PPL is the evolution of ETL. The PPL difference is that the data preparation is performed directly on the data arriving from source and is completed before it is loaded into the CryspIQ which makes streamed data load possible instead of the legacy batch methods if required.

CryspIQ organises all your business data in a single database schema, so that all data of similiar type is clustered or stored together making it easy to find, once the model is understood. What this means is that if you are looking for money items, such as budget amounts, invoice amounts, payment amounts, you will find them all stored in a single place.

Yes, This is one of the key benefits for implementing CryspIQ as it is completely agnostic to the system source from which the Data is derived.

You retain the history from the old system or technology through the existing mapping, and when the new system comes along, simply create new maps for messages from the new system into CryspIQ.

The result - No major end to end re-builds are required and all your history is retained with continuity from an analytics perspective.

Any output that can be produced in a tabular format - Databases, Rest APIs, flat files etc.

Please refer to System Plug-ins Library documentation for more information.

Yes, CryspIQ leverages the Azure Active Directory framework to control authentication and defines internal access privileges.

Data access level is controlled within CryspIQ and can be applied at a field level to ensure that people can only see what they are allowed to see irrespective of what queries they execute. Your data is controlled in a single place before consumption.

CryspIQ applies your data quality checks to the data Before entry into CryspIQ database which ensures quality data is provided to the consumers.

Additionally, by removing layers or handling steps in the data availability process, we remove inconsistencies derived from interpretation, manipulation during a Transform step and also enforce removal of duplications as a standard.

Any tool that allows relational record association using key value pairs (a relational database), e.g. SQL Server, Postgres, DB2, MySql, Oracle, Sybase, Amazon RDS, Apache Hive.

Yes, CryspIQ can reside alongside your existing Data Platform and enable you to start realising some of the cost saving benefits of CryspIQ. As an interim solution, you can leverage your existing Layers and redirect your Data into CryspIQ.

This means that you use your existing "Raw" or "Staging" layer and map the Data from there into CryspIQ for use. Please refer to the Architecture documentation for more information

• Data quality and governance.
• Digitisation and data-driven decision making.
• Analytics and reporting.
• Data science (Predictive Analytics, Artificial Intelligence and Machine Learning).

• Reducing data preparation and modelling effort for analytics and reporting by at least 50%.
• Increased trust and confidence in data through improved quality.
• Greater speed to insight, as new data can quickly be mapped, loaded and consumed.
• Increased business user self-service through simplicity and ease of use.

Yes, CryspIQ can handle the volume, velocity and variety characteristics of Big Data can be configured to use industry standard technology such as Apache Hadoop in any cloud platform. When CryspIQ is configured for Big Data, users can benefit by getting the same or faster results as on the relational storage engine and optimised storage cost through the use of the underlying Big Data technology. Note that Big Data Configuration is in addition to the standard CryspIQ offering.

Yes, the CryspIQ product is not limited to any specific industry.