BigQuery vs Snowflake: Which ETL tool is best?

By Kevin Colleran On Apr 6, 2022

ETL tool comparison: BigQuery verus snowflake lead image. — Image: Getty Images/iStockphoto/carloscastilla

With so many organizations utilizing software for their business processes, it is no wonder that ETL tools have become exceedingly necessary in recent years. These tools can enable organizations to access and process their data from multiple sites, like enterprise software systems and online engagement metrics.

BigQuery and Snowflake are two of the top ETL software solutions for organizations looking to manage their data from various sources and gain the most from their data insights. This resource will discuss and compare these two popular ERL software options.

What is BigQuery?

Google BigQuery ETL is a cloud-based data warehouse that serves as an ETL solution through the help of SQL queries. In addition, users can gain analytical insights from the serverless product with its built-in machine learning.

What is Snowflake?

Snowflake is a data lake and warehousing software solution for data management and processing. The platform simplifies complex data pipelines and can integrate with other data tools for greater data processing capabilities.

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BigQuery vs. Snowflake software comparison

Which has better data synchronization and processing?

With BigQuery’s ETL software, users can process data by uploading files from local sources, Google Drive, Data Fusion plugins or Google’s data integration partner tools. BigQuery can automatically transfer data from external sources to the platform on a scheduled and managed basis through the BigQuery Data Transfer Service.

In addition, the software can synchronize data through Datastream, its change data capture and replication service, for processing tables for analytics. This change data capture and replication can occur in real time across heterogeneous databases, applications and storage systems. Users can build their plugins with the plugin API or utilize the Cloud Data Fusion Plugins to extend the capabilities of Cloud Data Fusion. They can even query data stored outside of BigQuery through connections to external data sources within the Google Cloud Platform ecosystem.

The Snowflake software has native connectivity with many popular third-party solutions for integration, data extraction and transformation into the platform environment. The data integration tools that the product works with include Matillion, Informatica, Talend and Fivetran, to name a few. The software transforms data during and after it has been loaded within the platform, with data preparation, migration, movement and management. Snowflake can extract data from internal and external file locations through bulk loading, continuous loading and more.

Which has better visualization capabilities?

Users of BigQuery’s software can benefit from the visualization tools through Google Data Studio. Google Data Studio is a solution that integrates with BigQuery’s data sources to access data from its tables using the BigQuery connector. The data can then be transformed into reports, charts and other visual representations so users can identify trends, develop responses and generate predictions based on their data.

With the Google Sheets spreadsheet interface, users can present data within charts, pivot tables and other means to gain insights from their big data. Additionally, users can utilize BigQuery and business intelligence tools from Google Cloud partners to provide more visualization options. These visualization software integration options include AltScale, Domo, MicroStrategy, Qlik, SAP Analytics Cloud, Sisense, Tableau, Yellowfin, and Zoomdata.

Snowflake provides visual representations of users’ data and query results within its web interface, Snowsight. Users of Snowsight can visualize their data as heat grids, scorecards, bar charts, line charts, and scatter plots. In addition, they can customize their visualizations to view data from specific periods or adjust their data display without modifying their query by changing the column attributes, chart columns and chart appearances. Users can also determine single values from data points within a chart using the system’s aggregation functions.

SEE: Cheat sheet: Data management (free PDF) (TechRepublic)

Which has better analytics?

BigQuery’s data warehousing platform can process multicloud data through BigQuery Omni to generate real-time analytics. It allows users to analyze data with BigQuery’s interface through standard SQL seamlessly. Their machine learning enables users to create machine learning models using standard SQL as well. Big Query’s integrations with third-party business intelligence solutions can also deliver powerful insights about the data’s analysis.

The in-memory analysis service BI Engine can help accelerate the query response times via ODBC/JDBC drivers. Live BigQuery data can be analyzed through Google Sheets, which is an excellent option for those unfamiliar with SQL. Data can be utilized in real time with BigQuery’s high-speed streaming insertion API for immediate data analysis.

With the Snowflake Data Cloud, users can gain insights and information from their data through queries. The platform, which can be deployed across Google, Azure and Amazon Web Services, can analyze data within the cloud data lake to deliver insights by providing direct access to analysis tools. Using Snowflake in conjunction with other software systems enables a wide range of analysis functions, including predictive analysis, marketing analytics and big data analytics. Of course, Snowflake’s visualization capabilities can also help users derive insights from semi-structured data through queries with SQL statements.

Which is the best ETL solution for you?

Armed with the details of these two ETL solutions, you can decide which option is best for managing your organizational data. To make this decision, you should consider the functions and capabilities of the ETL solution that would be most beneficial for your organization.

For example, if your organization would benefit from using machine learning models directly within the ETL solution, you may decide upon BigQuery, as it has machine learning features. Similarly, if your organization regularly employs external Google software products like Google Sheets, an integration through BigQuery may be easier and more beneficial for analyzing data from these sources.

Since an organization operating within Amazon Web Services or Azure can’t use BigQuery, Snowflake would be the logical choice in that case.