Remove Aggregated Data Remove Relational Database Remove Unstructured Data
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Data Ingestion-The Key to a Successful Data Engineering Project

ProjectPro

Volume refers to the amount of data being ingested; Velocity refers to the speed of arrival of data in the pipeline; Variety refers to different types of data, such as structured and unstructured data. Why do you need a Data Ingestion Layer in a Data Engineering Project? application logs).

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Sqoop vs. Flume Battle of the Hadoop ETL tools

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Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., and Flume in Hadoop is used to sources data which is stored in various sources like and deals mostly with unstructured data. The complexity of the big data system increases with each data source.

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AWS Glue-Unleashing the Power of Serverless ETL Effortlessly

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This serverless data integration service can automatically and quickly discover structured or unstructured enterprise data when stored in data lakes in Amazon S3, data warehouses in Amazon Redshift, and other databases that are a component of the Amazon Relational Database Service.

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How To Choose Right AWS Databases for Your Needs

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They include relational databases like Amazon RDS for MySQL, PostgreSQL, and Oracle and NoSQL databases like Amazon DynamoDB. Types of AWS Databases AWS provides various database services, such as Relational Databases Non-Relational or NoSQL Databases Other Cloud Databases ( In-memory and Graph Databases).

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100+ Data Engineer Interview Questions and Answers for 2025

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Differentiate between relational and non-relational database management systems. Relational Database Management Systems (RDBMS) Non-relational Database Management Systems Relational Databases primarily work with structured data using SQL (Structured Query Language).

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Your 101 Guide to Becoming an ETL Data Engineer in 2025

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Their role involves data extraction from multiple databases, APIs, and third-party platforms, transforming it to ensure data quality, integrity, and consistency, and then loading it into centralized data storage systems. Clean, reformat, and aggregate data to ensure consistency and readiness for analysis.

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Data Pipeline- Definition, Architecture, Examples, and Use Cases

ProjectPro

In broader terms, two types of data -- structured and unstructured data -- flow through a data pipeline. The structured data comprises data that can be saved and retrieved in a fixed format, like email addresses, locations, or phone numbers. Step 2- Internal Data transformation at LakeHouse.