Today, industries and enterprises are dealing with massive amounts of data. Businesses must increase their data-management infrastructures quickly to keep up with the sheer amount of data. Multi-cloud, advanced data applications, and data-first architecture have ignited the need for enterprise data lakes. An agile data-lake development approach can help enterprises accelerate analytics strategy faster to derive valuable insights for achieving business goals. The blog post outlines the benefits of an enterprise data lake implementation for your organization.

What is an enterprise data lake (EDL)?

Data Lake is a reliable and scalable data management solution. All structured, semi-structured, unstructured, and binary data are stored in an enterprise data lake. Not just storing data, organizations can profile, cleanse, transform, model, and create data pipelines to meet specific functional and technological requirements. The end goal is-

• Breaking down the data silos
• Adapting to the evolving data requirements 
• Achieve faster analytics
• Develop real-time data-driven applications
• Quick integration of disparate data sources and formats

How does EDL function? 

The EDL centrally collects data from different sources in the raw format; however, the required data for analysis will only be pulled in, parsed, cleaned, and transformed with the additional processed data going to the EDL. It creates a loop where the raw data goes to the lake for processing and analysis while the processed data comes into the lake for further analysis. Organizations should follow an agile approach to the design and development of data lakes before pushing to the implementation stage. Research the wide range of technologies, testing methodologies, development iterations, and fine-tuning before employing EDL for data collection and storage.

Comparing Data Lake and Data Warehouse

Data lakes and warehouses store data, but each has specific storage capabilities. Both can be employed depending on the emerging scenario or functional needs. For example, a data warehouse demands a defined schema to deliver specific analytic requirements for business intelligence tasks like dashboards and data visualizations. Data warehouse works suitably for businesses requiring reporting outputs regularly but collects only structured data from transactional databases. On the other hand, data lakes can collect both structured and unstructured data. Data warehouses tend to perform efficiently, but it’s expensive, while Data lakes are slower in returning query results and are more affordable.

What are the benefits of an enterprise data lake?

• Democratize Data
• Collect better-quality data
• Data in native format
• Scalable and Cost-savings
• Advanced Analytics

Building a modern enterprise data lake

Enterprises must follow an agile process and move beyond hiring technological expertise. A successful enterprise data lake strategy needs every stakeholder from business, governance, and IT to work together. One must outline clear expectations, a proper implementation plan, rigorous testing, and monitoring before embarking on the data lake process.   An enterprise data lake can bring valuable analytics for faster time-to-market while lowering the cost and complexity of managing data at the same time. The storage capacity of data lakes is ideal for collecting enterprise data.