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 →

Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024