Spark is a programming methodology that is made up of analytical computing, which is quick and fast. The foundation of Spark is created on Hadoop. The development of spark occurs in three different ways. The three are very important to understand, and they are discussed below:
Standalone – This is a popular concept that comes into existence when a person has to work alone on the corresponding software. Standalone deployment considers the fact that spark occupies the highest position of the Hadoop Distributed File System (HDFS).
Hadoop Yarn – It is a process in Spark that does not need prior installation. Through it, components can run in different stacks.
Spark in MapReduce – It is used in the launching of spark jobs. It allows the user to use it if there is any need for administrative access.
Components of Spark
Spark provides an interactive shell, which is a tool of high importance in the analysis of interactive data. Here are the most popular components of Spark:
Apache Spark Core – It can be described as a complete platform that supports the building of a whole function. It is used in providing referencing datasets and In-Memory Computing with external storage systems.
Spark SQL – Spark SQL introduces a new level of data abstraction, which is popularly known as SchemaRDD, which is used in the provision of support for semi-structured and structured data.
MLlib – This Spark component is a machine learning framework that is composed of a distributed memory-based architecture. MLlib is known to be faster than the Hadoop disk-based version.
Spark streaming – This component can leverage spark core. It has a scheduling capability, which helps to efficiently perform streaming analytics. It supports data in the form of mini-batches and also helps Resilient Distributed Datasets transformations on the corresponding mini-batches of the specific data sets.
GraphX – this is a component with a distributed graph-processing framework that contains the provision of an API, which explains graph computation used in modeling a set of user-defined graphs through API abstraction.
Main features of Spark
These are the main features of Apache Spark:
Flexibility - Spark supports many languages. It is more flexible when compared to other models.
Fast processing – It helps in the processing of big data within a short period. Big data contains features such as variety, velocity, veracity, and volume, and that is why they must be processed at high speed. Spark saves a lot of time in both writing and reading.
In-memory computing – Spark stores memory in RAM. This ensures that there is faster access and also increases its analytics speed.
Compatibility with Hadoop – It can work independently as well as work with Hadoop.
Immediate processing - Through spark, there are immediate results
Better analytics – When compared to other languages, spark provides better analytics. This is why it is highly preferred.
Advantages of spark
Enhancement of enterprise adoption – Spark is becoming very popular especially among companies. This is because of its ability to handle big data technology.
Investment in big data
– Spark is very helpful for experts
who have enough information about Hadoop.
Increased data access – Spark has opened many doors for people to explore data. Through it, many companies can now sort big data issues without having to spend a lot on human resource and without taking much time on the data. This is why spark is in high demand among data engineers as well as data scientists because through it, one can store a lot of data.
Ease to use – Spark is made of easy-to-use APIs in operating large databases.
Disadvantages of Spark
1. No automatic optimization - you need to optimize your Spark code manually since it does not have an automatic one.
2. Spark does not come with its file management system. It solely depends on other platforms such as Hadoop.
3. It is not the best in a multi-user environment.