Apache Spark Write For Us – Apache Spark is a lightning-quick, unified analytics engine for large-scale data processing. Enables large-scale analytics using cluster machines. He is mainly dedicated to Big Data and Machine Learning.
What is Apache Spark?
For the curious, let’s go back to the creation of Apache Spark.
It all started in 2009. Spark was designed by Matei Zaharia, a Canadian computer scientist, during his Ph.D. at the University of California at Berkeley. At first, it was developed as a solution to speed up the processing of Hadoop systems.
Today it is a project of the Apache Foundation. Since 2009, more than 1,200 developers have contributed to the project. Some belong to well-known companies such as Intel, Facebook, IBM, and Netflix.
In 2014, Spark officially broke a new full-scale classification record. He won the Daytona Gray Sort contest by sorting 100TB of data in just 23 minutes. The earlier world record was 72 minutes, set by Yahoo using a 2,100-node Hadoop MapReduce cluster, while Spark only uses 206 nodes. This means it sorted the same data three times faster using ten times fewer machines.
Additionally, while there is no official petabyte classification competition, Spark goes further by classifying 1 PB of data, equivalent to 10 trillion records, across 190 machines in less than four hours.
This was one of the first petabyte-scale classifications done in a public cloud. Obtaining this reference marks a significant milestone for the Spark project. This shows that Spark is delivering on its promise to serve as the fastest and most scalable engine for processing data of all sizes, from GB to TB to PB.
What are the advantages of Spark?
As you may have guessed, Spark’s main advantage is its speed. Spark has been designed from the ground up with performance in mind. It does this by using in-memory computation and other optimizations.
Today, it is estimated to be 100 times faster than Hadoop for data processing, uses fewer resources, and has a simpler programming model.
The developers mainly highlight the speed of the product in terms of job execution compared to MapReduce.
Spark is also known for its ease of use and sophisticated analytics. In fact, it has easy-to-use APIs for working with large volumes of data.
Also, Spark has some versatility. It has a flow data processing software and a graphics processing system. You can also develop applications in Java, Scala, Python, and R in a simplified way, as well as perform SQL queries.
The analytics engine includes many high-level libraries that support SQL queries, streaming data, machine learning, and graph processing. These standard libraries allow developers to be more productive. They can easily be combined in the same application to create complex workflows.
Finally, Spark achieves high batch and streaming data performance thanks to a DAG scheduler, query optimizer, and physical execution engine.
The differences between Spark and MapReduce
Let’s quickly define what MapReduce is :
It is a programming model released by Google. MapReduce allows the manipulation of large amounts of data. To process them, it distributes them across a cluster of machines.
MapReduce has succeeded in companies with large data centers like Amazon or Facebook. Several frameworks have been developed to implement it. The best known is Hadoop, developed by the Apache Software Foundation.
Also, with MapReduce, iteration specification remains the responsibility of the programmer. Fault recovery management processes lead to poor performance. Spark uses a very different method. It consists of placing the data series in RAM memory and avoiding penalizing disk writes. In this way, Spark supports in-memory processing, which can increase the performance of Big Data analytics applications and, therefore, speed. It runs all data analysis operations in memory in real-time and only falls back to disks when memory is insufficient. Instead, Hadoop writes directly to disks after each operation and works in stages.
Who uses Spark?
Since its launch, companies in various industries have rapidly adopted the unified analytics engine. Internet giants like Netflix, Yahoo, and eBay have developed Spark on a large scale.
Spark has more than 1,200 partners, such as Intel, Facebook, IBM, etc.; now, it is the most critical community in the world of Big Data.
Allows you to unify all Spark Big Data applications. Spark is also suitable for real-time marketing campaigns, online product recommendations, and cybersecurity.
What are the different Spark tools?
- Spark SQL allows users to run queries in SQL languages to modify and transform data.
- Spark streaming offers its user streaming data processing. Use the data in real-time.
- Spark graph processes the information coming from the charts.
- Spark MLlib is a library containing all the classic learning algorithms and utilities, such as classification, regression, clustering, collaborative filtering, and dimension reduction.
The Apache Spark project is still alive and constantly evolving. Many companies around the world use it daily. It is an essential tool in the field of Big data and Data Science. If you are interested in this field, do not hesitate to make an appointment with our experts to learn more about Data Science and find the best training.
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