Move Zendrive Data into a Data Warehouse in Minutes

Panoply makes it easier for companies to store and manage Zendrive data in your own private cloud based data warehouse. Zendrive is a data collection and analytics company that compiles and analyzes fleet driving behavior for each individual driver within your team. Panoply comes with Zendrive integration, taking you from data extraction to analytics, all without a complex data infrastructure.

With Panoply, the time from storing data to assessing your drivers for risk is reduced dramatically. No ETL or ELT process is necessary to add your data successfully into your Panoply Smart Data Warehouse, where your analysts can combine your Zendrive analytics with any other data source you’ve compiled. Analysis can take place within the Panoply app, which has functionality for report generation, queries, custom dashboards, and more. Explore your Zendrive data better with Panoply’s end to end data management platform.

Panoply makes it easy to analyze Zendrive data

Why Panoply?

Use-Case Optimization

Analyzes queries and data – identifying the best configuration for each use case, adjusting it over time, and building indexes, sortkeys, diskeys, data types, vacuuming, and partitioning.

Query Optimization

Identifies queries that do not follow best practices – such as those that include nested loops or implicit casting – and rewrites them to an equivalent query requiring a fraction of the runtime or resources.

Server Optimization

Optimizes server configurations over time based on query patterns and by learning which server setup works best. The platform switches server types seamlessly and measures the resulting performance.

Updates, Upserts, and Deletions

Supports standard SQL update and upset operations out of the box – without worrying about vacuuming or rebuilding- unlike many analytical databases.

Semi-Structured Data Parsing

Supports semi-structured text values like nested JSON, user-agent strings, some standard log formats, CSV, and serialized Ruby objects – parsing these objects and normalizing them into a relational database design.

Nested Structures

Handles nested structures automatically, flattening them into several tables with a one-to- many relationship. The result is a ready-to- use relational database design for all current and future datasets.

Columnar Storage

Provides seamless data storage and management in a multitiered, columnar storage based on Amazon Redshift, Elasticsearch, and Hadoop or SS3.

Data Tracking and Alerts

Simplifies how you keep track of vast amounts of data – by identifying patterns, providing notification of anomalies, and generating alerts when the results of arbitrary SQL queries exceed predefined thresholds.

Backup and Recovery

Automatically backs up changes to data to a redundant S3 storage, optionally saved in two different availability zones across continents – enabling full recovery to any point in time.

From raw data to analysis in under 10 minutes

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