The Complex event processing streaming solution which Jeffrey is working on the IoT platform, is a hybrid cloud platform where few data sources are transformed into on-premises Big Data platform.
The on-premises data center and Azure services are connected via a virtual network gateway.
What kind of resources can he choose for this on-premises Big data platform connected to Azure via virtual network gateway, complex data processing and execution UDF jobs on java?
Click on the arrows to vote for the correct answer
A. B. C. D. E.Correct Answer: A.
Jeffrey can choose A. Spark Structured Streaming or Apache Storm for the on-premises Big Data platform connected to Azure via virtual network gateway, complex data processing, and execution of UDF jobs on Java.
Spark Structured Streaming and Apache Storm are both open-source distributed computing systems designed to process and analyze large volumes of real-time data streams. They both support Java as a programming language, making them a suitable choice for executing UDF jobs on Java.
Spark Structured Streaming is a real-time processing engine built on top of Apache Spark, a popular big data processing framework. It enables users to write streaming applications using SQL-like queries, which are compiled and optimized by Spark's query optimizer. Spark Structured Streaming also supports complex event processing (CEP) through its windowing and aggregation functions.
Apache Storm, on the other hand, is a real-time processing system that uses a directed acyclic graph (DAG) to process data streams. It provides low-latency processing and guarantees data processing at least once, making it a good choice for mission-critical applications that require real-time processing.
Apache Ignite is an in-memory data grid that provides high-performance computing capabilities for distributed systems. It is designed to perform real-time data processing and distributed computing, making it a suitable choice for some use cases. However, it may not be the best choice for executing UDF jobs on Java.
Apache Airflow is a platform for programmatically creating, scheduling, and monitoring workflows. It is primarily used for data processing pipelines that involve batch processing and data ingestion. While it can support some real-time processing capabilities, it may not be the best choice for complex event processing.
Apache Kafka is a distributed streaming platform that provides real-time data streaming and processing capabilities. It is designed to handle high volumes of data streams and provides low-latency processing, making it a suitable choice for some use cases. However, it is not designed to perform complex event processing and may not be the best choice for executing UDF jobs on Java.
Therefore, the best answer for this question is A. Spark Structured Streaming or Apache Storm.