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Migrating to BigQuery: A Well-Designed Repo for Data Orchestration

As we continue to grow our analytics company, serving the retail and consumer packaged goods (CPG) industries, we're faced with the challenge of migrating to a new data warehouse from PostgreSQL. In this article, we'll explore our journey to BigQuery and the importance of a well-designed and centralized repository for running tasks.

The Need for a Centralized Repository

In today's fast-paced data landscape, it's crucial to have a centralized repository that not only enables task execution but also concentrates subject matter expertise within your team. This allows knowledge sharing without wasting hours in meetings, where one person runs scheduled tasks in BigQuery and another builds Cloud Functions to load data.

We've seen firsthand the benefits of having a single, unified platform for our data operations. For instance, a friend of ours who owns a consulting firm that partners with Snowflake told us that Snowflake doesn't even bother trying to sell its services to prospective clients using BigQuery because it's that good.

Airflow: A Powerful Tool for Task Orchestration

While it's tempting to use a batteries-included service like Airflow, we believe it's essential to have a well-designed and centralized repository for running tasks. This approach allows us to maintain control over our data operations and avoid vendor lock-in.

We've had success with Snowflake in the past, and while it's a powerful tool, we're excited about BigQuery's capabilities. However, we do acknowledge that Snowflake has a lot of marketing muscle behind it, which can be misleading. It's crucial not to confuse Google's marketing efforts for BigQuery's actual limitations.

Building a Complex Data Warehouse in BigQuery

One of our primary concerns is the feasibility of building a complex data warehouse within BigQuery. We're interested in creating a system that can handle multiple clients, geographies, and objects talking to each other seamlessly.

While we've played around with BigQuery and are impressed by its ease of use and pricing model, we're not convinced it's the right tool for every job. Our worry is that BigQuery requires a deeper understanding of Google Cloud Platform (GCP) tools to execute properly.

The Role of Airflow in BigQuery

We've explored using Airflow with BigQuery and found that while it does provide a lot of structure and best practices built into its DB management, it's not necessarily a replacement for orchestration. In fact, we believe that having everything in one spot, staged, and scheduled would be ideal.

Including Python jobs would be fantastic for our use case, as we primarily work with SQL and Python. We're curious to know if BigQuery provides the level of development and ease of use we're looking for in a data warehouse solution.

As we migrate from PostgreSQL to BigQuery, we're excited about the potential benefits of this cloud-based data warehousing solution. While it's not without its limitations, BigQuery offers an easy pricing model and impressive capabilities. By designing a well-organized repository that centralizes our data operations, we can ensure seamless knowledge sharing within our team.

Whether you're considering Snowflake or BigQuery for your data warehouse needs, it's essential to weigh the pros and cons of each solution carefully. In our experience, having a clear understanding of GCP tools and Airflow is crucial for executing complex data warehousing projects effectively.

Atomix virtualdj 2018 pro infinity 8.3.4787 multilingual