Why ingestion pipelines feed the data lake in Hadoop - Quick Recap

Recall & Review

beginner

What is a data lake?

A data lake is a large storage system that holds raw data in its original form. It can store all types of data, like text, images, or logs, without needing to organize it first.

beginner

What is an ingestion pipeline in data processing?

An ingestion pipeline is a set of steps that collects data from different sources and moves it into a storage system like a data lake. It helps bring data in quickly and safely.

intermediate

Why do ingestion pipelines feed data lakes instead of databases directly?

Data lakes can store all kinds of raw data without changing it. Ingestion pipelines feed data lakes so data is ready for many uses later, like analysis or machine learning, without losing details.

intermediate

How does Hadoop support ingestion pipelines feeding data lakes?

Hadoop provides tools to store big data in a data lake and process it. It helps ingestion pipelines handle large amounts of data from many sources efficiently.

beginner

What is one key benefit of feeding data lakes with ingestion pipelines?

It allows storing data quickly and cheaply in one place, so teams can explore and analyze data anytime without waiting for complex setup.

What type of data does a data lake store?

A Raw data in any format

B Only images and videos

C Only structured data

D Only cleaned and processed data

Data lakes store raw data in any format, including structured, semi-structured, and unstructured data.

What is the main role of an ingestion pipeline?

A To visualize data

B To analyze data

C To delete old data

D To collect and move data into storage

Ingestion pipelines collect data from sources and move it into storage systems like data lakes.

Why feed data lakes instead of databases directly?

A Data lakes only store images

B Databases are faster for raw data

C Data lakes are cheaper and store raw data

D Databases cannot store any data

Data lakes store raw data cheaply and flexibly, making them ideal for large, varied data sets.

Which Hadoop component helps store data in a data lake?

A MapReduce

B HDFS (Hadoop Distributed File System)

C YARN

D Hive

HDFS is the storage system in Hadoop that stores data across many machines, forming the data lake.

What is a benefit of using ingestion pipelines with data lakes?

A Data is stored quickly and in raw form

B Data is only stored in databases

C Data is deleted after ingestion

D Data is stored slowly and carefully

Ingestion pipelines help store data quickly and in raw form in data lakes for flexible use.

Explain why ingestion pipelines are important for feeding data lakes.

Think about how data moves from where it is created to where it is stored.

Describe how Hadoop supports ingestion pipelines feeding data lakes.

Focus on Hadoop's storage and processing capabilities.