Did You Know?

There are around 3000 companies using Apache Spark today. The list includes top players such as Microsoft, Oracle, Facebook, Hortonworks, Cisco, Visa, Databricks, Amazon, Shopify, Yahoo, and many more.

The tagline of Spark says- “lightning-fast unified analytics engine.”

Just imagine how fast it can be.

Quick Bite:

Apache spark is popularly referred to as the “Swiss army knife of Big Data Analytics.”

The demand for Apache developers is soaring as there is a disparity between the demand for the developers and the presence of skilled and certified Spark developers. To get a steep competitive edge, you can learn Spark basics and get trained and certified.

Let us now explore what Apache Spark is and why you should learn it.

What is Apache Spark?

Simply put, Apache Spark is a lightning-fast, open-source data processing engine which is used for Artificial Intelligence and Machine Learning applications to be utilized for large datasets. It is meant to deliver excellent computational speed, scalability, and programmability, which is required for processing Big Data. This specifically includes streaming data, machine learning, graph data, and AI applications.

The analytics engine of Spark is capable of processing the data 10 to 100 times faster than other alternatives. This incredible speed is achieved as it scales by distributing processing work across large clusters/bundles of computers which consist of pre-built parallelism and fault-tolerance as well. Apache Spark includes APIs (Application Programming Interface), which are meant for programming languages popular among data scientists and data analysts, which include Python, Java, Scala, and R.

Quick Pick:

Apache Spark owns the largest open-source community in big data, which includes over 1000 contributors.

How does Apache Spark Work?

Spark constitutes hierarchical master/slave architecture. The master node is the Spark driver that monitors the cluster manager, which in turn manages the slave or worker node and delivers the resulting data to the client application.

Spark driver then generates the Sparkcontext, which is based on the application code which works with the cluster manager.

This cluster manager which is also called Spark’s standalone Cluster Manager, along with other cluster managers like Hadoop, YARN, Mesos, or Kubernetes, distributes and controls execution across the nodes. It is also responsible for creating RDDs or Resilient Distributed Datasets that account for Spark’s incredible speed.

Generally, Spark is compared to the MapReduce component of Hadoop, which is a native data processing unit. The most significant difference between both components is speed. This is because Spark processes and keeps the data in memory for subsequent steps, and that too without reading from or writing to the disk, which is the main reason for the high speed of Spark.

RDD or Resilient Distributed Dataset

RDDs are fault-tolerant collections of elements that are distributed among different nodes in a cluster and are worked on in parallel. RDDs form the basic structure in Spark.

Spark is capable of supporting a wide range of actions and transformations on RDDs. The users are free of worrying about computing the right distribution because Spark does it all; in other words, Spark performs the distribution.

DAG or Directed Acyclic Graph

To look after the tasks schedule and orchestration of worker nodes across the cluster, Spark creates a Directed Acyclic Graph or DAG. Since Spark works and transforms the data during the task execution process, the efficiency of Spark is enhanced by DAG as it orchestrates the worker nodes across the cluster. The fault-tolerance is made possible by this task-tracking, as it re-executes the recorded tasks to the data from a previous state.

Data Frames and Datasets

Apart from RDDs, Spark manages two other data types:

• Data Frames

  These are the most common structured application programming interfaces or APIs that represent a table of data with rows and columns. Since MLLib or the Machine Learning Library of Spark is gaining popularity, DataFrames play an important role as the primary API for MLLib. DataFrames are the ones accountable for providing consistency across various languages such as Java, Scala, Python, and R.

• Datasets

  These are an extension of DataFrames that are responsible for providing a type-safe, object-oriented programming interface. By default, Datasets are a collection of strictly typed JVM objects.

  SparkSQL enables you to query the data from DataFrames and SQL data stores, like Apache Hive. a DataFrame or Dataset is provided in return by Spark SQL queries when they are executed within other languages.

Spark Core

The base for all the data processing is the Spark core that handles scheduling, optimization, data abstraction, and RDD. It also provides the functional foundation for the Spark libraries Spark Streaming, Spark SQL, the MLlib or machine learning library, and graph data processing or GraphX. The Spark core and cluster manager collectively distribute the data across the Spark cluster for abstraction. This distribution and abstraction enables quick handling of Big Data and makes it user-friendly.

Spark APIs

There are a variety of APIs in Spark that bring competencies to audiences on a large scale. Spark SQL enables relational interaction with RDD data. It has a properly documented API for Scala, Java, R, and Python as well. There are specific nuances for each language API in Spark that handles the data. In each language API, there are RDDs, DataFrames, and datasets.

When there are APIs for such a huge variety of languages, it is possible for Spark to make Big Data processing accessible to professionals involved in data science, statistics, and development.

What’s so great about Spark?

There are some incredible features in Spark that make it a lightning-fast processing engine. Let us explore what they are.

1. Fault tolerance

   With RDDs and DAG, Spark is capable of handling worker node failures, thereby making it fault-tolerant data processing engines.

2. Lazy evaluation

   DAGs enable Spark for lazy evaluation, thus making it capable of making optimization decisions. This makes all the transformations visible to the Spark engine before performing any operation.

3. Speed

   RDD, DAG, query optimizer, and highly optimized physical execution engine are the features that contribute to the incredible processing speed of Spark.

4. Real-Time Stream Processing

   It is the language-integrated API that enables Spark for stream processing. This allows you to write streaming jobs the way you write batch jobs.

5. Dynamic Nature

   There are around 80 high-level operators that allow easy building of parallel applications.

   Apart from the features mentioned above, the other features of Spark include advanced analytics, in-memory computing, integration with Hadoop, support for multiple languages, and cost-efficiency.

Conclusion

With such a remarkable speed, Spark is adopted by almost every organization making the demand for Spark developers surge. If you wish to upgrade your current IT career or launch a career in Spark, taking up an online training course is a wise move.

Taking an online training course from a reputed institute makes your learning hassle-free which provides you with flexible learning hours and a mode of learning at your convenience.

Enroll yourself now!