Unleash Cloudian’s Power

Summary

This article provides a comprehensive guide on leveraging Cloudian for big data retention and analysis, using the PostFinance case study as a practical example. We’ll explore the challenges faced by PostFinance and the solutions they implemented using Cloudian’s HyperStore object storage, including integration with Splunk SmartStore. Discover how to optimize your data storage strategy for enhanced insights and cost efficiency.

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** Main Story**

Alright, let’s talk about big data and how Cloudian can really make a difference. We’ve all heard the buzzwords, but what does it really mean to wrangle massive amounts of information effectively? I think the PostFinance story is a really good example, actually, and a great starting point.

PostFinance, a pretty big player in the Swiss financial world, had a specific problem: they wanted to personalize the customer experience, something that’s pretty common these days. But, they were hitting a wall with their old-school storage when it came to big data analytics. It just wasn’t cutting it. It wasn’t scalable enough, it was costing them a fortune, and honestly, it was just slowing things down. Maybe you’re seeing some of the same issues?

Spotting Your Own Big Data Roadblocks

Before you jump into any new solutions, it’s worth taking a good, hard look at what you’re dealing with. Ask yourselves some tough questions:

• Is our current storage setup going to handle what we need, not just now, but a year or two down the line?
• Are we spending way too much on storage? It’s easy for those costs to creep up.
• And, most importantly, is our current storage actually holding us back from doing the cool, insightful analytics we want to do?

If you’re nodding along to any of those, it’s probably time to explore other options.

Why Cloudian? PostFinance’s Pick

So, PostFinance did their homework, and they landed on Cloudian’s HyperStore object storage. Now, why that one? Well, a few key things stood out. First off, it could scale practically forever. You can just keep adding storage as you need it, without a massive overhaul. Plus, it was way cheaper than what they were using before, a huge win for the budget.

But here’s the really clever bit: it’s fully compatible with the Amazon S3 API. Which means, you can use a ton of existing tools and applications that already work with S3. It’s like speaking a language that everyone understands. For example, my previous company used the S3 API to develop a streamlined process for data backup and retrieval that integrated seamlessly with our existing cloud infrastructure; the cost saving alone justified the switch from a legacy system.

When you are looking at different platforms it’s key to consider the following:

• Scalability: Can this thing handle petabytes of data without breaking a sweat?
• Cost-effectiveness: Are we actually going to save money, or is this just a fancy new toy?
• Compatibility: Will it play nicely with the stuff we already have? It’s no good if it doesn’t integrate.

Getting Down to Business: Implementation

PostFinance didn’t just buy the software and hope for the best, of course. They rolled out HyperStore in their data centers, hooked it up to their big data platform, and started moving data over from their old Hadoop system. They even used Cloudian HyperStore File to make file services easier.

However, before you start migrating stuff, here’s the key:

• Data Migration Strategy: How are you going to move your existing data over to the new system? That’s gotta be thought out. You don’t want any data loss.
• System Integration: How will this new system talk to what you already have in place? Make sure they get along.
• Data Protection: And, obviously, how are you going to keep your data safe and sound? Redundancy is your friend.

Beyond the Initial Use Case

This is where it gets really interesting. PostFinance didn’t just use HyperStore for their initial big data project. They started finding other uses for it! They used it for a huge document archive, for regulatory data, and even as a landing zone for data analysis with Apache Spark.

That’s the beauty of object storage; it’s really versatile. I think it’s worth considering these for yourself:

• Archiving: Can you ditch those old tape backups and move to something more modern?
• Backup and Recovery: Can it make your data protection strategy simpler and better?
• Content Distribution: Can it speed up access to those massive media files you’ve got?

Level Up: Splunk SmartStore Integration

PostFinance decided to push things even further. They hooked up HyperStore with Splunk SmartStore. I mean, you want to get the most out of your investment don’t you? This let them index, store, and analyze even more data, all while keeping costs down.

Splunk SmartStore, if you’re not familiar, uses S3-compatible storage (like Cloudian) to store the less frequently accessed data. This means you’re not paying a premium for that data. The indexer keeps the really hot, active data on faster, local storage. This all meant PostFinance saw:

• Lower Splunk Costs: Because they could scale storage and compute independently, they saved a bunch of money.
• Better Analytics: Accessing data directly from HyperStore made things faster.
• Scalability++: Scaling both storage and compute became way easier.

The Takeaway

The PostFinance story is a pretty compelling argument for Cloudian object storage, isn’t it? If you’re facing those big data challenges, it’s worth a look. Assess your needs, pick the right solution, implement it carefully, and keep finding new ways to use it. And keep in mind that as data volumes continue to explode, having a modern, scalable storage solution is crucial if you want to stay ahead of the game and get real value from your data.