Explained: The Big Deal About BigQuery

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BigQuery from Google helps manage terabytes of data and helps analyse that data with built-in features like business intelligence, machine learning, and geospatial analysis. Such a ready-to-use tool can be a gold mine for marketers without the need for deep technical expertise.

To reduce the time required to capture data in the log pipeline and more effectively transmit the log data to analysts and engineers, Twitter Sparrow, a new Twitter effort formed from an internal Hack Week project, employed cloud solutions like BigQuery – a fully managed and serverless data warehouse.

What exactly is BigQuery?

The greatest challenge for marketers is deriving insights from their data. Consider running a startup or a business and the need to analyse data. Initially, it’s easy with hundreds of data points, but as the operations expand, it rises to terabytes and petabytes. It’s hard to ingest, store and analyse such humongous data scalably.

When using tools like MySQL, the first thing to do before even considering a query is to set up an architecture that can accommodate this volume of data. It will be challenging to design this system because you need to decide on the RAM capacity, DCOS or Kubernetes, and other details. Additionally, there will be a need to set up and maintain a Kafka cluster if the company has incoming streaming data.

Enter BigQuery

BigQuery from Google has the capability to handle all such backend tasks. Data analysts or engineers at the firm only need to upload their CSV/JSON data files, and the tool takes care of the analytics. The need to handle massive sets of data—either millions in log data from tens of thousands of retail systems or IoT data from millions of vehicle sensors across the globe—gives birth to BigQuery.

To process and manage data, developers and data scientists can use client libraries with common programming languages like Python, Java, JavaScript, and Go, as well as BigQuery’s REST API and RPC API.

How does it work?

Combining cloud-based data warehouse and analytical tools, BigQuery helps focus on data.

• Firstly, when it comes to data storage, it uses a columnar storage structure ideal for analytical queries. BigQuery completely supports database transaction semantics and displays data in tables, rows, and columns.

• Secondly, it provides prescriptive and descriptive analysis. A data analyst can execute queries using external tables or federated queries, such as cloud storage, Google Sheets saved in Google Drive, or BigQuery, or conduct queries on data wherever it resides. Further, it supports multiple business intelligence tools, including third-party tools like Tableau and Power BI.

• Finally, BigQuery offers centralised control over data and computes resources, and Identity and Access Management (IAM) aids in securing those resources using the Google Cloud access architecture.

Need For Marketers

Marketers get a lot of raw data, and tools like BigQuery could help turn such data into actionable insights. Further, marketers today have tons of data stored across several platforms, thanks to the growth of the martech stack. With BigQuery, gathering all that data from multiple sources into a data warehouse, including ad, site analytics, pre-conversion, and post-conversion data, is quick and easy.

Many marketing platforms have limitations on the amount of historical data one can store. However, preserving all of it in a data warehouse is possible, providing a vast pool of information to research and draw conclusions.

Also, by linking BigQuery to a data analysis/visualisation tool, routine analytical activities, such as running reports with parameterised inputs, displaying visualisation based on complicated filters, and constructing hierarchical groups of dimensions, may be quickly and easily accomplished.

Conclusion

With multiple customer touchpoints and an increased marketing budget, marketers require different skill sets to aggregate fragmented data and convert them into insights. Google BigQuery is a significant step in this direction as it enables marketers to deploy sophisticated data-driven marketing strategies with little to no capital outlay, IT dependence, and—most importantly—without any deep technical expertise.

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