Very simply put, a data lake is a place to store data from disparate sources in a central storage repository. The unique thing about a data lake is that it allows you to store data in its original format until it needs analysis. A variety of data can be stored in a data lake — from written communications (blogs, e-mails, tweets, etc), audio, images, and video to operational data (sales data, inventory data, etc) and machine-generated data (log files, IoT sensor readings, etc.).
Unlike a data warehouse where you need to process the data before you can store it, a data lake allows you to store data in its original format. Any governance, processing, or structuring of the data is done on its way out when the data is actually needed for exploratory analysis.
While very few people dispute the benefits of having a data lake, there is some controversy around whether an on-premise structure is better or a cloud-based solution. There are some legitimate reasons why people hesitate to move from on-premise infrastructure to cloud. Here are some of them:
Let’s take a look at some of the biggest advantages offered by cloud providers.
With on-premise infrastructure, the initial cost of set-up can be huge, sometimes even prohibitive. With cloud services, on the other hand, there’s more flexibility. You can scale up and down very easily, depending on your requirements. So, let’s say you need only a 20-node cluster to begin with. You will only need to pay as per use. As your requirements change, you can then scale up to 100 nodes without any difficulty. In fact, some cloud-based models also allow you to pay per hour — so, let’s say you need to compute for three hours, you only have to pay for those three hours.
With on-premise software, upgrades can often be time-consuming and costly. There are so many things to take into account — from legacy infrastructure to operations to software. Cloud providers on the other hand just add services from different vendors so that you can upgrade to the latest technologies without too much hassle.
When you build your on-premise infrastructure, you have to manage both the hardware infrastructure as well as the software. This means that building the data pipeline can become very complex for data engineers, as they need to integrate a wide variety of tools. With cloud-based tools, the data pipeline is usually pre-integrated. This means you don’t have to invest a lot of engineering hours to get the solution up and running.
One of the major concerns in the past with cloud providers has been data security. However, in recent years, with finance and healthcare companies moving their data to the cloud, cloud providers have had to start maintaining the highest security and privacy standards. Today, most cloud vendors already provide most of the standard regulatory requirements and compliances.
One of the biggest fears with on-premise solutions is losing all the data in case of a disaster. This means you usually have to maintain a backup data center, which again involves a huge investment of resources. In the case of cloud-based tools, regional and cross-country data recovery strategies are already in place, with availability across a number of data centers. This makes a cloud-based solution far more resilient and reliable.
As data volumes and data types change quickly and dramatically, traditional data architectures that were sufficient in the past may not serve you as well anymore. In order to make the best out of Big Data, it’s a good idea to start by re-examining your current data architecture and then switch to the most efficient way of storing data. If you’re looking for a great tool for migrating to the cloud then CloudBlaze is a great option. CloudBlaze is an enhancement of ADV2 for faster and efficient migration to the cloud, catering to Microsoft Azure users. For more details about CloudBlaze and how it can assist in migration, book a demo today.