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In this video, we're going to dive into the details of three popular ways to store data: databases, data lakes, and data warehouses. These terms are often thrown around in the tech world, especially during job interviews, so it's important to know what each one means and how they differ. We'll start off by explaining what each of these data storage options is and highlight their main features. Next, we'll look at the advantages and disadvantages of using each type of storage solution, helping you understand when and why you might choose one over the others. We will also compare how each system handles data. This includes discussing how they manage various forms of data, from well-organized structured data to more flexible semi-structured data and even unstructured data, which can be more complex to organize. After that, we'll go over some typical uses for databases, data lakes, and data warehouses, sharing examples to show how each is applied in real-life situations. By the end of this video, you'll have a solid grasp of the key differences between a database, a data lake, and a data warehouse. This knowledge will not only boost your understanding but also prepare you to discuss these topics confidently in job interviews.

00:00:00 - Introduction to Data Storage
00:01:58 - What is a Database?
00:03:37 - What is a Data Warehouse?
00:05:29 - What is a Data Lake?
00:07:14 - A Quick Recap
00:09:32 - Factors to Consider to Choose the Right Platform

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✅ Is BigQuery a data lake or data warehouse?

BigQuery is a data warehouse. It is a fully managed, serverless, highly scalable, and cost-effective multi-cloud data warehouse offered by Google Cloud. BigQuery is designed for analyzing large datasets quickly and efficiently using SQL queries.

✅ Is data lake SQL or NoSQL?

A data lake itself is neither SQL nor NoSQL; rather, it is a storage repository designed to hold large amounts of raw data in its native format, including structured, semi-structured, and unstructured data. The type of data stored in a data lake can vary, and both SQL and NoSQL databases can be part of a data lake ecosystem.

✅ Does data lake use ETL?

Yes, data lakes use ETL (Extract, Transform, Load) processes, but they can also use ELT (Extract, Load, Transform) processes.

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#Database #DataLake #DataWarehouse #CloudComputing #2024 #simplilearn

➡️ About AWS Cloud Practitioner Course
AWS Cloud Practitioner course is for individuals seeking an overall understanding of Amazon Web Services Cloud, independent of specific technical roles. To build AWS Cloud knowledge, you will learn about AWS Cloud concepts, services, security, architecture and support. This course also helps prepare for the AWS Certified Cloud Practitioner exam.providers: Microsoft Azure, AWS, and GCP.

✅ Key Features
- Official AWS live class training content
- Self-paced learning videos authored by AWS
- Live online training from AWS Authorized Instructors
- 1-day Instructor-Led Training Course for accelerated learning

✅ Skills Covered
- AWS Cloud Adoption Framework
- AWS Global Infrastructure
- AWS data migration solutions
- Storage and databases
- AWS IAM security levels
- AWS Compute Services
- Amazon Cloud
- Front and Edge locations
- Networking concepts
- Payasyougo pricing model
- AWS pricing models and support

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