Apache Spark as Dominating Force for Data Analysts

Being introduced in 2009, Apache Spark has turned out as a dominating big data platform for data analysts. The diverse portfolio of Spark ranges from assisting telecommunications, banks, and gaming enterprises to catering the giants like IBM, Facebook, Apple, and Microsoft. When comes to real-time processing, the implementation of Apache Spark with Hadoop helps to get the potential and success for the companies.

Spark, nowadays, is incorporated into most Hadoop dispersions. Moreover, it is possible for data scientists to use Spark and run it in a standalone cluster mode that only requires the Apache spark framework and a JVM on every single machine within the cluster. Data scientists can deploy spark in different ways, and avail features like native bindings for the Scala, Java, Python, and R programming languages. Apache Spark also offers support for SQL, Machine Learning, Streaming Data, and graph processing.

Spark – Hadoop Comparison

When we talk about Big Data, we first think about Hadoop. After the introduction of Apache Spark and its ability to integrate with available frameworks, Spark has become an ideal option recently.

Spark encourages the implementation of both iterative algorithms that visits their data set numerous times in a loop and intuitive data analytics.

Data scientists are taking interest in Spark and it can be found in the most Hadoop distribution in present time. The user-friendly approach and speed of Apache Spark have made it an ideal framework to process big data and eclipse MapReduce.

In-memory data engine of the Spark is designed to perform tasks up to one hundred times faster than MapReduce under specific circumstances. Apache Spark works where the data scientists are unable to store data within memory around 10 times faster than MapReduce.

User-friendly Apache Spark API has hidden complexity that comes with a distributed processing engine behind simple method calls.

Here is an instance to show how Spark reduced the stress level of data scientists by performing one task with a few lines that could have taken 50 lines in MapReduce –

val textFile = sparkSession.sparkContext.textFile(“hdfs:///tmp/words”)

val counts = textFile.flatMap(line =>
line.split(“ “)).map(word => (word, 1)) .reduceByKey(_ +
_)counts.saveAsTextFile(“hdfs:///tmp/words_agg”)

The above example explains the compactness of the spark.

The implementation of Apache Spark can be done for Business Intelligence, Designers, and embedded use. Spark allows app developers and data scientists to leverage its speed and scalability in an accessible way. It provides bindings to popular and most used languages for data analyses like R and Python and more business-friendly Scala and Java. You can consider Apache Spark as a vendor-neutral platform where enterprises are free to develop spark-based analytics infrastructure without taking the stress of Hadoop vendor.

Features Putting Spark on the Map

Apache Spark is designed on the concept of RDD (Resilient Distributed Dataset). RDD is a programming abstraction that represents an immutable object collection that data, scientists can split across a computing cluster. This RDD concept allows traditional map and lower functionality and also offers built-in support for filtering, joining data sets, sampling, and aggregation.
Spark SQL designed to focus on the processing of structured data with the help of a data frame approach taken from R and Python (in Pandas). Spark SQL offers a standard interface to the professionals for reading from and writing to different data stores such as HDFS, JSON, JDBC, Apache Hive, Apache ORC, Apache Parquet, etc. That is supported out of the box.
Apache Spark offers libraries to data, scientists for deploying machine learning and graph analysis techniques to data. Spark MLLib has a framework that helps in creating ML pipelines, allowing for simple implementation of feature extraction, transformations, and selections on any structured dataset.
Structured Streaming is a professional API and user-friendly abstraction for writing apps. This API allows developers to develop infinite streaming data frames and data sets.

Apache Spark offers a framework of advanced analytics that includes special tools that experts can use for accelerated queries, graph processing engine, and streaming analytics.

Apache Spark comes with a library, including routine Machine Learning services named MLLib. The MLLib assists data, scientists in data development and interpretation.

Structured Streaming is the future of streaming apps with Apache platform. This means if you are creating a new streaming app, you should apply Structured Streaming to it. The Apache Spark officials have plans to bring continuous streaming without micro-batching in order to avoid low latency responses.

You can build software apps through Apache Spark Implementation and gain insights from the data analytics for your business. Spark offers a faithful community for developers and the introduction of new features frequently making it one of the best versatile platforms used by data analytics for data processing.