Apache Spark a.k.a. Spark is an open-source data processing engine which is mainly used to handle and analyze large datasets. Apache Spark was originated to improve the efficiency of Apache Hadoop. Ideally an engine to process data, Spark can also be leveraged as a database where we can use it as a table and query them as per our needs.
Components of Apache Spark ecosystem
Spark Core – Spark Core can be termed as the base of the whole project on which Spark functions. It performs the core functions such as: it holds the components scheduling of tasks, fault recovery, memory management and storage procedures.
Spark SQL – a Spark which functions as a distributed SQL query engine. We can execute SQL queries along with Spark functions. It was initially started as Apache Hive to run on top of Spark; now it is integrated with Spark stack. Spark SQL comes as a replacement of Apache Hive which can overcome the limitations of the latter. Developers can: import relational data from Hive tables, run SQL queries on imported data.
Spark streaming – This extension of Spark helps in processing live streaming of data.
Spark MLlib – Apache Spark MLlib is a machine learning library which helps to perform machine learning algorithms via Spark. It offers a high-level API that can be built on top of DataFrames to construct ML pipelines. It includes various algorithms like classification, regression, clustering, and even gradient descent optimization technique.
SparkR – As the name suggests, it is an R package which can be used via Spark. ‘SparkSession’ is the entry-point for SparkR. This helps to connect Spark clusters with R programs. Similar to MLlib, SparkR is also equally capable of executing ML algorithms.
Spark GraphX – The Spark API is termed as Spark GraphX. It can unite the ETL process and graphical computation in a single syatem. This API helps users to view the data both as graphs and as collections, without duplicating or moving the data. GraphX is faster as compared to other graph-processing systems.
Benefits of Spark
Being the largest open source project till date, Apache Spark has has the room for may improvements. Some of the visible advantages which Spark provides us are discussed below.
It is suitable for handling Big Data.
Today in many of the Hadoop distributions, Spark is included. This is mainly because it can handle the nuances of MapReduce.
It can run batch processing jobs about 10 to 100 times faster then MapReduce.
Spark requires less time as it dos not read and write all intermediate data to disks, but uses RAM to cache partial or the total results across nodes, whereas MapReduce requires larger disk space, thus requires more time.
Spark codes can be used in batch processing.
It can run both independently as well as with Hadoop integration.
Apache Spark supports numerous languages such as Scala, R, SQL, Python. Another reason why it is dynamic in nature.
Apache Spark has come a long way so far. It can run distributed SQL, run ML algorithms, create data pipelines, work with graphs, etc. It can easily integrate with popular libraries like TensorFlow, PyTorch, Scikit-Learn. Due to its ease of use and speed, Spark has become immensely popular these days. Though security is one aspect it still needs to look into.
