Data Lake Fundamentals: Key Concepts and Best practices

Data Lake Fundamentals: Key Concepts and Best practices free download

Key concepts and fundamentals of Data Lake implementation

Hello! Welcome to the "Data Lake Fundamentals" course!!

Did you know companies generate massive amounts of data every year? Data that, if used correctly, can transform businesses.

Traditional data management solutions struggle with today's data volumes. Data Lakes are the modern solution, offering a way to store, manage, and analyze all types of data efficiently

A Data Lake is a centralized repository that stores raw data in its native format, whether structured, semi-structured, or unstructured. It provides the flexibility needed for advanced data analysis."

Data Lakes offer scalability, cost efficiency, and support for IoT and regulatory compliance. Companies using Data Lakes turn data into valuable insights, driving innovation and gaining a competitive edge.

In our 'Data Lake Fundamentals' course, you'll learn:

-What a Data Lake is.

-Key features and benefits.

-How to architect and govern a Data Lake.

-Practical use cases and real-world examples

This course has three parts:

Part I: Course Introduction

In this section we can understand clearly what is a Data Lake and why they are so important.

Classes:

• Course Introduction

• What is a Data Lake?

• Why should I learn about it?

Part II - Characteristics and Comparisson of current scenario

In this section we will deep dive in the concepts and comparisson between traditional warehouses and Data Lakes. Also, we will understand some challenges for implementation.

Classes:

• Characteristics of a Data Lake

• How does a Data Lake adds value?

• Data Lake Vs Data Warehouse

• Data Lake challenges

Part III - Lamda Architecture implementation

Here we will understand how a lambda architecture data lake is splitted.

Classes:

• Core architecture principles

• Lambda architecture-drive Data Lake

• Data Ingestion layer

• Batch speed layer

• Storage layer

• Serving layer

• Data Acquisition layer

• Messaging layer

• Object storage vs HDFS