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Data Lake serves a similar function to a Data Warehouse. The main difference is that in the Data Lake, big data and unstructured data can be stored in the original form. When required, these data can be referenced for analytics purpose. Thus, the data in the Data Lake can be in any format and often, is unstructured images, text, pdf, videos and audio files. This makes them very agile. Data Lakes follow schema-on-read – where any new data will be stored in any format but when the data has to be used, the schema will need to be defined . The most popular Data Lake providers are AWS, Azure etc. The data is conducive for Data Exploration and Data Mining.

The Data Warehouse is a conventional store of data, stored in a structured format.  This primary key (eg. unique customer id ) enables a relational coupling of the data fields to ensure reliable and fast access to data , specially business data, which shows very less variability in type . Warehouses follow schema-on-write – meaning the structure of the any new data will be modified at the point of new data import into the data warehouse.  The most common warehouses are from Oracle, Teradata etc. The data is conducive to Machine Learning and deployment of predictive analytics algorithms.

Organizations with standard ERP systems, like Banks, Financial Institutions and Manufacturing companies, find it easy to keep the Business Data in Data Warehouses and some less used, yet to be used for critical Business decisions data . This may include  CCTV camera feeds, employee email backup, scanned images of employee/ customer/ vendor documents, production equipment, delivery vehicles, customer feedback etc. in a Data Lake format. Thus, a Hybrid structure with a combination of Data Warehouse and Data Lake, can often be an optimal solution for an organization.

Data is only valuable if it can be utilized to help make decisions in a timely manner. A user or a company planning to analyze data stored in a data lake will spend a lot of time finding it and preparing it for analytics—the exact opposite of data efficiency for data-driven operations. Only teams with a very clear understanding of outputs required succeed in this very adaptable and agile systems.

  1. Data lakes allow you to store anything without questioning whether you need all the data. This approach is faulty because it makes it difficult for a data lake user to get value from the data.
  2. Data lakes do not prioritize which data is going into a supply chain and how that data is beneficial. This lack of data prioritization increases the cost of data lakes (versus data warehouses and databases) and muddies any clarity around what data is required. There’s no “prioritisation” — only “gathering.” Data is not necessarily being gathered with a specific “mission” in mind.
  3. Data latency is higher in data lakes. Data lakes are often used for reporting and analytics; any lag in obtaining data will affect your analysis. Latency in data slows interactive responses, and by extension, the clock speed of your organization. Your reason for that data, and the speed to access it, should determine whether data is better stored in a data warehouse or database.

Data lakes foster data overindulgence. Too much unprioritized data creates complexity, which means more costs and confusion for your company—and likely little value. Organizations should not strive for data lakes on their own; instead, data lakes should be used only within an encompassing data strategy that aligns with actionable solutions.

A Hybrid structure with a combination of Data Warehouse and Data Lake, can often be an optimal solution for an organization. A process of  discovering value in the Data Lake data and then moving them into the Data Warehouse (if value is discovered) is a better way to use the organizational data than to use any one of the above structures.

Business analytics systems can use data lakes to perform automated reporting and serve analytical insights to digital dashboards. But for day-to-day functions that require access to more structured data assets, reports and other types of files and resources, a business might have a database or data warehouse in addition to a data lake.

Do you want to discuss and review the current capabilities of your Analytics Architecture ? Feel free to reach us at score@pexitics.com


  1.  Databases vs data lakes: Which should you be using? (information-age.com)
  2. Data Storage Explained: Data Lake vs Warehouse vs Database – BMC Software | Blogs


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