10 Things You Need to Know to Get Started with BigQuery

BigQuery is Google Cloud’s fully-managed enterprise data warehouse that enables scalable analysis over massive datasets. As one of Google’s most popular analytics products, BigQuery is used by media, retail, financial services, and other data-intensive organizations.

If you’re new to BigQuery, here are 10 key things to understand to get started on the right foot:

1. BigQuery Overview

BigQuery is a cloud-native data warehouse that handles petabyte-scale analysis using standard SQL. It enables analysts, data scientists, and developers to run fast SQL queries on vast amounts of data.

Key features include:

• Serverless architecture — no infrastructure to manage
• Real-time analysis of streaming data
• In-memory BI Engine for interactive analysis
• Integrated machine learning for predictive insights
• Geospatial analytics and visualization
• Granular access controls and encryption

These capabilities make BigQuery ideal for analytics use cases like customer intelligence, fraud detection, operational analytics, and IoT data science.

2. BigQuery Data Types

BigQuery uses familiar SQL data types like INT64, FLOAT64, NUMERIC, BOOLEAN, STRING, BYTES, DATE, TIMESTAMP, etc. It also supports nested and repeated data structures through RECORD and ARRAY types.

For machine learning, BigQuery ML (BQML) accepts FLOAT64 columns for training models. Numeric values should not have scales.

The maximum row size is 1 MB, with a limit of 4 MB for persistent UDF values. For optimal performance, rows should be under 10 KB.

3. Loading Data into BigQuery

There are several options for loading data into BigQuery:

• Batch loading from Cloud Storage — load CSV, JSON, Avro, ORC, Parquet and more
• Stream inserts from apps via the BigQuery API
• Ingest data directly from external sources via Data Transfer Service
• Query federated data sources like Cloud Bigtable and Google Drive without replication

To optimize costs and performance, use partitioned tables, clustering, compression (GZIP or Snappy), and data conversions.

4. Querying in BigQuery

BigQuery uses SQL for analysis, including:

• Standard SQL — uses standard syntax with some extensions like UNNEST. Great for portability.
• Legacy SQL — BigQuery’s original SQL dialect with some unique functions.

Both support features like wildcards, UDFs, subqueries, JOINs, WINDOW functions, etc. Legacy SQL may offer better performance in some cases.

The BigQuery sandbox provides a free playground for experimenting with queries using public datasets.

5. BigQuery Pricing

BigQuery uses a pay-as-you-go model with no upfront costs. Primary charges include:

• Data storage — charged per GB of data stored, can lower with compression.
• Streaming inserts — per 100,000 rows inserted.
• Queries — per TB of data processed by SQL queries. Prices decrease at higher tiers.
• Flat-rate pricing — fixed monthly fee for predictable workloads.

Execute queries efficiently and leverage features like caching query results or reusing computation between queries. Set budget alerts to control unexpected costs.

6. Access Controls in BigQuery

Manage access to datasets and jobs using:

• IAM roles — Predefined roles like BigQuery Admin, BigQuery Data Owner, BigQuery User, etc.
• Access control lists — Finer-grained control for specific resources.
• Authorized views — Restrict access to parts of a dataset at the row level.

Access controls enable securely sharing datasets with business units or departments. Audit logs provide visibility into query history.

7. Integrations with BigQuery

BigQuery integrates tightly with other Google Cloud services:

• BigQuery Omni brings multi-cloud analytics over data in Azure and AWS.
• BigQuery ML for in-database machine learning modeling.
• Dataflow ETL jobs can read/write BigQuery datasets.
• Dataproc can connect to BigQuery for big data pipelines.
• Looker, Tableau, Power BI via BigQuery BI Engine connectors.

This ecosystem enables real-time cloud analytics from ingestion to visualization.

8. BigQuery Best Practices

Follow these optimization best practices:

• Partition tables by date or region to prune scanning.
• Cluster tables to collocate related data.
• Use materialized views to pre-compute aggregates.
• Compress data for cheaper storage.
• Slot allocate reservations to get guaranteed capacity.
• Cache frequent query results.
• Leverage BigQuery geography tools for location-based analysis.

9. Getting Started in the Console

The web UI makes BigQuery approachable for beginners:

• Try the sandbox to query open datasets.
• Use schema and validation tools when loading data.
• Get guided help from BigQuery assistants.
• Review job logs to optimize and debug queries.
• Set alerts to monitor usage and billing.

10. Expand Your BigQuery Skills

To go further:

• Take Google’s BigQuery for Data Analysts course on Coursera.
• Join the BigQuery community forum to ask questions.
• Read the official documentation and API references.
• Follow blogs and tutorials from experts.
• Watch videos explaining BigQuery best practices.
• Review related tools like Data Studio, Dataprep, and Looker.

With the right guidance, anyone can leverage BigQuery’s power and scale. These tips will give you the knowledge to get started. Try BigQuery today and see how it can accelerate your data analytics.10 Things You Need to Know to Get Started with BigQuery

Here are some resources to get you started:

• Google BigQuery documentation: The official documentation is the most comprehensive resource, covering everything from basic concepts to advanced features: https://cloud.google.com/bigquery/docs
• BigQuery tutorials: Google offers several interactive tutorials that walk you through using BigQuery: https://m.youtube.com/watch?v=MH5M2Crn6Ag
• Coursera BigQuery course: This free course from the University of California, San Diego, provides a good introduction to BigQuery: https://www.coursera.org/courses?query=bigquery
• Books: Several books are available on BigQuery, such as “BigQuery for Data Warehousing” by Peter Bakkum and “BigQuery for Everyone” by Miguel Grinberg.

Additional tips:

• Start with the basics: Don’t try to learn everything at once. Focus on understanding the core concepts of BigQuery first.
• Practice with small datasets: Start with small datasets before working with larger ones. This will help you get comfortable with the interface and syntax.
• Join the community: There are many online communities where you can ask questions and get help from other BigQuery users.