Data Engineering Lifecycle Stages

A source system, such as an IoT device, an application message queue, or a transactional database.

Because data architectures often leverage several storage solutions, and storage solutions support complex transformation queries, and it frequently touches on other stages, such as ingestion, transformation, and serving.

To store data for further processing and analysis.

<p>Special techniques, such as HDFS, Amazon AWS, and Google Big Table, and special tools to analyze it.</p> Signup and view all the answers

<p>When the data is small.</p> Signup and view all the answers

<p>It's a critical stage that touches on other stages, such as ingestion, transformation, and serving, and has a significant impact on the rest of the data lifecycle.</p> Signup and view all the answers

<p>To consume data from multiple sources and transfer it into a destination database or data warehouse for further processing and analysis.</p> Signup and view all the answers

<p>Volume, Velocity, and Variety.</p> Signup and view all the answers

<p>To convert, cleanse, and structure data into a usable format for analysis and decision-making.</p> Signup and view all the answers

<p>Constructive, Destructive, Aesthetic, and Structural.</p> Signup and view all the answers

<p>Serving.</p> Signup and view all the answers

<p>The rows and columns are aligned consistently throughout the file.</p> Signup and view all the answers

<p>Because it ensures that data is in a usable format that can be analyzed to support decision-making and drive business growth.</p> Signup and view all the answers

<p>Semi-structured data</p> Signup and view all the answers

<p>Extracting valuable knowledge, patterns, and insights</p> Signup and view all the answers

<p>Unstructured data</p> Signup and view all the answers

<p>Generate the data</p> Signup and view all the answers

<p>Visualize the results</p> Signup and view all the answers