Hadoop is commonly used by an enterprise to analyze their data sets. It is because the Hadoop Architecture depends on the basic MapReduce programming model, which makes a scalable, robust, adaptive, and cost-efficient computing solution. The critical concern is to maximize the speed to handle massive datasets in terms of the waiting period between queries and period to execute the program.
As Big Data has taken the technology and business worlds by storm, there has been a tremendous increase in Big Data tools and platforms, particularly Apache Hadoop and Apache Spark. Today, let’s focus solely on Apache Spark and explore its functionality and scope in detail.
Apache Spark is not a modified version of Hadoop, as opposed to traditional beliefs, and does not rely on Hadoop because it manages its cluster. Hadoop is just one way to deploy Spark.
In two ways, Spark uses Hadoop – one is storage, and the other way is processing. Spark uses Hadoop only for storage purposes because it uses its cluster management computation.
What Exactly is Apache Spark?
Apache Spark was developed for high speed, simple to use, and detailed analysis in AMPLab of the University of California, Berkeley. Apache Spark is a real-time data processing system that can quickly perform processing tasks on vast data sets and can also spread data processing tasks across many devices, on its own, or in conjunction with others.
Importance of Apache Spark
Apache Spark is a distributed, open-source method of computing used for the workloads of Big Data. Spark uses in-memory caching and optimized query functions for fast queries of any data size. Spark is a quick and general large-scale data processing engine.
Apache Spark is a real-time computing open-source cluster platform and is one of the Apache Software Foundation’s most successful projects.
Spark has emerged as the industry leader for Big Data processing. Today, Spark’s implementation is being carried out by big players such as Amazon, eBay, and Yahoo! Several businesses run the Spark on clusters with thousands of nodes. There exists a huge career growth for Apache Spark certified professionals.
Top Features to Know about Apache Spark
- Speed: For large-scale data analysis, Spark runs up to 100 times faster than Hadoop MapReduce. Through controlled partitioning, Spark can achieve this speed. It handles data using partitions to process distributed data with minimal network traffic concurrently.
- Flexibility: Apache Spark supports several languages and enables developers to write Java, Scala, R, or Python applications. This tool is very rich in this field and features over 80 high-level operators.
- In-memory Process: We can increase the processing speed with in-memory processing. There is no need to fetch data every time from the disk as data is being cached and saves time. Spark has a DAG execution engine that allows for in-memory calculations and high-speed acyclic data flow.
- Real-Time Stream Processing: Spark has a system for real-time stream processing. Previously, the issue with Hadoop MapReduce was that data that is present, but not real-time, can be managed and processed. But we can solve the problem with Spark Streaming.
- Hadoop-Compatible: Spark not only works independently but can also operate on Hadoop. Not only that, but Spark is also compatible with both the versions of the Hadoop ecosystem.
- Active and Growing Community: From over 300 companies, developers contributed to design and build Apache Spark. Since 2009, Spark has been successfully provided by more than 1200 developers to make Spark today! Spark is supported by an active developer community that works continuously to enhance its functionality and efficiency. You can use mailing lists for every query, and you can attend Spark meet-up groups and conferences to reach the Spark community.
Why Earn an Apache Spark Certification?
Any course certification will differentiate you from the competitive crowd. And it is well known that certification is a validation of your competence and a boost in confidence in the work. Certified Apache Spark offers the freshers’ resume an immense increase. Individuals whose certifications are accurate are always chosen over those who do not. In addition to licensing you as an Apache Spark developer, you will benefit from an Apache Spark certification. A certified Apache Spark developer earns an even better salary than a novice developer.
How to become Apache Spark Certified?
Apache Spark is a popular platform for structured and unstructured processing data. You should take a course on the same aspect to become a Certified Apache Spark Developer.
Too many certified courses are available on the market. You can review which is the right one for you and why. Candidates should be highly dedicated and focused on the course concepts that are discussed.
The statistics and data analysis course demonstrate the fundamentals of working with Spark and allows you with the framework to delve deeper into Spark. You can learn about the architecture and programming model of Spark, including widely used APIs. You can write and debug simple Spark applications after completion of this course. The Spark certification course will also demonstrate how to use the Spark platform user interface (UI), how common coding errors can be identified, and how mistakes can be avoided proactively. Spark Core and Spark SQL are the primary focus areas in this course.
The course covers advanced undergraduate-level study material. It needs the background and experience of programming and practicing with Python. If you are interested in stepping into Big Data career, choose the best-reputed training course providers to excel in your field.
Apache Spark – Future Outlook
Spark is a very flexible big-data platform with impressive features. Since it is an open-source platform, it continually develops and evolves, introducing new functionality and functions. There will be a growing demand in the Big Data industry for Spark developers in the coming years. Apache Spark is a more intelligent and active platform. The need and requirement of Spark developers are increased, along with the Hadoop developers.
Apache Spark is also used for data processing specifications in the big data industry. Apache Spark plays a leading role in the next generation of Business Intelligence applications. Therefore, Spark’s practical training program and workshops are an excellent choice to make a brilliant contribution to the big data industry.