When it comes to storing big datasets, cloud based data lakes are the place where the activity is. However, many are not aware of data lakes as the foundation of modern IT infrastructures.
Data lakes have several advantages over traditional server-based architectures. First of all, they do not require any extra hardware or software besides the main database server. They are a collection of servers, which store and serve data. These can be private or public. Private data lakes will normally be served by the provider or server administrators, whereas public data lakes will probably be served by the hosting services such Amazon Web Services.
Data lakes make it easier to access business analytics through a central data center. It also helps in reducing the operational cost of your organization. For instance, the cost of delivering the ordered product from the manufacturer to the store can be calculated and better merchandising strategies can be developed. If you already have a data warehouse established, then you can access all the data stored in it through a single interface. This makes the entire process more efficient and also saves time.
Today, a number of companies use batch data lakes that are quite similar to traditional ones. The key difference is the automated generation of relevant structured data. The batch data lakes often contain one or more sources and then utilise a scheduling system to randomly access different data sources. This increases the possibility of obtaining more relevant structured information as the system learns from past patterns. Data from these additional sources can be applied directly in the manufacturing process and can consequently improve efficiency.
While there are some advantages of using a data lakes, they have their own disadvantages as well. Data lakes are susceptible to system crash due to overload or too much data. They are also vulnerable to system failure if the IT team neglects to update data on a regular basis. In addition, centralised data warehouses are vulnerable to system failure because IT teams fail over a period of time. They may not be able to handle any workloads related to the back-up and restoring data in case of a disaster.
Data lakes may be unsuitable for all kinds of business needs. For instance, in healthcare and life sciences, it is not feasible to store huge amounts of medical records as they are mostly used on a daily basis. A centralised data lake provides only basic functionality such as time and temperature and is therefore not suitable for this industry. Data lakes suitable for other businesses include financial services, industrial services and even legal businesses.
A good solution for handling large quantities of data is to use cloud platforms such as Microservice Delivery Managed Services. Cloud computing architecture removes the need for data lakes completely and thus makes data governance much easier. Cloud storage services are very scalable and elastic and thus allow businesses to easily scale up and down depending on their requirements. Using a Microservice architecture also enables businesses to get started quickly without any deployment challenges and thus proves to be an excellent choice for any business that requires increased processing power and a better and more robust solution.
Data Lake Management
With regards to data lake management, two key points are worth considering:
- As a business owner, you have a responsibility to yourself to ensure your data is appropriately managed and stored. It is absolutely critical that you understand the current state of your data access and storage management system – and the challenges that your data lake might be facing. In fact, you must stay abreast of those challenges, lest they become major issues as your company grows.
- You need to engage a company with the right expertise and tools to bring your IT and data governance goals into focus – and this typically involves an in-house team of information security specialists.
However, there is an incredibly valuable third aspect to a good data lake management strategy: it must be able to provide a high degree of flexibility, so that business-owners can continue to make use of their existing data assets even as their data resources (servers, laptops, etc.) become more strained by the natural growth of the company. This is most important if your data lake represents your entire data network, rather than just a part of it. The ability to allow your existing data resources to grow and expand according to your changing needs is the most significant advantage of a DMS has over all other data management strategies: by offering your business fast and flexible data lake management, you’ll ensure that your business will always remain competitive. You’ll also ensure that your data assets are always stored in a highly secured, protected environment, so that your data is always ready to be used and reused whenever it is needed, wherever it might be.