Batch Pipeline Assessment
Current-state review of scheduled jobs, ETL workflows, data dependencies, SLAs, failure patterns and migration risks.
Move from delayed batch processing to real-time data flows your teams can trust.
Logiciel helps enterprises migrate batch data pipelines into streaming architectures that support real-time analytics, automation and AI-ready systems. From pipeline assessment and event-driven design to CI/CD pipeline automation, DevOps CI/CD practices, observability and managed operations, we build streaming data systems that are reliable, scalable and production-ready.
Most enterprises do not struggle because batch pipelines never worked. They struggle because business needs have moved faster than overnight processing, scheduled jobs and delayed reporting.
We help your teams migrate batch workflows into real-time streaming foundations without disrupting business reporting.
A clear batch-to-streaming migration roadmap tied to business priorities.
Assessment of current batch jobs, dependencies, data flows and latency needs.
Streaming architecture designed around events, topics, schemas and consumers.
CI/CD pipeline automation for safer release, testing and rollback.
DevOps CI/CD practices applied to data pipelines, streaming jobs and infrastructure.
Observability for lag, throughput, schema changes, failures and downstream impact.
A practical streaming data operating model your teams can maintain after launch.
We cover the full migration lifecycle. Streaming architecture, CI/CD, observability and operations need to work together.
Current-state review of scheduled jobs, ETL workflows, data dependencies, SLAs, failure patterns and migration risks.
Event-driven architecture for producers, consumers, topics, queues, schemas, processing jobs and downstream data products.
Streaming ingestion, transformation, enrichment, aggregation, routing and delivery into analytics, applications and AI workflows.
CI/CD pipeline design for streaming jobs, data pipelines, schema changes, infrastructure updates and controlled deployments.
Continuous integration and continuous delivery practices that improve testing, release quality, rollback and environment promotion.
Monitoring for lag, throughput, errors, retries, schema drift, failed consumers, pipeline health and data freshness.
Ongoing monitoring, incident response, CI/CD improvement, performance tuning, cost review and continuous optimisation.
Dedicated Streaming Migration Squad
A standing team of data engineers, platform engineers, DevOps specialists and cloud architects embedded into your migration roadmap.
Streaming Migration Advisory and Staff Augmentation
Senior data engineers and CI/CD DevOps consultants who strengthen your internal platform, analytics, data or engineering teams.
Outcome-Based Batch-to-Streaming Migration
Fixed-scope engagements with defined streaming outcomes, migration milestones and success baselines agreed up front.
Detailed assessment of batch pipelines, source systems, dependencies, latency needs, CI/CD maturity, observability gaps and business priorities.
Event modelling, topic design, schema standards, producer-consumer contracts, stream processing patterns and downstream delivery.
Real-time ingestion, stream processing, filtering, enrichment, aggregation, routing, retries and dead-letter queue workflows.
Implementation of CI/CD pipeline tools, CI tools, release workflows, automated tests, deployment gates and rollback mechanisms.
Infrastructure-as-code, environment promotion, pipeline automation, cloud deployment patterns and CI/CD DevOps release controls.
Freshness checks, schema validation, lag monitoring, throughput tracking, error dashboards, alerts and incident workflows.
Ongoing monitoring, incident response, deployment support, platform tuning, cost review, data quality checks and continuous improvement.
Patterns from our data and DevOps engineering teams that help enterprises modernize pipelines without losing reliability.
How we structure migration ownership, CI/CD controls, release reviews, observability, incident response and continuous improvement across teams.
A practical approach to ranking migration candidates by latency need, business impact, batch complexity, CI/CD maturity and operational risk.
1. Batch Pipeline Diagnostic and Baseline
We assess batch jobs, data sources, dependencies, failure patterns, latency needs, CI/CD tools, monitoring gaps and business priorities.
2. Migration and Event Flow Mapping
We identify which batch workflows should remain scheduled, which should become streaming and what event flows must support them.
3. Streaming and CI/CD Engineering
We build streaming pipelines, event schemas, processing jobs, CI/CD pipelines, automated tests, release gates and deployment workflows.
4. Reliability, Observability and Release Control
We harden streaming systems with monitoring, alerts, schema checks, rollback workflows, runbooks and continuous integration and continuous delivery practices.
5. Streaming Operating Model
We hand over a repeatable streaming migration practice, including ownership, KPIs, dashboards, incident response, release cadences and improvement workflows.
Ready to turn Batch-to-Streaming Migration Services into a real-time foundation for analytics, automation and AI? Partner with Logiciel to modernize batch workflows, implement CI/CD pipelines and operate streaming systems with production-grade reliability.
Batch-to-Streaming Migration Services include batch pipeline assessment, event-driven architecture, streaming pipeline development, CI/CD pipeline automation, DevOps CI/CD practices, observability, data quality checks and managed streaming operations.
Enterprises should migrate from batch to streaming when delayed data limits operational decisions, real-time analytics, customer experiences, automation or AI systems that depend on fresh business events.
CI/CD pipelines help teams test, deploy and roll back streaming jobs, schema changes, infrastructure updates and data pipeline logic safely across environments.
Logiciel can work with common CI/CD tools, CI tools, cloud-native deployment services, infrastructure-as-code workflows and CI/CD pipeline tools depending on your technology environment.
DevOps CI/CD for data pipelines includes automated testing for transformations, schemas, data quality, streaming jobs, infrastructure and deployment workflows, not just application code.
Most engagements produce a diagnostic, roadmap and initial streaming pipeline within 4-8 weeks, while larger migration programs run across phased delivery waves over several months.
You retain ownership of all streaming pipelines, event schemas, CI/CD workflows, integrations, dashboards, monitoring rules, governance assets, runbooks and implementation materials.
Yes. We run managed operations with monitoring, incident response, deployment support, data quality reviews, CI/CD improvements, cost tracking and continuous improvement.