Data Engineering

Consulting
Data Engineering

Cloud-Native

Cloud Data Services

Data
Engineering

Data is the greatest driving force in business today. We build to answer the questions that matter: where is your data stored, is it safe, and are you getting the most from it?

We specialise in maximising your data's potential — designing, building, and managing robust pipelines, architectures, and warehouses in the cloud so you can make decisions with confidence.

What we deliver

Data ingestion and integration

Data warehousing and architecture

Data pipelines and architecture

Real-time data processing and analytics

Data governance and quality

Our Technology Stack

The Data Pipeline

We work across the full data lifecycle — from raw ingestion through to analysis-ready storage.

01Ingestion

Apache Kafka

A distributed streaming platform that enables high-throughput and provides low-latency, fault-tolerant, and scalable data processing.

AWS Kinesis

A fully managed service that makes it easy to collect, process, and analyse real-time, streaming data.

02Storage

Apache Cassandra

A distributed, NoSQL database designed to handle large amounts of data across many commodity servers.

Amazon S3

A cloud-based object storage service that provides highly durable and scalable storage for data.

03Processing

Apache Spark

An open-source data processing engine that provides high-level APIs in Java, Python and Scala.

AWS Glue

A fully managed extract, transform and load (ETL) service that makes it easy to prepare and load data for analysis.

Beyond pipelines — store it at scale

Storage & Architecture

Data Lakes

Unlock the full potential of your data with tailored lake solutions. We assist businesses in storing large amounts of structured and unstructured data — creating a centralised repository that encourages data-driven innovation and enhances business intelligence.

Unlike traditional data warehouses, data lakes enable organisations to store data in raw format without a predefined schema — providing unified storage, facilitating exploration, and supporting advanced analytics at any scale.

Data Lake Solutions:

Amazon S3/LakeFormation:

Amazon S3 is a popular storage platform for building and storing data lakes due to its high availability and low latency access. It's especially suitable for organisations using other AWS services or database engines like Aurora. S3 integrates seamlessly with AWS Glue, Amazon Athena, and Amazon Redshift for data cataloguing, querying, and warehousing. However, navigating the AWS ecosystem requires specialised expertise due to its complexity. Without a metastore/catalogue solution like Glue, S3 lacks a metadata structure for advanced data management tasks.

Google Cloud Platform / Big Lake:

Google offers two options for building data lakes: Google Cloud Storage (GCS) for storing data and BigLake for building a distributed data lake across warehouses, object stores, and clouds. GCS is suitable for staying within Google's cloud ecosystem. BigLake is ideal for managing distributed data across lakes, warehouses, and clouds, simplifying access control management. BigLake also offers added structure and governance with Dataplex, making it an intriguing data lakehouse option. This allows users to manage their data as if it were BigQuery tables.

Azure Data Lake Storage:

Azure Data Lake Storage (ADLS) is a prominent data lake vendor, particularly suitable for businesses using or considering Azure services. ADLS is implemented as a set of capabilities within the Blob Storage service of an Azure Storage account. It stands out from competitors with its focus on enterprise-grade security, data governance, and compliance features. ADLS provides built-in data encryption, granular access control policies, and comprehensive auditing capabilities for meeting security and compliance requirements.

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