Apache Spark is a distributed, in-memory, and disk-based optimized open-source framework that does real-time analytics using Resilient Distributed Data (RDD) sets. It includes a streaming library, and a rich set of programming interfaces to make data processing and transformation easier. It is not a database like an RDBMS or NoSQL database, but a data processing engine. Apache Spark is 100% open-source software as part of Apache Software Foundation, which is a vendor-independent platform. That’s why it is free to use for personal or commercial purposes.
The Spark engine runs in a variety of environments, from cloud services to Hadoop as a standalone instance or Mesos clusters. It is used to perform ETL (Extract, Transform, and Load), interactive queries (SQL), advanced analytics (e.g. machine learning), and streaming over large datasets in a wide range of data stores like HDFS (Hadoop Distributed File System), Cassandra, HBase, and Amazon S3. It supports a variety of popular development languages including Java, Python, Scala, and R.
Furthermore, it has an advanced execution engine supporting cyclic data flow with in-memory computing functionalities. It is capable of accessing diverse data sources including HDFS, HBase, and Cassandra among others.
Types of Applications in Apache Spark
Two styles of application benefit significantly from the Spark processing model.
- Iterative Algorithms
Here, a function is applied to the dataset repeatedly until an exit condition is met.
- Interactive analysis
Here, a user issues a series of ad hoc exploratory queries to Spark datasets.
Components of Spark
- General Execution: Spark Core
It is the general execution for the Spark framework, which sits at the base of the Spark stack. It is responsible for managing the memory, monitoring the jobs, maintaining the Spark, scheduling the job, and interacting with the Storage system.
- Streaming Analytics: Spark Streaming
This layer of Spark enables powerful interactive and analytical applications across both streaming and historical data while inheriting Spark’s ease of use and fault tolerance characteristics. It readily integrates with a wide variety of popular data sources, including HDFS, Flume, Kafka, and Twitter.
- Graph Computation: GraphX
GraphX is a graph computation engine built on top of Spark that enables users to interactively build, transform, and reason about graph-structured data at scale.
- Structured Data: Spark SQL (Structured Query Language)
Many data scientists, analysts, and general business intelligence users rely on interactive SQL queries for exploring data. Spark SQL is an engine for Hive data that enables unmodified Hadoop Hive queries to run up to 100x faster on existing deployments and data. It also provides powerful integration with the rest of the Spark ecosystem (e.g., integrating SQL query processing with machine learning).
- Machine Learning: ML lib
Machine learning has quickly emerged as a critical piece in mining Big Data for actionable insights. Built on top of Spark, ML lib is a scalable machine learning library that delivers both high-quality algorithms (e.g., multiple iterations to increase accuracy) and blazing speed (up to 100x faster than MapReduce). The library is usable in Java, Scala, and Python as part of Spark applications, so that you can include it in complete workflows.
Key features of Spark
Spark allows Integration with Hadoop and files included in HDFS.
- It has an independent language (Scala) interpreter and hence comes with an interactive language shell.
- Spark consists of RDDs (Resilient Distributed Datasets), which can be cached across computing nodes in a cluster.
- It supports multiple analytic tools that are used for interactive query analysis, real-time analysis, and graph processing.
- Allows integration with Hadoop and files included in HDFS.
- It provides lightning-fast processing speed for certain Use cases
When it comes to Big Data processing, speed always matters, and Spark runs on Hadoop clusters way faster than others. Spark makes this possible by reducing the number of reading/write operations to the disc. It stores this intermediate processing data in memory.
- Support for real-time querying
In addition to simple “map” and “reduce” operations, Spark supports SQL queries, streaming data, and complex analytics such as machine learning and graph algorithms. This allows users to combine all these capabilities into a single workflow. Spark supports multiple analytic tools that are used for interactive query analysis, real-time analysis, and graph processing.
- Real-time stream processing
Spark can handle real-time streaming data. MapReduce primarily handles and processes previously stored data. Spark does this in the best way possible.
Language Supported by Apache Spark
Apache Spark supports multiple programming languages. Some native languages are supported below.
- Java
- Scala
- Python
- R Language
Apache Spark in the real world
Apache Spark is one of the most popular in-memory data processing engines worldwide. Some important customers of Apache Spark are listed below:
- Software Companies (Uber/Lyft/Snapchat/Facebook/TikTok)
- Product-based companies (Apple/Samsung/LG)
- Telecommunication (AT&T, Verizon, T-Mobile)
- Media (Disney/HBO/Comcast)
- Insurance (Geico/Liberty Mutual)
- Banks (Wells Fargo/Bank of America)
- Healthcare (United Health Care, Aetna, Cigna)
Conclusion
In this blog post, we read about Apache Spark, the types of applications that can run on it, and the key features of Apache Spark. We also read about the components of Spark, the programming language supported by Apache Spark, and where Apache Spark is used in the real world.
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References
[1] Apache Spark official Documentation
