Data Engineering

Transform data into the building blocks of your success.

VedRock Consulting helps you drive critical business insights by building and deploying scalable data processing pipelines.

What does it mean for you?

Data-driven insights can unlock exciting opportunities for your business. However, turning raw data into actionable insights is demanding task. In most cases, it requires skillful combining data from various systems with the support of a solid data engineering foundation and efficient data pipelines to prepare, integrate, and enrich your data for business intelligence, advanced analytics, machine learning, and AI.

What are examples of data engineering services that we can deploy immediately to improve your data practice?

Data Engineering Strategy Development

Clearly articulate the purpose of the data engineering practice and its alignment with business objectives.

Design a data strategy document with a special focus on data engineering practice.

Identify a short-term and long-term goals (e.g., improved data availability, cost-effective storage, advanced analytics enablement).

Articulate key performance indicators (KPIs) for success (e.g., pipeline uptime, data latency, stakeholder satisfaction).

Assess Current State and Identify Requirements

Conduct a comprehensive audit of existing data assets, sources, and workflows.

Identify key business use cases that require data engineering support (e.g., real-time reporting, centralized analytics).

Understand gaps in the organization’s ability to manage, process, and utilize data effectively.

Collaborate with stakeholders to prioritize needs.

Design and Implement Core Data Engineering Processes

Data ingestion: Collecting data from various internal and external sources.

Data transformation: Cleaning and transforming raw data into usable formats.

Data storage: Structuring data for optimal accessibility and performance.

Data delivery: Making data available to end-users and systems in an efficient manner.

Implement Key Use Cases

Collaborate with stakeholders to identify and prioritize impactful use cases.

Examples:

Automating the extraction of data from critical business systems.

Creating a unified data repository for centralized reporting and analysis.

Delivering curated data for specific teams or applications.

Automate and Optimize

Implement CI/CD pipelines for data pipelines to ensure reliable deployments.

Automate data workflows using orchestration tools to minimize manual intervention.

Set up alerts and reporting mechanisms to detect and address data issues promptly.

Monitor and optimize data pipeline performance

Monitoring and Performance Optimization

Real-time monitoring of data workflows and systems.

Alerts and issue resolution protocols to address disruptions quickly.

Regular performance reviews and optimizations.

DataOps

We use DataOps principles to govern the flow of data and code moving through each step of the data lifecycle. Our process includes best industry practices for test automation, source code sharing, team collaboration, framework orchestration, and workflow automation.

Need to collect, store, transform, and prepare data for use in advanced analytics?

We can help. Let's talk!