Why You Need Your Own Data Lake for Your Business
These days, data storage solutions that used to do the job are rapidly being obsolete, as the needs of businesses are not uniform, and the size of necessary data for business operations grows at an ever-increasing rate. One of the most common methods of data storage was to house a series of servers and scatter data across them, often spanning several rooms or even buildings. This method is often referred to as a data warehouse. While a generally effective method, data warehouses have a considerable downside, that being the chore of concentrating data in one place for analytical purposes. However, an alternative solution has entered the fray fairly recently – the data lake.
What is a data lake?
A data lake is a competitive alternative to the tried and tested data warehouse. It is much more capable of holding your data in a single place, able to pull data from any system, regardless of whether it was initially stored on a cloud, a computer, or on-premises. Once held, a data lake can store data in its full form, allowing for unparalleled ease of access and thereby making it perfect for storing data for analytical purposes. Though a data lake can store any type of data, it is especially suited to storing large quantities of unstructured data. This includes images, documents, sound files, and other similar types of data, alongside pretty much anything from social media.
Why use a data lake?
There is a plethora of reasons to implement data lakes into your business. From flexible scaling to streamlined analysis, opting for a data lake as your means of storage could be exactly what your business needs. Let’s dive into the key advantages of using a data lake.
Data lakes don’t need to be the endpoint for your data
While it may seem as though this defeats the object of data storage, this can be a great way to blend a data lake with a data warehouse. By implementing a data lake as a node through which your data passes, you can benefit from the ingestion speed and analytical tools inherent to data lakes. Once your data lake has the raw data, you can determine exactly where you want it to go beforehand, avoiding any potential sorting issues of the past. Here, your data lake can store anything unnecessary to your day-to-day operations, while the important stuff heads to where it needs to go.
Data lakes make analysis easier
Analysing data is crucial for several reasons; you need to know how to improve your service, how to target an audience effectively, and even fulfil regulatory necessities. To properly handle these tasks, good data analysis is a must, one that a data lake might be able to help you with.
Metadata is one key factor. Knowing more about your customers, such as what demographics they fit and how they interact with your site, is of high importance. It can help guide marketing campaigns, fine-tune your service, and let you know what your customers think of what you do, if only in a roundabout way. Data lakes make gathering this data simple, taking note of any new data coming in and tracking who is responsible and when the data was ingested. This makes it an ideal option for fulfilling any regulatory requirements also, as an exact line from the origin to the endpoint is recorded for every piece of data.
Another way data lakes make themselves useful in analysis is in how they store data. As data lakes store data in its raw form, your data analysts will have access to the pure data, rather than data sheets created by people interpreting data and subject to further interpretation as it changes hands. This often has the effect of muddying otherwise clear data, causing problems down the line when using it as a reference point for how a project should be. While not always disastrous, small adjustments over a long stretch can lead you to completely miss your original mark. Provided you take advantage of the raw data stored in a data lake, you can mitigate this risk, possibly even avoiding it completely.
Data lakes can help reduce costs
Data lakes can bring down the cost of storing data, an advantage that every business can benefit from, regardless of size or industry. As more and more data is stored digitally, and that data is ever-growing in volume, data storage is something that will only get more expensive as time goes on. Therefore, planning ahead and devising methods of reducing this cost is key for any business with long-term plans, and that means moving away from densely packed server farms full of data, especially ones that store data and process it simultaneously.
By using a data lake, you can cut costs simply by reducing the amount of hardware in the mix. For data warehouses, there’s no getting around using physical servers, and the costs pile up quickly as you add more into the equation. Data lakes, on the other hand, can take a lot of this strain off. They run data storage and processing separately, while keeping data off physical servers to boot. This allows any physical servers you might have to focus on data storage, rather than both tasks, and reduces the amount of hardware you need, lowering the costs of both purchasing and maintaining your physical data storage solutions.
An added benefit to this is in scaling your data lake. With a data warehouse, expanding can be an expensive endeavour, as the usual approach would be to pack a room or building with servers. When the time comes to implement upgrades, it can be difficult and costly to do so with limited space. As data lakes support serverless data storage, this problem can be omitted entirely.
Final thoughts
Although data warehouses still solve the problem of data storage well enough, they are at considerable risk of falling behind as digital data and our reliance upon it changes. Data lakes, on the other hand, offer a future-proof solution to your data storage needs, alongside providing a roster of other noteworthy benefits to your data handling process. As the digital world continues to grow more mainstream, the importance of finding an effective means of data storage that can adapt to what the future brings is equally growing. To this end, there is no better solution currently available than a data lake.
