How to tackle cloud projects with high data complexity and handling requirements
If you’re tackling a cloud project with extreme data complexity and large data volumes, you might not be 100% sure where to begin.
Projects with this high level of complexity occur in many industries, but what they all have in common is a need for seamless handling of data. Every part must be watertight; from the collection of data to the way it’s processed, stored, and protected.
Numerous data sources need to be perfectly managed, including IoT data, databases, dynamic third-party data feeds, and data from users. Many devices and device types can be involved, and data might need to flow outwards too.
What’s the best way to approach a cloud task with this complexity?
At Blackbird Cloud we’ve worked on a lot of different projects, including ones with incredible data complexity and volumes – so we’d like to share our experience of these. This way, your project can get straight onto the track to success and avoid the common problems people usually encounter on their own.
The overarching view
It’s vital your project always considers the big picture, even from the earliest stages. This isn’t easy, and most developers cannot be expected to have this overview. But someone needs to have this vision, and use it to guide the development teams effectively.
Your project design should start by looking at what is needed in terms of the access pattern for data. This is essential for designing a proper infrastructure that can handle your needs.
Security is always a top priority, of course. This should also be worked into the design at the earliest stages as well, but for now let’s focus on the ‘nuts and bolts’ of your core functionality.
Read, Write, or both?
When looking at the access pattern for data, you should determine if the use case is primarily Read-heavy or Write-heavy, as this will shape the architecture you need.
For a Read-heavy situation, your priority should be to make queries as fast and efficient as possible. If your application is more Write-heavy, then you’ll need to create a series of ‘buffers’ that can manage this heavy workload.
It’s good to think about your inbound data flows as streams, or queues. This can help avoid situations where data is lost when a component like a database or a data transformation process is missing or out of action. This can easily happen due to maintenance or calamity, but by managing data with streams or queues, you can overcome this common occurrence by implementing buffers.
The pitfall of ever-increasing data
Something you should be wary of (and avoid) is the compounding costs generated when unnecessary data is kept around for longer than needed.
This problem starts out small, so it’s easy for you to miss it early-on, or feel it’s not a priority. But, as your excess data accumulates, it will start to rack up serious costs due to the exponential growth of your data size.
To avoid this situation, it’s absolutely essential you leverage data lifecycle management, especially for large data storage.
You don’t need to spend a lot of time on creating a custom solution for this - instead, we recommend using a cloud storage solution that has lifecycle management already built-in.
There are several providers that offer this, including AWS S3, Azure Data Lake, and Google Cloud Storage. These services can also be combined with data streaming services too, in most cases, including Apache Kafka and AWS Kinesis.
Is there an ideal setup for large data flows?
When a lot of data needs to be stored, it’s best to do this with a ‘streaming’ setup, consisting of this basic structure:
Ingress ➡ Processor ➡ Ephemeral fast storage ➡ Off-loader ➡ Long term storage
Ingress
This is the entry point for data entering your stream from various sources like IoT devices and external data-sources.
Processor
This part may be optional, depending on the specific use case. A processor may be required if data needs to be processed or translated. This if often the case with IoT data, which is in a binary format that needs to be unpacked and converted into a readable format.
Ephemeral fast storage
This acts as an intermediate storage point between the source/ingress and your long-term storage. When your cloud project is Write-heavy, you’ll want to conserve the number of files and indexes in your long-term storage by batching it over a certain time-frame. This kind of fast storage can provide rapid responses to queries, depending on the amount of data involved.
Off-loader
This part is a simple process which moves data from ephemeral fast storage to your long-term storage.
Long-term storage
Your long-term storage is the final resting place for data, subject to your lifecycle management policy. Almost all your data should end up here, as it is cheaper than fast storage. The downside is that it is slower to respond to queries, so look carefully at how you designate which data is kept in fast storage, and the balance between performance and cost.
Pre-built or custom-made?
Each of the above components is necessary for seamless data flows, especially when the volumes of data are very high. You will find a number of pre-built solutions for these, however you need to think about how well they serve the business case involved.
In our experience, it is often better to build your own components to make sure they really match the use case and the business needs.
Data security in complex Cloud projects
As we touched upon earlier, security needs to develop in parallel with the rest of your project. This starts at the design phase, ensuring that you have a solid foundation for cloud security.
The cloud is a frequent venue for malicious attacks, and the main method is to target the developers themselves with social engineering tactics. However, you should think about all three parts of maintaining your data: security, integrity, and processing.
You can maintain data integrity with proper identity and access management (IAM) and version control, and you can avoid problems with processing by using technical measures that limit exposure and by monitoring the cloud environment.
When it comes to data security for cloud projects, however, you need to tackle the human element as well as system architecture. With a big project, you will work with many partners and teams, so it’s vital you use your IAM in combination with good governance and best practices. Use ‘least privilege’ tactics and data encryption wisely, and use tools that actively monitor and detect threats.
A good Cloud security posture management (CSPM) tool we like to use is AWS Security Hub. This gives you an instant overview and the ability to act instantly when something is amiss.
Want help?
Even simple cloud projects can get complicated fast. Projects destined to be complex from the outset need more than just great developers and patience – expertise is also needed to take these projects across the finish line.
You can gain this expertise yourself through a process of trial and error, but this takes time and resources to develop. If you want to take the shortest route to success, we can help.
Get in touch, and let’s talk about how we can make your project fly.
