ASSEMBLE: Spark
A distributed clustering platform
The ASSEMBLE Protocol is a blockchain-based global point integration platform that exploits ASM utility tokens, whilst establishing a business ecosystem that can integrate, utilize existing points and miles with point providers, consumers and retailers.
Welcome back, readers! Today, we’re going to introduce you to Spark Technology, which is a distributed clustering platform, designed to perform universal and fast big data computation tasks. ASSEMBLE Protocol uses Spark to build Big Data processing platforms. For example, the ASM Big Data processing platform, which can be used for more accurate marketing practices. It investigates the behavioral patterns of consumers who exhibit a particular type of consumption.
What is SPARK?
Spark is a distributed clustering platform designed to perform universal and fast big data computation tasks.
Spark Features
Fast Processing Based on In-Memory
Spark has in-memory-based processing, with disks 10x faster and memory operations 100x faster than MapReduce operations. MapReduce writes the intermediate results of the task on a disk, so I/O limits the speed of the task. However, Spark stores the intermediate results in its memory, making it easier to process repetitive tasks.
Provides a Variety of Components
Spark supports stream processing within a single system using Spark streaming. It also supports SQL processing using Spark SQL, Machine learning processing using MLib, and GraphX processing. You can implement a variety of applications without the need for installing additional software.
Support for Various Languages
Spark is convenient for developers because it supports various languages such as Java, Scalar, Python, and R. However, since each language has a different processing speed, I recommend using Scalar for performance.
ASSEMBLE × Spark
ASSEMBLE uses Spark to build a big data processing platform. For example, ASM Big Data processing platform can be used for more accurate marketing. It investigates the behavioral patterns of consumers who make purchases in a particular type of way.
Specifically, Spark is an open-source cluster computing environment similar to Hadoop, but there are some differences between the two. Because of these differences, Spark performance is even better under some workloads. In particular, Spark can activate datasets distributed in memory, providing mutual queries as well as optimizing repetitive workloads.
