Accelerate your path to AI.

LeapAnalysis is a very new approach to Data Integration and Analysis because we thought about the challenge differently.

Accelerate your path to AI.

LeapAnalysis is a very new approach to Data Integration and Analysis because we thought about the Challenge differently.

Common Issues of Data Integration

Data Integration traditionally takes a long time and a large investment to initialize. Many companies spend more than 1 year and more than 1 million to integrate data in order to run search and analytics. The usual suspects for widening the amount of Data sources are:

Relational DB’s / Text Documents / File stores / Excel Sheets / NoSQL DB / Image Files etc.

Previously people used Data Warehouses or Data Lakes for integrating and analyzing this data. But these solutions require that the data is put in one place, which can lead to multiple copies and ownership problems. 

Data Integration traditionally takes a long time and a large investment to initialize. Many companies spend more than 1 year and more than 1 million to integrate data in order to run search and analytics. The usual suspects for widening the amount of Data sources are:

Relational DB’s / Text Documents / File stores / Excel Sheets / NoSQL DB / Image Files etc.

Previously people used Data Warehouses or Data Lakes for integrating and analyzing this data. But these solutions require that the data is put in one place, which can lead to multiple copies and ownership problems. 

Data Warehouse

  • High ETL – difficult and slow to construct
  • Works best with structured data
  • Answers only select business problems

Data Lake

  • No ETL – Schema on read issues
  • Requires complex indexing, which is hard to build in advance

The New Approach to Data Integration LeapAnalysis

Why use LeapAnalysis?

Reduce the need for up-front investment in Data Warehouse Technology and Data Lake Infrastructure.

True Data Federation

Data stays in place – no copies, no heavy ETL processing, no point-to-point integration.

True Data Federation

  • Data Sources stay in place with LA – no heavy ETL or data migration needed & no copies of data are created – with LA you “Embrace Your Data Silos”
  • LA is composed of data connectors designed for specific data types (via direct connection or APIs)
  • LA 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 LA
  • LA can speak numerous machine languages allowing for connection to a wide range of data sources

Virtualization

  • LA acts like a virtualized data lake, so data can remain federated while queries and analyses exist in an abstraction layer
  • Virtualization provides a means to spin up new queries & analytics on-demand
  • Reduces time-consuming and costly integration phases – new sources can be spun up & used in a fraction of the time required in Data Lakes or Data Warehouses
  • Users can drive connectivity directly without the need for IT to migrate data together

Virtualization

Search and analyze data from a single unified layer that virtually spans across multiple data sources & services.

Semantics

Meaning & context of data is captured in powerful knowledge graphs that provide multiple perspectives for users.

Semantics

  • LA employs powerful semantic models, which exist in multiple layers of complexity
  • Layered semantics allows for a base Knowledge Graph (KG) plus limitless perspective-driven models to be layered on top of the KG
  • LA’s semantics capture a data source’s schema as metadata and align that metadata to the Knowledge Graph
  • Business rules and perspectives can change over time and must only be replaced 1 time within the Knowledge Graph

User-Defined Functions

  • LA allows not only for user-driven queries and analysis, it provides a means to insert code directly into the engine
  • Alogrithms, Python Scripts, R, etc., can all be employed within LA and run automatically against connected data sources
  • This provides a powerful machine-to-machine form of automation for complex applications

User-Defined Functions

Advanced analytics where users can enter custom code to automate processing of data.

Minimal IT Footprint Security

No storage of instance data is needed – only metadata models and alignments are stored.

Minimal IT Footprint

  • LA stores minimal data within it – instance-level data remains in the data sources themselves
  • LA stores the base Knowledge Graph and relationships (i.e., joins) across federated sources
  • This reduces the amount of space required inside of LA – keeping it nimble.
  • Queries and analytics can be re-constituted on-the-fly using the metadata in the Knowledge Graph combined with the joins across data sources

Data Governance & Stewardship

  • LA doesn’t require new governance or stewardship policies to be created, since they should already exist within the data sources themselves
  • Data owners can maintain localized control of their systems with ease
  • Data sources can be presented to LA in their original formats or as views

Data Governance & Stewardship

Control of data stays at the source and reduces risk – no added governance and stewardship is needed.

Data Security

No storage of instance data is needed – only metadata models and alignments are stored

Data Security

  • LA allows IT organizations to maintain security protocols directly on data sources (via federation)
  • This reduces liability and risk for organizations, since data must not be copied and maintained separately – especially helpful with 3rd party data
  • Internally, LA has role-based security where users are recognized via single sign-on & only that data (or metadata) they have permission to see is shown

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