Did you know that companies are twice as likely to outperform their peers if they use advanced analytics? It’s no wonder then that today about 74% of firms say they want to become more insight-driven and introduce analytics and data science at every corner of their application in business. However, only about 23% have been successful so far.
Why Hybrid Transactional / Analytics Processing?
A common theme across all of our customers, whether it be the Retail, eCommerce, Telecommunications or other industries, is this shift towards real-time. This shift is happening because organizations want to be able to converge the systems that run the business with the systems that manage it, otherwise known as hybrid transactional / analytics processing.
We invite you to watch our recorded webinar series focusing on solution architectures leveraging a platform which tightly integrates in-memory computing and a high-performance Spark distribution for insight and action at the point of decision. Learn about the “why” and “how” of HTAP capabilities and how HTAP platform can help you directly from GigaSpaces IMC division’s VP of Products and Strategy, Ali Hodroj. Click below to watch recorded webinars or join our upcoming webinar in the series:
HTAP is the cornerstone of modern big data architectures which mainly addresses the initial stage of fast data. Today, leading analysts are discussing how companies are incorporating technology to more readily generate actionable insights. Both Gartner’s HTAP (Hybrid Transactional/Analytical Processing), and Forrester’s Insight Platforms address how the industry is witnessing a convergence of workflows and technology platforms for real-time, analytics, cloud, and in-memory processing to effectively address time-sensitive business decisions that involve the volumes of big data.
HTAP described a new generation of in-memory data platforms that can perform both online transaction processing (OLTP) and online analytical processing (OLAP) without requiring data duplication. This concept of a real-time data pipeline can only exist in a world where the analytical workload is co-located with the transactional processing which is being executed on the same data. The result is that both analytical and transactional processing are being executed against one single source of truth of data, removing data replication out of the equation and increasing performance.
The need for HTAP arose when organizations realized that traditional architecture, which separates the data workflow between transactional and analytical systems, cannot respond to business requirements in real-time, but rather only provide after-the-fact analysis. Enterprises discovered that they were still holding back when it came to fully and meaningfully harnessing of their data due to limited experience, skills, and undefined best practices.
An HTAP architecture supports the needs of many new IoT use cases that require scalability and real-time performance. It enables instant decision making by bringing transactional data and analytics together at the time of the transaction. An HTAP architecture is best enabled by IMC techniques and technologies to provide analytical processing on the same (in-memory) data that is used to perform transaction processing.