At the core of every data engineering project is the overall data processing system architecture. Learn the core components and best practices for data processing in GCP.

• High level architectural concerns such as security, core components or subsystems, and data sovereignty.
• Data reliability, such as preparing data, monitoring core components, disaster recovery, and data integrity concerns.
• Data flexibility and portability, mapping business requirements, data cataloging, and data governance
• Designing data migrations on a high level. This includes gathering stakeholder requirements, GCP migration components, migration validation strategy, and best practices for dataset architecture.

The first component of any pipeline is to ingest whichever data you are working with. GCP has a number of proprietary technologies to ensure consistency and high-performance throughout your stack.

• Planning the data pipelines, such as data transformations, networking, and encryption.
• Building the data pipelines, from cleansing the data to identifying core technologies, data transformations, and data integrations.
• Deploying and operationalizing the data pipelines, such as implementing Cloud Composer and CI/CD pipelines.

Ingested data must be stored and how you choose to architect your storage layer will set the tone for your entire project. GCP offers a number of fully managed storage options to choose from. The correct choice will depend upon a number of trade-offs between availability, durability, cost, and performance.

• Selecting the storage systems among a series of performance, cost trade-offs, and life-cycle management
• Planning for using a data warehouse including modeling data, mapping business requirements, and data access patterns.
• Using a data lake including management, data processing, and monitoring.
• Building a data mesh from GCP native technologies, segmenting data across teams, and building a federated governance model.

Certainly, the primary purpose of ingesting and storing data is to analyze and report upon that data in support of organizational decision making. Managing the data analysis process can make the difference for a data engineering project. You could have the most efficient ingestion and storage systems, but if your analysis and presentation layer are lacking then that is all the stakeholders will remember.

• Preparing data for visualization. There are a few techniques which GCP recommends to ensure a high performance architecture to power bi tools. These include BigQuery materialized views, time granularity considerations and data loss prevention.
• Sharing data by defining rules to share data, publishing data sets, reports, and visualizations, and using Google's Analytics Hub.
• Exploring and analyzing data. This including data discovery and preparing data for feature engineering.

Effective data engineering includes ensuring the integrity and readiness of your data workloads. This is achieved with advanced automation techniques using tools including Cloud Composer, logging and monitoring using GCP native tools, and recovery from failure.

• Optimizing Resources including minimizing costs according to business requirements, proper resource management, and job management techniques.
• Designing automation and repeatability using tools such as Cloud Composer.
• Organizing workloads based upon business requirements. This includes knowing the tradeoffs and best uses for slot pricing, caching, and when it is best to use interactive or batch jobs.
• Monitoring and troubleshooting processes by ensuring data process observability, monitoring usage, troubleshooting error messages, and managing workloads.
• Maintaining awareness of failures and mitigating impact be designing for fault tolerance, running jobs in multiple regions or ones, accounting for bad data, and ensuring data availability.