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Custom Data Pipeline Development Services for Enterprise-Scale Data Delivery
Batch & real-time pipelines | ETL/ELT development | Databricks, Snowflake, Azure & AWS | Built for reliability, governance, and AI readiness
Databricks Elite C&SI Partner - 100+ certified engineers building production-grade data pipelines for Fortune 500 enterprises across the US.
Our data pipeline development services help US enterprises design and build custom ETL, ELT, batch, and real-time streaming pipelines – transforming disconnected, unreliable data sources into clean, governed, analytics-ready infrastructure. From greenfield pipeline builds to legacy ETL modernization, we deliver production-grade pipelines that your analytics and AI teams can depend on.
Years
Building Enterprise Data Pipelines for Leading Global Enterprises
Fortune 500 Clients
Across Technology, Financial Services, CPG, Retail & Manufacturing
Databricks Certified Engineers
What is Data Pipeline Development?
Data pipeline development is the process of designing, building, testing, deploying, and maintaining automated systems that move data from source systems – databases, APIs, SaaS platforms, IoT devices, and streaming systems – through transformation and quality logic, and deliver it to analytics targets like data warehouses, lakehouses, or operational applications.
A well-developed enterprise data pipeline does more than move data. It enforces data quality at every stage, applies transformation logic that matches business definitions, tracks lineage from source to destination, handles failures gracefully, and scales with increasing data volumes without manual intervention.
Modern enterprise data pipeline development covers batch pipelines for scheduled analytics workloads, real-time streaming pipelines for operational decisions, ETL/ELT development using dbt and cloud-native tools, pipeline orchestration with Airflow or Dagster, AI/ML feature pipelines, and ongoing pipeline monitoring and DataOps across Databricks, Snowflake, Azure Data Factory, AWS Glue, and Apache Kafka.
Our Data Pipeline Development Services & Capabilities
Custom Data Pipeline Development
Purpose-built pipelines engineered for your data, your systems, and your SLAs
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- Design and build pipelines from the ground up: Architect custom ingestion, transformation, and delivery pipelines tailored to your specific source systems, data volumes, latency requirements, and target platforms whether that’s Databricks Lakehouse, Snowflake, Redshift, or BigQuery.
- Build modular, reusable pipeline components: Develop pipeline frameworks that are modular, well-documented, and reusable across domains reducing time-to-delivery for new data products and making future changes low-risk.
- Engineer for fault tolerance and reliability: Build retry logic, error handling, dead-letter queues, and circuit breakers into every pipeline so failures are contained, logged, and automatically recovered without impacting downstream consumers.
ETL/ELT Pipeline Development
From raw source data to analytics-ready tables — with clean transformation logic your team owns
- Build dbt-based transformation layers on Snowflake or Databricks: Develop SQL-based transformation pipelines in dbt – modular, version-controlled, tested, and documented so your data models are transparent, trustworthy, and easy to evolve.
- Develop cloud-native ELT pipelines on Azure and AWS: Build ELT pipelines using Azure Data Factory, AWS Glue, or Databricks Workflows that leverage cloud compute for transformation replacing expensive ETL middleware with scalable, maintainable code.
- Implement incremental load patterns and SCD logic: Engineer incremental ingestion, slowly changing dimension (SCD) handling, and merge logic that keeps data fresh without full-table reprocessing cutting compute costs and improving pipeline throughput.
Real-Time & Streaming Pipeline Development
Sub-second data delivery for fraud detection, personalization, and live operational analytics
- Build Apache Kafka and Spark Structured Streaming pipelines: Architect event-driven streaming pipelines using Kafka, AWS Kinesis, Azure Event Hubs, or Google Cloud Pub/Sub designed for high-throughput, low-latency data delivery at enterprise scale.
- Implement Delta Live Tables for unified streaming and batch: On Databricks, build Delta Live Tables pipelines that handle both real-time streaming and batch workloads on a single, governed platform with declarative pipeline definitions, auto-scaling, and built-in data quality constraints.
- Power real-time operational use cases: Deliver streaming infrastructure for fraud detection, real-time inventory tracking, dynamic pricing, clickstream analytics, IoT sensor processing, and customer behavior event streaming where delays cost revenue.
Pipeline Orchestration & Automation
Replace fragile cron jobs with governed, observable, production-grade pipeline workflows
- Deploy Apache Airflow, Dagster, or Prefect orchestration: Build workflow orchestration systems that schedule, monitor, retry, and alert on every pipeline step with dependency management, SLA enforcement, and environment-based deployment across dev, staging, and production.
- Implement CI/CD for data pipelines: Apply software engineering best practices to pipeline development – git based version control, automated testing with Great Expectations or dbt tests, and deployment pipelines that validate every change before it touches production data.
- Automate end-to-end pipeline workflows: Eliminate manual triggers, manual data loads, and hand-rolled scripts. Build fully automated ingestion-to-consumption workflows that run reliably on schedule with alerting when something breaks before the business notices.
AI & ML Pipeline Development
Data infrastructure that makes production AI and GenAI reliable not just possible
- Build feature stores and ML training pipelines: Design pipelines that compute and serve ML features consistently between training and production environments eliminating training/serving skew and ensuring models are built on the same logic they score against.
- Develop RAG and GenAI data pipelines: Build document ingestion, chunking, embedding, and vector store pipelines for Retrieval-Augmented Generation applications connecting enterprise knowledge bases to LLMs on Azure OpenAI, AWS Bedrock, or Databricks Model Serving.
- Engineer data quality pipelines for AI training datasets: Build automated validation, deduplication, and enrichment pipelines that ensure AI training data meets quality thresholds so model performance doesn’t degrade due to upstream data issues.
Pipeline Modernization
Retire legacy ETL without disrupting the business
- Migrate Informatica, SSIS, or DataStage to modern frameworks: Re-engineer legacy ETL transformation logic into dbt, Azure Data Factory, AWS Glue, or Databricks Workflows with version control, automated testing, and cloud-native scalability replacing proprietary licensing.
- Accelerate migration with MigrateMate: Our proprietary accelerator automates pipeline inventory, code conversion, and parallel validation reducing legacy ETL migration effort by 30%–40% while maintaining data integrity throughout the transition.
- Execute phased cutover with zero data loss: Run legacy and new pipelines in parallel with automated reconciliation checks at every stage validating output consistency before switching off the legacy system so there is no risk of data loss or downstream disruption.
Data Pipeline Development Challenges We Solve
Fragile, undocumented, or poorly architected pipelines are the single biggest obstacle between enterprises and reliable analytics. Here is what we fix:
Delayed reporting from brittle legacy ETL
Proprietary ETL jobs built in Informatica, SSIS, or DataStage break on schema changes, take hours to run, and have no observability leaving analytics teams constantly firefighting instead of delivering insights
No real-time data capability
Business runs on batch jobs scheduled overnight while competitive decisions require same-minute data. Customer behavior, fraud signals, and inventory movements can't wait for a 2AM batch window
Manual, error-prone pipeline maintenance
Pipelines built as scripts with no testing, no documentation, and no version control create single points of failure when engineers leave or data sources change
Data arriving without quality guarantees
Pipelines that move data without validation push bad rows, nulls, and duplicates directly into dashboards and AI models eroding trust in analytics across the business
AI and ML initiatives blocked by bad data
Machine learning teams can't build reliable models when pipeline output is inconsistent, undocumented, or missing entirely from key sources
Runaway cloud compute costs
Poorly optimized pipelines run unnecessary full-table scans, redundant transformations, and inefficient compute configurations inflating cloud bills without adding business value
Our Proven Data Pipeline Development Process
Modern data pipeline development is not just about moving data. It is about engineering reliable, governed, and scalable pipelines that power every analytics and AI decision across the enterprise. Explore our full pipeline development capabilities
Discovery & Assessment
- Inventory all data sources, existing pipelines, and transformation logic
- Evaluate data volumes, velocity, latency requirements, and SLA expectations
- Identify technical debt, performance bottlenecks, and compliance constraints
- Define pipeline scope, priority domains, and build vs. migrate decision
Architecture & Design
- Design pipeline patterns (batch, streaming, or unified) for target platforms
- Define ingestion strategy, transformation logic, schema design, and data models
- Establish data quality rules, lineage tracking, and governance standards
- Produce a phased build roadmap with rollback planning and risk assessment
Development & Integration
- Build ingestion, transformation, orchestration, and delivery layers
- Implement version control, automated testing, and CI/CD from day one
- Integrate with source systems, cloud platforms, and downstream consumers
- Document all pipeline logic, dependencies, and operational runbooks
Testing & Validation
- Execute data reconciliation testing against source system row counts and checksums
- Run performance and load testing against production data volumes
- Validate data quality outputs against defined business rules
- Sign off on production readiness with full test evidence
Deployment & Ongoing Operations
- Deploy pipelines to production with monitoring, alerting, and SLA tracking
- Hand over operational documentation and runbooks to your team
- Provide ongoing pipeline support, optimization, and evolution
- Continuously improve performance, cost efficiency, and governance coverage
Swift, seamless, and secure pipeline migration 30%–40% faster
Accelerate Pipeline Development & Migration with MigrateMate
MigrateMate is LatentView’s proprietary pipeline migration accelerator. It automates legacy ETL inventory, code conversion, and validation – cutting migration effort and cost by 30%–40% across Informatica, SSIS, DataStage, and Ab Initio to Databricks, Snowflake, AWS Glue, and Azure Data Factory.
What Enterprises Achieve After Pipeline Development with LatentView
Across Fortune 500 engagements in the US, our pipeline development work delivers measurable outcomes:
Faster data pipeline throughput vs. legacy ETL
Reduction in data infrastructure and compute costs
Decrease in time-to-insight for analytics and BI teams
Pipeline reliability with automated monitoring and alerting
Improvement in downstream data quality scores
Faster AI/ML model deployment enabled by consistent, governed pipeline output
Platforms and Tools We Use for Enterprise Data Pipeline Development
Awards & Recognitions
Start Your Enterprise Data Pipeline Assessment
We help you build reliable, scalable data pipelines that empower your data teams and drive better business outcomes.
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FAQs
01What is data pipeline development?
Designing, building, and maintaining automated systems that move, transform, and deliver data from source systems to analytics targets covering ETL/ELT, streaming, batch, orchestration, and quality frameworks on Databricks, Snowflake, Azure, and AWS.
02What is the difference between ETL and ELT pipeline development?
ETL transforms data before loading it. ELT loads raw data first, then transforms inside the warehouse using native compute. Modern cloud-native development favors ELT with dbt on Snowflake or Databricks faster, cheaper, and easier to test and version-control.
03How long does it take to develop an enterprise data pipeline?
Assessments take 2–3 weeks. A focused pipeline build takes 6–10 weeks. Enterprise-wide programs covering multiple systems, batch and real-time, are scoped as 3–6 month phased engagements. We provide a detailed timeline during discovery.
04What does data pipeline development cost?
Cost depends on pipeline complexity, source systems, data volumes, and target platform. A focused build typically starts at $75K–$150K. Our MigrateMate accelerator reduces migration-specific costs by 30–40% through automation.
05How do you ensure data quality in pipelines you build?
We embed validation at every stage schema enforcement, null checks, row count reconciliation, and business rule tests using dbt and Great Expectations. Alerting ensures failures are caught before reaching dashboards or AI models.
06What platforms do you build data pipelines on?
Databricks, Snowflake, Azure Data Factory, AWS Glue, Google Cloud Dataflow, Kafka, Airflow, Prefect, Dagster, dbt, Fivetran, and Microsoft Fabric. Platform selection is driven by your ecosystem, latency needs, and total cost of ownership.
07Can you modernize or replace our existing legacy ETL pipelines?
Yes. We migrate from Informatica, SSIS, DataStage, and Ab Initio to cloud-native frameworks. MigrateMate reduces migration effort by 30–40% through automated inventory, code conversion, and parallel validation before cutover.
08How do you handle data security and compliance?
We implement encryption, role-based access controls, data masking, audit logging, and compliance tagging. Unity Catalog handles governance on Databricks. Pipelines meet US standards including SOC 2, HIPAA, PCI-DSS, and CCPA.
09How is LatentView different from other pipeline development companies?
20+ years of Fortune 500 delivery, 100+ Databricks-certified engineers, proprietary accelerators like MigrateMate, and DataOps built in from day one ensuring pipelines directly power analytics and AI, not just move data.
