What You Need to Know About Google BigQuery

In digital marketing, Business Intelligence is key for success. An increasing number of businesses are utilising Google BigQuery as a tool for data-driven marketing.

Our data engineers have been using BigQuery to extract more value from our client’s data for years as a Google Cloud Platform Partner. 

What is BigQuery?

BigQuery is a fully managed enterprise data warehouse. It comes with built-in features like machine learning, geospatial analysis, and business intelligence to help you manage and analyse your data. The serverless architecture of BigQuery allows you to use Structured Query Language(SQL). The scalable, distributed analysis engine in BigQuery allows you to query terabytes of data in seconds and petabytes of data in minutes.

What are the Benefits of Using Google BigQuery?

Google BigQuery is capable of performing extremely complex queries in a short amount of time across large, complex data sets. It is also a great platform for businesses of all sizes, rather than being restricted to large corporations.

• Flexible Pricing Structure: BigQuery has the advantage of having a flexible pricing structure, which means businesses only pay for what they use. It is a cost-effective solution whether you just want to store your data or want to query. 

• Automatic Data Transfer Services: Google BigQuery includes a Data Transfer Service that enables you to schedule and fully manage the automatic transfer of data. From external data sources into BigQuery, including native integrations such as the Google Marketing Platform and Google Ads. As well as external sources such as Amazon S3. Users can now stream hit-level data directly into the data warehouse for interrogation, sorting, and analysis.

• Security: BigQuery is a highly secure data warehouse that is part of the Google Cloud Platform, so any data you store is safe. Administrators can restrict dataset access by roles, groups, and individual users. Allowing data to be shared easily while remaining secure. If a more granular look at certain datasets is required, users can also be granted access to view filtered data.

• Scalability: BigQuery, unlike many large data warehouses, still allows users to query their data using SQL rather than more complex syntax like map-reduce. This means that even though the volume of data can almost infinitely grow, querying it is still possible.

• Built-In Machine Learning and AI Integrations: BigQuery Machine learning (ML) allows users to create, train, and call machine learning models. These models can then be run on the BigQuery data sets, providing advanced predictive analytics that can be used to inform your digital strategy. Vertex AI, Google Cloud’s machine learning platform, can be integrated with BigQuery. We can also use it to invoke some TensorFlow models.

• Real-Time Analytics: As soon as data becomes available, we can stream it into BigQuery. This, combined with BigQuery’s ability to process data in seconds, means the platform provides real-time analytics, allowing businesses to make decisions as soon as the data indicates they should.

Why you should use Google BigQuery?
The most compelling reason to use it is for analytical querying. You can use BigQuery to run complex analytical queries on large datasets. Data requests include calculations, changes, merges, and other data manipulations. It has a high throughput and is designed to perform analytical queries that go beyond simple create, read, update and delete(CRUD) operations. 

Final Thoughts

At Alkye Services we help our clients keep up with market, competitor, and technological trends. We can also help you design and develop cutting-edge technology for new apps and websites. Our mission is to help you grow your company, stay competitive, and stay relevant to your customers.