Business users now require new and innovative ways for using data for operational reporting and implementing advanced analytics. Data lake is an advanced next-generation data storage and management solution, which was developed to meet the ever-evolving, needs of increasingly enterprise users. Thus, considering its importance, businesses are required to understand the designing of a data lake.

Customer requirements are constantly evolving, and along with advances in data storage technologies, the current enterprise data warehousing solutions are proving to be inadequate. Businesses dealing with advanced analytics require a data storage solution that is based on an IT “push” model, which enables multiple and varied analytics use cases across the enterprise. This new solution is called a ‘data lake’, which supports multiple reporting tools in a self-serve capacity, and allows rapid ingestion of new datasets without extensive modeling. While designing a data lake you must allow users to cleanse and process the data iteratively and easily track the lineage of data for compliance. Moreover, it should also be able to incorporate technologies like machine learning and text analytics.

While designing a data lake, you must first completely understand its architecture. A data lake consists of a data-centered architecture that features a repository capable of storing vast quantities of data in various formats. Data from various sources such as web server logs, social media, databases, and third-party data is ingested into the data lake. In a data lake, curation occurs by capturing metadata and lineage, and making it available in the data catalog. Data flows into the data lake either in real-time processing or batch processing of streaming data.

After you properly understand the data lake’s architecture, you should now know about the key elements that are necessary while designing a data lake.

Domain specifications

The data lake should be tailored to the specific industry. For example, a data lake customized for retail industry would be significantly different from one tailored for the manufacturing industry. A data lake requires data-locating capability to enable business users find, explore, and understand the data. The search capability that you provide should facilitate an intuitive means for navigation, including graphical search.

Designing a data lake with PoC

While designing a data lake you must create and execute Proofs-of-Concept (PoC) for demonstrating the viability of the design approach. Key capabilities of your data lake should be demonstrated using leading-edge bases and other selected tools.

Operating model design and rollout

You should customize your operating models to meet the requirements of individual client’s processes, organizational structure, governance, and rules. This includes establishing chargeback models, reporting mechanisms, and consumption tracking techniques.

Data lake is an effective data management solution for advanced analytics experts and business users as it allows them to analyze a large variety and volume of data, when and how they want. Thus, for designing a successful data lake you must focus on implementing a multitude of products, while being relevant to the industry and providing users with extensive, scalable customization.