomaly detection & AI inference

  • Live product updates & notifications

  • Event-driven automation & workflows

Real-time pipelines deliver:

  • Instant insights & dashboards

  • AI/ML-ready data streams

  • High-volume, fault-tolerant ingestion

  • Operational reliability & data freshness

Our Realtime Data Pipeline Services

Data Ingestion & Streaming Architecture

  • Kafka, Kinesis, Pulsar, DynamoDB Streams, EventBridge

  • Outcomes: reliable, high-throughput ingestion

Event-Driven Pipelines

  • Stream processors, enrichment, aggregation, retries

  • Tools: Flink, Spark Streaming, Lambda

  • Outcomes: clean, transformed, actionable data

Change Data Capture (CDC)

  • Sync databases in real time for microservices, search, ML updates

  • Tools: Debezium, AWS DMS, Kafka Connect

Real-Time ETL/ELT

  • Parsing, enrichment, validation, anomaly detection

  • Tools: Spark Structured Streaming, Flink, AWS Glue

Live Analytics & Dashboards

  • QuickSight, Grafana, Kibana, custom dashboards

  • Instant visibility & better decision-making

Search & Indexing Pipelines

  • Elasticsearch, OpenSearch, Pinecone, Weaviate

  • Low-latency indexing for improved user experience

AI Integration

  • Streaming inference, embeddings, RAG context injection

  • Tools: SageMaker, MLflow, Triton Server

Monitoring & Observability

  • Lag, throughput, error tracking, alerts, schema evolution

  • Tools: Datadog, Prometheus, CloudWatch

How Logiciel Works With You

  • Architecture Design: Define SLAs, tools, throughput, cost

  • Implementation: Build ingestion, processing, storage, serving

  • Validation: Load testing, monitoring setup, failure simulation

  • Optimization: Tune partitions, throughput, cost, resilience

  • Scaling & Evolution: Expand pipelines, ML, analytics layers

Success Stories

Why Logiciel

We build streaming systems that power AI, analytics, and modern product experiences.

  • Expert in real-time systems & streaming architectures

  • AWS + Kafka + Flink + Spark Streaming specialists

  • Proven enterprise-scale pipelines handling millions of events/day

  • Strong AI/ML integration capability

  • Performance, resilience & observability-first approach

Build Real-Time Pipelines That Power AI, Analytics & Modern Applications.

Start your real-time data engineering transformation today.

FAQs

Yes, hybrid architectures are common.
Designed for enterprise scale (from thousands to millions of EPS).
Kafka, Kinesis, Flink, Spark Streaming, Lambda, Debezium, Redis, more.
Yes, modernizing brittle ETL is a core specialization.
Absolutely, embeddings, inference, anomaly detection, and RAG.