Enterprise-class JDBC Connectivity
The Google BigQuery JDBC Driver offers the most natural way to access BigQuery data from any Java/J2EE application. Simply use the Google BigQuery Driver to connect and access data just as you would access any traditional database. The drivers is completely self-contained - no additional software installation is required!
- Access BigQuery data through widely available tools. The JDBC interface offers the greatest accessibility to BigQuery data from Java based applications and developer technologies.
- Advanced wire-protocol SSL security for remote connectivity.
- Faster application deployment and configuration.
Connect To BigQuery From BI, ETL, & Reporting Tools
JDBC is the most widely supported interface for connecting Java-based applications with data. All kinds of BI, Reporting, ETL, Database, and Analytics tools offer the ability to read and write data via JDBC connectivity.
The Google BigQuery Driver wraps the complexity of accessing BigQuery services in an easy-to-integrate, pure Java JDBC Driver. Applications then access BigQuery through the Google BigQuery Driver with simple Transact-SQL.
The CData JDBC Driver for Google BigQuery hides the complexity of accessing data and provides additional powerful security features, smart caching, batching, socket management, and more.
BigQuery Application Integration
The Google BigQuery Driver has the same JDBC architecture as the JDBC drivers for MySQL and OLEDB, including Connection, Statement and ResultSet objects. Because of this you can now access BigQuery data in an easy, familiar way. You can use the Google BigQuery Driver through popular IDEs (Eclipse, IntelliJ, NetBeans, etc.), in code through familiar classes, and in data controls available through Swing, Eclipse SWT Widgets, etc.
Connection conn =
boolean ret = stat.execute("SELECT * FROM Dataset");
More Than Read-Only: Full Update/CRUD Support
Google BigQuery Driver goes beyond read-only functionality to deliver full support for Create, Read Update, and Delete operations (CRUD). Your end-users can interact with the data presented by the Google BigQuery Driver as easily as interacting with a database table.
Connection conn =
String query = "UPDATE Dataset SET Where= ...";
PreparedStatement pstmt = conn.prepareStatement(query);
- Pure Java Type 4/5* Drivers: 100% Java architecture based drivers that implement the native protocol without reliance on client-side libraries.
* Our drivers fit the definition of Type 5 drivers; however, there are only 4 official JDBC Driver types.
- All-in-One Deployment: Single JAR that supports JDBC 3.0 and JDBC 4.0 specification and JVM versions 1.5 and above.
- Certified Compatibility*: Our drivers undergo extensive testing and are certified to be compatible with leading analytics and reporting applications like SAP Crystal Reports, Pentaho, Business Objects, Crystal Reports and many more.
* Please visit our knowledge base to access how-to articles.
- Developer Friendly: Design-time support for all major Java IDEs, including Eclipse, IntelliJ, and NetBeans.
- JDBC Remoting: Our exclusive remoting feature allows hosting the JDBC connection on a server to enable connections from various clients on any platform (Java, .NET, C++, PHP, Python), using any standards-based technology (ODBC, JDBC, etc.). JDBC Remoting is enabled using the popular MySQL wire protocol server.
- Replication and Caching: Our replication and caching commands make it easy to copy data to local and cloud data stores such as Oracle, SQL Server, Google Cloud SQL, etc. The replication commands include many features that allow for intelligent incremental updates to cached data.
- String, Date, Numeric SQL Functions: The driver includes a library of over 50 functions that can manipulate column values into the desired result. Popular examples include Regex, JSON, and XML processing functions.
- Collaborative Query Processing: Our drivers enhance the data source's capabilities by additional client-side processing, when needed, to enable analytic summaries of data such as SUM, AVG, MAX, MIN, etc.
- Easily Customizable and Configurable: The data model exposed by our JDBC Drivers can easily be customized to add or remove tables/columns, change data types, etc. without requiring a new build. These customizations are supported at runtime using human-readable schema files that are easy to edit.
- Secure Connectivity: Includes standard Enterprise-class security features such as TLS/ SSL data encryption for all client-server communications.
MySQL/SQL Database entry points for Google BigQuery Data
The CData JDBC drivers include powerful fully-integrated remoting capabilities that makes Google BigQuery data accessible from virtually anywhere. The drivers include the optional ability to accept incoming SQL and MySQL client connections and service standard database requests.
With the CData JDBC drivers, users can interact with Google BigQuery data from any client that supports SQL Server or MySQL: from web & mobile applications, to CRM and CMS systems, BI tools like SQL Server Analysis Services, and even through popular management applications like MySQL Workbench.
- Access Google BigQuery data from virtually any application that can access external data. Applications that can access SQL Server or MySQL data can now connect to Google BigQuery with this driver.
- Connect Google BigQuery data with popular BI tools like SQL Server Analysis Services.
- Enable enterprise Google BigQuery data integration through SQL Linked Server connectivity
- Includes support for the MySQL and SQL (TDS) remote access protocols - industry standards for remote database connectivity.
- Offers advanced wire-protocol SSL security for remote connectivity
Connect to Google BigQuery Data from Code
The BigQuery Driver offers a standard SQL Server or MySQL interface to data, with read/write (CRUD) support. This powerful remoting features makes it easy to connect your applications with Google BigQuery data regardless of your solution architecture.
Use one of the standard client libraries for accessing SQL/MySQL from .NET, Java, PHP, Python, Ruby, Objective-C, etc. to start your integration with BigQuery.
JDBC Driver Performance
With traditional approaches to remote access, performance bottlenecks can spell disaster for applications. Regardless if an application is created for internal use, a commercial project, web, or mobile application, slow performance can rapidly lead to project failure. Accessing data from any remote source has the potential to create these problems. Common issues include:
- Network Connections - Slow network connections and latency issues are common in mobile applications.
- Service Delays - Delays due to service interruptions, resulting in server hardware or software updates.
- Large Data - Intentional or unintentional requests for large amounts of data.
- Disconnects - Complete loss of network connectivity.
The CData JDBC Driver for Google BigQuery solves these issues by supporting powerful smart caching technology that can greatly improve the performance and dramatically reduce application bottlenecks.
Smart caching is a configurable option that works by storing queried data into a local database. Enabling smart caching creates a persistent local cache database that contains a replica of data retrieved from the remote source. The cache database is small, lightweight, blazing-fast, and it can be shared by multiple connections as persistent storage.
Caching with our JDBC Drivers is highly configurable, including options for:
- Auto Cache - Maintain an automatic local cache of data on all requests. The provider will automatically load data into the cache database each time you execute a SELECT query. Each row returned by the query will be inserted or updated as necessary into the corresponding table in the cache database.
- Explicit Cache - Cache only on demand. Developers decide exactly what data gets stored in the cache and when it is updated. Explicit caching provides full control over the cache contents by using explicit execution of CACHE statements.
- No Cache - All requests access only live data and no local cache file is created.
This powerful caching functionality increases application performance and allows applications to disconnect and continue limited functioning without writing code for additional local storage and/or data serialization/deserialization.
More information about JDBC Driver caching and best caching practices is available in the included help files.
Getting Started with BigQuery JDBC Driver
Working with the new Google BigQuery JDBC Driver is easy. As a 100% pure Java JDBC Driver, the Google BigQuery Driver integrates seamlessly with popular IDEs like Eclipse, IntelliJ and NetBeans, as well as any Java/J2EE application.
Eclipse Integration - Data Explorer
As a JDBC Driver, Google BigQuery JDBC Driver can be used to access and explore BigQuery data directly from the Data Source Explorers included in popular java IDEs.
It's easy. As a standard JDBC Driver, developers can connect the Data Source Explorer to Google BigQuery JDBC Driver, just like connecting to any standard database.
- Add a new Data Connection from the server explorer and select the BigQuery Data Source
- Configure the basic connection properties to access your BigQuery account data.
Explore real-time data! The Google BigQuery JDBC Driver makes it easy to access live BigQuery data directly from any modern Java IDE.
- After configuring the connection, explore the tables, views, and stored procedures provided by the BigQuery JDBC Driver.
- These constructs return live BigQuery data that developers can work with directly from within the IDE.