Prepare and test your skills
Prepare and test your skills
Deriving business insights through descriptive statistics and aggregation means turning many raw rows into small, trusted totals and averages that answer a business question. BigQuery (opens in a new tab) is the central service where GoogleSQL queries calculate those metrics, and Jupyter notebooks extend the same results with Python statistics and presentation.
SQL aggregation in BigQuery summarizes groups of rows into metrics that describe performance. BigQuery supports ANSI-standard SQL with joins, nested and repeated fields, analytic and aggregation functions, and multi-statement queries. Functions, operators, and conditional expressions shape each calculation, so raw transactions become totals, averages, and other central measures.
Segmenting data means splitting those summarized results by a category such as product, store, region, or time period. The query first selects business data from BigQuery tables, then applies aggregation within each segment, and finally returns one row per segment for comparison. This flow from tables to grouped metrics to one-row-per-segment lets teams move from a general question to evidence about which segment performs best or needs attention.
Jupyter notebook analysis brings BigQuery results into Python for statistical calculation and exploration. The pandas-gbq library lets analysts interact with BigQuery data in Jupyter notebooks, while the choice between pandas-gbq and the BigQuery Python client library sets the access path for the workflow. The pandas-compatible BigQuery DataFrames API is used to analyze and transform BigQuery data with familiar DataFrame operations.
Data flows from BigQuery tables to the notebook and back when needed. A query runs against BigQuery storage, the result rows flow into a pandas DataFrame in the notebook for statistical methods and visualization, and cleaned or enriched DataFrame contents can be loaded back to a BigQuery table. BigQuery Studio (opens in a new tab) supports this workflow with Python notebooks, saved queries, and version control for both notebooks and saved queries, so analysis steps remain repeatable.
Connected analytics tools present aggregated metrics as visualizations, dashboards, and spreadsheets without moving data out of BigQuery. BigQuery integrates with analytics and business intelligence tools that query BigQuery data where it lives:
These tools depend on BigQuery for computation and storage, while BigQuery depends on the tools to format results for decision makers. A view can be created in BigQuery to share a defined analysis, and BI Engine provides fast, in-memory analysis for business intelligence use. Together, SQL aggregation, Python notebooks, and connected tools form one path from raw data to quantified performance and clear business answers.
Exam tip: Connected Sheets runs BigQuery queries on your behalf either upon your request or on a defined schedule, and results are saved in your spreadsheet.
Exploratory Data Analysis (EDA) in BigQuery is the process of using SQL queries and notebooks to understand what is inside a dataset before answering a business question. BigQuery (opens in a new tab) supports both descriptive and predictive analytics for this work through SQL, machine learning, notebooks, and third-party integrations. EDA examines structure, distributions, relationships, and unusual values such as missing values, outliers, and anomalies.
BigQuery Studio is the workspace in BigQuery for discovering, analyzing, and running inference on data. Data flows from BigQuery tables into the Studio tools where you explore it, and results can flow back to BigQuery tables or into charts for business review. BigQuery Studio provides several connected tools for EDA work:
Choose the SQL editor when the question can be answered with a query and summary numbers. Choose a notebook when you need to combine SQL, Python, statistics, and charts in one place.
Discovering distributions and relationships with SQL means generating descriptive statistics that reveal how values spread out and move together. A query reads rows from a dataset or table, groups and summarizes them, and returns metrics such as counts by category, averages by group, and correlations between numerical features. Exploring distributions of categorical features shows which categories appear most often or perform best, while comparing numerical features shows trends and patterns.
You can run an exploratory query through several paths that all send the query to BigQuery for execution and return results for review:
bq query command in the bq command-line tooljobs.query or jobs.insert method in the BigQuery REST APIWhen a query runs, BigQuery builds an execution tree that breaks the work into stages, moves intermediate data between stages through a fast distributed shuffle tier, and writes the final results to persistent storage. You can view the query plan in the Google Cloud console to see stages and steps and to spot problems, such as a stage that writes much more output than other stages and may need an earlier filter.
Finding data quality issues is a core EDA task in BigQuery that protects later analysis from bad conclusions. Missing values appear as empty or null entries that reduce the usable rows for a metric. Outliers appear as values far above or below the normal distribution of a variable, and anomalies appear as unexpected patterns or breaks in trends and relationships. SQL exploration finds these issues by summarizing ranges, counting nulls, examining the distribution of key variables, and comparing subgroups against each other.
Once an issue is found, the next EDA actions are cleaning and wrangling before deeper analysis. Cleaning removes or corrects problem points, such as removing outlier rows that distort an average. Wrangling prepares the data for comparison, such as converting categorical features into numerical representations with one-hot encoding, label encoding, or BigQuery ML feature transformation tools, and creating new features for analysis. Cleaned data then supports reliable calculation of business metrics and fair evaluation of categorical performance.
Visualization in BigQuery turns descriptive statistics into pictures that make distributions, relationships, outliers, and anomalies easier to see. Charts are created from query results or from Python tables backed by BigQuery data, and they flow from the analysis environment to tools like integrated notebook charts or Looker Studio. Common EDA visualizations include:
After you connect a charting tool to a BigQuery table, the table appears as an available data source for building those views. In conversational analytics, you select Connect to data, choose BigQuery, select a project, dataset, and table, and then click Connect to make that table available for questions and charts.
Exam tip: Use a histogram or bar chart to see the distribution of one variable, and use a scatter plot to see the relationship between two variables.
SQL cells (opens in a new tab) in Colab Enterprise notebooks let you run BigQuery SQL inside a Python-based EDA workflow. The notebook depends on a connected runtime to execute code, and the SQL cell depends on BigQuery tables or Python variables for its inputs. You can refer to a Python variable inside a SQL expression by enclosing the variable name in braces, such as {my_threshold} in a WHERE x > {my_threshold} clause.
To create and run a SQL cell for exploration, follow this order:
The resulting DataFrame then flows into later Python steps for further statistics, visualization, feature engineering, or model training without leaving the notebook.
Identifying and interpreting data trends is the process of querying datasets in BigQuery to find meaningful patterns and using Jupyter notebooks to visualize those patterns to answer a business question. BigQuery (opens in a new tab) handles large-scale processing of data that is too large to fit in memory on a single machine, while notebooks provide Python runtimes for exploration, calculation, and charting. Together they let an analyst move from raw tables to metrics, comparisons, and visuals that explain what is happening in the business.
Exploring datasets in BigQuery Studio means checking structure, quality, and distributions in one workspace. BigQuery Studio combines a SQL editor with code completion, query validation, and estimates of bytes processed, embedded Python notebooks built on Colab Enterprise, a PySpark editor for Apache Spark procedures, and asset management with version history built on Dataform. BigQuery Studio also provides job history on a per-user or per-project basis and Knowledge Catalog features for data discovery, data profiling scans, and data quality scans.
Data exploration in BigQuery Studio means checking the structure of a dataset, identifying issues such as missing values and outliers, and examining the distribution of key variables with SQL or Python. The SQL editor path keeps the work in queries and summary numbers, while the notebook path keeps SQL, Python, statistics, and charts together for deeper trend work.
Analyzing patterns and relationships in BigQuery is the calculation of how variables connect to reveal trends and categorical performance. Analysis includes calculating correlations between numerical features and exploring distributions of categorical features to look for patterns and trends over time, across segments, or across categories. Clean analysis depends on prior cleaning and wrangling, where outliers are removed, categorical features are converted to numerical representations with one-hot encoding or label encoding or with BigQuery ML feature transformation tools, and new features are created for analysis.
For large datasets, this work uses distributed processing with BigQuery ML, BigQuery DataFrames, or Managed Service for Apache Spark so cleaning, transforming, and analyzing can run efficiently without moving all data into a single machine. The processing service depends on BigQuery tables for input storage, and BigQuery depends on the processing service to return trends and metrics at scale.
Visualization of findings in Jupyter notebooks turns query results into charts that make distributions and relationships easy to interpret against business objectives. A SQL cell in a Colab Enterprise notebook runs a BigQuery query and the output is automatically saved as a BigQuery DataFrame with the same name as the title of the SQL cell, so data flows from the BigQuery table to the SQL cell to the named DataFrame in the notebook runtime. Python code in the notebook then reads that DataFrame to create charts for tables stored in BigQuery. Common visualizations for trend analysis include:
Each chart type answers a different trend question, so the choice of chart controls whether the viewer sees a distribution, a relationship, or a comparison across groups.
The Data Science Agent (opens in a new tab) (DSA) is a Gemini-powered assistant that writes, explains, and troubleshoots analysis code directly within a notebook. The Data Science Agent supports comma-separated values (CSV) files and BigQuery tables as data sources, and the code it produces only runs in the notebook runtime connected to that notebook. A typical trend-finding workflow with the Data Science Agent follows an ordered sequence:
project_id:dataset.table in the prompt, or type @ to search with the @mention function.The first run in a project can take approximately five to ten minutes during initial setup, the notebook must be in a region supported by the Data Science Agent, and PySpark generation only produces Managed Service for Apache Spark 4.0 code.
Exam tip: The @mention function only searches for BigQuery tables in the current project, while the table selector can search across projects you have access to; use + to search for uploadable data files.
Choose the SQL editor in BigQuery Studio when the question can be answered with a query and summary numbers. Choose a notebook when you need to combine SQL, Python, statistics, and charts in one place.
A query runs against BigQuery storage and the result rows flow into a pandas DataFrame in the notebook for statistical methods and visualization. Cleaned or enriched DataFrame contents can then be loaded back to a BigQuery table for repeatable analysis.
Looker, Looker Studio, Connected Sheets, and Tableau query BigQuery data where it lives to create visualizations, dashboards, and spreadsheets. Looker Studio can be launched directly from BigQuery in the Google Cloud console, Connected Sheets runs BigQuery queries on your behalf and saves the results in a spreadsheet, and BI Engine provides fast, in-memory analysis for business intelligence use.
A retail analyst is investigating sales patterns across multiple product lines over the past two years to identify seasonal trends for an upcoming inventory planning meeting. The analyst uses a Jupyter notebook running in Vertex AI Workbench to examine the data stored in BigQuery.
The analyst has the following requirements:
Which approach should the analyst use to accomplish these tasks?
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