Spark is based on the Hadoop distributed file system but does not use Hadoop MapReduce, but its own framework for parallel data processing, which starts with the insertion of data into persistent distributed data records (RDD) and distributed memory abstractions, which computes large Spark clusters in a way that fault-tolerant. Because data is stored in memory (and on disk if necessary), Apache Spark can be much faster and more flexible than the Hadoop MapReduce task for certain applications described below. The Apache Spark project also increases flexibility by offering APIs that developers can use to write queries in Java, Python, or Scala.
Apache Spark main features
What’s good about Apache Spark?
Spark is suitable for most applications that require fast processing – repetitive processing, interactive processing, streaming, high-performance graphics, and batch calculations, as well as a combination of these historically different workloads.
By storing data in memory for better access time, you can improve the performance of repetitive algorithms and retrieve data.
Common examples of Apache Spark are:
- Real-time queries – Spark superfast queries can be run on data in Hive, HDFS, HBase, and Amazon S3.
- Stream event processing – signaling, aggregation and analysis of event-intensive applications such as algorithmic trading, fraud detection, location-based service monitoring, sensor data, social media and input and click processing.
- Iterative Algorithm – Spark is ideal for accelerating repetitive processing needed for iterative algorithms such as grouping and classification.
- Machine Learning – ML Lib is based on Spark and is a scalable machine learning library that complements Spark’s processing speed with high-quality algorithms.
- Big Data Graphics Spark also includes a distributed graphics system called GraphX. Social networking targeted advertising, and geolocation is just a few of the many applications that require great graphics. They can be counted intensively without Spark’s power.
- Faster batch processing In batch processing, large data records are used as input, are processed and large outputs are written. While Hadoop MapReduce processes batch processing, Spark can process batch tasks faster. By reducing the number of records and metrics on the disk, Spark can perform batch tasks 10 to 100 times faster than the Hadoop MapReduce engine.
- Uniform Big Data Analysis Once stored in memory, data can be shared between iterative processing, interactive processing, streaming, graphics, and batch calculations. This aggregation offers a number of interesting opportunities for new and innovative big data applications that address previously shared workloads such as real-time and historical data analysis, as well as an exploration of top-down and bottom-up data. Large data graphics are also a great addition to machine learning and data extraction applications. Unified Big Data Analytics also has the added benefit of eliminating the need to create, manage and maintain fewer separate processing systems for different computing needs.
Why Apache Spark a Service from Cloudaeon?
Cloudaeon understands the value of the Apache Spark project in big data analytics and aims to give Hadoop Spark users technical and business performance. Cloudaeon offers Spark as a service to help companies launch Spark on AWS. With this service, we have a platform that users can use to launch and use the Spark cluster and start polling in minutes. Spark as a Service makes it easy to process and request data stored on Hive, HDFS, HBase, and Amazon S3.
In the future, it is important to have access to a number of data sources and only combine their data with Spark. For example, various SQL, NoSQL and data sinks can be accessed through the interface, and their data can be combined and loaded into each of them (the latter is currently being developed). The QDS Query Editor and Visual Query Maker offer data developers and scientists an easy way to access Spark data without special coding knowledge.