Application-Driven Analytics: Why are Operational and Analytical Workloads Converging?

Mat Keep
May 8, 2023 | Updated: March 22, 2024

Fifteen years ago, our vision was to provide developers a new approach to databases. As industry change is constant, we are working to bring you another shift so you can stay ahead of the curve – application-driven analytics.

Application-driven analytics isn’t about replacing your centralized data warehouse or data lakehouse. Rather it’s about augmenting them; bringing a new class of analytics directly into applications where they are built by developers.

In his recent Analyst Perspective, Matt Aslett, VP & Research Director at Ventana Research was very clear there remains different functional requirements for dedicated operational and analytical systems. However, he noted the growth in intelligent applications infused with the results of analytic processing. This in turn is driving operational / OLTP data platforms such as MongoDB to integrate native analytics functionality.

In the Perspective, Aslett goes on to describe some of the recent product enhancements introduced by MongoDB to support analytics, and wraps up with this advice:

I recommend that organizations evaluating potential database providers for new, intelligent operational applications include MongoDB Atlas in their evaluations.

If you are interested in learning more about Ventana Research’s insights, take a look at the company’s Analyst Perspective: MongoDB’s Atlas Delivers Data-Driven Applications.

From manufacturing to retail and finance

Beyond the research from industry analysts, organizations are increasingly working to capture the opportunities presented by application-driven analytics.

Bosch Global Software uses MongoDB at the core of its IoT systems powering automotive, industrial, and smart home use cases. Being able to generate analytics in real time is a key capability of the company’s applications. As discussed in his recent article in The New Stack, Kai Hackbarth, senior technology evangelist at Bosch, talked about the value MongoDB provides:

From my history [of doing this for] 22 years, we never had the capabilities to do this before.

Global retailer Marks and Spencer rebuilt its Sparkes rewards program, moving from a packaged app to an in-house solution with MongoDB. The company reduced its time to build one million personalized customer offers from one hour to just five minutes. It is now able to serve those offers at 10x lower latency with the loyalty program driving 8x higher customer spend.

As part of its digital transformation initiative, Toyota Financial Services built a new operational data layer powered with MongoDB Atlas. The data layer connects the company’s internal mainframe backend systems with customers engaging the company through new digital channels. MongoDB handles customer onboarding along with fraud prevention.

The native OLTP and analytics capabilities provided by MongoDB Atlas were key. They eliminated the need for Toyota Financial Services to integrate and build against separate database, cache, object store, and data warehouse technologies. All dramatically simplifying the company’s technology estate.

MongoDB helps us make better decisions and build better products.

Ken Schuelke, Division Information Officer, Toyota Financial Services

Enabling developers for application-driven analytics

How is MongoDDB helping developers make the shift to smarter apps and intelligent software?

MongoDB Atlas unifies the core transactional and analytical data services needed to deliver app-driven analytics. It puts powerful analytics capabilities directly into the hands of developers in ways that fit their workflows. With Atlas, they land data of any structure, index, query, and analyze it in any way they want, and then archive it. All while working with a unified API and without having to build their own data pipelines or duplicate data.

MongoDB Atlas supports any level of application intelligence. From querying and searching records to aggregating and transforming data through to feeding rules-based engines and machine learning models.

Figure 1:   MongoDB Atlas combines transactional and analytical processing in a multi-cloud data platform.

Atlas automatically optimizes how data is ingested, processed, and stored, maximizing the efficiency of the application’s operational and analytical workloads. These capabilities are packaged in an elegant and integrated multi-cloud data architecture.

Getting started

There are many ways you can get started in building more intelligent apps. If you want to read more about the use-cases and business drivers, download our App-Driven Analytics whitepaper.

Alternatively if you want to dive straight in, sign up for an account on MongoDB Atlas. From there, they can create a free database cluster, load your own data or our sample data sets, and explore what’s possible within the platform. The MongoDB Developer Center hosts an array of resources including tutorials, sample code, videos, and documentation organized by programming language and product.

← Previous

What's New in Atlas Charts: Suggested Charts, Auto Activation, and Contextual Help

Atlas Charts is the native data visualization tool for quickly and easily analyzing your data in MongoDB Atlas. Today, we’re announcing a collection of updates that further streamline the Charts experience: Suggested charts: a quicker way to build visualizations More contextual help in the chart builder Automatic Charts activation for all project members Suggested charts Charts has always offered a simple UI with an easy to use, drag and drop interface that lets you quickly build charts and visualize your application data. However, we still found that some users could benefit from extra help when building out new charts. Rather than starting from an empty screen where you need to drag appropriate fields into the chart type selected, what if you could simply select an automatically suggested chart, and start applying customization from there? That’s exactly what suggested charts offer. We experimented with this feature late last year and now we have turned it on for everyone! Simply add a chart into one of your dashboards to try it out today. Figure 1: Using the new auto suggested charts in the chart builder. Help button in the chart builder As you might expect, the chart builder is where you do your chart creation. Similar to suggested, last year we experimented with ways to provide more contextual help for users when building new charts. Now, we are surfacing helpful docs articles to educate users on key chart building topics like: filtering, adding fields, selecting the right chart type, and more. Sometimes it can be intimidating to know exactly what chart to use and how to achieve the style and customization you want – the help button in chart builder will make this much easier. Building a chart and have a question? Just click into the Get help button and check out one of our highlighted topics, or choose View all topics to read the main Charts documentation. Streamlining Charts activation We’re constantly looking for ways to help Atlas users with data visualization quicker. So starting with this latest release, when you click into the Charts tab from the Atlas UI, you will automatically be set up to start visualizing your data – no activation required. Additionally, Atlas users browsing collections within a specific cluster, can now more quickly navigate directly into Charts for quick visualization. When viewing a collection, the Visualize your data button, seamlessly opens in the chart builder with the current collection selected as the chart’s data source. Paired with the new suggested chart, users see a list of chart suggestions to quickly and easily build a relevant chart based on their collection data. Note: you may see a “Charts” tab in the collection view instead of the Visualize your data button, as shown below. This is due to an experiment we are currently running. FIgure 3: Seamlessly navigate from an Atlas collection into the chart builder in Atlas Charts. This is a continuation of our effort to optimize the overall Charts experience. Last year we made strides in this area by introducing features like streamlined data sources and org-wide sharing . Keep on the lookout for more Charts features that further simplify your experience visualizing Atlas data across your team. New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas , deploying or selecting a cluster, and activating Charts for free.

May 4, 2023
Next →

Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024