In terms of today’s enterprise data management services, a shift is now happening from the traditionally popular data warehouses to the less structured data lakes. Although there are skeptics against data lakes, many also believe that unlike data warehouses, it enables businesses to have a more unlimited view of data.
In terms of today’s enterprise data management services, a shift is now happening from the traditionally popular data warehouses to the less structured data lakes. Although there are skeptics against data lakes, many also believe that unlike data warehouses, it enables businesses to have a more unlimited view of data.
Data lakes are defined as “a massive, easily accessible, centralized repository of large volumes of structured and unstructured data”. Whereas data warehouses store data from various sources in specific static structures and categories, data lakes do not classify data when they are stored.
However, just having a data lake is not enough. A successful one must be able to provide real-time response to queries and give users an easy and uniform access interface. To ensure your data lake’s success, we have compiled a list of tips shared by data management experts.
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Identify Use Cases
In his article on InfoWorld, Strategic Developer Andrew Oliver suggests that businesses must have some use cases in mind before constructing a data lake. He says they can either be existing ones or any case that your business wants to do but couldn’t.
Work with Data Scientists
Oliver likewise suggests that businesses work with data scientists. Data scientists and engineers provide the necessary expertise required to make the data lake a successful data and analytics tool. Businesses may choose to work with data management firms. Oliver points out, however, that there is no unicorn data scientist. Instead, the key is in hiring “technically adept facilitators”.
Use of Multiple Tools and Products
Knowledgent, suggests the next five characteristics they believe are necessary for a successful data lake and the first one involves customizing it based on multiple technology stacks. This is because there is no single open-source platform available right now that can extract maximum value out of the data lake.
Domain Specification
Data lakes must be industry-specific to cater to the industry’s unique needs. Make sure that IT intervention is not necessary to enable users to obtain data when they need it. A user interface that allows keyword, faceted and graphical search is likewise necessary.
Automated Metadata Management
Knowledgent states that “without a high-degree of automated and mandatory metadata management, a Data Lake will rapidly become a Data Swamp” and that “attributes like data lineage, data quality, and usage history are vital to usability”.
Configurable Ingestion Workflows
New sources of external information will continuously be available. Make sure to have an easy, secure and trackable content ingestion workflow mechanism that can rapidly add these new information into the data lake.
Integrate with the Existing Environment
A lot of businesses already have existing enterprise data management systems. The data lake must be able to support this and be well integrated into it to avoid replacing or ripping apart the existing environment.
Optimized Scalable Multi-Protocol Storage
Senior Consultant and Technologist Ed Walsh says in his article that enterprise data lakes have three critical storage requirements. First, they must be scalable to enable the business to expand capacity as needed and prevent service interruptions. Second, they must be optimized for low cost per gigabyte. Lastly, they must have multiple storage protocols to allow for simultaneous access.
These are the characteristics that data lakes must have to ensure its success. Data lakes can be an effective and successful data management solution for businesses provided that they allow users to analyze an extensive array and volume of data when and how they want it. The key is to design and implement one that is tailored specifically to address business needs.
