Built With MongoDB: Alloy Transforms Ecommerce With No-Code Integrations

Steve Jurczak
August 31, 2022 | Updated: October 17, 2022
#BuiltWithMongoDB#Startups

Gregg Mojica and Sara Du knew there was a need for simpler integrations with ecommerce platforms because they had experienced it themselves. After becoming friends through the open source community, they started a Shopify store as a side project and became intrigued by the multitude of apps available in the Shopify ecosystem — a large selection of integrations for things like ERP, email and social media marketing, ads, marketing analytics, and more. Mojica and Du also found that stitching together these disparate tools was overly complex and that the tools were not geared toward ecommerce.

Their company, Alloy Automation, is a no-code integration solution that integrates with and automates ecommerce services, such as CRM, logistics, subscriptions, and databases. For example, Alloy can automate SMS messages to go out upon reaching fulfillment milestones. It can automatically start a workflow when an event occurs in an online store or in another app, create logic to define whether a follow-up action will be taken, and use conditions like order tags or customer location to set up automated actions that will pull and push data from connected apps. If order status is updated to paid and the total value of order is greater than $100, for example, Alloy Automation can automatically send a text message with a discount for an additional purchase.

Alloy is part of the MongoDB for Startups program, and this article looks at how Alloy uses MongoDB and also benefits from the partnership to overcome startup challenges.

Jobs to be done

Mojica, co-founder and CTO of Alloy, sympathized with merchants that were trying to connect multistage workflows using the limited tools that were available. "A lot of merchants have relatively complex flows," he said. "They're cycling through abandoned carts, checking if certain line items are present, and setting up very aggressive rules that historically you would have to program yourself. But a lot of merchants don't have the operating budget to hire expensive engineers to set up these rules."

Mojica applied the knowledge he had gained as an engineer in financial services to address the integration problems he and Du were experiencing as online merchants. Although Alloy was initially focused on solving general ecommerce problems, Mojica says he realized that the tools he was building could apply to more than just ecommerce. "Not only are we solving problems for merchants but also for software and SaaS companies," Mojica said. "Now anybody can build relatively complex automations without having engineering expertise. Alloy can templatize those things and offer them as recipes on our platform – we offer a business facing product called Alloy Embedded that allows anyone to effortlessly connect to our integrations by implementing our SDK. Businesses can get started very easily with just a few lines of code."

Early stages

Alloy is a Y Combinator company — part of the cohort that was scheduled to demo their products in March 2020, the very moment the world “locked down” because of the COVID-19 pandemic. It still raised $5 million in a seed round, followed by $20 million in Series A funding in February 2022. In that time, the company has expanded its platform to include more than 220 integrations, including MongoDB.

Alloy is a member of the MongoDB for Startups program, which provides Atlas credits among other benefits for young companies, and it uses MongoDB Atlas as the underlying database. Mojica cites several reasons for the close partnership between the two companies. "Atlas was the database we chose from the beginning. I personally have used MongoDB before, so I have a certain comfort level, and I was the first person that wrote code in Alloy," Mojica said. "But another big reason why I wanted to use MongoDB is the freeform nature of much of the data that we ingest. We connect over 220 integrations, each one has its own schema, and it's typically in JSON. So having a less structured way to store that information compared with something highly delineated like SQL has been very valuable to us."

Growing pains

Mojica and Du are acutely aware of the challenges startups face, especially managing technical resources. "We like the fact that MongoDB has really good support, there's built-in monitoring, and backups,” Mojica said. “These things allow you to get going quickly. There's a lot of pressure, especially in the very beginning, to get into Y Combinator. You've got to build the product, get customers, and start your fundraise. That's a lot to do in three months. What you don't want to worry about is all the DevOps stuff."

As startups begin to scale, they often become subject to compliance requirements that present new technology hurdles. Alloy went through the compliance process seamlessly thanks to the security capabilities and certifications behind MongoDB Atlas.

"We're servicing larger clients and seeing different use cases," Mojica said. "The compliance process involved questions about where we're storing data and if we're in different regions. Once your company is big enough, it's a major concern. Just having SOC 2 certification and making sure we're following all the various data privacy rules is really important. We're effectively an intermediary for customer data, so compliance is really important, like when we are deleting data for GDPR requests. MongoDB Atlas helps us with that. It's SOC 2 certified, and we can deploy in any region on any of the major cloud providers. For us, that meant setting up a Network Peering Connection to our AWS VPC from Atlas. So, from a security and compliance perspective, we know that's all taken care of."

Making the MongoDB connection

"We added a MongoDB connector to our platform because we were hearing interest from our user base,” Mojica explained. “If you want to integrate with a series of different tools and you're also sending data to MongoDB Atlas, instead of having to build those integrations every time, Alloy already has the infrastructure. You can just connect your system, stream the data, and we handle all the architecture. Something that would normally take weeks or months now takes only a few hours. That's the power of the no-code platform."

The Alloy–MongoDB integration includes bidirectional sync. "Your connection with MongoDB Atlas can go both ways,” Mojica added. “You can pull data and you can push data. You can do scheduled workflows once an hour or once a day, make a query, get some data from MongoDB, check if a record was added, and then send the data to another platform or destination. The bidirectional sync is really important, because integration really is the ability to get data, but also push data."

Support for startups

As a member of the MongoDB for Startups program, Alloy enjoys access to a wide range of resources, including free credits to our best-in-class developer data platform, MongoDB Atlas, and personalized technical advice, among other perks. Alloy leveraged the program from an early stage, as Mojica explained, "The credits were very helpful in the beginning, especially when you're in Y Combinator and don't have a lot of money in the bank. We recently started getting in touch for support. In fact, just knowing that we have support is very valuable."

To learn more about Alloy, check out runalloy.com.

Are you part of a startup and interested in joining the MongoDB for Startups program? Apply now.

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3 Reasons (and 2 Ways) to Use MongoDB’s Improved Time Series Collections

Time series data, which reflects measurements taken at regular time intervals, plays a critical role in a wide variety of use cases for a diverse range of industries. For example, park management agencies can use time series data to examine attendance at public parks to better understand peak times and schedule services accordingly. Retail companies, such as Walmart , depend on it to analyze consumer spending patterns down to the minute, to better predict demand and improve shift scheduling, hiring, warehousing, and other logistics. As more sensors and devices are added to networks, time series data and its associated tools have become more important . In this article, we’ll look at three reasons (and two ways) to use MongoDB time series collections in your stack. This in-depth introduction to time series data features MongoDB Product Manager Michael Gargiulo. Reason 1: Purpose-built for the challenges of time series data At first glance, time series collections resemble other collections within MongoDB, with similar functionalities and usage. Beneath the surface, however, they are specifically designed for storing, sorting, and working with time series data. For developers, query speed and data accessibility continue to be challenges associated with time series data. Because of how quickly time series data can accumulate, it must be organized and sorted in a logical way to ensure that queries and their associated operations can run smoothly and quickly. To address this issue, time series collections implement a key tenet of the MongoDB developer data platform: Data that is stored together is accessed together. Documents (the basic building block of MongoDB data) are grouped into buckets, which are organized by time. Each bucket contains time series data from a variety of sources — all of which were gathered from the same time period and all of which are likely to show up on the same queries. For example, if you are using time series collections to analyze the rise in summer temperatures of Valencia, Spain from 1980 to 2020, then one bucket will contain temperatures for August 1991. Relevant, but distinct buckets (such as temperatures for the months of June and July 1991) would also be stored on the same page for faster, easier access. MongoDB also lets you create compound indexes on any measurement field in the bucket (whether it’s timeField or metaField) for faster, more flexible queries. Because of the wide variety of indexing options, operations on time series data can be executed much more quickly than with competing products. For example, scan times are reduced by indexing buckets of documents (each of which has a unique identifier) rather than individual documents. In terms of the previous example, you could create an index on the minimum and maximum average summer temperatures in Valencia, Spain from 1980 to 2020 to more quickly surface necessary data. That way, MongoDB does not have to scan the entire dataset to find min and max values over a period of nearly four decades. Another concern for developers is finding the last metadata value, which in other solutions, requires users to scan the entire data set — a time-consuming process. Instead, time series collections use last point queries, where MongoDB simply retrieves the last measurement for each metadata value. As with other fields, users can also create indexes for last points in their data. In our example, you could create an index to identify the end of summer temperatures in Valencia from 1980 to 2020. By indexing the last values, time series collections can drastically reduce query times. Another recurring challenge for time series applications is data loss from Internet of Things (IoT) applications for industries such as manufacturing, meteorology, and more. As sensors go offline and gaps in your data appear, it becomes much more difficult to run analytics, which require a continuous, uninterrupted flow of data. As a solution, the MongoDB team created densification and gap filling. Densification, executed by the $densify command, creates blank, placeholder documents to fill in any missing timestamps. Users can then sort data by time and run the $fill command for gap filling. This process will estimate and add in any null or missing values in documents based on existing data. By using these two capabilities in tandem, you will get a steady flow of data to input into aggregation pipelines for insights. Reason 2: Keep everything in house, in one data platform Juggling different data tools and platforms can be exhausting. Cramming a bunch of separate products and technologies into a single infrastructure can create complex architectures and require significant operational overhead. Additionally, a third-party time series solution may not be compatible with your existing workflows and may necessitate more workarounds just to keep things running smoothly. The MongoDB developer data platform brings together several products and features into a single, intuitive ecosystem, so developers can use MongoDB to address many common needs — from time series data to change streams — while reducing time-consuming maintenance and overhead. As a result, users can take advantage of the full range of MongoDB features to collect, analyze, and transform time series data. You can query time series collections through the MongoDB Compass GUI or the MongoDB Shell , utilize familiar MongoDB capabilities like nesting data within documents, secondary indexes, and operators like $lookup or $merge, and process time series data through aggregation pipelines to extract insights and inform decision making. Reason 3: Logical ways to organize and access time series data Time series collections are designed to be efficient, effective, and easy to use. For example, these collections utilize a columnar storage format that is optimized for time series data. This approach ensures efficiency in all database operations, including queries, input/output, WiredTiger cache usage, and storage footprints for both data and secondary indexes. Let’s look, for example, at how querying time series data collections works. When a query is executed, two things happen behind the scenes: Bucket unpacking and query rewrites. To begin with, time series collections will automatically unpack buckets — similar to the $unwind command. MongoDB will unscroll compressed data, sort it, and return it to the format in which it was inserted, so that it is easier for users to read and parse. Query rewrites work alongside bucket unpacking to ensure efficiency. To avoid unpacking too many documents (which exacts a toll in time and resource usage), query rewrites use indexes on fields such as timestamps to automatically eliminate buckets that fall outside the desired range. For example, if you are searching for average winter temperatures in Valencia, Spain from 1980 to 2020, you can exclude all temperatures from the spring, summer, and fall months. Now that we’ve examined several reasons to consider MongoDB time series collections, we’ll look at two specific use cases. Use case 1: Algorithmic trading Algorithmic trading is a major use case for time series data, and this market is predicted to grow to $15 billion by 2028 . The strength of algorithms lies in their speed and automation; they reduce the possibility of mistakes stemming from human emotions or reaction time and allow for trading frequency beyond what a human can manage. Trading algorithms also generate vast volumes of time series data, which cannot necessarily be deleted, due to compliance and forecasting needs. MongoDB, however, lets you set archival parameters, to automatically move that data into cheaper cloud object storage after a preset interval of time. This approach preserves your valuable storage space for more recent data. Using MongoDB products such as Atlas, materialized views, time series collections, and triggers, it is also possible to build a basic trading algorithm. Basically, time series data will be fed into this algorithm, and when the conditions are ideal, the algorithm can buy or sell as needed, thus executing a series of individual trades with cumulative profits and losses (P&L). Although you’ll need a Java app to actually execute the trades, MongoDB can provide a strong foundation on which to build. The structure of such an algorithm is simple. Time series data is loaded from a live feed into MongoDB Atlas, which will then input it into a materialized view to calculate the averages that will serve as the basis of your trades. You can also add a scheduled trigger to execute when new data arrives, thereby refreshing your materialized views, keeping your algorithm up to date, and not losing out on any buying/selling opportunities. To learn more, watch Wojciech Witoszynski’s MongoDB World 2022 presentation on building a simple trading algorithm using MongoDB Atlas, “Algorithmic Trading Made Easy.” Use case 2: IoT Due to the nature of IoT data, such as frequent sensor readings at fixed times throughout a day, IoT applications are ideally suited for time series collections. For example, Confluent, a leading streaming data provider, uses its platform alongside MongoDB Atlas Device Sync , mobile development services, time series collections, and triggers to gather, organize, and analyze IoT data from edge devices. IoT apps often feature high volumes of data taken over time from a wide range of physical sensors, which makes it easy to fill in meta fields and take advantage of densification and gap filling features as described above. MongoDB’s developer data platform also addresses many of the challenges associated with IoT use cases. To begin with, MongoDB is highly scalable, which is an important advantage, given the huge volumes of data generated by IoT devices. Furthermore, MongoDB includes key features to enable you to make the most of your IoT data in real time. These include change streams for identifying database events as they occur, or functions, which can be pre-scheduled or configured to execute instantaneously to respond to database changes and other events. For users dealing with time-based data, real-time or otherwise, MongoDB’s time series collections offer a seamless, highly optimized way to accelerate operations, remove friction, and use tools, such as triggers, to further analyze and extract value from their data. Additionally, users no longer have to manually bucket, query, or otherwise troubleshoot time series data; instead, MongoDB does all that work for them. Try MongoDB time series collections for free in MongoDB Atlas .

August 30, 2022
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MongoDB Database Observability: Integrating with Monitoring Tools

This post is the final in a three-part series on leveraging database observability. Welcome back to our series on Leveraging Database Observability! Our previous post showcased a real-world use case highlighting how MongoDB Atlas’s observability tools effectively tackle database performance challenges. Whether you’re a developer, DBA, or DevOps engineer, our mission is to empower you to harness the full potential of your data through our observability suite . Integrating Atlas metrics with your central enterprise observability tools can simplify your operations. By seamlessly working with popular observability tools, our approach helps teams streamline workflows and enhance visibility across systems. Integrating MongoDB Atlas with third-party monitoring tools MongoDB’s developer data platform combines all essential data services for building modern applications within a unified experience. Our purpose-built observability tools for Atlas environments offer automatic monitoring and optimization, guiding diagnostics tailored specifically for MongoDB. Additionally, we extend Atlas metrics into your existing enterprise observability stack, enabling seamless integration without replacing your current tools. This creates a consolidated, single-pane view that unifies Atlas telemetry with other tech and application metrics, ensuring comprehensive visibility into both database and full-stack performance. This integration empowers you to monitor, receive alerts, and make data-driven decisions within your existing workflows, driving greater efficiency. Below is a quick guide to modifying integration settings through the Atlas UI and the popular integrations we support: Navigate to the Project Integrations page in Atlas. Choose the organization and project you want to configure from the navigation bar. On the Project Integrations page, select the third-party services you’d like to integrate. Configure the chosen services with the required API keys and regions. Critical integrations for your observability platform With Atlas’s Datadog and Prometheus integrations, you can send critical MongoDB metrics to these platforms, empowering detailed, real-time monitoring. Through Datadog , you can track database operation counts, query efficiency, and resource usage, ideal for pinpointing bottlenecks and managing resources. Similarly, Prometheus enables you to monitor essential metrics like query times, connection rates, and memory usage, supporting flexible tracking of database health and performance. Both integrations facilitate proactive detection of issues, alert configuration for resource thresholds, and a cohesive view of Atlas data when visualized in Grafana. Atlas’s integration with PagerDuty streamlines incident management by sending metrics like performance alerts, billing anomalies, and security events directly to PagerDuty. This integration records incidents automatically, notifies teams upon alerts, and supports two-way syncing, ensuring resolved alerts in Atlas are reflected in PagerDuty. It enables efficient incident response and resource allocation to maintain system stability. With Atlas integrations for Microsoft Teams and Slack, you can route key metrics—such as query latency, disk usage, and throughput—to these channels for timely updates. Teams can use these insights for real-time performance monitoring, incident response, and collaboration. Notifications through these platforms ensure your team stays informed on database performance, storage health, and user activity changes as they occur. Use case: Centralized observability with MongoDB Atlas, Datadog, and Slack Let’s walk through a hypothetical scenario for ShopSmart, an e-commerce company that leverages MongoDB Atlas to manage its product catalog and customer data. As traffic surges, the DevOps team faces challenges in monitoring application performance and database health effectively. To tackle these challenges, the team leverages MongoDB Atlas’ integration with Datadog and Slack, creating a powerful observability ecosystem. Integrating MongoDB Atlas with Datadog: The team pushes key MongoDB Atlas metrics into Datadog, such as query performance, connection counts, and Atlas Vector Search metrics. With Datadog, they can visualize these metrics and correlate overall MongoDB performance with their other applications. Out-of-the-box monitors and dedicated dashboards allow the team to track metrics like throughput, average read/write latency, and current connections. This visibility helps pinpoint bottlenecks in real time, ensuring optimal database performance and improving overall application responsiveness. Setting up alerts in Datadog: The team configures alerts for critical metrics like high query latency and increased error rates. When thresholds are breached, Datadog instantly notifies the team. This proactive approach allows the team to address potential performance issues before they impact customers. Integrating Datadog with Slack: To ensure fast communication, alerts are sent directly to the dedicated Slack channel, “ShopSmart-Alerts.” This integration fosters seamless collaboration, enabling the team to discuss and resolve issues in real-time. With these integrations, ShopSmart’s engineering team can monitor performance quickly and address issues efficiently. The unified observability approach enhances operational efficiency, improves the customer experience, and supports ShopSmart’s competitive edge in the e-commerce industry. By leveraging MongoDB Atlas, Datadog, and Slack, the team ensures scalable performance and drives continuous innovation. Conclusion MongoDB Atlas empowers developers and organizations to achieve unparalleled observability and control over their database environments. By seamlessly integrating with central enterprise observability tools, Atlas enhances your ability to monitor performance metrics and ensures you can do so within your existing workflows. This means you can focus on building modern applications confidently, knowing you have the insights and alerts necessary to maintain optimal performance. Embrace the power of MongoDB Atlas and transform your approach to database management—because your applications can thrive when your data is observable. And that wraps up our Leveraging Database Observability series! We hope you learned something new and found value in these discussions. Sign up for MongoDB Atlas , our cloud database service, to see database observability in action. To dive deeper and expand your knowledge, check out this learning byte for more insights on the MongoDB observability suite and how it can enhance your database performance.

November 14, 2024