G2 Introduces New Real-time Analytic Databases Category

With organizations getting data rich each day, the conversation around real-time databases is getting rave in the industry.

Every organization today has loads of data, and almost all the decisions are based on processed data. Thus, it is imperative for the data to be processed as it comes. This is where real-time analytic databases come in. 

Companies have understood the importance of reducing data latency in today’s times to gain a competitive edge.

One of the most relevant uses of real-time analytic databases is smart pricing. Smart pricing strategies automatically change the prices based on certain factors like season, demand, and so on, say, on hotel aggregator platforms.

Keeping this in mind, G2 debuted the Real-time Analytic Database Software category in June 2023.

The need for real-time analytic databases

With the advent of IoT and connected devices, much data is generated in real time, which helps derive actionable insights, monitor device performance, and predict maintenance. Real-time data from disparate sources help companies identify process bottlenecks, improve resource allocation, and improve operational efficiency.

Traditional analytic databases use relational data models with predefined schemas to store and process data. The data that is processed is historical. And thus, it has to go through the extract, transform, and load (ETL) process before the analysis.

Real-time analytic databases vs. conventional analytic databases

Real-time analytic databases can be integrated with stream analytics. G2 hosts the Stream Analytics Software category as well. With the introduction of a Real-Time Analytic Database category, it will be easier for buyers to look for a combined package of both platforms.

Unveiling G2 data for the Stream Analytics category

Product review addition to the Stream Analytics Software category for 2023 shows an overall rise of 45.45% from February to June.

The category has seen a 178% rise in product review additions in May.

Companies are looking to streamline data management with ever-growing data, which can be attributed to this rise in the popularity of stream analytics. The market seems to stabilize the next month onwards with the review additions still being higher than April.

According to G2’s Buyer Behavior Report 2023, executive leaders like CEOs, VPs, and SVPs are quite optimistic about the buyers’ spending on software purchases in 2024. The chart below shows the percentage of respondents that believe software purchase spend will increase in 2024 by role.

Because of exponentially growing data, it has become crucial for organizations to make faster and better decisions. This is where the companies will consider integrating various tools like stream analytics and real-time analytic databases to obtain quicker and more accurate insights.

This will ultimately increase the company’s spendings too.

Integration with stream analytics 

Stream analytics and real-time analytic databases integrate to process, store, and analyze streaming data in real time, thus helping organizations make better-informed decisions.

• Data ingestion: Stream analytics platforms ingest data from various sources like social media feeds, sensors, logs, and IoT devices. It carries out real-time transformations and filtering on the streaming data.
• Real-time processing: Stream analytics platforms also perform data cleansing, enrichment, aggregations, and calculations with external data sources.
• Output to real-time analytic database: The processed data is then streamed into a real-time analytic database. It is usually done by updating records in the database in real time or near real time.
• Querying and analysis: The real-time analytic database acts as a querying layer for the processed streaming data. The time taken to query the data and to analyze it is extremely low in a real-time analytic database.
• Visualization and reporting: Real-time analytic databases can integrate with visualization tools. This helps in creating visualizations and dashboards.

The future of real-time analytic databases

The future of real-time analytic database platforms looks promising. There are advancements in data velocity, analytics capabilities, and integration with new technologies. 

These databases will continue to evolve and meet the increasing demand for real-time insights, personalized experiences, and operational intelligence, helping organizations thrive in the fast-paced, data-driven world.

What is database as a service (DBaaS)? Learn what makes it the next best "as a service" offering. 

Edited by Shanti S Nair