Why Cloud-Centric Architectures Are Breaking Under Data Scale
Your cloud bills are too high and data is slow? Here’s a better way than just using the cloud!
If high costs, slow data, and compliance headaches sound like your problems, your current cloud setup might be the cause. So, let's explore a more efficient way to manage your data.
The cloud changed how IT works, offering great ways to grow and be flexible. But for those who are dealing with huge amounts of distributed data, the cloud isn't always the best answer. What often happens? Costs go up, data moves slowly, and following the compliance rules becomes a big problem.
This post explains why the old way of using the cloud for data isn't working well anymore. We'll also show you a newer, better solution: Bacalhau and a computing paradigm called Compute-Over-Data.
The Cloud Isn't Always Cheap: Costs Go Up, Benefits Go Down
When you first look at it, the cloud seems cheaper. You pay for what you use instead of buying your own hardware. But when you have data in many different places, that changes. Moving all your data to the cloud and working on it there can get very expensive. Here’s why cloud costs can get too high:
Fees for moving data: You pay to move data into the cloud because you need its powerful computers. If your data is in many places, these fees add up quickly.
Costly data storage: Cloud companies have different ways to store data. If you need to get to your data fast for quick checks, you often have to use expensive "hot" storage.
High costs for central computers: Working on huge amounts of data in one main place needs a lot of computer power. Companies often buy more than they need just to be safe, which costs more. You can try to fix this by paying only for what you use, but this is more like a quick fix than a real solution.
These high costs make companies ask if sending all their data to one place is still a good idea.
Slow Data: Why Your Information Can't Keep Up
Slow data is a problem for many companies. When you send all your data to a centralized cloud service, it takes time for information to go from where it starts, to the cloud, and back. This delay can make information old and useless by the time it’s ready.
As you get more and more data, this problem gets bigger. Trying to push tons of data through pipes to one main spot causes traffic jams. This can take days for very large amounts of data. This is a big problem for:
Edge computing: This is when you need to make decisions right away, where the data is generated. Sending data far away to a cloud and waiting for instructions is too slow for things that need to happen fast.
Quick data checks: Slow data makes it hard to get fast answers. People who study data often need information quickly to make good choices. If they wait too long for data, they might miss chances to act on time.
Real-time warnings: Delays can make you miss big problems in some cases. For example, systems that find fraud must spot bad actions right away. Hospitals need to warn staff immediately about big changes in a patient's health.
When data has to travel far because it's spread out, it slows down. Adding more computers or faster internet to cloud services doesn't fix the problem.
Rule Troubles: Following Data Laws in a Central Cloud
Following data rules is a big and tricky job for businesses. Laws like Europe's GDPR, California's CCPA, and healthcare's HIPAA are very strict. If you don't follow them, you can get big fines. Using a centralized cloud can make it harder to follow these rules because:
Data location: Many laws say that companies must keep people's data in their own country. Using a central cloud could mean you store data in the wrong country. This can break laws and lead to big problems and fines.
Moving data across borders: Sending data to computers in other countries is not simple. It needs careful legal and tech planning because the country it goes to must protect data just as well. This means more paperwork, costs, and risks, especially with private data like medical records.
Showing you follow rules: It's hard to prove you're following the rules when all your data is in one big place. Showing officials exactly where certain data is, who has used it, and that everything you do follows the rules can be a very big job.
Dealing with many rules: Companies that work worldwide have to follow international, national, and local data rules. Trying to use all these different, and sometimes clashing, rules with data in one main place can be hard. Because of this, companies might make things too strict and costly, or they might miss some rules to try and follow others.
Information leaks: Even if your main data is safe, systems in one place often gather logs, passwords, and details about computer jobs. If this extra information (called metadata) leaks, it can show private details about your data, what you're computing, or your computer systems. This can give attackers useful information for possible attacks.
Why Your Current Infrastructure Tools Struggle with Spread-Out Data
Even common infrastructure tools have trouble with today's spread-out data. As we talked about in our "Kubernetes vs Nomad vs Bacalhau" article, these tools are made to manage apps that are close to their data. Usually, this means in a single data center or cloud area with a good, fast network.
Also, how they are built still expects data to be easy to get to and brought to the apps. So, trying to make these tools work well with data that's very spread out often means using complicated fixes. This can include:
Setting up complex data pipelines to move parts of the data.
Trying to link groups of computers (clusters) across far distances.
Building your own special ways to get data ready and keep it in sync.
This way of doing things adds more complications, makes more work to keep things running, and can reduce the real benefits these tools are supposed to give.
The "Send Everything" Habit Costs a Lot
A big part of the problem is a common IT habit: “We send everything." Many systems, data setups, and processes are made to collect all data from everywhere and send it all to one main place–like a data lake or warehouse–before anyone even looks at it or figures out if it's actually useful.
This habit means you pay a lot to move, store, and work on "data noise." This "data noise" uses up good resources and causes many costs:
Costs to move data out of where it resides.
Costs for the main system to take in data.
Storage costs.
Costs to search and study the data.
This leads to high bills for internet use, storage space, and computer time. All for a lot of data you don't really need.
You keep doing this inefficient thing because most tools you have are built for it. You put everything in one place because you need the computer power to study the data.
Luckily, now we have a good, modern answer to this problem!
A Smarter Way: Compute-Over-Data with Bacalhau
The problems with working on data in one central place show that you need a new way. Instead of fighting against where your data is, you should work with it. This means:
Stop moving lots of data to computers.
Start using a Compute-Over-Data (CoD) plan.
The New Idea: Send Computer Power to Your Data, Not Data to Your Computers
Compute-Over-Data changes how you work with data. You don't send large, raw amounts of data to one place just because you don’t have enough hardware resources. Instead, Compute-Over-Data moves computer tasks to where your data already is.
Your data might be on edge devices (like sensors), local company computers, or spread across many data centers. The CoD way of computing moves much less data and solves the main problems with central models, like:
Cost.
Speed.
Rule-following.
When you work on data near where it starts, you can filter, combine, and change it locally. Then, you only send to the cloud the data that's useful for other tasks, and only if you need to!
What is Bacalhau? Your Tool for a World of Spread-Out Data
Bacalhau is an open-source tool for computing that's spread out. It's built for the Compute-Over-Data idea. Here are some of its main features:
Simple to use: Bacalhau works as a client (user tool), an orchestrator (manager), and a compute node (worker computer) all in one small package. This makes it easy to set up and use in many different places. It also lets you do fast tests on your own computer that act like a whole cluster deployment, but without all the work of managing infrastructure.
Flexible design: It supports different ways to run code, like Docker containers and WebAssembly (WASM). It works with different storage systems (like S3, IPFS, and gets data from websites).
Handles different job types: It supports different kinds of jobs: "batch" jobs for tasks you do once, "ops" jobs for specific tasks on certain computers, "daemon" jobs for tasks that keep running in the background, and "service" jobs for programs that run for a long time.
Works reliably: Bacalhau is made for systems that are spread out. It can handle times when network connections are not steady. This is important for edge computing and data that is far apart.
How Bacalhau Solves Data Problems
By using Compute-Over-Data, Bacalhau fixes the headaches of working with data in one central place:
Cost savings: Working on data locally with Bacalhau cuts fees for moving data. By preparing and filtering data where it starts, you can stop paying to move and store "data noise." This saves a lot on network, storage, and central computer costs.
Faster data: Running jobs close to where data is made means less network travel and faster results. You get answers quicker, can make decisions in real time, and have faster apps.
Better data security and following rules: Bacalhau lets you work on private data within its safe and legal limits. This means less risk of data getting exposed when moving it. It also makes it easier to follow data rules like GDPR and data location laws.
Real Examples: Where Compute-Over-Data With Bacalhau Makes a Difference
Bacalhau's Compute-Over-Data way helps in many areas, such as:
Working with large amounts of logs: Study, filter, and combine logs on the computers or edge devices where they are made. This can cut the amount of data sent to central logging systems by over 90%, saving on network, intake, and storage costs.
Spread-out data warehouses: Run search queries directly on data stored in different local databases or cloud storage. This speeds up queries and helps follow data location rules.
Machine learning at the edge (edge ML): Train ML models or run them directly on edge devices. This allows for quick predictions, uses less internet by working on raw sensor data locally, and improves privacy by keeping private data at the edge.
Managing spread-out devices: Safely run commands, update software, and collect information from many spread-out devices or computers without needing direct access to each one or constant network connections.
Conclusion
Using one central cloud for everything causes problems when you have large amounts of data distributed over different locations. These problems include higher costs, slow data movement, and complex rule-following.
There's a different way, and it's called Compute-Over-Data (CoD). Bacalhau is a system designed for this CoD idea that sends computer tasks to where the data is, instead of moving large amounts of data.
Using Bacalhau reduces data movement, which lowers transfer and storage costs. It also makes it easier to follow data rules by letting you work on data within certain areas or security limits.
For companies working with distributed data, Bacalhau gives a way to fix the limits of old central ways of working with data.
What's Next?
To start using Bacalhau, install Bacalhau and give it a try.
If you don’t have a group of computers (a network) ready and would still like to try Bacalhau, we suggest using Expanso Cloud. You can also set up your own group of computers (we have setup guides for AWS, GCP, Azure, and more 🙂).
Get Involved!
We'd like you to be part of Bacalhau. There are many ways to help, and we’d love to hear from you. You can find us here:
Commercial Support
Bacalhau is open-source software, but the official Bacalhau program files are made by Expanso with a careful security, checking, and signing process. You can read more about the difference between open-source Bacalhau and Bacalhau with commercial support in our FAQ. If you want to use our pre-built program files and get commercial support, please contact us or get your license on Expanso Cloud!