BigQuery vs. Snowflake | ETL tool comparison
While many companies use software for their business processes, it is not surprising that ETL tools have become increasingly important in recent years. These tools can enable companies to access and process their data from multiple sites, such as enterprise software systems and online engagement metrics.
BigQuery and Snowflake are two of the top ETL software solutions that companies want to manage their data from different sources and get the most out of 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 for SQL queries. Also, users can gain analytical insights from serverless products, including its built-in machine learning.
What is a snowflake?
Snowflake is a data lake and warehousing software solution for data management and processing. The platform simplifies complex data pipelines and integrates 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 a local source, Google Drive, Data Fusion Plugin, or Google’s Data Integration Partner tool. The BigQuery data transfer service can automatically transfer data from external sources to the platform on a scheduled and managed basis.
Also, the software can synchronize data via datastream, its change data capture and copy service, for processing tables for analysis. These changes can occur in real time across different databases, applications and storage systems for data capture and copying. Users can create their own plugins with the Plugins API or use the Cloud Data Fusion plugin to expand the capabilities of Cloud Data Fusion. They may even ask for data stored outside of BigQuery by connecting to external data sources within the Google Cloud Platform ecosystem.
Snowflake software has local connections to 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 Matilion, Informatica, Talend and FiveTran, to name a few. The software converts data during and after loading into the platform, including data preparation, transfer, movement and management. Snowflake can extract data from internal and external file locations through bulk loading, continuous loading and more.
Which one has better visualization ability?
Users of BigQuery software can benefit from visualization tools through Google Data Studio. Google Data Studio is a solution that integrates with BigQuery’s data source to access data from its table using the BigQuery Connector. The data can then be converted into reports, charts and other visual representations so that users can identify trends, develop responses and make predictions based on their data.
With the Google Sheets spreadsheet interface, users can present data in charts, pivot tables, and other ways to gain insights from their big data. In addition, users can use 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 a visual presentation of its web interface, user data and query results within SnowSite. Users of SnowSite can imagine their data as hit grids, scorecards, bar charts, line charts and scatter plots. Additionally, they can customize their visualization to view specific time data or adjust their data display without changing their query by changing column properties, chart columns and chart appearances. Users can set single values from data points in a chart using the system’s integration function.
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Which is better analysis?
BigQuery’s data warehousing platform can process multicloud data through BigQuery Omni to create real-time analytics. It allows users to seamlessly analyze data via standard SQL with the BigQuery interface. Their machine learning enables users to create machine learning models using standard SQL. The integration of Big Query with third-party business intelligence solutions can also provide strong insights into data analysis.
In-Memory Analysis Services BI Engine can help speed up answering questions via ODBC / JDBC drivers. Live BigQuery data can be analyzed through Google Sheets, an excellent alternative for those unfamiliar with SQL. Data can be used in real time with BigQuery’s high-speed Streaming Insert API for instant data analysis.
With Snowflake Data Cloud, users can get 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 provide insights by providing direct access to analytics tools. Enables a wide range of analytics functions using Snowflake in conjunction with other software systems, including predictive analytics, marketing analytics and big data analysis. Of course, Snowflake’s visualization capabilities can help users gain insights from semi-structured data through queries with SQL statements.
Which is the best ETL solution for you?
Equipped with details of these two ETL solutions, you can determine which option is best for managing your organizational data. In making this decision, you should consider the functions and capabilities of your ETL solution that will be most beneficial to your organization.
For example, if your organization directly benefits from using machine learning models in ETL solutions, you may want to consider BigQuery, as it has machine learning features. Similarly, if your organization regularly employs external Google software products such as Google Sheets, integration through BigQuery can be easier and more useful for analyzing data from these sources.
Since an organization working in Amazon Web Services or Azure cannot use BigQuery, Snowflake would be a reasonable choice.
Further comparison of ETL solutions
For additional information on popular ETL platforms, see Firebolt vs Snowflake: Compare Data Warehousing Platform, Databrix vs Snowflake: ETL Tool Comparison, Snowflake vs ENFLEK, ENVS REDShift: DataWarfLeaf: DataWarHowTL Tool comparison.