Data Integration traditionally requires a long time and a large investment to initialize. Many companies have spent more than 1 year and 10’s of millions of dollars to integrate data to run search and analytics.
Traditional data lake and data warehouse solutions require that the raw data is migrated into a physical data layer, so that search and analytics can access the instance data. This approach is time- consuming and can lead to the creation of multiple copies of data. Copies of data are expensive and difficult to manage over time across large industries.
Because LeapAnalysis uses advanced data connectors to integrate data virtually, there is no need for the creation of a physical data layer. Instead data can be accessed directly from sources and connected using only metadata. Virtually connected data provides immediate search and analytics results for our customers. Patterns, trends, and insights can be moved from LeapAnalysis to the physical data layer if the customer chooses. This provides “auto indexing” for data that is most commonly used.
True Data Federation
Data Sources stay in place – no heavy ETL or data migration needed and no copies of data are created – with LeapAnalysis you “Embrace Your Data Silos."
LeapAnalysis employs powerful data connectors that can directly link to & utilize an extremely wide range of data sources.
Data sources are linked using only metadata – no migration, no copies, no pipeline to build.
Queries each data source independently in its native language (SQL, CSV, etc) – filters and other functions are done within the data source directly, then passed back to LeapAnalysis.
Our world-class, award-winning virtualization engine connects data via Restful APIs to a common reference model so your data looks and feels like it is physically connected.
Acts like a virtualized data lake, so data can remain federated while queries and analyses exist in an abstraction layer.
Reduces time-consuming and costly integration phases – new sources can be spun up & used in a fraction of the time required for Data Lakes & Data Warehouses.
Create “Virtual Entities” which can 1) mask data fields, 2) combine data fields into new views, or 3) do calculations on data fields.
LA’s virtualization engine provides added data security so that only allowable portions of your data get exposed to end users based on their user role and profile.
Meaning and context of data is captured in powerful knowledge graphs that provide multiple perspectives for users.
LeapAnalysis employs a common reference model that connects all your data sources at the metadata level.
Layered semantics allows for a base Knowledge Graph (KG) plus limitless perspective-driven models to be layered on top of the KG.
LeapAnalysis’ powerful AI engine reads the schemas off data sources & recommends appropriate classes, attributes, or relationships to map inside the reference model.
LeapAnalysis takes the guesswork out of mapping and aligning your data sources – it provides an intelligent virtual data modeler for your organization.
LeapAnalysis provides users with a variety of advanced analytics capabilities. Users can build custom analytics in a variety of languages such as Python, R, R-Shiny, TensorFlow, etc. or connect to 3rd party analytics engines via APIs.
Analytics can be automated into workflows so that query results are automatically analyzed by LA.
Analyze all of your data seamlessly without moving, migrating, or copying it. Analysis happens directly against source data.
LeapAnalysis combines logical reasoning with statistical reasoning to mirror the ways the human brain conceptualizes and computes over data.
Minimal It Footprint
No storage of instance data is needed – only metadata models and alignments are stored.
LA does not require IT pipelines and indices to be built. This speeds access to your valuable data.
LeapAnalysis allows users the ability to directly write, execute & save queries and analytics without the need for involving IT.
LeapAnalysis pushes query filters down into the data sources themselves, reducing the size and complexity of result sets to save you money in cloud operating costs.
LeapAnalysis can run on premises, in a private cloud, or public cloud environment using moderately-sized inexpensive servers.
Are you ready for AI?
We’re excited to show what LeapAnalysis can do for your business.
LeapAnalysis allows customers to easily and flexibly manage and govern their metadata, reference data, or master data.
LeapAnalysis can utilize your existing data governance platforms or can provide you with it’s own data governance capabilities – your choice.
LeapAnalysis manages data vocabularies for you, without having to change data labels at the source. Labels are all managed internally by LA. Query all of your data seamlessly, no matter how many different terms your organization uses.
Data owners can maintain localized control of their systems with ease.
LeapAnalysis secures your data across 3 levels of access, making sure your data is safe and only accessible by the right people.
The 1st layer of security is when users log into LA, it knows who you are, your role and what you have access to. We can use your organization’s existing Single Sign-On (SSO) credentials.
The 2nd layer of security exists on the data sources themselves. Nothing can be connected or accessed without permission from the data owner. All usernames & passwords are maintained in their current state.
The 3rd layer of security exists on the Data Connectors so that query results brought back can be hidden or “masked” to hide sensitive information from human and artificial users.
LeapAnalysis allows you to search and analyze your data with the confidence that your valuable information is protected and preserved.