Apache Spark, a unified analytics engine designed for large-scale data processing environments, provides an open-source interface to process large data sets effectively. The technology allows data teams to have quick and efficient access to analyze structured, semi-structured, and unstructured data; with a variety of interfaces to explore, collaborate, and work in a centralized environment.
Developed by Apache Software Foundation, Apache Spark is popular amongst businesses that need to execute queries quickly against big data workloads for five key reasons:
- SPEED - Apache Spark is fast as it uses the DAG Scheduler and other elements for both batch and streaming data.
- USABILITY - Apache Spark enables developers to develop parallel applications using interactive technologies like Scala, Python, SQL shells, and R.
- AVAILABILITY - Apache Spark can run on Hadoop, Kubernetes, or any other cloud and enable access to diverse data sources.
- GENERALITY - Apache Spark is backed by solid libraries, including SQL and DataFrames, GraphX, Spark Streaming.
- INTEGRATION - Apache Spark system comprises closely integrated components such as languages,SQL components, MLlib components, and graph computations that seamlessly combine to support a range of use cases.
What is an Apache Spark Ecosystem?
Various Apache Spark components combinedly make an apache spark ecosystem. Let’s look at the core components one by one.
Language components
Apache Spark comprises various languages like Scala, Python, R, Java.
Scala
Spark framework is developed in Scala language. The Scala for Spark provides exclusive access to all advanced features that are available in Spark only.
Python
Python has extended excellent libraries that can be used for data analysis.
R language
R language is used for machine language and statistical analysis. Developers use R language with Spark to enhance productivity and processing in a single machine.
Java
Developers prefer this language who are from Java + Hadoop background.
Spark Components
Apache Spark ecosystem is still under development phase. Some of the apache spark components that empower the current ecosystem are:
Apache Spark Core
Apache Spark Core is the foundation of parallel and distributed processing. It is responsible for all fundamental input/output functions. It monitors and schedules various jobs over the cluster. Apache Spark has in-memory computational capabilities using a unique data structure- Resilient Distributed datasets (RDD).
Apache Spark SQL
Apache Spark SQL is used for structured data analysis, especially in voluminous data. The information obtained from Apache Spark SQL is used to perform more optimization. Apache Spark SQL is compatible with Hive data. It can commonly access different data sources.
Apache Spark Streaming
This lightweight component helps developers perform batch processing, data streaming, real-time data processing, and scheduling capacity facilities. It is implemented across various sectors like cybersecurity, IoT platforms, diagnostic, etc., and is also used in online advertisements.
Apache Spark MLlib
Apache Spark is a scalable machine learning library. It provides both high-quality algorithms and lightning speed. This library supports not only machine learning but also all APIs like Java, Scala, Python. It is an essential component in Big data system mining. MLlib is compatible with various languages and scalable. MLlib library has the following ML algorithms:
Apache Spark GraphX
Apache Spark GraphX is a graph computation engine. It is used in building, transforming data at scale. With the GraphX clustering, parallel execution of Graph and GraphX is possible.
Apache Spark R
If you want to handle processing a single machine, R language cannot beat any other technology. Apache Spark R is best as it combines features of R language and Spark.
Thus, Apache Spark components make the robust ecosystem that is efficiently used for all types of data processing and leads progress.
Who Uses Spark?
Key users of Spark application are data engineers, data scientists, and application developers. Spark helps data scientists by supporting the entire data science workflow, from data access and integration to machine learning and visualization using the language of choice—which is typically Python. It provides a growing library of machine-learning algorithms through its (MLlib). To data engineers, it provides the ability to abstract data access complexity. Spark also enables near-real-time solutions at a web scale, such as pipelined machine-learning workflows.
It helps application developers through its support of widely used analytics application languages such as Python and Scala. Spark helps eliminate programming complexity by providing libraries such as MLlib, and it can simplify development operations (DevOps). Spark also makes embedding advanced analytics into applications easy.
Thus, Apache Spark components make the robust ecosystem that is efficiently used for all types of data processing and leads progress.