If you have built or are building a Data Lake on the Google Cloud Platform (GCP) and BigQuery you already know that BigQuery is a fully managed enterprise data warehouse that helps you manage and analyze your data with built-in features like machine learning, geospatial analysis, and business intelligence. BigQuery’s serverless architecture lets you use SQL queries to answer your organization’s biggest questions with zero infrastructure management. BigQuery’s scalable, distributed analysis engine lets you query terabytes in seconds and petabytes in minutes. BigQuery maximizes flexibility by separating the compute engine that analyzes your data from your storage choices. You can store and analyze your data within BigQuery or use BigQuery to assess your data where it lives. Federated queries let you read data from external sources while streaming supports continuous data updates. Powerful tools like BigQuery ML and BI Engine let you analyze and understand that data. While in my opinion, BigQuery is the best Data Lake platform on the planet let me tell you why we need more than just BigQuery.
Knowledge worker tools
Knowledge workers, the people who build insights from data and help your organization make data-driven business decisions need data visualization tools. Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Additionally, it provides an excellent way for knowledge workers to present data to non-technical audiences without confusion. In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven business decisions.
What data visualization tools should I use?
There are many data visualization tools to choose from and your organization may have tools like Qlik Sense, Power BI, Tableau, or others, but let me tell you why Looker Studio is a better choice for a GCP BigQuery Data Lake. All the products I mentioned have similar visual graphs, search, presentation, and data management capabilities, but Looker Studio is significantly different in one specific area. Looker Studio does not require that data be ingested into an in-memory visual engine before using it. If you have built your Data Lake on BigQuery then the last thing you want to do is extract data from BigQuery so that you can visualize the data. Instead, Looker Studio uses the speed and performance of BigQuery and the built-in BigQuery analytics engine to optimize performance. There is no need to extract data from BigQuery and there is no need to keep updating in-memory data. This is a significant performance and data management benefit for data and knowledge workers.
Looker Studio Versions
Looker Studio comes in two versions. The first version of Looker Studio is now and has always been free. The second version is called Looker Studio Pro and costs $9.00 per month per user. Recognizing the appeal of Looker Studio, Google introduced Looker Studio Pro, a paid version enriched with additional features. Looker Studio Pro enhances team collaboration, governance, and support. If your Data Like is built on the Google Cloud Platform and BigQuery I recommend the use of Looker Studio and Looker Studio Pro for data visualization.
Perficient’s Cloud Data Expertise
The world’s leading brands choose to partner with us because we are large enough to scale major cloud projects, yet nimble enough to provide focused expertise in specific areas of your business. Our cloud, data, and analytics team can assist with your entire data and analytics lifecycle, from data strategy to implementation. We will help you make sense of your data and show you how to use it to solve complex business problems. We will assess your current data and analytics issues and develop a strategy to guide you to your long-term goals.
Download the guide, Becoming a Data-Driven Organization with Google Cloud Platform, to learn more about Dr. Chuck’s GCP data strategy
