Designing a modern Data Platform: Simplicity, power and security

Empowering Decisions with Cutting-Edge Data Integration and Analysis

In today’s world, data analysis is crucial for well-informed decision making. A cutting-edge data platform can be a game-changer, enabling companies to seamlessly integrate, manage, and analyze diverse data sources. But what defines a modern data platform? And how do you implement it, and what technologies do you need?

Key characteristics of a modern Data Platform

A robust data platform should consolidate all your data in one place, regardless of how it’s spread across various systems. It must be reliable, with intelligent controls ensuring everything functions as expected. Keeping the software up-to-date is equally important to guarantee data security.

A modern data platform must handle both large and small datasets efficiently, optimizing resource use. It scales automatically when more capacity is needed and adjusts back down as the load decreases. This all happens within a secure, centrally managed environment where you always have full visibility.

At Cadran, we’ve developed a data platform that meets all these criteria. In this blog, we’ll explore how the Cadran Analytics Platform works and what makes it unique.

Cadran Analytics Platform: A Closer Look

The Cadran Analytics Platform supports a variety of data formats, from tables to images and PDFs. This versatility makes it suitable for diverse needs and situations.

The platform offers over 700 integrations with systems like Oracle NetSuite, Exact Online, and LinkedIn Ads. This allows seamless data connections and retrieval—essential for companies managing multiple data sources. If issues arise, our experts are immediately alerted to resolve them quickly.

Data Transformation for valuable insights

Once your data is retrieved, it’s transformed into actionable insights. This process, known as data transformation, combines information from different sources and enriches it with analyses. For example, calculations for inventory management, scheduling, or forecasting. These insights can then be used in dashboards or sent back to systems like your ERP to enhance business processes.

For users, we’ve developed standard standard JD Edwards models, allowing you to gain insights faster without needing to configure everything yourself

Ensuring a robustness and reliable Data Platform

Ensuring platform robustness is key. Our software undergoes automated testing. Whenever a developer suggests a change, a series of tests are performed to ensure that existing logic still produces the desired output. This approach helps us to prevent regressions, and ensures that the code is well-maintained. Once the software change is both validated and tested, the code is deployed automatically using a continuous integration and deployment pipeline to the Cadran Analytics Platform. This streamlined process allows the Cadran Analytics Platform to undergo reliable and quick iterations, making it possible to design a solution to a business problem at a record pace.

Furthermore, the platform automatically adjusts its scale based on workload demands. When there’s a surge in required workloads, it scales up by acquiring additional machines, and when the demand decreases, it scales down by removing excess machines. This not only makes the platform financially efficient, but also contributes to lowering our environmental impact through reduced energy usage. The implementation of this scaling mechanism ensures that we are ready for any workload in the future, without concern about the computational requirements.

The platform is managed and monitored centrally, which means that changes can be easily applied, with a comprehensive history of previous states always accessible. If for any reason the platform would be deleted, we could reproduce it within minutes. This rapid recovery extends to both our  infrastructural components and the systems running on our platform. To monitor all the systems and processes that run on our platform, we designed a mechanism that keeps track of events and resource utilization. This enables us to closely monitor the resources our platform uses, and identify the processes responsible for certain loads.

Keeping your data platform secure

To ensure the highest security for our data platform, we’ve implemented a two-fold approach. Firstly, our infrastructural components, including servers, software and databases are updated automatically on weekends without causing any downtime. Utilizing a cluster-based platform allows us to apply these updates with minimal implications. We’ve streamlined the process by initiating a new node with the upgraded OS versions before phasing out the current one.

Secondly, active monitoring of vulnerabilities in our code is a top priority. Out scanning system notifies us of any potential vulnerabilities in externally used Python packages, enabling us to quickly address and rectify any issues.

In conclusion, as you strive to become a more data-driven company, making well-informed decisions hinges on a robust and modern data platform. The techniques explained in this blog give an insightful overview of the essential elements a modern data platform should consist of. For more information about the Cadran Analytics Platform, don’t hesitate to contact us!