Developer data platforms (DDPs) are growing in popularity because of their use in data engineering. Data has become a business in itself, with every other industry making use of it in one way or another. And with practically every device intrinsically built to become fully integrated into people’s lives, it’s no wonder that the world is producing so much data. Statistics from Tech Jury found that the world will likely generate about 181 zettabytes of data by 2025.
With how much data engineers need to interact with and manage workloads, DDPs are useful and accessible tools once you know how to work them. Thankfully, there are plenty of robust and centralized suites that make the whole thing more approachable and easy to learn. A great DDP for both newbies and more experienced users is Atlas, which is free as long as you register on their official platforms. You can create your account here on MongoDB.
MongoDB Atlas should be all you need whether you’re working with transactional or analytical workloads. Here’s how the DDP addresses each.
The Difference Between Transactional and Analytical
The first step is to understand what kind of model you’re going to need. A lot of people don’t get to optimize their data management because they end up confusing one for the other. Although new platforms are able to merge both types of workloads these days, they aren’t suddenly interchangeable.
The simplest way to remember the difference is to consider that transactional focuses on enabling transactions whereas analytical is just about analysis. Engineers must consider their use case to maximize their workload and identify the right one for their needs. A good method is to ask yourself first what end result you need, and this should naturally reveal whether you should opt for a transactional or analytical workload.
Transactional Workloads in Developer Data Platforms
You’ll commonly see transactional workloads in developer data platforms because of the number of industries that rely on data that gets changed and interacted with by or from different users. Think point of sale, accounting, shipments, and the like. More complex cloud-integrated systems now work out transactional workloads using generative AI. This is the same technology that powers ChatGPT, but in this case, enables developers with prompts and code.
In the case of Atlas, developers will want to make use of its workload isolation features. This will allow you to separate instances between those that are solely for reporting workloads and those that functionally parse data and the like. Developers can also tailor their data model to focus on shifting data and tons of read-and-writes that come naturally with transactional workloads. The transactions API comes in handy here, as this will provide the atomicity needed to execute said transactions and distribute relevant data to multiple documents.
Multi-document transactions should also be easier to manage because of Atlas’ ability to work with different data formats. The most common formats include CSV, AVRO, and JSON (which happens to be similar to the native BSON of Atlas.)
Analytical Workloads in Developer Data Platforms
Developer data platforms should also be a great way to work with analytical workloads. This type of workload should be your application or database’s primary route if you are aiming for machine learning, performance tracking, data mining, and pattern analysis. In Atlas, you can use analytics nodes so you can specifically isolate queries without interfering with any other workload.
There are plenty of tools and APIs within the ecosystem that help you glean relevant insights in a more efficient and digestible manner. Because of the DDP’s noSQL format, you also have the benefit of faster querying and low latency. This proves most useful if you are dealing with complex queries or a huge pool of data. Consider the fact that SQL generally struggles with managing time series data because of its architecture, and it’s enough to opt for the unstructured data sets and flexibility of noSQL.
One of the biggest tips to remember when learning ‘How to Become a Data Engineer’ is to minimize operational creep. In analytical workloads, this tends to seep in via scope creep. Those dealing with growing projects that suddenly integrate more complexity should be able to manage the horizontal scalability within Atlas. Expect to connect MongoDB with R to maximize the analytical and retrieval capacity of the platform.
