The storage of data is a big deal. Companies that USE big data have been in the news a lot lately, as they try to maximize its potential. Data storage is usually handled by traditional databases for the layperson. For storing, managing, and analyzing big data, companies use data warehouses and data lakes.

Data Warehouse: This is considered an ideal place to store all the data you gather from many sources. A data warehouse is a centralized repository of data where data from operational systems and other sources are stored. It is a STANDARD tool for integrating data across the TEAM- or department-silos in mid-and large-sized companies. It collects and manages data from varied sources to provide meaningful BUSINESS insights. Data warehouses can be of the FOLLOWING types:

• Enterprise data warehouse (EDW): Provides decision support for the entire organization.
• Operational Data Store (ODS): Has functionality such as reporting sales data or employee data.

Data Lake: Data lakes are basically large storage device that stores raw data in their original format until they are needed. with its large amount of data, analytical performance and native integration are improved. It exploits data warehouses' biggest weakness: their incapacity to be flexible. In this, neither planning nor knowledge of data analysis is required; the analysis is assumed to happen later, on-demand.

The purpose of Data Analysis is to transform data to discover valuable information that can be used for making decisions. The use of data analytics is crucial in many industries for various purposes, hence, the demand for Data Analysts is therefore high around the world. Therefore, we have listed the top data analyst interview questions & answers you should know to succeed in your interview. From data cleaning to data validation to SAS, these questions cover all the essential information related to the data analyst role.

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