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/ Data Engineering /

Big Data Engineering Malta

Big data engineering services in Malta. Distributed processing, large-scale data platforms, and high-volume data infrastructure for Malta businesses.

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/ the solution /

Big Data Engineering 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.

  • Distributed Processing Frameworks

    Design and implement distributed data processing using Apache Spark, Flink, and cloud-nati…

  • Scalable Storage Architecture

    Build storage platforms that handle petabyte-scale data volumes efficiently using data lak…

  • High-Volume Data Ingestion

    Ingest millions of events per second from diverse sources including IoT sensors, web appli…

  • Performance Optimisation

    Optimise query performance, processing throughput, and resource utilisation across big dat…

/ how it works /

Live in weeks, not months.

We profile your data volumes, growth rates, processing patterns, and latency requirements to determine the right big data architecture. Not every organisation needs distributed processing, and we ensure the solution matches the actual scale challenge.

We recommend specific big data technologies based on your workload characteristics, team skills, and cloud platform. Spark, Databricks, Snowflake, BigQuery, and other options are evaluated against your specific requirements and constraints.

We design distributed processing architectures including cluster configurations, storage layers, partitioning strategies, and integration patterns. Architecture decisions account for cost, performance, operational complexity, and future scalability needs.

We build the big data platform with production-grade reliability, implementing processing jobs, ingestion pipelines, quality checks, and monitoring. Load testing validates performance at expected and peak data volumes before production deployment.

We optimise cluster sizing, partition strategies, caching, and query plans to achieve target performance levels at minimum cost. Continuous performance monitoring identifies optimisation opportunities as data volumes and usage patterns evolve.

We transfer operational knowledge to your team with comprehensive documentation, runbooks, and training. Your engineers learn to monitor, troubleshoot, and extend the platform independently with ongoing support available as needed.

/ what you get /

Everything you need. Nothing you don't.

Distributed Processing
Frameworks
Scalable Storage
Architecture
High-Volume Data
Ingestion
Performance Optimisation
/ 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

Compre Group Dashboard

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

Processing 12+ data sources with distributed pipelines

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Data Engineering & ML

Tipico AML

We migrated Tipico's AML data science workflows from KNIME to Python-based big data analytics with AWS Airflow automation, achieving up to 70% faster ETL pipeline execution and improved risk-ranking accuracy.

Real-time processing of millions of transactions for compliance

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/ questions /

Big Data Engineering FAQ

When does a business actually need big data engineering?
Big data engineering becomes necessary when your data volumes exceed what traditional databases and single-server processing can handle efficiently, typically above 1-10TB of active data or when processing millions of events per second. If your queries take too long, your storage costs are escalating, or your analytical tools are hitting capacity limits, big data architecture is the solution.
Is Spark still the best choice for big data processing?
Apache Spark remains the dominant general-purpose distributed processing framework, and its ecosystem including Databricks has only strengthened. However, for specific workloads, alternatives like Apache Flink for streaming, Snowflake for analytical queries, or BigQuery for serverless analytics may be better fits. We recommend based on your specific workload mix.
How does big data engineering relate to AI and machine learning?
AI and machine learning require large, clean datasets for training and large-scale scoring in production. Big data engineering provides the infrastructure to prepare training data, run distributed model training, and deploy models that score millions of records. Without big data engineering, ML initiatives are limited to small datasets and toy problems.
What cloud platform is best for big data?
All major cloud platforms offer strong big data services. AWS has the broadest service range with EMR, Glue, and Redshift. Azure integrates well with Microsoft tools via Synapse and Databricks. GCP offers BigQuery, one of the best serverless analytics engines. Your existing cloud presence and team skills often determine the best choice.
Can you optimise our existing Spark or Databricks workloads?
Yes, performance optimisation of existing big data workloads is one of our most common engagements. We typically find 30-60% cost savings and significant performance improvements through cluster sizing, partition optimisation, query refactoring, caching strategies, and job scheduling improvements.
How do you handle data quality at scale?
We implement distributed data quality checks that run alongside processing pipelines without becoming bottlenecks. Great Expectations, Deequ, and custom validation frameworks catch quality issues at ingestion and transformation stages, preventing bad data from propagating through the platform to downstream analytics and AI consumers.
What about real-time big data processing?
We build real-time processing using Spark Structured Streaming, Apache Flink, and cloud-native streaming services. These systems handle millions of events per second with sub-second latency, enabling real-time dashboards, fraud detection, IoT analytics, and event-driven automation at scale.
How do you control costs with big data infrastructure?
Cost control is central to our architecture decisions. We use spot instances for batch processing, autoscaling for variable workloads, storage tiering for cold data, and compute-storage separation to avoid over-provisioning. Regular cost reviews identify optimisation opportunities, and we typically achieve 40-60% savings compared to unoptimised deployments.
/ get started /

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