Apache Spark hottest new trends,What is Apache Spark? A unified platform for data management 2020 –

Original article was published by Seema Durgapal on Artificial Intelligence on Medium

Apache Spark hottest New Trends | What is Apache Spark? A unified platform for Data Management 2020

Apache Spark is one of the hottest new trends in the technology domain.

It is the framework with probably the highest potential between Big Data and ML.

It runs fast (up to 90x faster than traditional Hadoop MapReduce due to in-memory operation, offers robust, distributed, fault-tolerant data objects, and integrates beautifully with the world of ML and graph analytics through supplementary packages like Mlib and GraphX.

The best part about using Spark is that you can write Spark apps in Java, Scala, or even Python, and these apps will run nearly ten times faster and 100 times faster than MapReduce apps.

Apache Spark works —

Apache Spark has a hierarchical master/slave architecture.
Based on the application code, Spark Driver generates the SparkContext, which works with the cluster manager—Spark’s Standalone Cluster Manager or other cluster managers like Hadoop YARN, Kubernetes, or Mesos— to distribute and monitor execution across the nodes.

Best Features of Apache Spark —

Lighting-fast processing speed–

Big Data processing, enterprises want such frameworks that can process massive amounts of data at high speed. As we mentioned earlier, Spark apps can run up to 100x faster in memory and 10x faster on disk in Hadoop clusters.

It relies on Resilient Distributed Dataset that allows Spark to transparently store data.This helps to reduce most of the disc read and write time during data processing.

Ease of use –

Spark allows you to write scalable applications in Java, Scala, Python, and R. So, developers get the scope to create and run Spark applications in their preferred programming languages. Moreover, Spark is equipped with a built-in set of over 80 high-level operators. You can use Spark interactively to query data from Scala, Python, R, and SQL shells.

It offers support for sophisticated analytics –

Spark supports SQL queries, streaming data, and advanced analytics, including ML and graph algorithms. It comes with a powerful stack of libraries such as SQL & DataFrames and MLlib (for ML), GraphX, and Spark Streaming.

Active and expanding community —

Developers from over 300 companies have contributed to design and build Apache Spark.

Naturally, Spark is backed by an active community of developers who work to improve its features and performance continually. To reach out to the Spark community, you can make use of mailing lists for any queries, and you can also attend Spark meetup groups and conferences.

Users of Spark —

Yahoo –

Yahoo uses Spark for two of its projects, one for personalizing news pages for visitors and the other for running analytics for advertising. To customize news pages, Yahoo makes use of advanced ML algorithms running on Spark to understand the interests, preferences, and needs of individual users .

Uber —

Uber uses Spark Streaming in combination with Kafka and HDFS to ETL (extract, transform,vast amounts of real-time data of discrete events into structured and usable data for further analysis. This data helps Uber to devise improved solutions for the customers.

Conviva –

As a video streaming company, Conviva obtains an average of over 4 million video feeds each month, which leads to massive customer churn. Conviva uses Spark Streaming to learn network conditions in real-time and to optimize its video traffic accordingly. This allows Conviva to provide a consistent and high-quality viewing experience to the users.

Why Industries Running behind Spark — “25 Reasons why spark is important” –

Apache Spark has replaced Hadoop and became most popular Big Data Engine”.

25 reasons why you should choose Spark —

1) Simplicity
2) Speed
3) Multiple Languages support
4) General Purpose Distributed Processing engine
5) Active and Expanding community
6) Spark can work in an independent manner as well as integration with Hadoop.
7) Apache Spark has automatic memory tuning.
8) Fault Tolerant
9) Supports Multiple formats
10) Lazy Evaluation
11) Reusability
12) Dynamic in nature
13) Real- Time Stream Processing
14) Cost Efficient
15) Support for Sophisticated Analysis
16) Powerful Caching
17) Deployment
18) Immutability
19) Partitioning
20) Location stickiness
21) Multiple Sources support
22) Multiple Commercial Support
23) Coarse Grained Operation
24) Persistence
25) Open Source

Apache Spark is the fastest big data engine, it is widely used among several organizations in a myriad of ways.

Finance industry
Health care
Media and entertainment
E-commerce industry
Travel industry
Data streaming
Machine learning
Fog computing
Interactive analysis

Top 5 Free Apache Spark Courses for Programmers —

1- Spark Starter Kit.

2– Scala and Spark 2 -Getting Started .

3- Hadoop Platform and Application Framework .

4- Python and Spark — Setup Development Environment .

5- Apache Spark Fundamentals .

Apache Spark can deployed in many ways, and it also offers native bindings for Java, Scala, Python, and R programming languages. It supports SQL, graph processing, data streaming, and Machine Learning. This is why Spark is widely used across various sectors of the industry, including banks, telecommunication companies, game development firms, government agencies, and of course, in all the top companies of the tech world – Apple, Facebook, IBM, and Microsoft.

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