Hydrolix has shipped a new Apache Spark connector, which enables Databricks users to apply the analytical power of Databricks to the entire scope of their event data rather than data sets limited by sampling, aggregation and shorter retention windows, tactics typically used to control data storage costs. Now, Databricks users can economically store full-fidelity event data, such as logs, in Hydrolix and rapidly extract information from both real-time and historical data, thereby gaining valuable new business insights.

With the Hydrolix Spark connector, Databricks users can query all their event data for data science, business intelligence and machine learning needs. The connector makes it easy to replace the current log data infrastructure with Hydrolix. All other data infrastructure remains the same and functions as it always has.

Users of the Hydrolix Spark Connector with Databricks can:

• Explore log data in Databricks.
• Use Databricks notebooks to analyze and visualize Hydrolix data.
• Join log data in Hydrolix with data from other sources to generate new insights.
• Use MLib for machine learning tasks to address business-critical use cases such as fraud detection, capacity prediction and anticipating customer churn.
• Use the power of Hydrolix summary tables for real-time summaries in Databricks.

Use Cases
The Hydrolix integration with Databricks can deliver impactful new insights in a variety of use cases, such as:

• Predicting inventory and product demand
• Capacity planning
• Detecting outliers for anomaly and threat detection
• Fraud detection
• Training machine learning models

“The Hydrolix Spark Connector allows Databricks users to store massive amounts of time series data over long periods of time at full fidelity in the Hydrolix data lake,” said Alok Aggarwal, director of the Innovation Lab at Hydrolix. “With this connector, Databricks users can unleash the full power of Databricks against all of their data and model across longer time periods such as year-over-year and multiyear data sets quickly and cost-effectively.”