Start analyzing your Twitter data with Databricks
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Integrate with Twitter in minutes
On Twitter’s social media platform, individuals and enterprises share photos and talk about almost anything they want to in 160 characters or less. Posts can include links to items for sale and to ecommerce or other websites. The platform collects data on views, likes, shares, clicks, and overall audience engagement. Sales aren’t the only way that businesses can benefit from their Twitter accounts. Analysis of Twitter engagement data can provide important marketing insights for planning campaigns targeted to specific demographics.
If you collect, process, and analyze Twitter data to grow your business, an enterprise-level ETL + Smart Data Warehouse can make the most of that data while keeping it safe. The one-click Panoply integration connects your Twitter account to a powerful, fast, and secure cloud data pipeline. With Panoply, you can automatically combine all your data sources with your Twitter data and connect to more than 100 BI tools for big-picture analysis.
Learn more:
Documentation
Twitter Data Warehousing
Twitter Business Intelligence
Other integrations with Twitter
Integrate with Databricks in minutes
Databricks Unified Analytics was designed by the original creators of Apache Spark. This paid BI tool combines data science and engineering to perform massive-scale ML data operations. It’s an integrated platform that prepares data, runs experiments, and continuously trains and builds ML models. Then Databricks deploys the AI apps you create across multiple platforms.
Expand Databricks capabilities by integrating it with Panoply with one click. Panoply is the only cloud service that combines an automated ETL with a data warehouse. With Panoply’s seamless Databricks integration, all types of source data are uploaded, sorted, simplified and managed in one place. The Panoply pipeline continuously streams the data to your Databricks output. So your models and apps are always delivering real-time analytics.
Other integrations with Databricks
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