The amount of data processed globally increases every moment, with the rise in internet use, IoT networks, online purchases, social media activity, and apps in the play. It is estimated that the beginning of 2020 alone saw 44 zettabytes of data on the internet and 71.5 billion apps downloaded worldwide. Google, Facebook, Microsoft, and Amazon stored more than 1,200 petabytes of information. Facebook alone generates 4 petabytes of new data daily, and the gaming industry about 50 terabytes of data.  It is the future of the Big Data industry, like IT, eCommerce and social media companies continue to collect hundreds of petabytes of data every day. This means an enormous scope for professionals certified in Big Data tools and technologies that support the Big Data Analytics landscape. 

The Apache Spark is one such tool that has witnessed rapid growth in its deployment since its release in 2009. The open-source Big Data community supporting Spark has also grown, with global names adding to the momentum with Spark hiring. Some of the leading names recruiting Spark skillsets are Yahoo, Netflix, and eBay.

IT executives and Big Data practitioners must learn Spark basics to land a job at one of these global enterprises and enhance their prospects of career growth in the Big Data job market.

What is Spark

The Apache Spark was developed at UC Berkeley as an advanced framework for handling lightning speedy computations. Spark is a data processing engine for handling a wide range of workloads like batch processing, interactive, iterative, and stream processing. It offers a unified platform for streaming analytics in a Big Data environment by distributing tasks across multiple computers for faster data crunching. 

Phenomenal speed

Spark has high performance for both batch and streaming data. It handles workloads 100x faster for large-scale data processing using in-memory computing. 

Generality

The Spark framework combines a stack of libraries in the same application offering support for SQL queries, streaming data, machine learning, and graph processing. These standard libraries can be seamlessly combined for complex workflows.

Spark allows you to query structured data using either SQL or the DataFrame API, and the MLlib for high-quality machine learning algorithms. The Spark Streaming extension of language-integrated APIs enables scalable, high-throughput, fault-tolerant stream processing of live data streams from multiple disparate sources, thus letting you write streaming jobs the same way you write batch jobs. The pipeline structure of MLlib allows you to call into deep learning libraries and construct classifiers with just a few lines of code, or apply custom TensorFlow graphs or Keras models to inward bound data. Spark supports the fastest graph system, GraphX, to unify ETL, exploratory analysis, and iterative graphical computations supported by a mix of graph algorithms.

Flexibility

The flexibility of Apache Spark to run in a standalone cluster mode or resource cluster management system is another reason why it is a popular go-to tool for Big Data analytics.

Easy to use

Spark has easy-to-use APIs with over 100 operators for data transformation of large datasets and building parallel apps. The data frame APIs support processing of semi-structured data. Besides, Spark allows applications to be written interactively from Java, Scala, Python, SQL, and R, making it one of the easiest to use tools for Big Data Analysis.

It runs everywhere

Spark runs on Hadoop, Apache Mesos, EC2, and Kubernetes, using its standalone cluster mode, or in the cloud. It can also access diverse data sources including HDFA, Apache HBase, Apache Hive, and hundreds of others. 

Apache Spark application has two main components: 

• the driver that converts user code into multiple tasks distributed across multiple computers and worker nodes, and 
• the executors, which run on these nodes to execute the tasks. 

Benefits of using Spark

Spark is a data analytics engine popular among Data Scientists for the following benefits: Fast, streaming analytics in real-time.

• Only 20,000 lines of code, thus making it run faster.
• A developer-friendly environment with the code being reused for various tasks and queries.
• SQL queries can be run for various functions on the go.
• Can run in a standalone cluster environment or dedicated cluster frameworks.

However, Spark does not replace Hadoop.  Rather, when used together with Hadoop MapReduce, HBase, or other big data frameworks, the capability of Spark is enhanced.

What are the prerequisites to learn Spark?

Any programming basics such as loop, functions, interface, objects, and so on, will be familiar to an IT professional. So besides these known topics, there are no rigid prerequisites to learn Spark.

At the same time, knowledge of any or some of the following will make it easier to master Apache Spark:

Spark

However, since Spark is built in Scala, the functional concepts like the map and flat map are much similar to Scala. So working knowledge of Scala makes it easier to learn Spark. 

SQL

Apache Spark’s SQL engine performs SQL-type queries in real-time projects, using the Spark SQL. So SQL is another path to learning Spark basics.

Python

Apache Spark also provides API in other languages such as Java and Python. Knowledge of either of these languages can fast-track your Spark learning curve.

Experience with Python and Panda libraries, although not an absolute must, can help you to master Apache Spark. 

Java

Knowledge of Java and lambda expressions help to use all the features in Spark.

Cloud technology

A good working experience of any cloud technology like AWS helps in implementing Big Data projects tied to a cloud. 

Hadoop

As Apache Spark can run with or without Hadoop, prior knowledge of Hadoop is not a prerequisite. But Spark is based on the Hadoop file format, so a hands-on experience of the Hadoop file system supports faster learning. 

Architecture and Database

Understanding the backend framework is important. So brushing up on any distributed database like Hbase or Cassandra, and YARN architecture must be included in your Spark learning plans.

Summary

At the end of the day, if you want to launch your career in the Big Data industry, then you must learn Spark basics. Register for an Apache Spark certification and be a part of the Big Data industry.