The Engine
SECTION One
ETL-less Data Integration
Panoply automatically aggregates data as it streams in, allowing you to analyze everything in seconds – regardless of scale, and without data configuration, schema, or modeling.
Panoply offers a collection of pre-defined data source integrations to all of the popular databases and services – open-sourced – and provides an array of SDKs in many of the most common programming languages, so that you can easily tailor the platform to your needs and connect to any data source.
SECTION TWO
Auto-Generated Schemas
When you insert data into Panoply, the platform scans through the data and discovers the underlying schema and metadata that best describe it – including all columns, data types, and foreign keys. It constructs this schema based on the data, or alters the existing schema in real time (when necessary), thereby eliminating the need to explicitly design database tables and columns.
Panoply makes it easy to change data types or add columns – you can simply input different value sets into the platform. If necessary, manual adjustments and customizations can also be made.
SECTION Three
Real Time Transformations
Panoply uses common transformations automatically, including the identification of data formats like CSV, TSV, JSON, XML, and many log formats – and flattens nested structures like lists and objects into different tables with a one-to-many relationship.
Remodeling and reindexing are also automatic processes, taking place whenever the system detects changes in query patterns. Panoply uses statistical analysis to identify the columns and tables that are used most frequently in filters and group-bys, and uses that information to rebuild indexes.
SECTION Four
3-Tier Storage Architecture
Panoply has a 3-tier stack of storage systems abstracted away behind a single JDBC end point: AWS S3 is used at the backend, as a massively scalable storage engine for semi-structured data; Redshift is used for most of the data, and especially for structured and frequently accessed tables and rows; and Elasticsearch provides fast access and searches through data and aggregations, and handles the indexing and storage of common daily queries.
SECTION Five
Streamlined Data Utilization
Panoply delivers a set of pre-integrated, cloud-based analysis tools through a Data Apps framework, which is easily extendable to your own tools and platforms.
Panoply exposes a standard JDBC end point with ANSI-SQL support, providing plug-in support to your Tableau, Spark, or R analytics tools. The platform also allows you to write your own SQL code and build apps on top of the data.
SECTION Six
Simplified User Management
Panoply provides streamlined management of users and permissions, avoiding the cumbersome SQL configuration generally required to manage lists of users, passwords, grants, and denies – and allowing you to send out Invites via an easy-to- use UI.
Panoply allows you to specify what permissions users have and which tables they can access, and to view a complete activity log of activities per user – making it easy to pinpoint why changes were made.
SECTION Seven
Enhanced Privacy & Security
Built on top of AWS, Panoply uses the latest security patches and encryption capabilities provided by the underlying platform including permission controls, TLS, and hardware accelerated RSA encryption.
Panoply also offers an extra layer of security built to enhance data protection and privacy, that includes columnar encryption, two-step verification, anomaly detection, and handling expiring accounts.
SECTION Eight
Efficient Monitoring
Panoply is a fully managed analytical data platform that provides maximum transparency about everything from uptime and average query time, to low- level details such as the IO throughput of the physical disks.
Panoply’s monitoring capabilities include an analysis of all queries performed on the data by all users, making it easier to identify bottlenecks, catch unexpected behaviors, and “rewind” a database to any previous point in time.
SECTION Nine
Adaptive Auto-Scaling
Panoply handles the entire data infrastructure, eliminating the traditional concerns about scale, caching, IOPS, and memory. The platform auto-scales clusters seamlessly to keep up with the organization’s needs while reducing server costs.
Panoply adapts server configurations over time based on data scale and query patterns – scaling up or scaling out servers, as necessary. Scale changes take place on a regular basis and can occur multiple times throughout the week, optimizing the system’s performance.
