Whether your business already operates within the cloud or you’re considering migrating and becoming cloud native, you’ll likely be aware of the importance of effective data storage and management. But while you may know what a database is, what about data lakes?
In this article, we’ve explored what data lakes are and how they differ to traditional databases, as well as their benefits and how to build a data lake within your cloud environment.
What are data lakes?
In simple terms, a cloud data lake is a huge storage repository for vast amounts of raw data, including structured and unstructured data (and everything in between). It’s effectively a place to store any and all data, ready to be analysed and sorted at a later point.
Data lake vs database
It’s important to be able to distinguish between data lake architecture and cloud databases, because they both serve different purposes, and can be useful in their own rights.
While a cloud data lake is a virtual vault designed to securely store huge amounts of diverse information and raw data in its native format, a database is much more structured in design and optimised for organisation. If a database is a filing cabinet, a data lake is a huge storage unit.
What are some common use cases of data lakes?
Data lakes are becoming increasingly popular, due to their vast capabilities and effortless scalability. Examples of common use cases and when to use a data lake include:
Advanced data research
As identified above, data lakes are effectively enormous virtual warehouses, jam-packed full of data and useful information. This makes them a gold mine for researchers hoping to establish relationships between data points, uncover complex patterns, and test hypotheses – and all within an environment that’s not bound by a strict structure.
Data archiving
Because data lakes can hold such huge amounts of data, they’re the ideal archiving solution for long-term data retention and disaster recovery. Especially as they’re so flexible and scalable – as your archive increases, you can expand your data lake architecture to match.
Machine learning
There are benefits to storing such huge amounts and types of data, including using it to advance machine learning. With a vault of information at your disposal, you can experiment and develop algorithms, which can ultimately be used to deliver advanced analytics, natural language processing, and object recognition – among other applications.
What are the benefits of a data lake?
It’s one thing knowing what a data lake is, but how can your business benefit from implementing this popular solution?
1. Centralised storage
Due to the nature of what a data lake is, it’s inevitable that large amounts of data are stored within one centralised hub. This can make it much easier to locate information from across multiple sources, which helps to streamline processes – and, as a bonus, it means multiple people can access and work from the same data lake, which encourages collaboration.
What’s more; having a centralised data storage system allows you to maintain closer control over your environment, compared to managing multiple databases, which can improve consistency and efficiency.
2. Scalability
Scalability is an important consideration if you’re thinking about migrating to a cloud native environment, so you’ll be pleased to hear it’s relatively straightforward to scale data lake resources!
Because they’re designed to manage huge amounts of data, they can easily accommodate growing volumes without requiring significant infrastructural changes – meaning you can add and remove resources to meet demand.
3. Flexibility and agility
Data lakes offer extreme flexibility and agility for users, who are able to manage and analyse raw data of varying types, structure, and scale. This practicality lends itself nicely to many business models – especially those that anticipate changing database demands, encourage testing, and require growing storage.
Boasting such strong flexibility in the handling of huge amounts of raw data can allow you to more effectively and efficiently react to market changes, make informed data-led decisions, and gain a competitive advantage.
4. Cost-effectiveness
There are various cost-related benefits of data lakes, compared to more structured or traditional data storage solutions. For a start, and perhaps most obviously, because you’re housing data in its raw format, you don’t have to pay for it to be cleaned (which comes with its own advantages, including retaining the original context of the data and reducing the risk of corruption during processing).
Additionally, the common pay-per-use model allows you to scale up and down depending on business need, meaning you’re only ever paying for what you’re using. Finally, because data is stored in a centralised system, you minimise your risk of data duplication (and the additional cost of storage that comes with managing twice the information).
5. Data analysis and innovation
Another of the stronger data lake benefits is that they naturally facilitate innovation and testing, which has the potential to not only help you keep up with market trends but actually anticipate them to position yourself as an authority.
As we’ve explored above, data lakes are a huge hub of information, across multiple types and categories, all of which can be analysed by experts to establish relationships, spot trends, optimise existing operations, and highlight new opportunities.
6. Data security
When managed and handled correctly by experienced IT professionals, data lakes can offer effective and robust security advantages – largely, because data is held within a single, centralised virtual unit, which makes it more straightforward to cast a security net. That said, because you’re holding such large volumes of data in one place, you increase the ‘surface area’ of attack, so it’s imperative that you implement strong internal measures.
On that, as with anything related to the cloud, there is a shared responsibility between provider and user, and there are steps you can take to further-improve security measures, such as using strong passwords and implementing encryption.
Next steps and how to build a data lake
If a cloud data lake seems appropriate for your business, there are numerous platforms for you to consider across top-rated providers such as AWS and Microsoft Azure (if you want any advice as to which best-suits your business model, get in touch with our experienced cloud architects, who’ll be more than happy to help).
Once you’ve chosen your cloud platform, you’ll need to transfer your data to its new environment, before organising it in such a way that complements your processes.
Finally, implement appropriate security measures and ensure your data is encrypted and regularly monitored against threats. With your data secure and in place, you can begin storing and analysing your resources, to drive innovation and growth.
Hopefully, you’ve found the above useful and you now feel confident taking the next steps and exploring cloud data lake platforms. If so, work with a cloud consultant to get your virtual environment data-ready! Alternatively, explore even more useful insight and guides like this over on our blog.
