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AWS Data Engineering Services Malta

AWS data engineering services in Malta. Build scalable data platforms using S3, Glue, Redshift, EMR, Kinesis.

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/ the solution /

AWS Data Engineering Services 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.

  • AWS Glue & Serverless ETL

    Serverless ETL pipelines using AWS Glue that automatically scale with data volume without …

  • Amazon Redshift Data Warehouse

    Data warehouse deployment on Amazon Redshift with optimised distribution keys, sort keys, …

  • S3 Data Lake with Lake Formation

    S3-based data lakes governed by Lake Formation for centralised security, fine-grained acce…

  • Real-Time Streaming with Kinesis

    Amazon Kinesis implementations for real-time data ingestion and processing at scale. Strea…

/ how it works /

Live in weeks, not months.

We evaluate your current AWS usage, VPC configuration, IAM structure, and compliance requirements. For new AWS deployments, we design the landing zone with proper account structure, networking, and security foundations.

We design the data architecture selecting optimal AWS services for each component. Glue vs EMR for processing, Redshift vs Athena for querying, and Kinesis vs MSK for streaming are evaluated against your specific workload requirements.

We deploy AWS data infrastructure using Terraform or CloudFormation for infrastructure-as-code. VPC configuration, IAM policies, encryption settings, and monitoring are configured according to AWS Well-Architected Framework principles.

We build data pipelines using Glue ETL, Step Functions orchestration, and Lambda for event-driven processing. Each pipeline includes comprehensive error handling, monitoring, and documentation.

We configure Redshift warehouses, Athena workgroups, or QuickSight dashboards for analytical consumption. Query performance optimisation and cost management ensure responsive analytics within budget constraints.

We establish monitoring, alerting, and operational procedures using CloudWatch, SNS, and custom dashboards. Your team receives training on AWS data service operations, troubleshooting, and cost management.

/ what you get /

Everything you need. Nothing you don't.

AWS Glue &
Serverless ETL
Amazon Redshift
Data Warehouse
S3 Data Lake
with Lake Formation
Real-Time Streaming
with Kinesis
/ 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.

Data Engineering & AI

GPT Cloud Migration

Complete migration of Malta Tourism Authority legacy licensing data to cloud using GPT-powered NLP for error detection, achieving over 90% reduction in migration errors.

40% cost reduction migrating to AWS cloud-native architecture

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.

AWS-powered automated data collection across 50+ categories

Read case study → 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.

AWS streaming architecture for real-time transaction monitoring

Read case study →
/ questions /

AWS Data Engineering Services FAQ

Should we use Glue or EMR for data processing?
Glue provides serverless, managed ETL that is ideal for straightforward transformation workloads with minimal operational overhead. EMR provides full Apache Spark and Hadoop clusters for complex processing, ML workloads, and scenarios requiring custom libraries. We recommend Glue for most ETL workloads and EMR for advanced processing requirements.
When should we choose Redshift versus Athena?
Redshift delivers consistent, sub-second query performance for dashboards and concurrent users with predictable workloads. Athena provides serverless, pay-per-query analytics ideal for ad-hoc exploration and variable workloads. Many organisations use both: Redshift for production dashboards and Athena for exploration and cost-effective queries on data lake data.
How do you manage AWS data costs?
We implement reserved instances for predictable workloads, spot instances for batch processing, serverless options for variable loads, and S3 Intelligent-Tiering for storage. Cost allocation tags, AWS Budgets, and Cost Explorer dashboards provide visibility, while regular optimisation reviews identify savings opportunities.
Can you migrate our data platform to AWS?
Yes, we handle migrations from on-premises, Azure, GCP, and legacy platforms to AWS. AWS Database Migration Service, Schema Conversion Tool, and custom migration pipelines ensure data accuracy. We run parallel validation and maintain rollback capabilities throughout the transition.
How do you secure data on AWS?
We implement VPC isolation, IAM least-privilege policies, KMS encryption, S3 bucket policies, and CloudTrail audit logging. Lake Formation provides fine-grained data access controls. AWS Config and SecurityHub monitor compliance continuously, and GuardDuty detects threats across your data infrastructure.
What about real-time data processing on AWS?
Kinesis Data Streams and Kinesis Data Firehose handle real-time ingestion at any scale. Kinesis Data Analytics or managed Apache Flink processes streams in real time. Lambda functions provide event-driven processing for lighter workloads. We design streaming architectures based on latency, throughput, and processing complexity requirements.
Should we use AWS-native services or open-source alternatives?
We balance managed service convenience with portability. AWS-native services like Glue and Redshift reduce operational overhead. Open-source alternatives like Airflow on MWAA and Spark on EMR provide portability. We recommend a pragmatic mix based on your lock-in tolerance and operational capabilities.
Can AWS data services support AI and ML workloads?
Absolutely. S3 and Glue prepare training data, SageMaker provides ML training and serving, and Redshift ML enables in-warehouse predictions. EMR runs distributed training with Spark MLlib. We design AWS data architectures that serve both analytics and ML workloads from shared data infrastructure.
/ get started /

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