Translytical database

What is a translytical database?

A translytical database is a unified database that supports transactions, analytics, and operational insights in real-time retaining transactional integrity, efficiency, and scale.

Companies often use several databases, data warehouses, or data lakes to accommodate various workloads, such as transactional and analytical which can be responsible for sacrificing transactional integrity performance. Separating operational and analytical systems has long been the best strategy for preventing analytic workloads from interfering with operational processes.

However, having several layers typically necessitates the employment of multiple technologies to divide the effort, such as one technology for running applications and another for batch analysis. In such cases, data must be moved from transactional to operational to analytical systems, slowing down processing and limiting integration and real-time insights and analytics.

At present, companies need real-time access to information to take the next best action when it comes to customers. This is where translytical databases come into play. 

Acting as a data platform, these databases have a single technology layer that leverages in-memory to provide application transactions and analytics and support multiple workloads. With translytical databases, the user benefits from real-time insights, machine learning (ML), streaming analytics, and extreme transactional processing, within a single database.

Business benefits of using translytical database

  • In-memory
    Provides low-latency access to critical data in real-time without slowing down or hindering the ongoing processes for multiple workloads.

  • Accurate analytics
    Sensor innovation combined with streaming and machine learning can provide precise analytics and forecasts using the Internet of Things (IoT). The combination of sensors and the IoT allows businesses to operate with confidence because errors get detected before they occur, allowing companies to manage risks effectively.

  • Critical business data management
    The platform ensures that data is processed correctly to reflect a trustworthy view of data. With the use of multiple apps, data is stored consistently across platforms to deliver critical data accurately.

  • Up to date machine learning (ML)
    Translytical databases ensure information is up to date for training and machine models.
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