- Apache Flink Tutorial
- Apache Flink - Home
- Apache Flink - Big Data Platform
- Batch vs Real-time Processing
- Apache Flink - Introduction
- Apache Flink - Architecture
- Apache Flink - System Requirements
- Apache Flink - Setup/Installation
- Apache Flink - API Concepts
- Apache Flink - Table API and SQL
- Creating a Flink Application
- Apache Flink - Running a Flink Program
- Apache Flink - Libraries
- Apache Flink - Machine Learning
- Apache Flink - Use Cases
- Apache Flink - Flink vs Spark vs Hadoop
- Apache Flink - Conclusion
- Apache Flink Resources
- Apache Flink - Quick Guide
- Apache Flink - Useful Resources
- Apache Flink - Discussion
Apache Flink - Big Data Platform
The advancement of data in the last 10 years has been enormous; this gave rise to a term 'Big Data'. There is no fixed size of data, which you can call as big data; any data that your traditional system (RDBMS) is not able to handle is Big Data. This Big Data can be in structured, semi-structured or un-structured format. Initially, there were three dimensions to data − Volume, Velocity, Variety. The dimensions have now gone beyond just the three Vs. We have now added other Vs − Veracity, Validity, Vulnerability, Value, Variability, etc.
Big Data led to the emergence of multiple tools and frameworks that help in the storage and processing of data. There are a few popular big data frameworks such as Hadoop, Spark, Hive, Pig, Storm and Zookeeper. It also gave opportunity to create Next Gen products in multiple domains like Healthcare, Finance, Retail, E-Commerce and more.
Whether it is an MNC or a start-up, everyone is leveraging Big Data to store and process it and take smarter decisions.