Data Sources

Nowadays there are different sources of data from databases, websites, applications and spreadsheets to live data streams. As a result, we mostly encounter with a massive amount of data from various sources and in a variety of data formats which could be structured or even unstructured. These circumstances arise the absolute necessity of utilizing some alternative strategies for storing, manipulating and visualizing the data, called Big Data Analytics.

In order to analyze and extract useful information from such a huge volume of data, specialized Big Data tools and software programs should be developed which could also ensure the viability of the required high-performance analyses. Thanks to our rich and exhaustive data model at Eris Innovation, we are perfectly able to get access to different sources of data no matter how big and complex data is.

Data Marts

A Data Mart is a subset of a data warehouse which is more focused and oriented to a specific subject or business line. Users without expertise in working with databases and data warehouses can simply consult data marts to get access to domain-specific data for reporting, analysis and supporting the decision-making processes.

We are using several data marts to accelerate our business processes by avoiding the time-consuming tasks of collecting information directly from the source databases.

IoT

Internet of Things (IoT) refers to a system in which the physical objects, devices or machines are connected to the Internet by sensors. IoT-based technologies can produce vast quantities of data regarding their sampling frequencies and running time. These sensors can provide data from different sources:

  • Passive data which means that the data is readable and accessible through an API
  • Active data which refers to a steady stream of data samples
  • Dynamic data from smart sensors that can communicate in a bidirectional way with IoT applications

Database Silos

A Data Silo is an isolated repository which has remained fairly segregated from other parts of the system's architecture. Since data silos are closed off from other elements in an organization, generally they have been considered as a problem for efficiency and data integration of the system.

However, at Eris Innovation we are heavily investing in opening up data silos applying complex network design, modern cloud services and advanced software management tools.

Legacy Data

Legacy data is a kind of disparate data which nearly comes from everywhere, including existing relational/hierarchical or object/relational databases. However, the main sources of legacy data are those from antiquated and outdated legacy systems and applications which many companies still keep running them for some convincing reasons to avoid costly and sophisticated changes, damaging business stoppages or even privacy or political issues.

At Eris Innovation we are planning to extract, transform, anonymize and eventually convert disparate data not only from legacy systems but also from other disparate data sources such as some ERP (Enterprise Resource Planning) systems or their related modules like CRM (Customer Relationship Management) systems for example.

Cloud Applications and Cloud Data Sources

As Cloud Computing continues to deliver IT services via the Internet, efficient data modeling is becoming more and more important for many companies and organizations to make the most benefit out of the cloud-based data sources.

At Eris Innovation we are developing a broad framework that employs several service-level or application-level APIs to provide access and functionality for a cloud environment in addition to connecting the application-layer with the cloud and underlying IT infrastructure such as ERP and CRM cloud-based application extensions.