How to Crush Database Workloads by Combining Transactions and Analytics on the Same Platform

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Click to learn more about author Neil McGovern.

The rise of Big Data over the past decade has made businesses think more critically about their analytics strategies, and about how they use the exponentially growing amount of data available to them to drive positive business outcomes.

Now that these modern organizations understand the business value of analytics, there is a growing need to access those data-driven insights in real time. The traditional data management model of using disparate databases to support siloed workloads is no longer adequate for today’s enterprise. This requirement to alleviate data movement between data silos and business applications – along with the rise of machine learning, predictive and streaming analytics – has continued to fuel the growth of transactional and analytic data platforms.

Translytical Data Platforms Explained

In the Forrester Wave™: Translytical Data Platform report, Forrester Research documented the rise of what they called “translytical” data platforms, an emerging technology that combines transactions and analytics on a single platform. Today, these platforms can support real-time applications for stock trading, fraud detection, counterterrorism, patient health monitoring and machine operation analysis. Similarly, the translytical platforms support asset monitoring for IoT devices that keeps sophisticated machinery operating with little to no downtime.

The key to a translytical data platform’s success is the ability to perform multiple workloads within a single framework, using a single data tier that can serve both transactional and analytical workloads. This means that businesses can store and analyze customer data in a single integrated translytical platform, enabling them to upsell and cross-sell new products based on known customer preferences, buying patterns and previous orders.

Alleviating Data Challenges

When combined with a true in-memory database and persistent memory, a transactional and analytical data platform can deliver dramatic performance improvements and cost savings. IDC estimates that an in-memory database can deliver performance improvements of 10x for transactions and up to 100x for analytics. It can also simplify your data model and streamline your IT landscape, reducing total RDBMS costs (including server, storage, network administration, database administration, and database-related operations) by 40-45%. Additionally, with persistent memory, your transactional and analytical data platform delivers much higher memory capacity and persistent storage of the data with near DRAM-like performance. Persistent memory is non-volatile so data stays in-memory when the system is shut down, which enables significantly faster data loads when powering up.

Forrester identified four of the most common database workloads and how translytical data is alleviating data challenges, including real-time applications, IoT analytics operational data, connected data apps, and continuous learning in which translytical databases are applied to train and retrain machine learning models. With IoT sensors, streaming analytics, machine learning and in-memory technologies, manufacturers can track their devices and machines every second to predict likely failures, as well as to decide when to take the machine offline for regular maintenance to avoid any downtime due to a breakdown. Another database workload identified was connected data applications, and the report detailed how integrated business data is critical to their success. Translytical data platforms deliver a real-time, trusted view of critical business data. For example, a customer’s address might be stored on five or more different databases, and a change by one application might not be visible to other app users right away. In this case, storing all customer-critical data in-memory in a transactional and analytical database allows all business applications to apply it, delivering consistent service.

Adding Business Value in The Real World

Many businesses today are already starting to reap the benefits of transactional and analytical platforms. Take Convergent IS, which assists companies across a wide range of industries, including the public sector, utilities, and oil and gas, to build mobile apps that make business processes faster and more efficient. Prior to using an integrated and transactional platform, their finance department had severe data silo challenges that were causing reporting delays and user frustrations. The accounts payable team had to log in to its Expensify expense reporting system, export each report to a spreadsheet, then create a table with manual filters to transfer the data to their data storing technologies. Now, with powerful in-memory database centered on a new ERP system, operational data is presented alongside analytical data to help make more informed decisions and manage exceptions. Not only does this dramatically speed up invoicing and increase accuracy, but it also significantly improves the user experience – empowering the accounts payable team in the same way Convergent IS empowers its customers.  The accounts payable team is no longer burdened with manual data reconciliation from multiple systems. Instead, it can automatically process invoices through a single interface – saving time and improving accuracy.

Transactional and analytical platforms are setting new standards for real-time data and in-event analytics, allowing organizations to make decisions in-transaction. Industries have come to terms with the escalated levels of connectivity and data collection in today’s IoT-driven environment—now it’s time to continue growing on these data journeys and making the most of transactional data in real time using the right technologies.

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