Information is the oil of the 21st century and analytics is the combustion enginePeterSondergaard, Senior Vice President, Gartner

Data is everything in this 21st century. However, unless insight is extracted from it, data remains useless. Big data analytics exists to analyze data to discover valuable insights, hidden patterns, and other information to enhance decision making. To this effect, the world has witnessed the development of powerful analytics tools with real-time analysis capabilities including Spark, Hadoop, MongoDB, and others. It takes professionals with Hadoop and Spark certification training and experience to make the most out of big data.

How is the big data market growing?

Large volumes of data are generated daily from internet queries, social networks, and most recently, from IoT devices. Big data is growing in complexity and is today defined by the four Vs including Volume – very large data sets, Velocity- high speed with which data is generated, Variety- different forms of data, and veracity – biases and abnormalities in data. The big data sector is made up of architecture, technologies, tools, applications, as well as security services and was valued at $169 billion as of 2018. It is expected to grow to $274 billion by 2022.

Key growth factors

Key growth factors in this sector include increased access to smartphones and internet-enabled mobile devices, cloud computing adoption, and the adoption of IoT and AI technologies. Big data analytics has gained significant traction with more than 58% of organizations across the globe considering adoption together with a robust infrastructure to harness complex data processes.

Real-time data, real-time analytics

Larger volumes of data will continue to be generated to the tune of 175 zettabytes by 2025 as projected by IDC in its Data Age 2025 report. To handle big data storage and processing challenges, sophisticated software like Apache Hadoop and Apache Spark is being developed. These tools are built to not only support cloud servers but also perform real-time analytics which is even more valuable to any business. As IDC has projected, by 2025, up to 30% of data will be real-time therefore real-time analysis will certainly put a business ahead in terms of performance and growth.

Big data and machine learning

Going forward, the integration of machine learning and big data will handle analytics even more efficiently by replacing human interaction with programmed computer function on predictable repetitive tasks. This will be very helpful in sectors like healthcare and banking where very accurate data processing and analytics are required.

What is Hadoop?

Hadoop is an open-source software framework for storing and processing big data. It does this by processing and distributing data in large sets to clusters of data nodes using the MapReduce programming model. Hadoop is built in the Java programming language and is popular among big names like AWS, IBM, Microsoft, and Facebook that are known to generate massive volumes of data.

The distributed file system works by scaling up from single servers to numerous machines, each with its own processing capabilities and storage. This accords Hadoop the capability of literally any type and volume of data from plain text, images, videos, JSON, and XML.

Components of Hadoop architecture

  • Hadoop distributed file system (HDFS). Data storage is done in replicas across a cluster of data nodes.
  • MapReduce programming model. A simple computational model used for writing software that runs on Hadoop. It processes large data sets in a parallel distributed model across a cluster.

Features of Hadoop

  • The MapReduce programming model is fast because data processing is distributed across data nodes and this significantly enhances processing speed
  • Uses a distributed file system that ensures fault tolerance because when data is stored in one node, it is automatically replicated to other nodes in the cluster. In case of a fault in a node, data can still be accessed from the other nodes.
  • Scalable, offering massive storage and robust computing for various types of data
  • Highly scalable by simply adding data nodes to the Hadoop distributed file system (HDFS) cluster rather than investing your resources in an entire operating system

What is Spark?

Spark is an open-source software framework for processing big data. It does so through a distributed file system and cluster-computing. Spark is hailed as a fast processing solution for large data sets. Spark runs on Spark core, an internal memory on which all other spark modules function. It supports a number of programming languages including R, Python, Scala, and Java.

Spark has been adopted by big names like Yahoo, Netflix, and eBay for big data analytics.

Since its release, Apache Spark, the unified analytics engine, has seen rapid adoption by enterprises across a wide range of industries. Internet powerhouses such as Netflix, Yahoo, and eBay have deployed Spark at a massive scale, collectively processing multiple petabytes of data on clusters of over 8,000 nodes. It has quickly become the largest open source community in big data, with over 1000 contributors from 250+ organizations.

Components of Spark architecture

  • Spark SQL. Spark SQL function is for processing structured data. Spark SQL provides a programming abstraction known as DataFrame and acts as a distributed query engine that queries different nodes in a cluster using SQL or Hive Query language.
  • Spark streaming. This component is for processing real-time streaming data generated from the Hadoop Distributed File System, Kafka, and other sources.
  • MLlib (Machine learning library). Spark’s scalable machine learning library features a range of ML algorithms, pipelines, featurization, and utilities for easy scaling of clusters. It features APIs in Python, Java, R, and Scala languages.
  • GraphX. GraphX is Spark’s graph library with tools and algorithms for manipulating graph’s structured data.

Features of Spark

  • Spark is used to run applications in Hadoop and runs on internal memory making it up to 100 times faster compared to when running on disk.
  • Supports more languages including Java, Scala, R, and Python.
  • Built for a range of workload and supports MapReduce programming, SQL queries, streaming data, ML, and graph computations.

Hadoop vs. spark

While spark integrates with Hadoop’s storage and processing, it is designed with its own cluster algorithms. This means that it can operate as part of the Hadoop ecosystem alongside MapReduce and HDFS components or independently depending on project requirements. However, both Hadoop and Spark are open-source Apache tools developed for big data analytics and can be implemented together with Hadoop performing big data analytics and Spark performing ETL and SQL batch tasks on large data sets.

Still, Hadoop and Spark are different in many ways.

  1. Hadoop has been developed with Java language while Spark is built with Scala but supports R, Python, and Java languages.
  2. Hadoop MapReduce allows for parallel processing of large data sets by breaking it down into smaller chunks and then distributing these chunks across different data nodes for processing, a feature that enhances performance. Spark, on the other hand, allows for multiple types of computations including real-time streaming, batch processing, interactive, and graphs processing all within the same cluster.
  3. In terms of speed, Spark is faster operating 100 times faster on RAM and 10 times faster on disk memory compared to Hadoop MapReduce which reads and writes on disk hence slowing down performance. Another function of speed is Spark’s built-in ML libraries.
  4. Hadoop is more fault-tolerant compared to spark because it replicates data across data nodes within a cluster. In the event that a node is down, data can be accessed from the other nodes.
  5. Spark is a low-latency computing framework that allows interactive data processing. Hadoop MapReduce, however, is a high-latency framework and does not offer an interactive mode.

There is really no competition between Hadoop and Spark. In fact, each has unique features to complement the other’s function. Organizations are leveraging both tools to gain the most insight from big data. As a professional, it pays to be skilled in both, therefore, acquiring training in both Hadoop MapReduce and Spark will go a long way in enhancing one’s career prospects.