- BigQuery is a serverless data warehouse that frees up data engineers from worrying about infrastructure management, especially for large-scale data.
- Businesses can find answers to complex queries using SQL along with streaming support for continuous, real-time data.
- Merit’s data and business intelligence engineers often recommend BigQuery in their strategic guidance in building optimised data ecosystem that are custom designed for each business. Here’s why:
How Toyota Canada used BigQuery to run targeted marketing campaigns
Toyota Canada leveraged BigQuery to run various marketing campaigns, targeted towards their website visitors. Based on various data points of the visitors and their user personas, the relevant campaign (and creative) was served to better convert new prospects.
Overall, it was able to improve the engagement of website visitors six fold. They also lowered their cost-per-customer acquisition by about 20% and saw a leap in the sales of the Toyota Corolla within a few months of the campaign.
One of the key reasons for this success was the company was able to leverage AI and ML capabilities of the BigQuery platform, which made its analytics-based predictions more accurate.
Storage and Analytics with BigQuery
BigQuery is a serverless, fully-managed enterprise data warehouse with built-in features such as machine learning, business intelligence and geospatial analysis. It enables managing and analysing data and requires zero infrastructure management to answer the organisation’s questions using SQL queries. It also comes with a scalable, distributed analysis engine that allows querying terabytes and petabytes of data quickly.
BigQuery can be used for:
- Loading and exporting data
- Running interactive or batch queries and storing data in virtual tables
- Managing data as projects, datasets, jobs, and tables
Storing and computing are decoupled in BigQuery, providing businesses with the flexibility to store and analyse data within the warehouse or store it elsewhere and use the tool for analysis.
Data can be stored in a columnar format optimised for analysis in BigQuery, which supports database transaction semantics (ACID). In addition, BigQuery ensures high availability by automatically replicating data across multiple locations.
As the query resources and storage resources are allocated dynamically based on requirement and usage, resources are provisioned based on usage. It ensures efficient storage by using several formats including proprietary format, proprietary columnar format, query access pattern, Google’s distributed file system, among others.
Streaming support allows continuous data updates while federated queries provide access to data from external sources. Machine learning, business intelligence, ad hoc analysis, and geospatial analytics can be used for descriptive and prescriptive analysis.
Identity and Access Management (IAM), the access model used across Google Cloud, facilitates secure, centralized data and compute resources management in Big Query.
Key Features of BigQuery
The features of BigQuery include:
- BQ Omni – A Multi Cloud Functionality: BigQuery enables data analysis across multiple cloud platforms securely using BigQuery Omni, which runs on Anthos clusters managed by Google Cloud. This enables breaking down silos and getting a holistic view of all the company’s data. Also, it provides flexibility and a consistent data experience across clouds.
- BQ ML – Built-in ML Integration: Using simple SQL queries, BQ ML facilitates the creation and execution of Machine Learning models in BigQuery. It does not require ML-specific knowledge and programming skills and uses models such as linear regression, Matrix Factorisation, Time Series, Binary and Multiclass Logistic regression, and Deep Neural Network based on the data. As it does not require data export, model development is faster using existing BI tools and spreadsheets.
- BQ BI Engine – Foundation for BI: An in-memory analysis solution, it provides high concurrency and speed when analysing the data stored in BigQuery. The SQL Interface allows it to interact with other BI tools such as Looker, Power BI, Tableau, etc. Its ability to integrate with custom applications helps with data exploration and analysis.
- BQ GIS – Geospatial Analysis: It converts latitudes and longitudes columns into geographical coordinates to provide information about location and mapping.
- BQ Data Transfer Service – Automated Data Transfer: This enables automating the data transfer into BigQuery periodically. This transfer can be scheduled easily without any coding. In case there are any gaps or outages at the time of ingestion, adding data backfills is also possible.
- BQ Sandbox – Free Access: The BigQuery Sandbox allows prospective customers to experience the features of BigQuery and the Cloud Console before investing in it. Users can run all the applications in a separate environment as part of the trial and decide to buy BigQuery if satisfied.
Use Cases and Benefits of BigQuery
Common use cases of BigQuery include:
- Running heavy and complex analytical queries on high-volume data sets (in addition to streaming data)
- Running queries on the data stored in the cache
- Reduce the load on the relational database by running the queries on a third-party service
Some of the benefits of BigQuery include:
- Ease of use and quick setup
- Seamless scalability
- Reduced time-to-insights
- Data protection
- Lower TCO
Merit Group’s expertise in cloud BI
At Merit Group, we work with some of the world’s leading B2B intelligence companies like Wilmington, Dow Jones, Glenigan and Haymarket. Our data and engineering teams work closely with our clients to build data products and business intelligence tools. Our work directly impacts business growth by helping our clients to identify high-growth opportunities.
Our team of data engineers can help you with faster time-to-insights using BigQuery, which is scalable, secure and has built-in machine learning features. BigQuery promises a 26%–34% lower three-year TCO for running analytics at scale when compared to other cloud data warehouse alternatives. It also lets you scale up data from bytes to petabytes in no time and at no additional operational overhead.
We’re experts in Cloud BI, helping companies streamline and migrate to a truly next-generation BI stack.
Our team also brings to the table deep expertise in building real-time data streaming and data processing applications. Our experience in data engineering is especially useful in this context. Our data engineering team brings to fore specific knowledge in a wide range of data tools including Airflow, Kafka, Python, PostgreSQL, MongoDB, Apache Spark, Snowflake, Redshift, Athena, Looker, and BigQuery.
If you’d like to learn more about our service offerings or speak to a BigQuery expert, please contact us here: https://www.meritdata-tech.com/contact-us
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