In the current data-based environment, companies are teeming with data platforms that amass, keep, process, and crunch mammoth data. A modern data platform should not be limited to being a data warehouse. It should help to generate real-time insights, ensure data integrity, and enable a quick, flexible decision-making process.
Regardless of whether you have nothing in place or you are enhancing the current stack you build, your modern data stack is supposed to contain the following seven features:
Key Features to Look Out For
Whether you are creating an AI data platform or a modern data system, specific features are essential. Below are some of the most important features of a modern data platform.
Scalability
A contemporary data platform should be able to scale out with the increasing data volume and user base. The platform must be able to support horizontal and vertical scaling, without compromising performance, whether you are dealing with terabytes or petabytes.
The primary forms of architecture that allow this type of scalability are cloud-native and elastic storage with distributed processing at their core.
Real-time data processing
In the process of making real-time decisions, batch processing is no longer adequate in industries that require it. The new age of businesses needs a platform that can consume, process and analyse data in real-time. Find support for streaming technologies like Apache Kafka, Spark Streaming, or Flink to prepare up-to-the-second analytics.
Integration abilities in data
A contemporary information platform must have a straightforward connection to various data sources. It should include structured, semi-structured, and unstructured data. This comprises APIs, databases, cloud services, and even legacy systems, as well as IoT devices.
Flexible ETL/ELT platforms and incorporation frameworks play a significant role in the data intake of various paths and consistency.
Security and governance
With the stricter data privacy policies, powerful administration and security functionality are non-negotiable. An efficient platform must have role-based access control (RBAC), encryption in transit and at rest, an audit trail, and support for compliance (e.g. GDPR, HIPAA and so on). Transparency and control over data use are also provided with metadata management and data lineage capabilities.
High-tech analytics and AI integration
In addition to storing and retrieving, a current platform is supposed to be able to support advanced analytics, machine learning, and artificial intelligence. Integration capabilities or built-in tools to work with AI frameworks enable data teams to construct models natively on the platform.
This facilitates the workflow and creates predictive insights without the need to transfer the data between systems.
User-friendly interface
Not every user is a data engineer. In order to achieve decision success, business analysts, marketers and other concerned parties also require insights.
Self-service and the intuitive dashboards, drag-and-drop features that enable non-technical users not only to explore but also visualise data without writing a single line of code on a modern platform should be there.
Monitoring and data observability
Data platforms should make sure they incorporate observability tools in order to guarantee reliability and trust. These characteristics assist in anomaly detection, data quality monitoring, or pipeline health monitoring. Observability guarantees that malfunctions such as broken data flows, stale data, or schema changes are detected in time.
Conclusion
Digital transformation is built on a modern data platform. Focusing on scalability, real-time processing, integration, security, analytics, usability, and observability, businesses can maximise the use of the data.
The advantage of investing in such features is not only strengthening the operational efficiency but also giving a competitive advantage in the hectic world of the digital economy.
