Apache spark is an open-source analytics engine that is gaining popularity among developers due to its speed, scalability and developer-friendly API. It can be used to process large amounts of data at once and supports multiple workloads, including batch processing, streaming and machine learning. Its thriving community provides valuable shared knowledge and troubleshooting assistance. It also integrates with different cluster managers and can be run on Kubernetes, enabling it to be easily integrated into modern data stacks.
Spark’s core is a distributed execution engine that builds Resilient Distributed Datasets (RDDs) and executes them across a cluster of worker nodes. Its architecture is based on a master/slave model with the driver program running on the master node and executors or slaves running on the worker nodes. RDDs are created through transformations that are applied to data sets and metadata is recorded to build a Directed Acyclic Graph (DAG). The DAG is then submitted to the Spark Scheduler, which distributes tasks over the cluster.
The core has a unified API that allows applications to write programs in languages of their choice, including Java, Scala, Python and R. It has libraries for data handling, SQL, machine learning and graph processing. The most popular of these are MLlib for machine learning and GraphX for graph-parallel computations.
The streaming component is a key feature of apache spark that has gained significant traction for real-time analytics needs. It has the ability to process data in near real-time, which can be useful for businesses such as financial institutions who want to track market trends and detect anomalies. It also has low-latency Continuous Processing mode, which reduces the maximum latency to a few milliseconds.
What is Apache Spark?
Apache spark is an open-source analytics engine that is gaining popularity among developers due to its speed, scalability and developer-friendly API. It can be used to process large amounts of data at once and supports multiple workloads, including batch processing, streaming and machine learning. Its thriving community provides valuable shared knowledge and troubleshooting assistance. It also integrates with different cluster managers and can be run on Kubernetes, enabling it to be easily integrated into modern data stacks.
Spark’s core is a distributed execution engine that builds Resilient Distributed Datasets (RDDs) and executes them across a cluster of worker nodes. Its architecture is based on a master/slave model with the driver program running on the master node and executors or slaves running on the worker nodes. RDDs are created through transformations that are applied to data sets and metadata is recorded to build a Directed Acyclic Graph (DAG). The DAG is then submitted to the Spark Scheduler, which distributes tasks over the cluster.
The core has a unified API that allows applications to write programs in languages of their choice, including Java, Scala, Python and R. It has libraries for data handling, SQL, machine learning and graph processing. The most popular of these are MLlib for machine learning and GraphX for graph-parallel computations.
The streaming component is a key feature of apache spark that has gained significant traction for real-time analytics needs. It has the ability to process data in near real-time, which can be useful for businesses such as financial institutions who want to track market trends and detect anomalies. It also has low-latency Continuous Processing mode, which reduces the maximum latency to a few milliseconds.