MongoDB: The Most Wanted Database by Developers for the 4th Consecutive Year

Since 2011, Stack Overflow has taken the pulse of the developer community, revealing the top trends, technologies, and work priorities for software engineers around the world. The survey always provides fascinating insight, and 2019 was no exception with nearly 90,000 developers completing it. And now the results are in.

You can learn what your peers rate as the top programming languages, web frameworks, libraries, tools, platforms, and of course, databases, picking out the most loved, the most dreaded, and the most wanted. Stack Overflow created the database-specific category back in 2017, and MongoDB was rated as the most wanted then. It was top again in 2018, and now in 2019, repeats the ranking for the third consecutive year.

Stack Overflow Most Wanted Database 2019 Results

Image courtesy of Stack Overflow 2019 Developer Survey, Most Wanted Databases

OK, but we said in the title that MongoDB was most wanted for the past four years...so what gives? Well in 2016, before databases were given their own category, MongoDB was the only database listed in the generic roll-call of “most wanted” technologies.

So what do we make of these results? Firstly, we want to thank the users of MongoDB out there whose enthusiasm for MongoDB is contagious, as the results show. They are the community that makes MongoDB better every day, innovating on some of the most demanding applications anywhere, guiding our roadmap, and educating others on how to get the most out of MongoDB.

Why do developers select MongoDB today, and why do so many others want to start using it? We believe it is because MongoDB enables them to build faster and better meet the demands of modern apps through three core design principles:

1: The document data model

The document data model is the best way to work with data. Documents match the objects you use in code, presenting a much more natural data structure than rows and columns in a table. You can modify the document schema at any time, index on any attribute and query pretty much any way you want to support a range of operational and analytical apps – from simple point and range queries, through to complex aggregations, faceted search, geospatial processing, graph traversals. And you can have all the ACID transactional guarantees you are used to in relational databases. All of this in a single database with a single API.

2: Distributed by design

MongoDB’s distributed systems design means you can put data where you need it to be. Not just for high availability, for scale-out or for workload isolation to separate transactional and analytics workloads on the same cluster. MongoDB gives you the ability to locate data around the world. You can pin data to shards in specific geographic regions so local users always get low latency access, while you can meet the data sovereignty demands of privacy regulations such as the EU’s GDPR.

3: Run Anywhere

You have the freedom to run anywhere – you can move MongoDB from your laptop to on-premise servers, to the cloud, to the fully managed global cloud MongoDB Atlas service – all without changing a single line of code. When you use MongoDB, your platform of choice becomes a deployment time decision, not a design time constraint that locks you into one provider.

You can learn more about MongoDB’s design principles in our Architecture Guide.

The Stack Overflow Developer Survey comes on the back of last month’s Forrester Wave™: Big Data NoSQL, Q1 2019, which recognizes MongoDB as a leader, while DB Engines Rankings show MongoDB continues to extend its lead as the world’s most popular non-relational technology. We believe all of these data points validate a path we've been pioneering to establish MongoDB as the next-generation database platform of choice.

Want to learn what’s coming next? Join us at MongoDB World between June 17th and 19th in New York City, and get all of the latest news, as it happens.

Want to get going right now? Get yourself a free M0 cluster on MongoDB Atlas to explore, dive into our documentation and sign up for a course at MongoDB University.

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T-9: The Countdown to MongoDB World 2019

As MongoDB World 2019 approaches, we thought it would be a good time to look back at MongoDB World 2018 and some of the great talks that took place there and where to find similar talks at this year's essential event. It's nine weeks to MongoDB World 2019 and this is your countdown...

April 15, 2019
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Accelerating to T+1 - Have You Got the Speed and Agility Required to Meet the Deadline?

On May 28, 2024, the Securities and Exchange Commission (SEC) will implement a move to a T+1 settlement for standard securities trades , shortening the settlement period from 2 business days after the trade date to one business day. The change aims to address market volatility and reduce credit and settlement risk. The shortened T+1 settlement cycle can potentially decrease market risks, but most firms' current back-office operations cannot handle this change. This is due to several challenges with existing systems, including: Manual processes will be under pressure due to the shortened settlement cycle Batch data processing will not be feasible To prepare for T+1, firms should take urgent action to address these challenges: Automate manual processes to streamline them and improve operational efficiency Event-based real-time processing should replace batch processing for faster settlement In this blog, we will explore how MongoDB can be leveraged to accelerate manual process automation and replace batch processes to enable faster settlement. What is a T+1 and T+2 settlement? T+1 settlement refers to the practice of settling transactions executed before 4:30pm on the following trading day. For example, if a transaction is executed on Monday before 4:30 pm, the settlement will occur on Tuesday. This settlement process involves the transfer of securities and/or funds from the seller's account to the buyer's account. This contrasts with the T+2 settlement, where trades are settled two trading days after the trade date. According to SEC Chair Gary Gensler , “T+1 is designed to benefit investors and reduce the credit, market, and liquidity risks in securities transactions faced by market participants.” Overcoming T+1 transition challenges with MongoDB: Two unique solutions 1. The multi-cloud developer data platform accelerates manual process automation Legacy settlement systems may involve manual intervention for various tasks, including manual matching of trades, manual input of settlement instructions, allocation emails to brokers, reconciliation of trade and settlement details, and manual processing of paper-based documents. These manual processes can be time-consuming and prone to errors. MongoDB (Figure 1 below) can help accelerate developer productivity in several ways: Easy to use: MongoDB is designed to be easy to use, which can reduce the learning curve for developers who are new to the database. Flexible data model: Allows developers to store data in a way that makes sense for their application. This can help accelerate development by reducing the need for complex data transformations or ORM mapping. Scalability: MongoDB is highly scalable , which means it can handle large volumes of trade data and support high levels of concurrency. Rich query language: Allows developers to perform complex queries without writing much code. MongoDB's Apache Lucene-based search can also help screen large volumes of data against sanctions and watch lists in real-time. Figure 1: MongoDB's developer data platform Discover the developer productivity calculator . Developers spend 42% of their work week on maintenance and technical debt. How much does this cost your organization? Calculate how much you can save by working with MongoDB. 2. An operational trade store to replace slow batch processing Back-office technology teams face numerous challenges when consolidating transaction data due to the complexity of legacy batch ETL and integration jobs. Legacy databases have long been the industry standard but are not optimal for post-trade management due to limitations such as rigid schema, difficulty in horizontal scaling, and slow performance. For T+1 settlement, it is crucial to have real-time availability of consolidated positions across assets, geographies, and business lines. It is important to note that the end of the batch cycle will not meet this requirement. As a solution, MongoDB customers use an operational trade data store (ODS) to overcome these challenges for real-time data sharing. By using an ODS, financial firms can improve their operational efficiency by consolidating transaction data in real-time. This allows them to streamline their back-office operations, reduce the complexity of ETL and integration processes, and avoid the limitations of relational databases. As a result, firms can make faster, more informed decisions and gain a competitive edge in the market. Using MongoDB (Figure 2 below), trade desk data is copied into an ODS in real-time through change data capture (CDC), creating a centralized trade store that acts as a live source for downstream trade settlement and compliance systems. This enables faster settlement times, improves data quality and accuracy, and supports full transactionality. As the ODS evolves, it becomes a "system of record/golden source" for many back office and middle office applications, and powers AI/ML-based real-time fraud prevention applications and settlement risk failure systems. Figure 2: Centralized Trade Data Store (ODS) Managing trade settlement risk failure is critical in driving efficiency across the entire securities market ecosystem. Luckily, MongoDB integration capabilities (Figure 3 below) with modern AI and ML platforms enable banks to develop AI/ML models that make managing potential trade settlement fails much more efficient from a cost, time, and quality perspective. Additionally, predictive analytics allow firms to project availability and demand and optimize inventories for lending and borrowing. Figure 3: Event-driven application for real time monitoring Summary Financial institutions face significant challenges in reducing settlement duration from two business days (T+2) to one (T+1), particularly when it comes to addressing the existing back-office issues. However, it's crucial for them to achieve this goal within a year as required by the SEC. This blog highlights how MongoDB's developer data platform can help financial institutions automate manual processes and adopt a best practice approach to replace batch processes with a real-time data store repository (ODS). With the help of MongoDB's developer data platform and best practices, financial institutions can achieve operational excellence and meet the SEC's T+1 settlement deadline on May 28, 2024. In the event of T+0 settlement cycles becoming a reality, institutions with the most flexible data platform will be better equipped to adjust. Top banks in the industry are already adopting MongoDB's developer data platform to modernize their infrastructure, leading to reduced time-to-market, lower total cost of ownership, and improved developer productivity. Looking to learn more about how you can modernize or what MongoDB can do for you? Zero downtime migrations using MongoDB’s flexible schema Accelerate your digital transformation with these 5 Phases of Banking Modernization Reduce time-to-market for your customer lifecycle management applications MongoDB’s financial services hub

May 25, 2023