Compared to predecessors such as Hadoop or competitors such as PySpark, Apache Spark is characterized by impressively fast performance, one of the most important aspects when querying, processing and analyzing large amounts of data. As a big data and In Memory analytics framework, Spark offers numerous benefits for data analytics, machine learning, data streaming, and SQL.
What is Apache Spark?
Apache Spark, Berkeley’s data analytics framework, is currently considered one of the most popular big data platforms in the world and is a Top Level Project of the Apache Software Foundation . This analytics engine is used to simultaneously process large amounts of data and analytics applications on distributed computing clusters. Spark was developed to meet the needs of big data in terms of calculation speed, extensibility, and scalability.
To do this, it offers integrated modules that provide many advantages for cloud computing , machine learning , AI applications, as well as streaming and graphical data. The engine is used even by large providers like Netflix, Yahoo, and eBay due to its performance and scalability.
What makes Apache Spark important?
Apache Spark is a significantly faster and more powerful engine than Apache Hadoop or Apache Hive . Processes jobs 100x faster compared to Hadoop’s MapReduce when processing occurs in memory and 10x faster when processing occurs on disk. Spark offers businesses such as custom software development company and other businesses performance that reduces costs and increases efficiency .
However, the most interesting thing about Spark is its flexibility. The engine can not only run independently, but also on YARN-controlled Hadoop clusters. Additionally, it allows developers to write applications for Spark in various programming languages . Not only SQL , but also Python, Scala, R or Java.
Other special features of Spark: It does not have to be configured on the Hadoop file system, but can also work with other data platforms such as AWS S3, Apache Cassandra or HBase . Additionally, if the data source is specified, it processes both batch and Hadoop processes, as well as stream data and different workloads with almost identical code. With an interactive query process, real-time current and historical data, including analytics, can be distributed across multiple layers on hard disk and memory and processed in parallel.
How does Spark work?
The way Spark works is based on the hierarchical primary-secondary principle (also known as the master-slave principle . For this, the Spark controller acts as a master node managed by the cluster administrator. This, in turn, controls the slave nodes and forwards data analysis to the client. Executions and queries are distributed and monitored through the SparkContext created by the Spark controller, which cooperates with cluster managers such as those offered by Spark, YARN, Hadoop or Kubernetes. This also creates Resilient Distributed Datasets (RDD).
Spark determines which resources are used to query or store data, or where the queried data is sent. By dynamically processing data directly in the working memory of server clusters , the engine reduces latency and delivers very fast performance. In addition, it offers parallel processing steps and the use of virtual and physical memory.
Apache Spark also processes data from various data stores . These include the Hadoop Distributed File System (HDFS) and relational data stores such as Hive or NoSQL databases. Additionally, there is in-memory or disk-based processing that improves performance, depending on the size of the data sets.
RDDs as distributed, fault-tolerant data sets
Resilient Distributed Datasets are an important foundation of Apache Spark for processing structured or unstructured data. These are fault-tolerant data aggregations that Spark distributes to clusters of servers through clustering and processes in parallel or moves to data warehouses. It is also possible to pass the data to other analysis models. RDD data sets are subdivided into logical partitions that can be retrieved, recreated or edited, and computed using transformations and actions.
DataFrames and Datasets
Other types of data that Spark processes are called DataFrames and Datasets. DataFrames are APIs like data tables structured in rows and columns. Datasets, on the other hand, are an extension of DataFrames for an object-oriented user interface for programming. Especially in relation to the Machine Learning Library (MLlib) , DataFrames play an important role as an API with a uniform structure across all programming languages.
What language does Spark use?
Spark was developed in Scala, which is also the core language of the Spark Core engine . Additionally, Spark offers Java and Python connectors. With Spark and in combination with other languages, Python offers many advantages for effective data analysis, especially for data science and information engineering . Spark also supports high-level APIs for the R data science programming language, so it can be applied to large data sets and used for machine learning.
The importance of Spark
Spark’s diverse libraries and data stores, APIs supporting many programming languages, and efficient in-memory processing make it suitable for a wide range of industries. When it comes to processing, querying, or computing large amounts of complex data , Spark’s high speed, scalability, and flexibility offer great performance, especially for large enterprise big data applications. Therefore, Spark is popular in digital advertising and e-commerce, in financial companies for the evaluation of financial data or for investment models, as well as for simulations, artificial intelligence and software development services forecasting.
Key highlights of Spark include:
- processing, integration and compilation of data sets from a wide range of sources and applications
- Big data interactive query and analysis
- analysis of data streams in real time
- machine learning and artificial intelligence
- large ETL processes
Important components and libraries of Spark architecture
As the foundation of the entire Spark system, Spark Core provides the basic functions of Spark and manages task distribution, data abstraction, development planning, and input and output processes. Spark Core uses RDDs distributed across multiple clusters of servers and computers as its data structure. It also forms the basis of Spark SQL, libraries, Spark Streaming, and all other important individual components.
This is a particularly frequently used library with which you can use RDDs as SQL queries. To do this, Spark SQL generates temporary DataFrames tables. With Spark SQL you can access various data sources, work with structured data, and use data queries through SQL and other DataFrame APIs. Additionally, Spark SQL allows you to integrate the HiveQL database system language to access a data warehouse managed with Hive.
With this high-level API functionality, highly scalable and error-resistant data flow functions can be used, and continuous data streams can be processed or displayed in real time. Spark generates individual packets for data actions from data streams. In this way, the machine learning models taught can also be used in data streams.
MLIB Machine Learning Library
This scalable Spark library provides machine learning code to apply advanced statistical processing on server clusters or to develop analytical applications. Common learning algorithms such as clustering, regression, classification and recommendation, workflow services, model evaluation, distributed linear statistics, and algebra or feature transformations are included. With MLlib, machine learning can be efficiently scaled and simplified.
Spark’s GraphX API is used for parallel computing of graphs and combines ETL, interactive graph processing, and exploratory analysis.
How was Apache Spark created?
Apache Spark was developed in 2009 at the University of California, Berkeley, as part of AMPlabs. Spark has been freely available to the public as an open source license since 2010. Continuation and optimization of Spark began in 2013 by the Apache Software Foundation. The popularity and potential of this big data framework led the ASF to declare Spark a “Top Level Project” in February 2014. Spark version 1.0 was then released in May 2014. Currently (April 2023), Spark is found in version 3.3.2.
The goal of Spark was to speed up queries and tasks on Hadoop systems. With Spark Core as a base, it allowed distributed task dispatch, input-output functionalities, as well as in-memory processing to surpass MapReduce, common in the Hadoop framework until then.
What advantages does Apache Spark offer?
Spark offers the following advantages for quickly querying and processing large amounts of data:
- Speed – Workloads can be processed and executed up to 100 times faster compared to Hadoop’s MapReduce. Additional performance benefits come from support for batch and streaming data processing, directed acyclic graphs, a physical execution engine, and query optimization.
- Scalability : Thanks to In Memory processing of data distributed in clusters, Spark offers flexible scalability of resources on demand.
- Consistency : Spark serves as a big data framework that unifies various functions and libraries into a single application. These include SQL queries, DataFrames, Spark Streaming, MLlib for machine learning, and GraphX for graph processing. In addition, it has HiveQL integration.
- Ease of use : With easy-to-use API interfaces for a wide variety of data sources, as well as more than 80 common operators for application development, Spark bundles numerous application possibilities into a single framework. Also especially practical is the interactive use of Scala, Python, R or SQL shells to write services.
- Open Source Framework – Through its open source approach, Spark offers an active, global community of experts who continually develop Spark, close security gaps, and ensure rapid innovation.
- Increased efficiency and reduced costs : Since Spark can also be used without physical high-end server structures, this platform for big data analytics offers capabilities that reduce costs and improve performance, especially for computational machine learning algorithms. intensive and complex parallel data processing.
What disadvantages does Apache Spark have?
For all its strengths, Spark also has some drawbacks. The first and most important is the fact that Spark does not have a built-in memory engine and therefore relies on many distributed elements. In-memory processing also requires a lot of RAM, which can impact performance if resources are insufficient. Additionally, using Spark requires a longer learning period to understand background processes when installing a standalone Apache server or other cloud structures.