Case Study - Big Data Ingestion

The video tutorial discusses the role of Kafka in data ingestion, highlighting its dual function as a speed layer for real-time applications and a buffer for data ingestion into analytic stores. It explains a typical big data ingestion framework, where Kafka acts as a massive buffer, receiving data from various sources and feeding it into real-time analytics dashboards. The tutorial also covers the batch layer, where data is stored in systems like Hadoop, Amazon S3, or RDBMS for batch queries, data science, reporting, and long-term storage. The video concludes by formalizing these concepts.

What are the two roles that Kafka serves in data ingestion?

• As a speed layer for real-time applications and a slow layer for buffering data

• As a storage system and a processing engine

• As a data visualization tool and a reporting tool

• As a database and a file system

In a big data ingestion framework, what is Kafka primarily used for?

• As a database management system

• As a massive buffer for data from various sources

• As a data cleaning tool

• As a data visualization tool

Which tools are mentioned as part of the real-time analytics dashboard fed by Kafka?

• Tableau and Power BI

• Spark, Storm, and Flink

• Hadoop and Amazon S3

• RDBMS and Elasticsearch

What is the purpose of the batch layer in a big data framework?

• To clean and preprocess data

• To provide real-time analytics

• To perform batch queries for data science and reporting

• To visualize data in dashboards

Which systems are used in the batch layer for long-term storage and processing?

• Hadoop, Amazon S3, and RDBMS

• Elasticsearch and Tableau

• Kafka and Spark

• Storm and Flink