Panoply Features

View ETL process data via reports and visualizations like charts and graphs.

Record ETL historical data for auditing and potential data correction needs.

Extract data from the designated source(s) like relational databases, JSON files, and XML files.

Cleanse and re-format extracted data to the needed target format.

Load reformatted data into target database, data warehouse, or other storage location.

Arrange ETL processes to occur automatically on needed time schedule (e.g., daily, weekly, monthly).

Capable of scaling processing power up or down based on ETL volume.

Consolidates, Cleanses and Normalizes data from multiple disparate sources.

Helps save storage capacity and improves query performance.

Eliminates data inconsistency and duplications ensuring data integrity.

SQL based analytics functions like Time series, pattern matching, geospatial analytics etc.

Provides built in capabilities like machine learning algorithms, data preparation functions, model evaluation and management etc.

Allows data querying across data formats like parquet, ORC, JSON etc and analyze complex data types on HDFS

Integrates with data science workflows, Machine Learning and artificial intelligence (AI) capabilities.

Integrates with BI Tools to transform data into Actionable Insights.

Provides speed in data processing and capturing unstructured, semi-structured and streaming data.

Provides On-Premise deployment options.

Provides Cloud deployment options (private or public cloud, hybrid cloud).

Manages huge volumes of data, upscale or downscale as per demand.

Policies, procedures and standards to manage and access data.

Restricts data access at a cell level, mask or hide parts of cells, and encrypt data at rest and in motion

Allows users to generate text based on a text prompt.

Condenses long documents or text into a brief summary.