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/ Data Engineering /

Data Lake Services Malta

Data lake services in Malta. Design and implement scalable data lakes and lakehouse architectures on AWS, Azure.

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/ the solution /

Data Lake 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.

  • Data Lake Architecture Design

    Design scalable data lake architectures with proper zone organisation including raw landin…

  • Multi-Format Data Ingestion

    Ingest structured, semi-structured, and unstructured data from any source into your unifie…

  • Data Lake Governance & Cataloguing

    Catalogue, classify, and secure data lake contents with automated discovery, metadata mana…

  • Lakehouse Implementation

    Modern lakehouse architectures using Delta Lake, Apache Iceberg, or Apache Hudi that add A…

/ how it works /

Live in weeks, not months.

We audit your data sources, volumes, formats, access patterns, and analytical requirements to design a lake architecture that addresses your specific needs. We identify data that belongs in the lake versus data better served by other storage patterns.

We design the lake architecture with appropriate zones, partitioning strategies, file formats, and governance layers. Medallion architecture patterns define clear boundaries between raw, cleaned, and curated data with transformation rules for each transition.

We deploy the data lake on your chosen cloud platform with proper security, networking, access controls, and cost management. Infrastructure-as-code ensures the environment is reproducible, auditable, and maintainable.

We build automated ingestion pipelines for each data source, handling batch loads, streaming ingestion, and file-based transfers. Each pipeline includes metadata tagging, quality validation, and cataloguing for ingested data.

We configure data cataloguing, classification, lineage tracking, and access control policies. Users discover and access data through governed interfaces that enforce security and compliance requirements.

We connect your data lake to analytical tools including Spark, Databricks, BI platforms, and ML environments. Query engines like Athena, Synapse, or Trino provide SQL access to lake data for analysts and dashboards.

/ what you get /

Everything you need. Nothing you don't.

Data Lake
Architecture Design
Multi-Format Data
Ingestion
Data Lake Governance
& Cataloguing
Lakehouse Implementation
/ 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.

Data lake architecture reducing storage costs by 60%

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.

Centralised lake storage unifying 12+ data sources

Read case study →
/ questions /

Data Lake Services FAQ

What is the difference between a data lake and a data warehouse?
A data lake stores raw data in its original format at low cost, supporting diverse processing workloads. A data warehouse stores structured, modelled data optimised for analytical queries. Modern lakehouse architectures combine both by adding warehouse-like capabilities to lake storage. Most organisations benefit from both patterns serving different needs.
How do you prevent a data lake from becoming a data swamp?
Data swamps occur when lakes lack governance, cataloguing, and quality controls. We prevent this through automated metadata cataloguing, zone-based organisation, data quality checks at ingestion, access controls, and retention policies. Every dataset is documented, classified, and discoverable through a central catalogue.
Which cloud platform is best for data lakes?
AWS S3 with Lake Formation is the most mature option with the broadest ecosystem. Azure Data Lake Storage integrates well with the Microsoft stack. Google Cloud Storage with BigQuery provides excellent serverless querying. Your existing cloud presence typically determines the best choice, and we support all three platforms.
What is a lakehouse and should we build one?
A lakehouse adds ACID transactions, schema enforcement, and fast SQL queries to data lake storage using technologies like Delta Lake or Apache Iceberg. If you need both the flexibility of a lake and the reliability of a warehouse, a lakehouse provides both without maintaining separate systems. It is increasingly the recommended default architecture.
How do you handle security and access control?
We implement fine-grained access controls using Lake Formation, Unity Catalog, or cloud IAM policies. Column and row-level security restricts data visibility based on user roles. Encryption at rest and in transit protects sensitive data. Audit logging tracks all data access for compliance.
Can we query data lake data with SQL?
Yes, query engines like AWS Athena, Azure Synapse serverless SQL, Google BigQuery, and Apache Trino provide full SQL access to data lake files. With lakehouse table formats like Delta Lake or Iceberg, SQL queries perform comparably to traditional data warehouses for most analytical workloads.
How do you handle schema evolution in a data lake?
Lakehouse formats like Delta Lake and Iceberg support schema evolution natively, allowing columns to be added, renamed, or reordered without breaking existing queries. We design ingestion pipelines that handle upstream schema changes gracefully, logging changes and alerting when unexpected modifications occur.
What about data lake costs?
Data lake storage on cloud object storage is extremely cost-effective, typically pennies per GB per month. Compute costs for processing depend on workload patterns. We optimise costs through storage tiering, partition pruning, file compaction, and appropriate compute sizing. Most organisations find data lakes significantly cheaper than equivalent database storage.
/ get started /

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