Big data has taken over many aspects of our lives and as it continues to grow and expand, big data is creating the need for better and faster data storage and analysis. Apache Hadoop and Apache Spark fulfill this need as is quite evident from the various projects that these two frameworks are getting better at faster data storage and analysis. These Apache Hadoop projects are mostly into migration, integration, scalability, data analytics, and streaming analysis. These Apache Spark projects are mostly into link prediction, cloud hosting, data analysis, and speech analysis. These projects are proof of how far Apache Hadoop and Apache Spark have come and how they are making big data analysis a profitable enterprise.
As we step into the latter half of the present decade, we can’t help but notice the way Big Data has entered all crucial technology-powered domains such as banking and financial services, telecom, manufacturing, information technology, operations, and logistics.
With Big Data came a need for programming languages and platforms that could provide fast computing and processing capabilities. The parallel emergence of Cloud Computing emphasized distributed computing and there was a need for programming languages and software libraries that could store and process data locally (minimizing the hardware required to maintain high availability). Apache™, an open-source software development project, came up with open-source software for reliable computing that was distributed and scalable.
Hadoop and Spark are two solutions from the stable of Apache that aim to provide developers around the world a fast, reliable computing solution that is easily scalable. Built to support local computing and storage, these platforms do not demand massive hardware infrastructure to deliver high uptime. At the bottom lies a library that is designed to treat failures at the Application layer itself, which results in highly reliable service on top of a distributed set of computers, each of which is capable of functioning as a local storage point.
Apache houses a number of Hadoop projects developed to deliver scalable, secure, and reliable solutions. Hadoop Common houses the common utilities that support other modules, Hadoop Distributed File System (HDFS™) provides high throughput access to application data, Hadoop YARN is a job scheduling framework that is responsible for cluster resource management and Hadoop MapReduce facilitates parallel processing of large data sets. A number of big data Hadoop projects have been built on this platform and this has fundamentally changed a number of assumptions we had about data. Hadoop looks at architecture in an entirely different way. Hadoop projects make optimum use of ever-increasing parallel processing capabilities of processors and expanding storage spaces to deliver cost-effective, reliable solutions.
Owned by Apache Software Foundation, Apache Spark is an open-source data processing framework. It sits within the Apache Hadoop umbrella of solutions and facilitates the fast development of end-to-end Big Data applications. It plays a key role in streaming in the form of Spark Streaming libraries, interactive analytics in the form of SparkSQL and also provides libraries for machine learning that can be imported using Python or Scala.
It is an improvement over Hadoop’s two-stage MapReduce paradigm. By providing multi-stage in-memory primitives, Apache Spark improves performance multi-fold, at times by a factor of 100! It can interface with a wide variety of solutions both within and outside the Hadoop ecosystem.
Apache has gained popularity around the world and there is a very active community that is continuously building new solutions, sharing knowledge, and innovating to support the movement. A number of times developers feel they are working on a really cool project but in reality, they are doing something that thousands of developers around the world are already doing. The aim of this article is to mention some very common projects involving Apache Hadoop and Apache Spark.
RDBMSs were inefficient and failed to manage the growing demand for current data. This failure of relational database management systems triggered organizations to move their data from RDBMS to Hadoop. Data migration from legacy systems to the cloud is a major use case in organizations that have been into relational databases. Being open-source Apache Hadoop and Apache Spark has been the preferred choice of a number of organizations to replace the old, legacy software tools which demanded a heavy license fee to procure and a considerable fraction of it for maintenance. Unlike years ago, open-source platforms have a large talent pool available for managers to choose from who can help design better, more accurate, and faster solutions. Hadoop ecosystem has a very desirable ability to blend with popular programming and scripting platforms such as SQL, Java, Python, and the like which makes migration projects easier to execute.
Businesses seldom start big. Most of them start as isolated, individual entities and grow over a period of time. The Digital explosion of the present century has seen businesses undergo exponential growth curves. Given the operation and maintenance costs of centralized data centers, they often choose to expand in a decentralized, dispersed manner. Given the constraints imposed by time, technology, resources, and talent pool, they end up choosing different technologies for different geographies and when it comes to integration, they find going tough.
That is where Apache Hadoop and Apache Spark come in. Given their ability to transfer, process, and store data from heterogeneous sources in a fast, reliable, and cost-effective manner, they have been the preferred choice for integrating systems across organizations.
As mentioned earlier, scalability is a huge plus with Apache Spark. Its ability to expand systems and build scalable solutions in a fast, efficient, and cost-effective manner outsmart a number of other alternatives. Apache Spark has been built in a way that it runs on top of the Hadoop framework (for parallel processing of MapReduce jobs). As the data volumes grow, processing times noticeably go on increasing which adversely affects performance. Hadoop can be used to carry out data processing using either the traditional (map/reduce) or Spark-based (providing an interactive platform to process queries in real-time) approach.
Link prediction is a recently recognized project that finds its application across a variety of domains – the most attractive of them being social media. Given a graphical relation between variables, an algorithm needs to be developed which predicts which two nodes are most likely to be connected? This can be applied in the financial services industry – where an analyst is required to find out which are the kinds of frauds a potential customer is most likely to commit? It can also be applied to social media where the need is to develop an algorithm which would take in a number of inputs such as age, location, schools, and colleges attended, workplace and pages liked friends can be suggested to users.
Given Spark’s ability to process real-time data at a greater pace than conventional platforms, it is used to power a number of critical, time-sensitive calculations and can serve as a global standard for advanced analytics.
Apache Hadoop is equally adept at hosting data at on-site, customer-owned servers, or in the Cloud. Cloud deployment saves a lot of time, cost, and resources. Organizations are no longer required to spend over the top for procurement of servers and associated hardware infrastructure and then hire staff to maintain it. Instead, cloud service providers such as Google, Amazon, and Microsoft provide hosting and maintenance services at a fraction of the cost. Cloud hosting also allows organizations to pay for actual space utilized whereas in procuring physical storage, companies have to keep in mind the growth rate and procure more space than required.
Organizations often choose to store data in separate locations in a distributed manner rather than at one central location. Besides risk mitigation (which is the primary objective on most occasions) there can be other factors behind it such as audit, regulatory, advantages of localization, etc.
It is only logical to extract only the relevant data from warehouses to reduce the time and resources required for transmission and hosting. For example, in financial services, there are a number of categories that require fast data processing (time series analysis, risk analysis, liquidity risk calculation, Monte Carlo simulations, etc.).
Hadoop and Spark facilitate faster data extraction and processing to give actionable insights to users. Separate systems are built to carry out problem-specific analysis and are programmed to use resources judiciously.
To set the context, streaming analytics is a lot different from streaming. Streaming analytics is a real-time analysis of data streams that must (almost instantaneously) report abnormalities and trigger suitable actions. For example, when an attempted password hack is attempted on a bank’s server, it would be better served by acting instantly rather than detecting it hours after the attempt by going through gigabytes of server log!
Streaming analytics requires high-speed data processing which can be facilitated by Apache Spark or Storm systems in place over a data store using HBase. Streaming analytics is not a one-stop analytics solution, as organizations would still need to go through historical data for trend analysis, time series analysis, predictive analysis, etc.
Computer Telephone Integration has revolutionized the call center industry. Speech analytics is still in a niche stage but is gaining popularity owing to its huge potential. Consider a situation where a customer uses foul language, words associated with emotions such as anger, happiness, frustration, and so on are used by a customer over a call. Instead of someone having to go through huge volumes of audio files or relying on the call handling executive to flag the calls accordingly, why not have an automated solution? Hadoop and Spark excel in conditions where such fast-paced solutions are required. This reduces manual effort multi-fold and when an analysis is required, calls can be sorted based on the flags assigned to them for better, more accurate, and efficient analysis.
Big Data technologies used: AWS EC2, AWS S3, Flume, Spark, Spark Sql, Tableau, Airflow
Big Data Architecture: This implementation is deployed on AWS EC2 and uses flume for ingestion, S3 as a data store, Spark SQL tables for processing, Tableau for visualization, and Airflow for orchestration.
Hadoop Sample Real-Time Project #2: Movielens dataset analysis using Hive for Movie Recommendations
Problem: The movielens dataset contains a large number of movies, with information regarding actors, ratings, duration, etc. We need to analyze this data and answer a few queries such as which movies were popular etc.
Big data technologies used: Microsoft Azure, Azure Data Factory, Azure Databricks, Spark
Big Data Architecture: This sample Hadoop real-time project starts off by creating a resource group in azure. To this group, we add a storage account and move the raw data. Then we create and run an Azure data factory (ADF) pipelines. Following this, we spring up the Azure spark cluster to perform transformations on the data using Spark SQL. This makes the data ready for visualization that answers our analysis.