Offering an accelerated and unbeatable query performance, Google BigQuery has become a reliable Cloud Data Warehouse & Analytics solution worldwide. With its on-demand scaling, economical pricing, and the ability to efficiently handle fluctuating workloads, BigQuery provides a reliable and secure cloud platform for businesses of all sizes.
Often, you may require to transfer your data from BigQuery to your BI Tools for further analytics & reporting purposes. Magnitude Simba provides an easy-to-setup BigQuery ODBC Connector to seamlessly connect to your BI Tool instantly. Introduced by Microsoft in collaboration with Simba, ODBC(Open Database Connectivity) is a standard API used to access a database.
In this article, you will learn how to effectively set up & test the BigQuery ODBC Connector in 3 easy steps.
Table of Contents
What is Google BigQuery?
Launched in 2010, BigQuery is a Cloud-Based Data Warehouse service offered by Google. It is built to handle petabytes of data and can automatically scale as your business flourishes. Developers at Google have designed its architecture keeping the storage and computing resources separate. This makes querying more fluid as you can scale them independently without sacrificing performance.
Since there is no physical infrastructure present similar to the conventional server rooms for you to manage and maintain, you can focus all your workforce and effort on important business goals. Using standard SQL, you can accurately analyze your data and execute complex queries from multiple users simultaneously.
Key Features of Google BigQuery
Google BigQuery has continuously evolved over the years and is offering some of the most intuitive features :
- User Friendly: With just a few clicks, you can start storing and analyzing your data in Big Query. An easy-to-understand interface with simple instructions at every step allows you to set up your cloud data warehouse quickly as you don’t need to deploy clusters, set your storage size, or compression and encryption settings.
- On-Demand Storage Scaling: With ever-growing data needs, you can rest assured that it will scale automatically when required. Based on Colossus (Google Global Storage System), it stores data in a columnar format with the ability to directly work on the compressed data without decompressing the files on the go.
- Real-Time Analytics: Stay updated with real-time data transfer and accelerated analytics as BigQuery optimally allocates any number of resources to provide the best performance and provide results so that you can generate business reports when requested.
- BigQuery ML: Armed with machine learning capabilities, you can effectively design and build data models using existing SQL Commands. This eliminates the need for technical know-how of machine learning and empowers your data analysts to directly evaluate ML models.
- Optimization Tools: To boost your query performance, Google provides BigQuery partitioning and clustering features for faster results. You also change the default datasets and table’s expiration settings for optimal storage costs and usage.
- Secure: BigQuery allows administrators to set access permissions to the data by groups and individuals. You can also enable row-level security for access to certain rows of a dataset. Data is encrypted before being written on the disk as well as during the transit phase. It also allows you to manage the encryption keys for your data.
- Google Environment: Maintained and managed by Google, BigQuery enjoys the easy and fluid integrations with various applications present in the Google Ecosystem. With little to no friction at all, you can connect BigQuery to Google Sheets and Google Data Studio for further analysis.
What is ODBC?
Developed by Microsoft in collaboration with Simba in 1992, ODBC(Open Database Connectivity) is an open standard Application Programming Interface (API) for accessing both relational and non-relational databases. From a technical point of view, ODBC has based on the Call-Level Interface (CLI) specifications from Open Group and ISO/IEC for database APIs. ODBC allows you to easily connect your BI tools to a data source such as a file, a particular database on a DBMS, or even a live data feed to access databases using Structured Query Language (SQL).
Once connected, you can pass SQL(Structured Query Language) Statements to and receive results from the ODBC Driver Manager. The ODBC Driver Manager processes or passes this ODBC function calls to an ODBC driver. The ODBC Driver understands the ODBC function calls and submits SQL requests to a specific data source and returns results to your application/BI Tool.
What is Magnitude Simba?
Magnitude Simba is a complete package of data connectivity solutions that enable efficient and effective data access to applications, data platforms, and databases. These solutions include Simba Gateway connectivity as a service, standalone data connector, Simba SDK, as well as managed services with custom-built connectors via rigorous testing and authentication. For instance, the Simba BigQuery ODBC connector allows you to get data from BigQuery to your BI Tool.
Hevo Data, a No-code Data Pipeline helps to transfer data from 100+ sources to a Data Warehouse like Google BigQuery or a destination of your choice and visualize it in your desired BI tool. Hevo also supports Google BigQuery as a Source. Hevo is fully managed and completely automates the process of not only loading data from your desired source but also enriching the data and transforming it into an analysis-ready form without even having to write a single line of code. Its fault-tolerant architecture ensures that the data is handled in a secure, consistent manner with zero data loss.
It provides a consistent & reliable solution to manage data in real-time and always have analysis-ready data in your desired destination. It allows you to focus on the key business needs and perform insightful analysis by using a BI tool of your choice.
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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Understanding the Configuration of BigQuery ODBC Connector
Using the Simba BigQuery ODBC Connector, you can configure the connection settings according to your use case, thereby unlocking important performance benefits. To enjoy these broad ranges of configuration options offered by the BigQuery ODBC Connector, follow these simple steps:
Step 1: Install and Download Simba Google BigQuery ODBC Connector
- Step 1: Visit the BigQuery ODBC JDBC Drivers page and download the connector for your Operating System. For this article, the windows version is selected. After downloading the MSI File, open it.
- Step 2: The BigQuery ODBC Connector Setup window will pop up on your screen. Click on the Next button.
- Step 3: For accepting the licensing agreement, click on the Next button.
- Step 4: Provide the location on your system where you want to install the connector and click on the Next button.
- Step 5: Finally, click on the Install button. After the BigQuery ODBC connector is completely installed, click on the Finish button to close the setup window.
Step 2: License Allocation
After you have submitted the form, the driver’s license is sent to your email. Ensure that the file is stored in the /lib/ folder underneath where the connector has been installed.
Step 3: Configuring Simba Google BigQuery ODBC Connector
- Step 1: In your Windows system, click on the SEARCH and navigate to ODBC Administrator.
- Step 2: In the ODBC Administrator window, click on the System DSN tab. Search and click on the Simba Google BigQuery ODBC DSN. Then, click on the CONFIGURE button.
- Step 3: In the DSN setup, configure the settings according to your business needs.
- Step 4: After the configuration is done, click on the TEST button to test the connection.
- Step 5: Click on the OK test window to save your settings.
Following are some of the popular questions regarding the BigQuery ODBC Connector you may have:
What are the charges involved when querying BigQuery via the Driver?
The driver can be downloaded for free. Queries run by the driver are charged based on the driver’s configuration:
- By default, the price of the query is applied to all queries from the driver. The query price is the only price that applies provided that the driver is not configured to assist a large result set.
- For cases when the driver is configured to write a large result set to the target table, the storage charge will be added to the query charge. Data is stored 24 hours a day, and table results are subject to a 24-hour storage fee.
- Storage API charges apply when the Storage API is called using the driver. These prices apply to the data read from the query results, not the data scanned by the query. Note that the Storage API pricing applies only to large result sets.
Do the BigQuery ODBC Drivers support the SQL Query Prefix?
Unlike the Drivers, BigQuery supports switching between legacy SQL dialects and standard SQL dialects using query prefixes. The driver always maintains a specific status regarding the SQL mode used and automatically sets options when creating the connection. The driver does not support switching SQL dialects using query prefixes because the SQL mode is set when the connection is created.
How do the Drivers deal with BigQuery’s repeated and nested data schemas?
The ODBC data model does not provide a convenient way to represent data. Hence, the nested and iterative data (STRUCTS and ARRAYS in standard SQL) is represented as BigQuery API JSON output. You can execute queries that manipulate these types, but if the query’s output schema is of complex types, the driver encodes and renders them in JSON format.
Can these drivers be used to export or ingest data between BigQuery and your existing environment?
The BigQuery ODBC drivers use BigQuery’s query interface and do not provide the ability to take advantage of BigQuery’s rich ingest and export features. You can use the DML to issue a small set of INSERT requests, but this has its limitations.
Do the Drivers support Parameterized Queries?
The BigQuery ODBC Drivers provides complete support for Positional Parametrisation. It is to be noted that preparing a query before executing it provides validation information. Also, this doesn’t have any impact on the performance of the executed query.
In this article, you have learned how to effectively set up the BigQuery ODBC Connection. Magnitude Simba is a simple yet powerful solution to seamlessly connect your BigQuery Data Warehouse to your BI Tools. Offering more control and granular configuration options, the Simba BigQuery ODBC Connector allows you to connect and maintain your connection using an easy-to-use interface.
As you collect and manage your data across several applications and databases in your business, it is important to consolidate it for complete performance analysis of your business. However, it is a time-consuming and resource-intensive task to continuously monitor the Data Connectors. To achieve this efficiently, you need to assign a portion of your engineering bandwidth to Integrate data from all sources, Clean & Transform it, and finally, Load it to a Cloud Data Warehouse like Google BigQuery, BI Tool, or a destination of your choice for further Business Analytics. All of these challenges can be comfortably solved by a Cloud-based ETL tool such as Hevo Data.
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Hevo Data, a No-code Data Pipeline can seamlessly transfer data from a vast sea of 100+ sources to a Data Warehouse like Google BigQuery, BI Tool, or a Destination of your choice. Hevo also supports Google BigQuery as a source. It is a reliable, completely automated, and secure service that doesn’t require you to write any code!
If you are using Google BigQuery as your Data Warehousing & Analytics solution and searching for a no-fuss alternative to Manual Data Integration, then Hevo can effortlessly automate this for you. Hevo, with its strong integration with 100+ sources and BI tools(Including 40+ Free Sources), allows you to not only export & load data but also transform & enrich your data & make it analysis-ready in a jiffy.
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