The Data Platform Built for Analytics
Assemble all your data…
from popular cloud apps including Salesforce, HubSpot, Shopify, and many others
from MongoDB, DynamoDB, and Elasticsearch into tables automatically
of your data, using an ELT approach
including Postgres, MySQL, Microsoft SQL Server, Redshift, and BigQuery
CSV & TSV, XLS, JSON, and server log files into easy-to-access tables
with more than 200 additional data sources from ETL partners
In one place…
and cost-effectively, with no manual resizing needed
with role-based permissions and table-level access control
backed by industry-leading AWS and Microsoft Azure infrastructure
with standard SQL, and connect virtually any tool via industry-standard ODBC/JDBC
into your data pipeline with full control over your job schedules and detailed reporting
Easier than ever
and IT people: no extra infrastructure or technical resources required
and backup to keep data fresh and secure
and data structure automatically to improve runtime performance
your storage based on usage, including eliminating repetitive tasks such as vacuuming
from our Data Architects
Secure & Reliable
Secure, stable, and reliable infrastructure
As with any AWS cloud-hosted solution, responsibility for security is shared between Panoply and AWS.
Panoply is SOC 2 certified and in adherence with HIPAA protocol guidelines, in addition, it rests on Amazon’s AWS, which complies with standards such as: PCI-DSSL SOC 2, SOC 3, FISMA, and FedRamp.
Layered security and anomaly protocols
We take multiple precautions to ensure that all data is safe and secure.
Audits
Security measures are taken regularly and frequently to make sure Panoply is using the latest and greatest in security protocols.
Panoply under-the-hood
Cloud data pipeline automation to save you time and resources.
Automated ETL
Our AI-powered solution is based on an ELT process that uses natural language processing to automate extract, load, and transfer processes to save you endless hours of coding and modeling for data ingestion, integration, and transformation.
Data integration
Panoply can ingest data from over 100 data integrations, including databases, APIs, file systems and Panoply's SDK for pushing data from any current and future data source into Redshift, which is all done through Panoply.
- Automates data source connections
- Seamlessly connects to third party SaaS API's
- Easily connects to the most common storage services
- Build your own data source connection with Panoply’s SDK
Data schema modeling
Adaptive schema changes at real time along with the data. You don't need any prior knowledge and changes are seamless. Just load data in, everything else is automatic.
- Data types are automatically discovered and a schema is generated based on the initial data structure
- Likely relationships between tables are automatically detected and used to model a relational schema
- Slowly-changing tables are automatically generated
- Aggregations are automatically generated
- Table history feature allows you store data uploaded from API data sources so you can compare and analyze data from different time periods
Automated data transformation
Panoply automatically performs common transformations, including the identification of structures & semi-structured data formats like CSV, TSV, JSON, XML, and many log formats – and immediately flattens nested structures like lists and objects. Structured data can also be transformed into different tables with a one-to-many relationship.
- Common data formats are identified automatically, and parsed accordingly
- Compressed files are discovered and extracted
- Nested structures can be either flattened or placed into a sub-table
- Enhancement modules are automatically applied to the data
Query Performance Optimization
Panoply automatically reindexes the schema and performs a series of optimizations on the queries and data structure to improve runtime based on your usage.
Remodelling through continuous optimization
Panoply offers several tools for automated maintenance of your analytical infrastructure, but also provides transparency and full control over all processes, enabling you to apply changes manually when needed.
- Reindexing happens automatically whenever the system detects changes in query patterns
- Panoply automatically identifies columns used for joins, and re-distributes the data across nodes to improve data locality and join performance
Concurrency
Multi-cluster replication allows the compartmentalization of storage and compute. The number of available clusters scales with the number of users and the intensity of the workload, supporting hundreds of parallel queries that are load balanced between clusters.
Connect to any business intelligence tools
Panoply exposes a standard JDBC/ODBC endpoint with ANSI-SQL support to allow instant, seamless connection to any visualization or business intelligence tool, such as Chartio, Looker, and Tableau.
Storage optimization
Panoply is constantly running periodic processes to mark the data and optimize the storage based on your usage. Full and incremental backups are automated so you don't have to schedule them.
Scaling up and down the cluster
Panoply automatically scales up and down based on the data volume. Scaling happens automatically behind the scenes, keeping your clusters available for both reads and write, and thus ingestion can continue uninterrupted. When the scaling is complete, the old and new clusters are swapped instantly.
Automated maintenance
Panoply automates the vacuuming and compressing of tables to help improve Redshift database performance, continuously analyzes tables to better utilize the queries its receiving, and give you fresh metadata.
Check out Panoply’s transparent pricing for any type of business.
Need more info? Speak with an expert Data Architect by setting up a personalized demo.
