Architecting Success as a Woman in Tech

Celia Halenke
March 26, 2024 | Updated: April 8, 2024
#Culture

Celia Halenke, Solutions Architect at MongoDB, shares insight into the skills, experiences, and aspirations that shape her MongoDB journey in the dynamic world of technology. Plus, learn about her advice for teams wanting to build more inclusive environments for women in tech sales.

Mastering the balance: Technical and communication skills

In my role, it's all about having a strong blend of technical skills and effective communication. To succeed as a pre-sales Solutions Architect, you need to blend both seamlessly. I’ve had to learn the ins and outs of MongoDB's technology, but equally important is grasping the unique challenges and objectives my clients face. This allows me to craft solutions that are not just tailored but perfectly aligned with their needs.

Communication is just as important. From running demos to conducting workshops with people from diverse backgrounds, clear and concise communication is a must. It's not just about showcasing the technology; it's about ensuring everyone is on the same page.

Team collaboration is another vital aspect of my role. Working closely with sales reps, CSMs, product managers, and engineers requires building strong relationships. These connections are not just essential for success but play a significant role in personal growth.

Photograph of Celia with her team members
Celia and team members

Fostering inclusivity in tech

Being a woman in tech, I can't stress enough the importance of seeing more women in leadership roles. It's not just about breaking stereotypes; it's about having role models who inspire and motivate. That's why promoting women into leadership is crucial. Mentorship and leadership programs specifically designed for women can make a significant impact, providing the support and guidance needed to thrive in a historically male-dominated industry tech.

I'm proud to be part of MongoDB, where employee resource groups for women and other communities create a supportive environment. More companies should consider implementing similar initiatives to foster inclusivity and provide platforms for sharing experiences.

Celebrating success

One of the highlights of my journey at MongoDB has been working closely with the Product Led Sales team. They have recognized me for my efforts for two consecutive quarters, which is a testament to the trust and collaboration I’ve built within the team. It feels really good! Knowing that my work is valued and appreciated motivates me to keep pushing boundaries. I encourage women to make time to celebrate their accomplishments.

The joys of customer interaction

What I love most about my customer-facing role is the direct interaction with our customers. Understanding their projects, and the problems they aim to solve, and then offering them the perfect MongoDB Atlas feature brings me immense satisfaction. Recently, I had the opportunity to visit clients on-site during a business trip to Latin America. I enjoyed this experience and it changed my perspective on customer interactions: though not as quick as hopping on a video conference, in-person sessions are some of the most engaging.

Photograph of Celia in Latin America
Celia in Latin America

Aspirations and future growth

Looking ahead, my goal is to continue growing as a Solutions Architect at MongoDB! Embracing the evolving challenges of my role allows me to constantly learn and enhance my communication and technical skills. I aspire to work with larger customers, witnessing firsthand the positive impact MongoDB's applications can have on people's lives. As I gather more experience, I'm eager to take on a leadership role, guiding others in their MongoDB journeys.

My journey at MongoDB is a testament to the ever-evolving landscape of technology, where success is not just about technical expertise but also about building meaningful connections, fostering inclusivity, and celebrating every milestone along the way.

Learn more about Sell Like a Girl and MDBWomen, Employee Resource Groups supporting a community of women around the world at MongoDB.

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AI-powered SQL Query Converter Tool is Now Available in Relational Migrator

When I traveled to Japan for the first time it was shortly after translation apps on smartphones had really taken off. Even though I knew enough phrases to get by as a tourist I was amazed at how empowered I was by being able to have smoother conversations and read signs more easily. The power of AI helped me understand a language I had only a passing familiarity with and drastically improved my experience in another country. I was able to spend more time enjoying myself and spend less time looking up common words and sentences in a phrase book. So what does this have to do with application modernization? MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More Transitioning from relational databases as part of a modernization effort is more than migrating data from a legacy database to a modern one. There is all the planning, designing, testing, refactoring, validating, and ongoing operation that makes modernization efforts a complex project to navigate successfully. MongoDB’s free Relational Migrator tool has helped with many of these tasks including schema design, data migration, and code generation, but we know this is just the beginning. One of the most common challenges of migrating legacy applications to MongoDB is working with SQL queries, triggers, and stored procedures that are often undocumented and must be manually converted to MongoDB Query API syntax. This requires deep knowledge of both SQL and the MongoDB Query API, which is rare if teams are used to only using one system or the other. In addition, teams often have hundreds, if not thousands of queries, triggers, and stored procedures that must be converted, which is extremely time-consuming and tedious. Doing these conversions manually would be like traveling abroad and looking up each object one by one in a phrase book instead of using a translation app. Thankfully with generative AI, we are finally able to get the modern version of the translation app on your phone. The latest release of Relational Migrator is able to use generative AI to help your developers quickly convert existing SQL queries, triggers, and stored procedures to work with MongoDB using your choice of programming language (JavaScript, C#, or Java). By automating the generation of development-ready MongoDB queries, your team can be more efficient by redirecting their time to more important testing and optimization efforts — accelerating your migration project. Teams that are familiar with SQL can also use the Query Converter to help close their MongoDB knowledge gap. The SQL objects they're familiar with are translated, making it easier to learn the new syntax by seeing them next to each other. Let’s take a closer look at how Query Converter can convert a SQL Server stored procedure to work with MongoDB. Figure 1: The MongoDB Query Converter Dashboard We’ll start by importing the stored procedure from the relational database into our Relational Migrator project. This particular stored procedure joins the results from two tables, performs some arithmetic on some of the columns, and filters the results based on an input parameter. CREATE PROCEDURE CustOrdersDetail @OrderID int AS SELECT ProductName, UnitPrice=ROUND(Od.UnitPrice, 2), Quantity, Discount=CONVERT(int, Discount * 100), ExtendedPrice=ROUND(CONVERT(money, Quantity * (1 - Discount) * Od.UnitPrice), 2) FROM Products P, [Order Details] Od WHERE Od.ProductID = P.ProductID and Od.OrderID = @OrderID Developers who are experienced with the MongoDB aggregation framework would know that the equivalent method to join data from two collections is to use the $lookup stage. However, when migrating a relational database to MongoDB, it often makes sense to consolidate data from multiple tables into a single collection. In this example, we are doing exactly that, by combining data from the Orders , Order Details , and Products table into a single orders collection. This means that, when considering the changes to the schema, we do not actually need a $lookup stage at all, as the data from each of the required tables has already been merged into a single collection. Relational Migrator’s Query Converter works alongside the schema mapping functionality and automatically adjusts the generated query to work against your chosen schema. With JavaScript chosen as our target language, the converted query avoids the need for a costly join and includes MongoDB equivalents of our original SQL arithmetic functions. The query is now ready to test and include in our modernized app. const CustOrdersDetail = async (db, OrderID) => { return await db.collection('orders').aggregate([ { $match: { orderId: OrderID } }, { $unwind: '$lineItems' }, { $project: { ProductName: '$product.productName', UnitPrice: { $round: ['$lineItems.unitPrice', 2] }, Quantity: '$lineItems.quantity', Discount: { $multiply: ['$lineItems.discount', 100] }, ExtendedPrice: { $round: [ { $multiply: [ '$lineItems.quantity', { $subtract: [1, '$lineItems.discount'] }, '$lineItems.unitPrice' ] }, 2 ] } } } ]).toArray(); }; Relational Migrator does more than just query conversion, it also assists with app code generation, data modeling, and data migration, which drastically cuts down on the time and effort required to modernize your team's applications. Just like a language translation app while traveling abroad it can drastically improve your experience converting and understanding a new language or technology. The new Query Converter tool is now available for free for anyone to try as part of a public preview in the Relational Migrator tool. Download Relational Migrator and try converting your SQL queries and stored procedures today.

March 25, 2024
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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