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Data Pipeline Development Malta

Data pipeline development services in Malta. Build reliable ETL/ELT pipelines, real-time streaming.

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/ the solution /

Data Pipeline Development built around your business.

Every solution we deliver is built on three pillars: your data, your context, and continuous improvement. Each capability is traceable and measurable.

  • ETL/ELT Pipeline Engineering

    Build production-grade extract, transform, and load pipelines using Apache Airflow, dbt, S…

  • Real-Time Streaming Pipelines

    Event-driven streaming pipelines using Apache Kafka, AWS Kinesis, and Azure Event Hubs for…

  • Data Integration & Connectors

    Connect any data source to any destination with robust integration connectors. We integrat…

  • Pipeline Monitoring & Observability

    Comprehensive monitoring with automated alerting for pipeline health, data freshness, proc…

/ how it works /

Live in weeks, not months.

We catalogue your data sources, document their schemas, access patterns, change frequencies, and data volumes. This analysis determines the optimal extraction strategy for each source, whether full refresh, incremental, or change data capture.

We design pipeline architectures that balance processing latency, reliability, and cost. Batch, micro-batch, and streaming patterns are selected based on freshness requirements and data characteristics for each workflow.

We build pipelines with comprehensive unit tests, integration tests, and data quality checks embedded at every transformation stage. Test data generators and pipeline test harnesses ensure reliability before production deployment.

We configure pipeline scheduling, dependency management, and workflow orchestration using Airflow, Dagster, or cloud-native schedulers. Complex multi-pipeline workflows with conditional logic and cross-pipeline dependencies are managed centrally.

We implement monitoring dashboards and alerting rules that track pipeline execution, data quality, freshness, and volume metrics. PagerDuty, Slack, and email integrations ensure the right people are notified when issues arise.

Every pipeline is documented with data flow diagrams, transformation logic, scheduling details, and operational runbooks. Your team receives training on monitoring, troubleshooting, and extending the pipeline framework.

/ what you get /

Everything you need. Nothing you don't.

ETL/ELT Pipeline
Engineering
Real-Time Streaming
Pipelines
Data Integration
& Connectors
Pipeline Monitoring
& Observability
/ built for your situation /

Sounds familiar?

Head of Data, retail group
"Our sales data lives in three different systems — Shopify, our ERP, and a warehouse management tool — and we can't get a single view of inventory performance"

We build a unified data pipeline that ingests from all three sources, applies consistent business logic, and loads into a data warehouse your BI team can query in real time.

How Neural AI helps

We build a unified data pipeline that ingests from all three sources, applies consistent business logic, and loads into a data warehouse your BI team can query in real time.

/ proof /

Real deployments. Real results.

Business Intelligence

Social Benefits Dashboard

Two interactive public-facing dashboards using Google Looker Studio for real-time monitoring of social benefits and expenditure data across Malta.

Automated pipeline processing 500K+ records for policy analytics

Read case study →
Business Intelligence

Compre Group Dashboard

Power BI dashboard providing comprehensive visibility into payables, costs, and financial operations for Compre Group's insurance business.

12+ source system integrations unified through automated pipelines

Read case study → AI & NLP

Grocery Price Monitoring

An AI-powered system using web scraping and GPT-based NLP for product name matching across Maltese supermarkets, enabling real-time food price comparisons with predictive modelling for pricing trends.

Daily automated data collection across 50+ product categories

Read case study →
/ questions /

Data Pipeline Development FAQ

What is the difference between ETL and ELT?
ETL transforms data before loading it into the destination, typically used when the target system has limited processing power. ELT loads raw data first and transforms it within the destination, leveraging modern cloud data warehouse compute for transformation. We increasingly recommend ELT with tools like dbt for flexibility and auditability, but the right choice depends on your specific architecture.
How do you handle pipeline failures gracefully?
Every pipeline includes automated retry logic with exponential backoff, dead-letter queues for unprocessable records, and idempotent design that allows safe re-execution. When retries are exhausted, automated alerts notify your team with diagnostic information. Failed records are quarantined without blocking the rest of the pipeline from processing.
Can you integrate with legacy systems that do not have APIs?
Yes, we have extensive experience integrating with legacy systems through database connections, file-based transfers, screen scraping, and custom adapters. Change data capture from legacy databases enables near-real-time integration without modifying the source system. We work with whatever your systems provide.
How long does it take to build a data pipeline?
Simple pipelines connecting one source to one destination take 1-2 weeks. Complex multi-source pipelines with business logic, quality checks, and error handling typically take 3-6 weeks. Enterprise-scale pipeline platforms with dozens of integrations are delivered iteratively over 2-4 months.
Should we use Airflow, Dagster, or Prefect for orchestration?
Apache Airflow is the most mature option with the largest community and widest adoption. Dagster offers a more modern developer experience with better testing and data asset management. Prefect provides a simpler model for straightforward workflows. We recommend based on your team's skills, existing infrastructure, and workflow complexity.
How do you ensure data quality within pipelines?
We embed quality checks at every pipeline stage using Great Expectations, dbt tests, and custom validation rules. Checks cover completeness, uniqueness, referential integrity, range validation, and business rule compliance. Quality failures trigger alerts and can halt downstream processing to prevent bad data propagation.
Can pipelines handle schema changes in source systems?
Yes, we design pipelines with schema evolution handling that detects and adapts to source schema changes. New columns are added automatically, removed columns are handled gracefully, and type changes are caught and flagged. Schema registry integration provides advance warning of planned changes.
What about data pipeline costs?
Pipeline costs depend on data volume, processing frequency, and infrastructure choices. We optimise for cost-efficiency using serverless compute for variable workloads, spot instances for batch processing, and efficient transformation patterns that minimise compute usage. Most clients find pipeline automation saves far more in manual labour than it costs in infrastructure.
/ get started /

Ready to put AI to work in your business?

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