Introduction
Big Data analytics is the backbone of most business-critical applications in industries like Telecom, Online Retail, Finance, Healthcare, and Banking. All these domains use Big Data to handle their growing analytics requirements. The Big Data industry provides a wide range of solutions designed to solve different real-world problems in the analytics space.
Hadoop was the initial solution in this space. After Hadoop, multiple technologies emerged, including:
- MapReduce for processing batch data
- Storm for processing streaming data
- Apache Tez for batch and interactive data processing
- Apache Giraph for graph data processing
- Hive for structured data processing
We have also seen Apache Spark taking over Hadoop and becoming one of the most popular technologies in Big Data analytics.
Each of these frameworks is a specialized engine that solves a specific problem in managing or handling Big Data.
In the early stages of Big Data evolution, learning and using multiple tools in a single application was acceptable, even though it required developers and architects to master various tools.
Today, the industry seeks a generalized platform that can handle different types of data and workloads. Apache Spark is one such framework capable of handling batch, streaming, interactive, iterative, graph, and in-memory processing.
Why Flink when Spark is there?
At its core, Spark is a batch processing engine that processes streams as micro-batches. While Spark is significantly faster than Hadoop, it is still limited by its batch processing nature. This is where Apache Flink plays a crucial role.
Apache Flink is the next-generation Big Data platform capable of processing data at lightning-fast speeds. It was created with a vision to solve the limitations posed by existing data-driven engines and frameworks like Hadoop and Spark.
Unlike Spark, which processes streaming data as micro-batches, Flink is a true stream processing engine. It does not cut streams into micro-batches, reducing latency significantly. Flink’s query optimizer follows the principles of iterative algorithms, commonly used in machine learning to minimize errors. This allows Apache Flink to process streaming data with reduced latency compared to micro-batch architectures.
Conclusion
Apache Flink is designed to handle real-time, high-speed data processing efficiently. As businesses require faster and more dynamic analytics solutions, Flink’s advantages in stream processing make it a powerful alternative to Spark for specific use cases.