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
- Data Security
- Data Access
- API/Dev Tools
- High Availability and Disaster Recovery
- Pricing Transparency
- 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.