Analyst Perspectives

I recently wrote about the need for enterprises to harness events to process and act upon data at the speed of business. The core technologiesthat enable enterprises to process and analyze data in real time have been in existence for many years and are widely adopted. However, streaming and events technologies are also commonly seen as a niche requirement, separate from an enterprise’s primary...

I previously wrote about the importance of open table formats to the evolution of data lakes into data lakehouses. The concept of the data lake was initially proposed as a single environment where data could be combined from multiple sources to be stored and processed to enable analysis by multiple users for multiple purposes.

The final of the men’s 100 meters at the Paris Olympics this summer was a reminder that being successful requires not just being fast but performing at the right time. Being fast is obviously a prerequisite for participating in an Olympic 100-meter final, and all the competitors finished the race in under 10 seconds, with just 0.12 seconds separating the first man from the last. While all the...

I have written on multiple occasions about the increasing proportion of enterprises embracing the processing of streaming data and events alongside traditional batch-based data processing. I assert that, by 2026, more than three-quarters of enterprises’ standard information architectures will include streaming data and event processing, allowing enterprises to be more responsive and provide...

I previously wrote about the ongoing importance of event brokers and event management in enabling enterprises to adopt event-driven architecture and event stream processing. Many enterprises adopt EDA as the design pattern for maximizing events to deliver real-time business processes. There are many advantages to using EDA, including a cultural shift away from batch processing towards real-time...

Ventana Research recently announced its 2024 Market Agenda for Analytics and Data, continuing the guidance we have offered for two decades to help enterprises derive optimal value and improve business outcomes.

I previously wrote about the challenge facing distributed SQL database providers to avoid becoming pigeonholed as only being suitable for a niche set of requirements. Factors including performance, reliability, security and scalability provide a focal point for new vendors to differentiate from established providers and get a foot in the door with customer accounts. Expanding and retaining those...

In my past perspectives, I’ve written about the evolution from data at rest to data in motion and the fact that you can’t rely on dashboards for real-time analytics. Organizations are becoming more and more event-driven and operating based on streaming data. As well, analytics are becoming more and more intertwined with operations. More than one-fifth of organizations (22%) describe their...

The data platforms market may appear to have little or nothing to do with haute couture, but it is one of the data sectors most strongly influenced by the fickle finger of fashion. In recent years, various architectural approaches to data storage and processing have enjoyed a phase in the limelight, including data warehouse, data mart, data hub, data lake, cloud data warehouse, object storage,...

I recently wrote about the various technologies used by organizations to process and analyze data in real time. I explained that while the terms streaming data and events and streaming analytics are often used interchangeably, they are separate disciplines that make use of common underlying concepts and technologies such as events, event brokers and event-driven architecture. Confluent’s...

Real-time business is a modern phenomenon, and business transformation has accelerated many business events in recent years. However, the execution of business events has always occurred in real time. Rather, it is the processing of the data related to business events that has accelerated instead of the event itself.

As I have previously explained, we expect an increased demand for intelligent operational applications infused with the results of analytic processes, such as personalization and artificial intelligence-driven recommendations. These systems rely on the analysis of data in the operational data platform to accelerate worker decision-making or improve customer experience.

Organizations increasingly rely on real-time analytics to make informed decisions and stay competitive in today’s data-driven business landscape. As the complexity of data grows with the continuous addition of diverse sources, customers and workers alike expect real-time responsiveness. Accelerated query performance is crucial to process and extract valuable insights from data in a timely manner....

I have written recently about the increasing importance of managing data in motion and at rest as the use of streaming data by enterprise organizations becomes more mainstream. While batch-based processing of application data has been a core component of enterprise IT architecture for decades, streaming data and event processing have often been niche disciplines typically reserved for...

Success with streaming data and events requires a more holistic approach to managing and governing data in motion and data at rest. The use of streaming data and event processing has been part of the data landscape for many decades. For much of that time, data streaming was a niche activity, however, with standalone data streaming and event-processing projects run in parallel with existing...

Ventana Research recently announced its 2023 Market Agenda for Data, continuing the guidance we have offered for two decades to help organizations derive optimal value and improve business outcomes.

Ventana Research uses the term “data pantry” to describe a method of data storage (and the technology and process blueprint for its construction) created for a specific set of users and use cases in business-focused software. It’s a pantry because all the data one needs is readily available and easily accessible, with labels that are immediately recognized and understood by the users of the...

I have previously written about growing interest in the data lakehouse as one of the design patterns for delivering hydroanalytics analysis of data in a data lake. Many organizations have invested in data lakes as a relatively inexpensive way of storing large volumes of data from multiple enterprise applications and workloads, especially semi- and unstructured data that is unsuitable for storing...

Ventana Research’s Data Lakes Dynamics Insights research illustrates that while data lakes are fulfilling their promise of enabling organizations to economically store and process large volumes of raw data, data lake environments continue to evolve. Data lakes were initially based primarily on Apache Hadoop deployed on-premises but are now increasingly based on cloud object storage. Adopters are...

Earlier this year I described the growing use-cases for hybrid data processing. Although it is anticipated that the majority of database workloads will continue to be served by specialist data platforms targeting operational and analytic workloads respectively, there is increased demand for intelligent operational applications infused with the results of analytic processes, such as...