Apache Spark continues to gain momentum in today’s big data analytics landscape. Although a relatively newer entry to the realm, Apache Spark has earned immense popularity among enterprises and data Analysts within a short period. Apache Spark is one of the most active open source big data projects. The reason behind is its versatility and diversity of use.
Some of the key features that make Spark a strong big data engine are:
- Equipped with MLlib library for machine learning algorithms
- Good for Java and Scala developers as Spark imitates Scala’s collection API and functional style
- Single library can perform SQL, graph analytics and streaming.
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Spark is admired for many reasons by developers and analysts to quickly query, analyze and transform data at scale. In simple words, you can call Spark a competent alternative to Hadoop, with its characteristics, strengths and limitations. Spark runs in-memory to process data with speed and sophistication than the other complement approaches like Hadoop MapReduce. It can handle several terabytes of data at one time and perform efficient processing.
Spark versus Hadoop MapReduce
Despite having the similar functionality, there is much difference between these two technologies. Let’s have a quick look into this comparative analysis:
|Processing Location||In-memory||Persists on disk after map and reduce functions|
|Ease of use||Easy as based on Scala||Difficult as based on Java|
|Speed||Up to 100 times faster than Hadoop MapReduce||Slower|
|Computation||Iterative computation possible||single computation possible|
|Task Scheduling||Schedules tasks itself||Requires external schedulers.|
One of the excellent benefits of using Spark is that it is often used in Hadoop’s data storage model, i.e. HDFS and can well integrate with other big data frameworks like HBase, MongoDB, Cassandra. It is one of the best big data choices to learn and apply machine learning algorithms in real-time. It has the ability to run repeated queries on large databases and potentially deal with them.
Knowing the extensively excellent future growth and rapid adoption of Apache Spark in today’s business world, we have designed this Spark tutorial to educate the mass programmers on interactive and expeditious framework. The tutorial aims at training you on beginner concepts of using Spark as well as gain insights into its advanced modules. For all those who are seeing an expert Spark tutor, this learning package is the delightful and knowledgeable end to your search.
It includes detailed elucidation of Spark and Hadoop Distributed File System. The major topics include Spark Components, Common Spark Algorithms-Iterative Algorithms, Graph Analysis, Machine Learning, Running Spark on a Cluster. Further, you will be able to type in algorithms by yourself by learning to write Spark Applications using Python, Java, Scala, RDD and its operations. Since Spark has the ability to run on diverse platforms using various languages, it is an important phase to gain insights into developing application with various mentioned programming languages.
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This learning package also covers Spark, Hadoop, and the Enterprise Data Centre, Common Spark Algorithms and Spark Streaming, which is yet another important feature of Spark. Most application developers are frequently using this data streaming to keep a check on fraudulent financial transactions. If you find this tutorial helpful, you can browse through our multiple combo training courses of Spark, Storm, Scala and Spark with Python – which can help you grow technically and managerially.
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- Big Data Analysts and Architects
- Software Professionals, ETL Developers and Data Engineers
- Data Scientists and Analytics Professionals
- Beginner and advanced-level programmers in Java, C++, Python
- Graduates aiming to learn latest and efficient programming language to process Big data in a faster and easier manner.
- Before getting started with this tutorial, have a good understanding of Java basics and concepts of programming.
- For that matter, your knowledge of other programming languages like C, C++, Python and Big data analytics will be beneficial to decipher the topics better.