Most data practitioners trace the concept of the “modern data stack” all the way back to 2012, when Amazon launched its cloud data warehouse Redshift. Things have really been taking off recently, however, and we have seen exponential growth in the industry over the past year or so.
When the COVID-19 pandemic forced companies to operate remotely, adoption of digital technologies accelerated and many companies began migrating to the cloud. Tools and services that were once “nice to have” went mainstream almost overnight.
What’s even more exciting is that some experts believe that this is only the beginning. Almost certainly, we are on the cusp of an even more explosive growth across the industry.
The Modern Data Stack
Like its older counterparts, the modern data stack works in four main stages:
- building data pipelines for data extraction from multiple sources
- storing it in a centralized data warehouse
- processing it for analysis
- analyzing and visualizing the data
What sets tools in the modern stack apart from older counterparts is the “scalability and elasticity” they can provide. Managing data with older tools was both inefficient and expensive — and this meant that data was not as readily accessible.
Obviously, this is one of the main reasons to adopt more modern technologies, and a key reason you may be considering it for your organization.
But would you even know where to begin?
There are many tools available for data collection, storage, processing, and analysis. With so many options, in some ways, choosing your data stack may have become even more complex along with this growth.
But there's no need to worry. If you’re thinking about building a modern stack for your company and trying to figure out how to do it, there are five big factors you might want to consider first.
1. Where Is Your Data Coming From?
The modern data stack can give you more power to collect, process and analyze data efficiently. In legacy systems, the data process was usually ETL (Extract, Transform, and Load) and data coming in through the pipelines had to be transformed before storage. With the modern stack, the process is typically ELT (Extract, Load, and Transform) — meaning the data is transformed within the central warehouse.
What this means is that, with the modern data stack, storage and compute capabilities are separated and this allows for more efficient scalability. So, if your data is coming from a range of sources (including databases, applications, APIs and so on), you will need to have a layer that is able to convert your raw data into reliable datasets that are query-ready.
If you are still using legacy systems, make sure to also consider the tools that could allow you to blend these sources with your cloud-based data sources.
2. What Do You Need Your Data To Do?
While more companies have begun to realize the importance of data for making business decisions, many are still unable to harness its full power.
One of the reasons for this is that, by the time decision makers gain access to understandable data, it is already too late. A slow process can mean that data-driven decisions end up being made reactively.
Modern data tools can transform this into a more proactive process. Besides deploying operational analytics, which allows data monitoring in real time, it is also possible to implement analytics that feed the data directly back into the system.
Rather than having to wait for a person to look at the visualized data before taking action, the system can immediately adjust based on the data that it receives.
If your company would benefit from taking quick action based on data, implementing a data loop like this could do wonders for your business.
3. How Long Will Your Data Stack Last?
Since we are at the start of a potential industry transition, there will be more and more innovations in the field of data analytics. While having new products and services to choose from will be a good thing, it can also be hard to keep up.
Ideally, your analytics stack should last at least a few years, so you will want to select tools that aren’t using outdated technologies and make sure they can integrate well with both old and new technologies.
When you choose a tool to add to your modern data stack, you should also consider if their team understands the landscape well enough and will continue to update the tool accordingly.
Really, there isn't much point in investing in something that will move from "solution" to "problem" before you know it.
4. What Should Your Data Team and Processes Look Like?
When it comes to managing your data, technology is not the only thing that matters.
You must also consider your processes and the people managing them.
There are many data tools that have overlapping capabilities. At the same time, your team might also have different types and levels of of capabilities. You may have more data analysts than data engineers, for example.
When you design your data stack, you always need to consider how your tech can support your team — and vice versa — when it comes to achieving your required data processes.
5. How Will Your Data Tools Interact?
One final thing that is essential to think about is how the individual tools you select will work with the other tools in your arsenal.
No matter what choices you make in terms of tools, you will have to integrate them in order to achieve the results you want. Depending upon the solution, this can be relatively seamless, complicated, or a nightmare.
Naturally, a large project will always require some time and resources, including troubleshooting and solving issues that will arise. For most firms, the goal is usually to limit the headaches and reduce the scope — making integration a big, but often underrated, factor to consider before making any final decisions.
Simplifying the Modern Data Stack
There are no right or wrong answers for how you should build your modern data stack. What works for one company may not work for you. The key thing is to understand your own business processes, team capabilities, and data needs before making your decisions.
If you’re looking for a simple solution for building your stack, Shipyard can help. Not only do we scale your jobs automatically, we also have more than 50 code blueprint options so you can immediately integrate with your databases or cloud service providers.
You can easily test it out by signing up for a free 14-day Shipyard trial. With the trial, you can immediately log on, create workflows and run them.
Shipyard is a modern data orchestration platform for data engineers to easily connect tools, automate workflows, and build a solid data infrastructure from day one.
Shipyard offers low-code templates that are configured using a visual interface, replacing the need to write code to build data workflows while enabling data engineers to get their work into production faster. If a solution can’t be built with existing templates, engineers can always automate scripts in the language of their choice to bring any internal or external process into their workflows.
The Shipyard team has built data products for some of the largest brands in business and deeply understands the problems that come with scale. Observability and alerting are built into the Shipyard platform, ensuring that breakages are identified before being discovered downstream by business teams.
With a high level of concurrency and end-to-end encryption, Shipyard enables data teams to accomplish more without relying on other teams or worrying about infrastructure challenges, while also ensuring that business teams trust the data made available to them.