Getting your data from BigQuery to Apache Superset can be a pain. Managing pipelines for all your data sources just isn’t sustainable, but your reporting can’t be put on hold while you wait for IT or Engineering to get to your ticket.
With Panoply, you can eliminate that hassle—and all that wasted time. Set up code-free connections to both BigQuery and Apache Superset with just a few clicks. Then Panoply will automatically sync your data so it’s ready to analyze when you are.
It’s that simple: Unblock your data with Panoply.
BigQuery allows companies to perform analytics on very large read-only data sets stored within the Google Cloud. Being able to seamlessly access BigQuery data will empower Panoply customers to take this data, combine it with other data sources, and be able to query all the data in one place, which is crucial for data mining. BigQuery’s integration with Panoply lets you not only ingest any data source into one automated cloud data warehouse, but you can also analyze it with any visualization tool you need to get lightning fast insights.
Learn more:
Documentation
BigQuery Data Warehousing
BigQuery Business Intelligence
Apache Superset is an open-source data analytics platform. This BI tool features a user-friendly UI that builds interactive visualizations and shareable dashboards. With Superset, you can control permissions and also define what data can be displayed and what analytics functions can be accessed. It’s integrated with Druid.io, so you can get fast, complex analytics from event-driven data streams.
Superset connects to most SQL-based database management systems and 23 major data sources including MySQL, Google BigQuery, and Apache Hive. However, you can automatically connect all your data sources and types to Superset with the Panoply integration. With one click, the enterprise-level Panoply data management platform continuously extracts, simplifies, and stores all your data in one place. This AI-driven data pipeline is the only fully integrated cloud service that combines an automated ETL with a data warehouse.
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