The global landscape is currently amidst a digital transformation that’s pushing the boundaries of data processing and management
Startups are magic – taking an idea and ‘poof’, creating a successful company out of it. Well, everything is accurate except for that “poof”. Startups are more like treating a very delicate seed and attempting to grow a beautiful flower from it. Now, the same thing can be said for a startup’s data infrastructure, beginning as a seed and growing into a sustainable, scalable data infrastructure – or so you hope.
At first, it may seem minimal, plausible, doable – as it only includes a few factors, small amounts of limited data that need to be aligned at the internal level, but then, ‘poof’, external factors come into play. As your seed grows, the controllable environment you developed starts to be challenged by external interrupts and constraints such as third-party data sources, databases, and other services that have to be added. In doing so, your “data team” needs to take this once small and fairly straightforward operation and like magic, turn it into a blooming flower – or maybe a more accurate analogy, a beast that ought to be tamed.
That is the catch-22. It doesn’t make sense to invest a lot of expensive manpower to design a scalable data infrastructure from the get-go (just because you can), and it is even more mind-boggling to invest the needed resources to scale an element that may end up being just a utility for your growing company.
These main big data infrastructure challenges that a growing startup comes across can be summarized as a capacity problem: how to use the limited resources available to get your product to market.
From our own experiences at Datorios, the key to success is focus. Focusing on defining your MVP, focusing on your core, and making sure not to rack up technical debts when not needed. But, how do you implement a sustainable, scalable data infrastructure for your startup from the get-go? It may not be magic, but it can be as easy as 1,2,3.
1. Assume Every Data Infrastructure Process Is Permanent
From the start, adopt the mindset that every temporary solution may become long-lasting. Whether you are considering building your own data infrastructure in-house or working with third-party services, ask yourself, will this solution that works for my data needs now, still work for me in the future? What will be the costs of upgrading or changing it and how will it affect the company?
Startups typically begin with “a temporary solution” that works for their needs, but, in reality, that feeling of success is short-term and can lead to headaches down the road. Often, temporary fixes become permanent, leaving your startup with an unstable infrastructure that requires continuous, costly resources at crucial times, resources that could be used to develop your core product.
Datorios was founded to help startups overcome this exact dilemma. Do we continue with an ongoing investment of resources that were never planned or make a large investment into new resources we now need? It was this question, this seed, that led us to create a platform that adapts to ever-changing business needs while being limber enough to handle data capacity and any data type. Adopt the assumption that each data process is permanent before you realize your startup is standing on a temporary solution that won’t meet your future needs.
2. Ensure Flexibility in the Building Blocks of Your Data Infrastructure
In many cases, usually with software-based companies, the first instinct when looking to develop a data infrastructure is to build your own solution. In the hopes of saving money and time, those who haven’t gotten their hands dirty, incorrectly assume data engineering is only a branch of software engineering and attempt to build in-house. But, by gluing together various open-source software types using code and hoping for the best, you are left with a rigid data infrastructure solution that is unable to adapt to growing, changing business needs. Start with a low-cost solution with integration from the get-go, that can handle capacity, complexity, and performance using a platform built for developers offering the flexibility they need.
It is difficult if not impossible to assume what you will need in the future – that’s why the Datorios platform allows for any data feature including batch, CDC, and stream. Use the most current and common ways to send and receive data, whether you are working with, or want to work with Rest API, Data buckets, Kafka, MQTT, or any of the main cloud providers – so as you change and grow, your platform solution adapts with you. By doing so you avoid paying the cost of reconfiguring everything from the ground up or investing in internal resources attempting to work with tailored solutions that fail to offer the flexibility your startup requires. By using a robust and flexible solution that can grow with your business from conception, your team can spend more time on tasks that contribute to success.
Ready-Made Data Consolidation Tools for ETL Data Transformations
The only thing that takes longer than coding something from scratch, is attempting to debug a complex data system. Startups tend to have a high turnover of engineers and as a result, those who remain, are left attempting to work with code they’ve never seen before. As engineers ourselves, we know this conundrum way too well, which is why our platform is configuration-based, making situations such as this much easier to handle.
Datorios’s “as code” development for data pipelines with ready-made and user-defined data transformations guarantees easy pipeline maintenance and effective data consolidation regardless of the size or complexity of your data stack. You can benefit from the experience of another team by utilizing premade templates and professional help from industry veterans who saw the hardships of numerous data flows and developed tools to help you avoid them. From offering expert help to developing your first pipeline to delivering the complex tools required to shorten development processes, now the ideology of getting data pipelines as a service has become a reality.
Resources for Startup Scalability & More
The idea that sparked what is now Datorios, was the idea to offer any business the ability to implement a sustainable, scalable data infrastructure to rule over any ETL configuration. That was our seed, and the platform you see here today is that beautiful flower – if we do say so ourselves. We spent the time, human power, and resources to develop our solution so your business doesn’t have to.
By considering every mistake we made along the way, this is what we learned and hope to teach you. When creating or implementing a data infrastructure for your business consider that every data process is permanent, flexibility is built-in, and the features you need now and in the future are available. You can learn from our process and use the platform built by engineers, for engineers to strengthen your data flows and shorten development cycles. Save yourself time and effort creating your data infrastructure so you can get back to what you love – and we know it’s not maintaining your data pipeline.
If you missed Part 1, check it out here
The terms “workflow orchestration” and “data orchestration” are often used interchangeably, but there are important differences between the
Data is a critical asset for most enterprises and the trend is only increasing with the advent of