What is Apache Spark?
Apache Spark is an open-source software that uses distributed computing to process big data faster. Spark uses in-memory caching and optimized query execution to help enterprises enhance their Java applications.
What does Apache Spark do?
Apache Spark executes all the necessary components of big data processing, including analysis, in-memory processing, and real time processing. Additionally, all these processes can be executed by the software in a single action.
- In-memory processing
The software can store data on the computing system to be used again. This data storage increases processing speeds because the software can help the system create shortcuts to frequently run tasks and learn patterns from previously executed operations.
- Real-time processing
Real-time processing is another significant function of Apache Spark, which is executed by the Spark Streaming Component. An extension of the core Spark API, Spark Streaming enables the processing of real-time data from various sources and the distribution of this data to file systems, databases, and live dashboards.
The software can import data and process it at production speed. Alternative data processing methods have a delay between when information is received and processed. Apache Spark provides applications with the most relevant and up-to-date information to create shortcuts and learn patterns to enhance performance.
Apache Spark also contains MLlib, Spark’s scalable machine learning library, with APIs in Java, Scala, Python, and R. The tool provides a library of tasks for the application to call on to shortcut the processing.
- Data Analytics
Apache Spark contains tools to gather data from applications and analyze information. Developers can manage their data using the Spark SQL component, which can manage queries. The Spark SQL component is a distributed framework for structured data processing so Spark can perform extra optimization. It uses the same execution engine while computing an output. Data can be presented in a digestible manner to provide insights throughout the business.
Apache Spark also contains GraphX, a library to help developers analyze data presented in graph formats. GraphX is a graph processing framework for big data.
The software utilizes a distributed framework, meaning information processing occurs across multiple systems. When the software receives large quantities of data, the software distributes the data processing functions across multiple servers. This allows the processing to occur more efficiently, as workloads can be distributed evenly throughout a system, rather than relying on an individual computing resource to execute the entire process.
All the necessary functions can be run at once, whereas alternative data processing tools run as a series of individual processes. Distributed computing enables each process to be delegated to a different server and run at the same time.
What are the benefits of Apache Spark?
Apache Spark augments data processing in Java applications by utilizing the distributed computing framework and by incorporating added processing capabilities. This has led to benefits such as increased scalability, fault tolerance, adaptability, efficiency, and comprehensive performance enhancements.
Apache Spark can manage large quantities of data by delegating clusters of data to its various systems. This means that the application can be scaled to execute data processing for both small and large tasks. Apache Spark can also be integrated into cloud applications.
- Fault Tolerance
Distributed computing utilizes multiple systems. While these systems are connected and can communicate to execute the processing, they are not reliant on each other to function. If one system fails, the application can still continue to run.
The software can understand languages beyond Java, such as data languages like Python, Scala and R. Depending on the composition of your enterprise’s applications, this capability may be useful for application development.
By delegating tasks to multiple systems, Apache Spark can run all the components of data processing simultaneously. This allows processing to occur efficiently, at production speed.
- Performance Enhancements
In-memory processing allows Apache Spark to learn patterns and create shortcuts in data processing. When the application has already processed data, the information can be easily called on from the memory cache to use again.
What are the differences between Apache Spark vs Apache Hadoop?
While both Apache Spark and Apache Hadoop are designed for data processing, they differ in their overall capabilities. The two can be integrated together to perform all types of data processing.
- Apache Spark incorporates in-memory processing, while Apache Hadoop stores memory on disks. Apache Hadoop cannot quickly reference data in memory, as disk memory must be processed again.
- Apache Spark can process data at production speed, whereas Apache Hadoop must receive all the necessary data to begin processing. This makes Hadoop better suited for batch processing.
- Apache Hadoop has been around longer, so the open-source community has developed more tools for the program. However, MLlib, GraphX, Streaming and SQL are all unique to Apache Spark.
How does Azul help companies run Apache Spark?
Azul Platform Prime augments Java applications using Apache Spark by providing a better runtime environment for the software. Azul Platform Prime is the world’s only cloud-native JVM and is designed to boost speed and scalability of Java deployments and reduce cloud costs by up to 50%. This allows applications integrated with Apache Spark to use more in-memory data and deliver higher throughput with consistent response times. Azul Platform Prime is fully compatible with Spark, allowing enterprises to create the most value from their application enhancing investments.