Google BigQuery: Connect and Query with the DataDirect Connector for JDBC

Google BigQuery: Connect and Query with the DataDirect Connector for JDBC

January 15, 2020 0 Comments
Google BigQuery Connect and Query with the DataDirect Connector for JDBC_1200_620

Companies are gathering big data at a tremendous rate, which means they need a data warehouse that can scale as quickly as needed. Google BigQuery solves this challenge and our latest driver enables you to connect Google BigQuery to the applications your business relies on.

Using Google BigQuery, a serverless, highly scalable enterprise data warehouse, with JDBC helps you take full advantage of big data insights without needing to be a database administrator. The latest Progress DataDirect connector is an excellent benefit to your company, enabling you to:

  • Make full use of BigQuery through basic Java and SQL commands
  • Create an environment where less time is spent querying, and more time is spent analyzing to get the valuable data insights you need to meet your customer expectations
  • Consume fewer system resources, reducing drag on CPU performance by 76% and requiring up to 70% less memory compared to other third-party connectors

Getting the Most from Your BigQuery data

The Progress DataDirect JDBC Connector for Google BigQuery supports result sets with configurable fetching and paging options and returns data for complex data types. Additionally, it supports both legacy and standard SQL dialects and authentication mechanisms like OAuth 2.0 and service account authentication.

The data connector is a customizable solution that enables businesses to query their Google BigQuery data via Java APIs using simple SQL commands that uncover data insights from their cloud databases and local databases, empowering their own Java-scripted tools. The data connector integrates seamlessly with the leading analytics and reporting applications.

Getting Started with the BigQuery JDBC Connector

Market and business intelligence need new and versatile solutions for analyzing customer data to turn that data into knowledge about the customer. The Progress DataDirect JDBC connector allows interfacing with Google BigQuery databases using simplified SQL statements so those meaningful data relationships can be revealed.

The Progress DataDirect JDBC connector empowers organizations to connect to any data source, anywhere, giving you the advantage over your competitors.

BigQuery and Progress: Strong Together

Companies are gathering big data at a tremendous rate. Buying the hardware and creating the infrastructure necessary to store, query and analyze these massive datasets for market insights is not cheap in terms of time or money. Google BigQuery is a SaaS enterprise data warehouse that solves the need for such an ambitious investment and save upwards of 26-34% of what it would cost using other methods. The value of the infrastructure Google provides includes:

  • BiqQuery ML (Machine Learning) that enables you to create and operationalize ML models using structured or unstructured data using straightforward SQL quickly and inefficiently.
  • BigQuery BI Engine (Business Intelligence) to accelerate both data exploration and analysis. The analysis occurs in memory and provides a sub-second response time at a high rate of concurrency to process massive, complex datasets interactively.
  • BigQuery GIS (Geospatial Information System) is a capability for native geospatial analysis. Google BigQuery is the only cloud data warehouse with built-in GIS functionality.

Our Developer Advocate, Saikrishna Bobba, explains how to connect to Google BigQuery databases from your own SQL/BI/ETL tools using Progress DataDirect's JDBC connector for Google BigQuery in this tutorial.

You can also try our connector for Google BigQuery yourself.

Try Now

Howard Davidson

Howard Davidson

Howard Davidson is the proud brand manager for Progress DataDirect. He’s been developing b2b strategy, creating content, increasing brand awareness and driving leads for nearly several decades. Follow Howard on Twitter @hdavidson and on LinkedIn.

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