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Introduction to Apache Spark

Emerging as a next-gen engine for companies that actively use massive data sets in their projects, the Spark framework is in great demand now.

What is Spark framework?

The Spark framework is an open-code general-purpose computational engine that is ready to process vast data loads at a high speed. Spark’s high-level tools contribute to delivering powerful analytical insights that help to reduce developers’ time and build competitive applications and services.

Spark emerged to become one of the most perspective systems to process big data. But before we proceed to discussing the reasons for its success, let’s review the story of its emergence first.

When did Spark emerge?

Before Spark was introduced in 2013, Hadoop’s MapReduce hegemony was unquestioned. The framework was used to provide linear processing of huge data sets. However, it was slow in delivering the results when the system was running against steady streams of unstructured data.

Large amounts of unstructured data and the urge to acquire live analytics were driving enterprises to search for alternatives to MapReduce and other big data computational solutions. That’s where Spark has stepped in with its in-memory parallel processing as well as its ability to provide for prompt and insightful analytics.

Apache invested in Spark as a way to find a replacement for Hadoop. The Software Foundation wanted to work out an easy-to-use platform that could deliver the results fast and could also support iterative processing. Since 2013, Apache has been actively developing Spark to make it available for various big data projects.

Spark core capabilities

Spark is able to simultaneously process zettabytes of data in both real-time and batch modes, and distribute it across multiple servers. These capabilities make the framework the most powerful computational engine so far. What is more, Spark has a simple interface that makes it possible to track every operation with the aid of accompanying user-friendly tools.

Spark caters for a variety of tasks and its flexibility is exceptional for tackling use cases individually. The platform is actively attracting investors and developers. Let’s explore why.

Which features make Spark stand out

Fast processing

In contrast to MapReduce, the number of read-write operations within Spark is next to negligible. That is why the platform is ready to process the data ten times faster than MapReduce on both physical and virtual disks.

Ease of use

Spark provides native bindings to Java, Python, and several other languages. As compared to Hadoop that’s accessible only in Java, Spark is significantly easier to program.

In-memory processing

With Spark’s in-memory processing, the data flows into the RAM storage and is processed in parallel. This allows large data sets to be analysed for certain detectable patterns. The data that doesn’t fit into RAM can be recalculated or dumped to disk.

Tools for interactive analytics

Spark comprises several tools to analyze the incoming data through machine learning and interactive and SQL queries.

Stream processing

Spark solves the notorious problem with getting the results immediately. In a situation where data streams in from multiple sources, Spark can both process the data and take decisions on it simultaneously.

Fault tolerance

Spark relies on RDD (Resilient Distributed Datasets) to recover from faults. In case of a failure, Spark is able to retrieve the lost data from other nodes through various RDD methods. The latest versions of Spark are using DataFrame to organize the data into named columns for enhanced productivity.

Efficiency

Users can significantly minimize the time of each operation by computing only relevant values. This feature arises from the lazy nature of all transformations that take place inside Spark. Actual computations don’t occur until they are necessary. That is how programmers can cut down the time spent to execute the RDD operations.

Who uses Spark

These days we see massive loads of data coming in different chunks and through different engines. What’s more, we see the data coming arbitrarily in steady streams or in an unstructured format.

That is why more and more companies are seeking out an adequate tool for big data processing. Today, the spheres where the Spark framework is applicable range from Internet of Things and marketing to business and social media. Let’s see who uses Spark and where the technology is efficient most of all.

Where Spark is used: examples and use cases

Machine learning

Among the many useful components within the Spark framework, there is the Machine Learning Library (MLlib). As the Apache Spark tutorial indicates, this tool is able to combine several functions that are applicable to big data:
Customer segmentation
— Fraud detection and risk authentication
— Predictive analytics
— Network security
— Data analysis

Spark delivers interactive analytics combined with extensive visualization. It is crucial when the data comes from multiple sources and requires multiple approaches. This, together with a rich set of high-level APIs, simplifies data access from various programming languages.

Data streaming

Spark’s parallel processing capabilities go hand in hand with its computational powers. The platform combines static and interactive queries and makes it possible to “reuse the same code for batch processing, join streams against historical data, or run ad-hoc queries on stream state”, as Spark’s official webpage states.

Which industries use Spark

Today, more than 5,000 companies are actively using the Spark framework and its powerful features in their daily operations. As the list of major Spark clients suggests, it is evident that the scope of industries is rather varied:

The number of industries benefiting from Spark with its analytical insights and advanced reporting is expected to rise in the coming years.

Spark as an antidote against Big Data problems

Massive data sets are becoming the new norm almost everywhere. The need to process them as promptly and efficiently as possible is a major driver for changes in numerous industries. That is why more and more data engineers consider the prospect of combining batch processing and interactive data analysis. In this regard, Apache Spark is a robust computing engine that provides extremely powerful data pipelines to deliver the desired results with relative ease.

References

https://spark.apache.org/

Sergey

Sergey Lobko-Lobanovsky

Chief Executive Officer

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