MongoDB Named A Strong Performer in The Forrester Wave™: Translytical Data Platforms, Q4 2019

Mat Keep
November 4, 2019 | Updated: November 29, 2022
#Forrester

Forrester published a Translytical Data Platforms Wave in 2019. More recently, MongoDB was named a Leader in the 2022 Translytical Data Platforms Wave report: “MongoDB ramps up its translytical offering aggressively.” See our new blog covering the results here.

Forrester has released its report, the Forrester Wave™: Translytical Data Platforms, Q4. MongoDB's data platform has been named a Strong Performer among the top 14 providers in what Forrester observes as a “hot, emerging market”.

The power of Translytics is to consolidate previously separate transactional (System of Record), operational (System of Engagement), and analytical (System of Insight) workloads into a single, unified data platform. Combining these workloads enables you to get insights and take action on integrated data faster and at scale, with lower complexity and risk.

Before getting into the report let’s dig into what challenges Translytics help you solve, and why it matters.

The Rise of Translytics and Why it Matters to You

“I’m fine making personalized product recommendations based on an aggregation of last month’s sales numbers….” is not something you are likely to hear from your business partners.

Surviving and thriving in the digital economy relies on speed:

Microservices, agile, and DevOps help us build and ship software faster.
Streaming and event-driven architectures help us sense and respond to the digital environment around us in real time.

And yet getting insights from our data is often still slow. Nightly or weekly batch loads from our transactional and operational systems to our analytical data lakes and data warehouses via slow and fragile ETL processes remain the norm for many. As a result, the business takes decisions based on old and stale views of their data.

Beyond the speed of analytics insights, we must also handle all of the complexity that comes from running three separate systems to support these different workloads, and then try to share data between each of them efficiently and securely.

How do Translytical Data Platforms Help?

It's these problems that Translytics looks to address by changing the way we work with data, bringing our transactional, operational, and analytic workloads into one unified data platform that is capable of effectively handling all those varied tasks. This industry shift aligns with our product developments at MongoDB.

Forrester predicts the trend to translytical systems is poised to disrupt the traditional database market. This is because those platforms “are failing to meet new business requirements that demand a no compromises combination of real-time data, performance, scale, integrated data, and security.”

Key Translytics Findings from the Forrester Wave

Forrester evaluated 14 of the most significant translytical data platform vendors against 24 criteria, spanning current product offering, strategy, and market presence. This was the first time MongoDB participated in the research, and we are delighted that Forrester evaluated the MongoDB Data Platform as a Strong Performer, earning the highest possible scores in the following categories:

Data Pipeline
Multi-Model
Data Security
Data Access
API/Dev Tools
High Availability and Disaster Recovery
Pricing Transparency
Partners
Customer Adoption

The report cites how MongoDB “has gained momentum for translytical use cases over the past few years…. Companies use MongoDB to support real-time analytics, systems of insight, customer 360, IoT, and mobile applications.” It goes on to say, “Recent product and roadmap additions include innovations such as full-text search, on-demand materialized views, and data lakes, expanding translytical support.”

Why Translytics Needs a Data Platform, Not Just a Database

We believe our placement in Forrester’s Translytical Wave is the result of our investments in building out the MongoDB Data Platform, enabling you to meet the challenges of modern analytics by:

Delivering rapid insight and actions directly on fresh, operational data without time-consuming ETL.
Intelligently tiering data to serve the full spectrum of analytics use cases from real-time to offline batch.
Enabling data sharing and collaboration by serving multiple audiences with native, skill-appropriate tools.

Modeling and Querying your Data

The foundation of the MongoDB Data Platform is the document data model, allowing you to ingest, store, and combine data of any structure.

The MongoDB Query Language (MQL) and aggregation pipeline is comprehensive and expressive, so you can query, transform, and analyze data any way you need. It doesn’t matter whether that data is stored in your transactional and operational databases fully managed in cloud by us with MongoDB Atlas or run by you on-premises; in our Atlas Full-Text Search service; or with the MongoDB Atlas Data Lake, in cloud object storage. The result is that you can use MQL to efficiently reach into and query your data wherever it lives.

Analyzing your Data

You can work against that data with a dozen idiomatic native language drivers, including Python and R, along with a range of tools that integrate MongoDB into your analytics ecosystem. These tools cover a spectrum of different analytics use cases and teams across your organization:

MongoDB Charts – the fastest and easiest way to create visualizations of MongoDB data. You can create graphs and build dashboards, sharing them with other users for collaboration, and embed them directly into your web apps to create engaging user experiences.
The MongoDB Connector for BI lets you directly connect to MongoDB from your existing SQL-based BI and analytics platforms such as Tableau, Microstrategy, Looker, and more.
The MongoDB Connector for Apache Spark exposing all of Spark’s libraries, including Scala, Java, Python and R. MongoDB data is materialized as DataFrames and Datasets for analysis with machine learning, graph, streaming, and SQL APIs.

Distributing and Securing your Data

MongoDB’s distributed systems architecture enables you to elastically scale-out your data sets and parallelize your queries as well as enforce ACID transactional guarantees, while isolating different workloads across the nodes in a single cluster. You can dedicate one set of nodes to handle transactional and operational applications, while the platform automatically replicates data to another set of nodes handling analytical queries. Those queries could be updating reports and dashboards, or serving machine learning models.

Through process separation, the workloads never contend for system resources. You can serve your operational applications with millisecond latency, while concurrently generating real time insights from your live, operational data. And you can do all of this without the fragility and latency of ETLing your data into a separate database.

With a single data platform, you can apply global security and governance controls across your data, ensuring only authorized access and processing.

What’s Next in Translytics?

Being rated a Strong Performer in the Translytical Data Platform market is just the start for us. With our focus on documents and distributed systems as the foundation for modern transactional and analytical workloads and the cloud as the optimal route to deliver them to users, our strategy is to raise the bar in translytics for everyone.

You can get started by signing up today for a free MongoDB Atlas M0 tier instance and experience the MongoDB Data Platform for yourself.

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Being Latine in Tech: Two MongoDB Employees Share Their Advice on Building Careers in Engineering

Ashley Naranjo and Martin Bajana, members of MongoDB’s employee resource group QueLatine, share their career journeys and offer insight into how other members of the Latine community can build careers in tech. Jackie Denner: How did you make your way into the tech industry? Ashley Naranjo: I am a first-generation Latina with a passion for Information Technology and a knack for problem-solving. After graduating early from high school, I embarked on a career in Nursing. I chose Nursing initially because I wanted to make a difference and help others, but my path took an unexpected turn when COVID-19 reshaped our world. In light of the circumstances, I reevaluated my options and decided to seize an opportunity with a program called Year Up . During the intensive six-month training and deployment phase, I not only completed rigorous coursework but also obtained IT Google Coursera certifications and actively pursued CompTIA certifications. 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Martin Bajana: My interest in technology stems from a variety of sources. From a young age, I developed a strong passion for video games and exploring new technologies. Whether it was experimenting with the latest gaming consoles or delving into computer hardware, I relished the opportunity to learn and understand the inner workings of these technologies. In school, I discovered my affinity for mathematics, which further solidified my decision to pursue a career in the tech industry. Choosing to study computer science in college was a natural progression for me, as it allowed me to combine my love for technology with my aptitude for problem-solving. After completing my education, I was recruited by Verizon, where I worked on front-end applications and Android development. Although the transition was initially challenging, I persevered and regained my confidence. It was during this period that I realized a career in technology was my long-term aspiration. Throughout my tenure at Verizon, I embraced opportunities to work across various teams, acquiring valuable experience and honing my skills. Eventually, I made the decision to join MongoDB, which has provided me with an enriching journey and the chance to shape my career in the tech industry. JD: Have there been any challenges you've faced throughout your career? AN: Imposter syndrome has been a significant challenge for me throughout my career, and it's something I still deal with to this day. When surrounded by my talented colleagues, I would often compare myself to them and focus on my perceived weaknesses and flaws, leading to a lack of self-confidence. However, I tackled this issue by addressing my feelings with my manager. Her support and guidance helped me realize my own potential and acknowledge my accomplishments. Maintaining a positive mindset has enabled me to view myself as a competent engineer and recognize the value I bring to my team. I have learned to take ownership of my successes and embrace opportunities for growth. Stepping out of my comfort zone has become a regular practice, as personal and professional development often stems from embracing challenges and discomfort. By giving myself permission to take up space and be confident in my abilities, I have been able to overcome imposter syndrome and continue to thrive in my role. MB: I have been fortunate enough to work for companies and teams that value and respect me for the work I deliver. Being in the tech industry and growing up in a culturally diverse region of the country, I have had exposure to individuals from various backgrounds and identities, which has made me more comfortable as a Latinx individual in the industry. My personal goal is to promote a work environment where everyone is judged based on the contributions they bring to the team, rather than their identity. 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