Hundreds of BigQuery customers use Hex to perform exploratory analytics and data science. Today, we are thrilled to announce our official partnership with Google Cloud!
We are introducing a range of new integrations with Google Cloud, enabling teams to explore data through various data sources and harness the power of Google Cloud’s managed services for large-scale data processing and analysis.
Our BigQuery integration is becoming more robust with the addition of BigQuery support for DataFrames. Data teams can use Python to apply pandas-like syntax to their BigQuery data. This unlocks the capability to work with pandas-like DataFrames even for large datasets that would typically surpass memory limits in traditional notebooks. Users can authenticate to their BigQuery instance and seamlessly transition between SQL & Python workflows.
“Hex has allowed our best analysts to dive much deeper into data than our previous tools. Being able to switch between SQL and Python has led to more data science work getting done and integrating predictive models into our everyday work. We're incredibly excited to see the Hex team deepening their partnership with Google Cloud and are looking forward to leveraging their new integrations to scale our analytics”
- Carlo Pulcini, Data & Analytics Tech Lead at Bending Spoons.
Support for BigQuery DataFrames within Hex will be available soon. To express your interest and get early access, let us know!
We are also excited to announce our upcoming integration with Spark on Google Cloud. This integration will take advantage of Serverless Spark’s worry-free, on-demand autoscaling of clusters. Coming soon, users will have the ability to conduct ETL, data science, and machine learning workloads within Hex while harnessing the Serverless Spark infrastructure.
Before diving into exploratory analysis or machine learning, data scientists frequently need to transform and prepare massive amounts of data. Hex facilitates this process, enabling easy data transformations and creation of Spark jobs. Leverage Hex’s polyglot support to access native Python and SQL cells and eliminate the necessity for spark.sql(‘select * from my_table’). Once data has been transformed, scheduling a Hex project to run on a user-defined schedule or using native integrations with Airflow or Dagster is simple. For version control and production-ready pipelines, Hex supports a native integration with Git.
Once a user's data is primed for exploration and machine learning, they can train and develop their models while tapping into their serverless Spark infrastructure.
Support for Spark on Google Cloud within Hex will be available soon. To express your interest and get early access, let us know!
Finally, Hex is broadening its support for databases with our newest Google Cloud Ready - Cloud SQL designation. You can now connect to Google Cloud-hosted SQL Server, PostgreSQL and MySQL databases, further expanding the amount of database connections we offer our customers.
Cloud SQL support is available within Hex today.
To get started using Hex with a Google Cloud database, just click!
“With Hex, we can start from a point familiar to analysts— writing SQL. Hex’s query caching allows us to write and store queries in hosted notebooks, preserving both the query and making it simple to access and visualize data from our warehouse (BigQuery). We’ve found Hex’s integration with our warehouse to be simple, reliable, and fast. Rather than dealing with custom JDBC connectors to build custom libraries for executing queries via Jupyter notebooks (or importing CSVs), we can dive right into analysis. Hex’s managed environment abstracts away notebook configuration and resource monitoring (you can easily adjust compute).”
- Matt Palmer, Analytics Engineer at AllTrails
As we embark on this exciting journey with Google Cloud, we invite you to express your interest in early access to these integrations and join us in transforming the landscape of data analytics.