Technology is pacing up at an unprecedented rate and the world is more connected than ever, thanks to the rapidly increasing number of electronic devices and the use of the Internet. According to an IBM survey, we were generating 2.5 quintillion bytes of data every single day in 2013. That number has undoubtedly grown even higher. The data generated consists of structured, semi-structured, and unstructured data. To handle structured data, enterprises have started using data warehousing methodologies. Data warehouses are mature, easy to use and implement, and offer a high level of security. However, they can be quite expensive for large data volumes. To handle unstructured data, various Big Data technologies such as Hadoop are already in use. Irrespective of what the technology is, a relatively new technology — data lakes — has become a common abstract idea to store and manage unstructured data.
What are data lakes?
A data lake is essentially a large data repository that accommodates vast data sets in its native format. Unlike data warehouses, a data lake can accommodate raw data and it stores the data as it is using a schema-less flat file architecture. In data lakes, each data object is assigned with a unique identifier or a key and is tagged with a set of metadata tags.
When the need to access the data arises, the data is then queried, and this is when the data is given its shape and structure, known as schema-on-read. In data lakes, the schema and the data requirements remain undefined until the data is needed.
Data lakes are highly agile, as they don’t have a predefined structure of data storage. This gives its users the ability to easily configure and reconfigure the structure as per the need. Data lakes are also designed to offer low-cost storage, which is the primary reason for various organizations and enterprises to implement data lakes.
Data lakes are specially designed to overcome information silos by bringing the entire enterprise’s data under one roof to access, analyze, and put to use without any restrictions. Before we proceed further, it is very important to understand that data lakes are not a replacement for the traditional enterprise data management systems. For an organization to run smoothly, it is vital to have structured data that can be queried frequently. Therefore, traditional data management systems also hold its importance.
Advantages of data lakes
What can data lakes mean for business? Let’s take a look:
Handle all forms of data
Data lakes provide an easy and flexible access to your entire data. As mentioned earlier, data lakes can accommodate all forms of data be it structured or unstructured.
Due to the schema-on-read property of data lakes, data scientists or data analysts can explore and analyze wide possibilities from a data lake. Data lakes support rapid ingestion of data and enable easy integration and change in schema depending on the use case.
With the increasing amounts of data being generated, costs associated to filter and store data in data warehousing is also increasing. Data lakes, on the other hand, are capable of handling huge amounts of data and can be operated at very low costs.
In addition to all these benefits and features, a data lake can also be used as a source of data for various frontend applications.
Best practices in implementing data lakes
Before you can use data lakes effectively, there are several prerequistes. Here are a few:
Because data lakes can store all your data irrespective of its structure and format, to query the data when needed, metadata tags are defined and each data object is assigned with a unique identifier. Enterprises need to be cautious about properly defining the metadata as it is the key to effective use of the data lake. Defining metadata must not be treated as an additional burden and must be defined the moment new data is being stored in a data lake. Benefits from using data lakes are directly proportional to well-defined metadata.
Visibility and accessibility
An enterprise needs to have proper visibility and accessibility to the content of its data lake to get the most of it. Information catalogs have to be defined, which provides frameworks and guidelines to the enterprise and its employees to understand the data lake’s content. Catalogues are made based on the metadata defined and are essential in maintaining the consistency of the data. Based on these catalogs and metadata, data is classified and is made available to be queried based on the business need.
Integrate data lakes in data architecture
Businesses are constantly in search of methods and techniques to use data to ensure better operations and to accommodate advanced analytics. To get the most of a data lake, an enterprise must integrate it in the company’s data architecture. Often, data lakes are treated as a separate entity altogether and are not included in the business architecture. This is possibly the biggest blunder one could ever do while implementing or using a data lake. Data lakes provide a very efficient medium to maximize the analytical abilities of the company and to get the most from the data. Moreover, a data lake must be well integrated with the existing enterprise data management platform, methods, and tools.
Being domain specific
For any enterprise to get maximum benefits from its data lake, it is essential to tailor the data lake as per the company’s requirements and specifications. For instance, a data lake designed for a medical industry varies a lot from a data lake designed for a consultancy firm. The first one needs very accurate data segregation and grouping through metadata, whereas, the later needs to provide a more intuitive information for navigation.
Leverage data governance and security
Data lakes are not a huge repository of data; They are storage vault of all your valuable information that is stored together. Being the house of all the diverse information, a data lake possesses various governance and security-related challenges. Therefore, it is important to leverage strong security and governance policies. Also, proper access management policies need to be adapted to avoid any unwanted data breaches.
If implemented properly with all the right measures, data lakes can prove to be a game changer for many enterprises. They will not just save money but will also enable the business to analyze data in a better way. Apart from all these best practices, always try to keep the data lake as simple as possible. Also, a data lake must be designed keeping in mind both the technical and business goals. Always have proper communications, operational, and disaster-recovery plans to ensure business continuity. All business needs must be well-defined before implementing a data lake. And finally, do keep in mind that a data lake must be configured in such a way that it provides all the values that the business is not getting from a data warehousing system.