¿Cómo se puede planificar un proyecto de ingeniería de datos?

I usually begin with understanding what problem we are trying to solve, then look into solutions and lastly it's technical implementation. 1. Talk to stakeholder/customers gather requirements and dive deep into understanding them. 2. Understand scope of project and its impact. 3. Complexity of data processing, pipeline and consumption. 4. Due Diligence: Use existing solution if it exists. Steal/copy with pride. You don;t alway need to re-invent the wheel. 5. Break down Business problem into logical tasks and then look into process and tech solutions required to design.

>Scope and Objectives: Define business problems, outcomes, and data requirements. Identify data sources, formats, and quality needs >AWS Services Selection: consider Amazon S3 for scalable object storage or Amazon Redshift for data warehousing. For processing, evaluate options like AWS Glue for ETL or EMR for big data processing. >Data Security and Compliance:Ensure compliance with regulations such as GDPR or HIPAA. >Monitoring and Optimization: Set up monitoring using AWS CloudWatch for resource utilization and performance metrics. Optimize costs by leveraging AWS Cost Explorer and Trusted Advisor.

Clearly define the goals and objectives of the project. Define and document the problem statement. Define the scope of the project, including the data sources, technologies, and deliverables.

1. Define project goals and requirements. 2. Identify data sources and systems. 3. Design data pipelines and workflows. 4. Choose appropriate tools and technologies. 5. Develop and test the data infrastructure. 6. Implement data quality and monitoring mechanisms. 7. Deploy the solution and monitor performance. 8. Iterate and improve based on feedback and insights.

Understanding your goals and the data you already have is the first step in planning a data engineering project. After that, you have to find out how to compile and merge the data from various sources while maintaining its integrity. After that, you decide how to process it and where to keep it all in order to obtain the answers you require. Throughout the process, you monitor that everything is functioning properly and that the data is of high quality. When all is well, you share the knowledge you've gained and monitor the situation to ensure it continues to function well. And you make adjustments based on what you discover if anything needs to be improved.

Understand your data sources is critical step on data engineering project because: > Evaluate how ingest this dataset & and what tool use to do it. > What data quality metrics implement on it. > What data convertions process implement. > How data integration can do. > How business teams understand their data and use it. > What metadata will need to discovery and exploration the dataset to use in the future. > Solve design question like volume, velocity or variaty of data.

Following scoping, data exploration and analysis form the subsequent phase of a data engineering project. Here, delving into data characteristics, distribution, and patterns is pivotal. Identifying anomalies and understanding processing and transformation requirements aids in uncovering data quality issues, integration challenges, and modeling options. This exploration validates project feasibility and value, generating insights for continued investigation. Through meticulous analysis, data engineers refine project strategies and ensure alignment with overarching objectives.

After data exploration, the subsequent step in a data engineering project is designing the data pipeline. This entails selecting tools, technologies, and frameworks for each stage, encompassing data ingestion, cleansing, enrichment, transformation, storage, access, and delivery. Defining data flow, schema, quality checks, governance policies, security measures, and documentation standards are also integral. By meticulously designing the data pipeline, engineers ensure efficient data processing and adherence to quality and security standards, facilitating seamless data flow and access for stakeholders.

->Unit Testing: Test individual components of the data pipeline using AWS Lambda for serverless functions or AWS Glue for ETL scripts. ->Integration Testing:Validate interactions between different components using AWS Step Functions for orchestrating workflows or AWS Data Pipeline for scheduling and managing data processing tasks. ->System Testing: Test the entire data pipeline end-to-end using AWS services like Amazon S3 for data storage, AWS Glue for data transformation, and Amazon Redshift for data warehousing.

Following data pipeline design, thorough testing is crucial before production deployment in a data engineering project. This entails verifying functional and non-functional requirements like data accuracy, completeness, reliability, scalability, and security. Testing also validates expected outcomes such as insights, reports, and dashboards, ensuring value delivery. Employing diverse techniques like unit, integration, system, and user acceptance testing, along with data quality testing, ensures comprehensive validation of the data pipeline. By rigorously testing the pipeline, engineers mitigate risks and ensure seamless functionality, bolstering confidence in the system's performance.