A Data Scientist‘s Guide to Google BigQuery and Data Studio

ByJordan Brown

As a data scientist, you know the importance of having the right tools to efficiently store, process, analyze and visualize massive datasets. Two essential tools offered by Google Cloud that every data scientist should master are BigQuery, a fully-managed, petabyte-scale data warehouse, and Data Studio, a powerful reporting and dashboarding solution.

In this in-depth guide, we‘ll explore what makes these tools so valuable for data science, dive into their key features and use cases, and walk through practical examples to help you get started. Along the way, I‘ll share some insights and best practices based on my experience as an AI/ML expert.

Why BigQuery for Data Science?

Google BigQuery is a serverless, highly-scalable, and cost-effective cloud data warehouse that‘s transforming how data scientists work with big data. Some of the key benefits that make BigQuery an essential part of the data science toolkit include:

  1. Unmatched scale and speed – BigQuery can run complex queries on multi-petabyte datasets in mere seconds, thanks to its unique architecture that decouples storage and compute. It can scan terabytes of data per second, enabling data scientists to get insights from massive datasets in near real-time.

  2. Familiar SQL interface – Data scientists can leverage their existing SQL skills to quickly get started with BigQuery. It supports standard SQL dialect and common analytic functions. At the same time, BigQuery extends SQL with powerful features like nested and repeated fields, arrays, structs, and user-defined functions (UDFs).

  3. Seamless integration with GCP ecosystem – BigQuery integrates seamlessly with other Google Cloud services frequently used in data science pipelines, such as Cloud Storage for staging data, Dataflow for ETL and batch processing, Pub/Sub for streaming ingestion, and AI Platform for machine learning.

  4. Built-in machine learning – With BigQuery ML, data scientists can build and deploy ML models using familiar SQL queries, directly inside BigQuery, without moving data or needing to be an ML expert. Supported model types include linear regression, binary and multi-class logistic regression, k-means clustering, matrix factorization, time series, and more.

  5. Security and compliance – BigQuery provides robust security, identity, and access control features, including Cloud Identity and Access Management (IAM), column-level access controls, and Cloud Key Management Service (KMS) encryption. It is also certified for industry standards like ISO 27001, HIPAA, FedRAMP and others.

According to a Forrester study, BigQuery can deliver 676% ROI over three years and 81% cost savings compared to on-premises data warehouses, along with dramatically improved performance, scalability, security and availability (Source: Total Economic Impact of Google Cloud‘s BigQuery).

Getting Started with BigQuery

To start using BigQuery, you‘ll need to set up a Google Cloud project and enable billing. Once you have a project, navigate to the BigQuery console and click the + ADD DATA button to create a new dataset or load data into BigQuery.

There are several ways to get data into BigQuery:

  • Manual upload – You can upload data files (CSV, JSON, Avro, ORC, Parquet) up to 10 MB directly through the BigQuery console
  • Cloud Storage transfer – Larger data files can first be uploaded to Google Cloud Storage, then loaded into BigQuery via a load job
  • Streaming inserts – Data can be streamed into BigQuery one record at a time using the streaming API, typically from live data sources
  • Queries from external sources – BigQuery can query data directly from external sources like Cloud Storage, Cloud SQL, Cloud Bigtable and Google Drive via external tables
  • Public datasets – BigQuery hosts a variety of public datasets that you can access and analyze for free, such as Stack Overflow, COVID-19 open data, and GitHub repos

Once your data is in BigQuery, you can start running SQL queries on it via the query editor in the BigQuery console, bq command-line tool, or client libraries. BigQuery supports standard SQL (SQL 2011) and has all the usual SQL clauses like SELECT, FROM, WHERE, GROUP BY, HAVING, ORDER BY, LIMIT, etc.

However, to get the most out of BigQuery and optimize query performance, there are some key concepts and best practices data scientists should be aware of:

Optimizing Storage with Partitioning and Clustering

BigQuery allows you to partition and cluster tables to improve query performance and reduce costs. Partitioning divides a table into smaller segments based on a date or timestamp column, while clustering co-locates related data based on a set of columns you specify.

With partitioning, queries only scan the relevant partitions instead of the entire table. BigQuery supports ingestion-time partitioning based on a _PARTITIONTIME pseudo-column or integer range partitioning. Partitioning is most effective for large tables (> 1 GB) that are queried with predicates or aggregations over the partitioning column.

Clustering, on the other hand, can dramatically improve the performance of queries that aggregate or filter on the clustering columns. Up to four clustering columns can be specified. BigQuery automatically reclusters data in the background to maintain performance as data is added or modified.

As a best practice, data scientists should use partitioning and clustering whenever possible on large tables that are queried frequently. Tables can be partitioned and clustered at creation time using the CREATE TABLE statement or by configuring load jobs.

Caching and Materialized Views

To speed up frequently-used queries, BigQuery offers two features – query caching and materialized views.

Query results are automatically cached for 24 hours and can be reused by subsequent queries that have the same syntax and use the same tables. Using cached results avoids re-scanning tables and can significantly improve performance.

Materialized views take caching one step further by allowing you to store the results of a query as a separate table. Unlike normal views, which are virtual tables that run the underlying query each time they‘re accessed, materialized views are precomputed and periodically refreshed. This makes them ideal for storing summarized or aggregated data that is queried often.

Approximate Aggregations

Sometimes in data science you don‘t need perfectly exact results and trading off a bit of accuracy for a large gain in performance can be worthwhile. This is where BigQuery‘s approximate aggregate functions come in handy.

BigQuery provides functions like APPROX_COUNT_DISTINCT, APPROX_QUANTILES, APPROX_TOP_COUNT, and APPROX_TOP_SUM that use sophisticated algorithms like HyperLogLog++ to estimate results with high accuracy in a fraction of the time of their exact counterparts. These functions can achieve up to 99.9% accuracy while running up to 100x faster.

As a data scientist, you should consider using approximate aggregations when a small margin of error is acceptable and you‘re dealing with very large datasets or need near real-time results.

Machine Learning in BigQuery

One of the most exciting BigQuery features for data scientists is BigQuery ML, which allows you to create and execute machine learning models using SQL queries. With BQML, you can train ML models directly on massive structured or semi-structured datasets stored in BigQuery, without exporting data or needing to be an ML specialist.

Supported model types include:

  • Linear regression for forecasting
  • Binary and multi-class logistic regression for classification
  • K-means clustering for segmentation
  • Matrix factorization for recommendations
  • Time series for analyzing temporal data
  • Deep Neural Networks with TensorFlow in SQL
  • AutoML Tables for automatic model selection and tuning
  • Boosted Tree models for regression and classification using XGBoost

Training a model with BQML is as simple as using the CREATE MODEL statement and providing a SQL query that specifies the model type, training data, label column, and input features. You can evaluate trained models using ML.EVALUATE and get predictions on new data with ML.PREDICT.

For example, here‘s how you would train a logistic regression model to predict customer churn:

CREATE OR REPLACE MODEL `churn_model`
OPTIONS
  (model_type=‘logistic_reg‘,
  input_label_cols=[‘Churn‘]) AS
SELECT
  customerID,
  gender,
  SeniorCitizen,
  Partner,
  Dependents,
  tenure,
  PhoneService,
  MultipleLines,
  InternetService,
  Contract,
  PaperlessBilling,
  PaymentMethod,
  MonthlyCharges,
  Churn
FROM
  `telecom_churn`

And here‘s how you would get predictions on new customer data:

SELECT
  *
FROM
  ML.PREDICT(MODEL `churn_model`,
    (
    SELECT
      customerID,
      gender,
      SeniorCitizen,
      Partner,
      Dependents,
      tenure,
      PhoneService,
      MultipleLines,  
      InternetService,
      Contract,
      PaperlessBilling,
      PaymentMethod,
      MonthlyCharges
    FROM
      `new_customers`
  ))

BQML democratizes machine learning by putting the power of ML into the hands of data scientists and analysts who already know SQL. It eliminates the need for complex data movement and integration with external ML tools. Since the trained models reside in BigQuery, you can readily use them for analytics and reporting.

Data Studio: Visualizing Insights from BigQuery

While BigQuery lets you extract insights from massive datasets, Google Data Studio provides the tools to turn those insights into beautiful, interactive dashboards and reports that can be easily shared with stakeholders across your organization.

Data Studio offers a user-friendly, drag-and-drop interface for building visualizations and supports a variety of chart types including time series, bar charts, pie charts, scatter plots, geo maps, scorecards, tables, and more. You can customize every aspect of your charts and controls down to the colors, fonts, backgrounds and data labels.

Connecting Data Studio to BigQuery is a breeze – simply create a new data source, select BigQuery, and authenticate with your Google account. Then choose the project, dataset and table you want to visualize. You can also provide a custom SQL query to tailor the data for your specific needs.

One of Data Studio‘s most useful features for data scientists is the ability to define calculated fields to create new metrics and dimensions based on complex formulas. This allows you to perform additional data transformations and enrich your datasets without having to modify the underlying data in BigQuery.

Data Studio also allows you to blend data from multiple sources, including BigQuery and external sources like Google Analytics, Google Sheets, MySQL, and uploaded CSV files. You can blend data using join keys or by geo fields, making it possible to create rich, cross-channel reports all in one place.

With Data Studio‘s built-in sharing and collaboration features, you can easily publish your reports for others to view and embed them in websites and apps. Reports are fully interactive, allowing viewers to filter the data, hover over data points for more details, and drill-down into the underlying data.

Real-World Examples and Success Stories

Organizations of all sizes and industries are using BigQuery and Data Studio to harness the power of big data and AI for transformational insights and use cases. Here are a few examples:

  • Twitter uses BigQuery to analyze 230 million events per minute for ad analytics, storing over 100 PB of data. BigQuery forms the foundation of Twitter‘s data platform and has helped drive a 10% lift in engagement and 10% higher CTR on ads (Source: Twitter‘s BigQuery Story)

  • The Home Depot combines streaming and batch data from its stores, online, and supply chain in BigQuery to provide near-real-time inventory tracking, dynamic pricing, and predictive inventory planning (Source: The Home Depot: Big Data, Big Insights)

  • Major League Baseball uses BigQuery and Data Studio to analyze millions of rows of Statcast game data, delivering next-day insights to clubs on defensive positioning and outfield catches (Source: MLB hits a home run with BigQuery)

  • Helix uses BigQuery as their genomic data warehouse to query over 100 billion genetic records in seconds, enabling population-scale analytics and accelerated scientific discovery for over 1 million COVID-19 viral genomes (Source: Population genomics with BigQuery)

These examples demonstrate the massive scale, cutting-edge analytics and ML, and diverse use cases made possible by BigQuery‘s unique capabilities. Data Studio amplifies the impact of these insights by enabling engaging, interactive data storytelling.

Getting Started as a Data Scientist

Whether you‘re a seasoned data scientist looking to work with bigger datasets and more advanced analytics, or just starting your data science journey, BigQuery and Data Studio are powerful tools to add to your arsenal. Here are some tips for jumping in:

  1. Start with the basics – If you‘re new to BigQuery, begin by loading a sample dataset, writing basic queries, and creating your first Data Studio report. BigQuery Quickstarts and the Data Studio Report Gallery are great places to start.

  2. Learn by doing – The best way to learn is through hands-on practice. Take on a project to analyze a large dataset you‘re interested in, like Google Analytics sample data or public datasets like COVID-19 Open Data. Follow BigQuery best practices as you go.

  3. Get certified – Earning a Google Cloud certification, like the Professional Data Engineer or Professional Machine Learning Engineer, can validate your BigQuery and Data Studio skills and open up new career opportunities. Certifications are a great way to structure your learning.

  4. Join the community – There‘s a vibrant community of BigQuery and Data Studio users, developers and experts you can learn from. Join the Data Studio community connector group, follow the BigQuery blog, and connect with other data scientists using Google Cloud on platforms like Kaggle.

Conclusion

BigQuery and Data Studio are game-changers for data scientists, enabling the analysis of massive datasets, cutting-edge ML, and powerful data visualization at an unprecedented scale and speed. By mastering these tools, you can level-up your skills, accelerate your data science projects, and unlock transformational insights from big data.

Sources:

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