This is how we can help you
DataOps / Data Automation
In a innovative manner, we apply classic and modern software engineering principles to simplify and automate the practice of data engineering
For us, a data pipeline is an application and it must take advantage of well established software practices and methodologies such as Version Control, Continuous Integration and Delivery, and Infrastructure as Code (IaC)
Designing and building systems to access, store and analyse data in scalable and cost-effective manner is as much an art as a science
We can help you to implement, modern, resilient and scalable data pipelines using best-of-breed data architectures, patterns and technologies
The data space is ever-evolving with new architectures, principles, methodologies and technologies such as
Data Warehouse vs Data Lake vs Data Fabric vs Data Mesh vs Data Lakehouse vs Data Ecosystem
Lambda and Kappa data architectures
ETL, ELT and EL/T
Come have a chat about the latest new technologies coming to the fore
We can fast track the maturity of your enterprise and solution architectures by defining or peer reviewing the technique and approach to address the data needs of your organisation at multiple levels such as
Enterprise data architecture: taxonomies and data engineering patterns
Solution architecture: high-level and low-level (detailed) technical solution designs
Data Governance and Quality
We can help you to define or review the Vision, Ambition and Roadmap of your Data Strategy. Also, we can assist with the processes and tools associated to Data Ownership, Stewardship, Quality Assurance, Master Data, Metadata and Lineage
There are many different ways on how you can structure your data entities so they are fit for purpose, so what's the best way? Well, it depends...
On the one hand, we can help you to understand the theory and show you strengths and differences of classic data modelling methodologies such as Inmon, Kimball, and Data Vault
On the other hand, we can help you to model highly conformed and contextualised data entities by identifying and using the best data modelling methodology for your use case, for example:
Is time and expertise an issue? Then, you may want to use a simple Kimball-based star schema
Is the resilience of your data model absolutely imperative? Then, you may want to Data Vault your data model
Advanced Analytics and Last Mile Analytics
So, what's the value of all the hard and expensive work done on architecting and engineering a new data platform if your business is unable to derive insights and drive tangible outcomes from data?
Advanced Analytics - we can have help to prepare, analyse, transform, model, and visualise your data to derive trends and make predictions that can be translated into actionable insights
Last Mile Analytics - we can help to define strategies to connect actionable insights to the right people at the right time
Coaching and Training
We can help to harden your skills on various data-related concepts and tools by providing real-world insights and knowledge to help you reach your destination faster
These are some of the questions that we can help you to answer via our coaching and training services:
What data architecture should I use: Data Warehouse, Data Lake, Data Fabric, Data Mesh or Data Lakehouse?
How should I model my data entities: Inmon, Kimball, or Data Vault?
How can I monitor the quality of my data?
What's dbt and how can I use it for my use case?
What's Airflow and how do I use it for my use case?
How can I use Snowflake in a cost-effective manner for my use case?