A data lake is a central hub for storing original, unprocessed enterprise data.

With the emergence of big data analytics, a modern information storage paradigm called Data Lake is being implemented by several companies to overcome data management hurdles. For varied use applications, such as business intelligence, insights, regulatory enforcement, and fraud detection, the data lake framework is being embraced.

It is a central hub for storing original, unprocessed enterprise data.  It helps you to store data items such as business application line data and organizational databases, and non-relational data such as smart phone applications, IoT devices, and social media. It binds it with identifiers and attributes tags for quicker recovery when storing data.

It is obvious today; data lakes are the only option in the norm these days for addressing big data challenges. Even so, it is vital to seek solutions with enterprise-grade applications that extend the reach of analytics and insight generation while simplifying the end-to-end data management process as you begin analyzing and building data lakes for your business.

Enterprise Data Lake

Enterprise Data Lake is a joint self-service platform for data analysts and data scientists for the discovery and preparation of big data. It helps analysts to identify and transform raw knowledge into insight quickly and allows IT to guarantee consistency, visibility, and accountability. Analysts spend more time on analytics with Enterprise Data Lake, and less time on data finding and planning.

According to Accenture, “In recent years, demand for faster, more efficient data access and analytics at end-users’ fingertips have fueled the rise of enterprise data lakes: repositories designed to hold vast amounts of raw data in native formats until needed by the business. With these in place, companies have started to gain various benefits:

• Centralize enterprise content silos
• Overcome legacy source systems’ limitations
• Transform insight discovery and analytics processes
• Enrich data in ways that are not possible in the source systems”

Data Lake vs Data Warehouse

A data lake is a warehouse that stores piles of original data. It stays in its original form and is only converted when appropriate. It collects all kinds of data regardless of whether it is structured, semi-structured, or not.

A data warehouse, on the other hand, is a storage center that stores data in files and directories that are retrieved, converted and loaded. A data warehouse only retains structured data for business users from one or more varied systems, which are later processed. Data collected from a data center allows customers to make company decisions.

Usually, organizations require both. The former were created because of the need to leverage big data and benefit from original, granular structured and unstructured machine learning data. However, data warehouses for the use of analytics by enterprise clients still need to be developed.

Risks of Data Lake Usages are:

• It can lose importance and strength after certain time.
• When building them, a greater amount of risk is implied.
• It also boosts the cost of storage & computing.
• There is no way to obtain feedback from other people who worked on these data because the line of results of previous analysts are not taken into account
• Protection and access control are the biggest threat to data lakes. Data can sometimes be put in a lake without monitoring, since some of the information may have confidentiality and safety needs.