DataOps Solutions

All of our services employ DataOps principles and we can tailor our solutions to your needs.

Our DataOps solutions streamline your data analytics pipeline in order to provide enhanced product scalability, reliability, security and cost effectiveness. To achieve this we can work with your team to implement best practices and tools that subsequently help you to automate your data workflows and improve their transparency.

Repeatability, Reusability and Infrastructure as Code

In AWS repeatability and reusability are achieved through Infrastructure as Code (IaC) practices using tools such as the AWS Cloud Development Kit or AWS CloudFormation, which for example, allow for consistent and automated deployment of infrastructure across multiple environments.

NFRs and Well-Architected Cloud Framework

The NFRs (Non-Functional Requirements) and Well-Architected Cloud Framework’s data analytics lens in AWS focuses on evaluating and optimising data solutions to ensure they meet best practices and organisational needs.

Iterative Building, Testing (Continuous Integration) and Deploying (Continuous Deployment)

Iterative building, testing (Continuous Integration) and deploying (Continuous Deployment) involves continuously developing, validating and deploying code changes in small increments. The aim of this is to subsequently improve data quality and accelerate delivery via robust automation and fast requirements feedback loops.

Automation and Orchestration

Automation and orchestration of data pipelines in AWS involves using services such as AWS Glue, Step Functions, Apache Airflow or Lambda to streamline and manage the end-to-end processes of data extraction, transformation and loading, therefore ensuring efficient and reliable data workflows.

Monitoring, Logging and Observability

Monitoring, logging and observability in data pipelines involve tracking system metrics, recording data journey events, validating static data quality and sending system status alerts with tools such as Amazon CloudWatch, Glue DQDL rules and AWS X-Ray to ensure reliable, efficient and transparent data processing.

Service Level Objectives (SLOs) and Measurement

Service Level Objectives (SLOs) and measurement involve defining specific performance goals, tracking relevant indicator metrics and assessing these metrics to ensure that services meet their expected quality, performance and availability standards.

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Ready to solve your data challenges? Contact us to find out more about our services and pricing