Sincere enthusiast Data Engineer that really loves what it does.
Starting at
$
15
About this service
Summary
FAQs
What are the benefits of implementing cloud-based data engineering solutions?
Cloud-based data engineering offers scalability, flexibility, and cost-efficiency, allowing organizations to scale their data processing capabilities on-demand, access a wide range of powerful data tools and services, and reduce infrastructure costs by paying only for what they use.
How long does it typically take to complete a data engineering project?
The duration of a data engineering project varies depending on factors such as project scope, complexity, and available resources. However, a typical project may take anywhere from a few weeks to several months to complete, with timelines determined during the initial scoping and planning phase.
Can you work with our existing data infrastructure and tools?
Absolutely. I specialize in integrating with existing data infrastructure and tools to ensure seamless compatibility and minimal disruption to your operations. Whether you're using on-premises databases, legacy systems, or cloud-based platforms, I can adapt my solutions to integrate with your existing ecosystem effectively.
What's included
Cloud Data Architecture Blueprint
- Description: A comprehensive blueprint outlining the architecture design for cloud-based data processing and storage, tailored to the client's specific needs and requirements. - Format: Digital document (PDF or Word) detailing the architectural components, data flow diagrams, and technology stack recommendations. - Quantity: 1 blueprint document. - Revisions: Up to 2 rounds of revisions based on client feedback.
ETL Pipeline Implementation
- Description: Fully functional Extract, Transform, Load (ETL) pipeline deployed on the client's chosen cloud platform, integrating with their data sources and target systems for seamless data processing. - Format: Cloud-based solution deployed on the client's cloud environment (e.g., GCP Dataflow, Azure Data Factory). - Quantity: 1 implemented ETL pipeline. - Revisions: Testing and debugging support provided for up to 1 week post-deployment.
Data Quality Assurance Framework
- Description: A robust framework for ensuring data quality throughout the data lifecycle, including data validation, cleansing, and error handling mechanisms, tailored to the client's data governance policies. - Format: Documentation outlining the data quality framework, including code snippets, validation rules, and best practices. - Quantity: 1 comprehensive data quality assurance framework document. - Revisions: Up to 2 rounds of revisions to fine-tune the framework based on client feedback and evolving requirements.
Duration
1 week
