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Featured Posts (344)

  • Batch vs. Real Time Data Processing

    Guest blog post by Michael Walker

    Batch data processing is an efficient way of processing high volumes of data is where a group of transactions is collected over a period of time. Data is collected, entered, processed and then the batch results are produced (Hadoop is focused on batch data processing). Batch processing requires separate programs for input, process and…

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  • Is Big Data Harmful or Good?

    Data is an advantage for industries as it benefits them make up-to-date choices. Strange! Data is being produced at an extraordinary rate and establishments are hording it like there’s not any tomorrow, generating enormous data groups we call big data. But is big data serving these businesses or is it just obscuring the decision-making procedure? We will find out.

    Big data has numerous applications and, collective with analytics, is cast-off to find responses to glitches in a variation of businesses. For organisations, it can benefit them comprehend customer behavior and get most out of business procedures, all of which, in concept, should help administrators make sound choices to drive business development. But like so numerous things that complete good in concept, it’s not precisely working out for numerous organisations. In a worldwide review of over 300 C-level initiative administrators by Chartered Global Management Accountant (CGMA), which was complemented…

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  • Lambda Architecture for Big Data Systems

    Guest blog post by Michael Walker

    Big data analytical ecosystem architecture is in early stages of development. Unlike traditional data warehouse / business intelligence (DW/BI) architecture which is designed for structured, internal data, big data systems work with raw unstructured and semi-structured data as well as internal and external data sources. Additionally, organizations may need both batch and (near) real-time data processing capabilities from big data systems.…

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  • Price discrimination and downward demand spiral are widely used analytical concepts/practices in the Airlines and Hospitality industries respectively, long before the term Big Data Analytics was even coined. Incidentally, these concepts have been taught in global elite b-schools for decades. So, how come Analytics, which has been there in practice for decades experience a meteoric rise suddenly? To answer this question, we need to get the Big Picture. Given below are key factors that led to huge buzz around analytics today.

    • Proliferation of Data Sources – Every day we create 5 quintillion bytes of…
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  • The advent of sharing economy has brought a sea change in the way urban populace commute locally. The Ubers, Lyfts and many other local players have made taxi riding convenient, affordable and safe. These rides have emerged as a strong alternative to the public transport clocking millions of rides per month in some cities. The emergence of hyper-local delivery models to optimize the supply chain has also led to a large number of daily trips by these vehicles.

    These developments have mandated the installations of either standalone or smartphone app-based GPS devices to keep track of and better regulate these rides and a fleet of taxis. These GPS systems spew a ton of data generating up to GBs of…

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  • HDFS vs. HBase : All you need to know

    The sudden increase in the volume of data from the order of gigabytes to zettabytes has created the need for a more organized file system for storage and processing of data. The demand stemming from the data market has brought Hadoop in the limelight making it one of biggest players in the industry. Hadoop Distributed File System (HDFS), the commonly known file system of Hadoop and Hbase (Hadoop’s database) are the most topical and advanced data storage and management systems available in the market.

    What are HDFS and HBase?

    HDFS is fault-tolerant by design and supports rapid data transfer between nodes even during system failures. HBase is a non-relational and open source Not-Only-SQL database that runs on top of Hadoop. HBase comes under CP type of CAP (Consistency, Availability, and Partition Tolerance) theorem.

    HDFS is most suitable for performing…

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  • Introduction

    By now, you have probably heard of the Hadoop Distributed File System (HDFS), especially if you are data analyst or someone who is responsible for moving data from one system to another. However, what are the benefits that HDFS has over relational databases?

    HDFS is a scalable, open source solution for storing and processing large volumes of data. HDFS has been proven to be reliable and efficient across many modern data centers.

    HDFS utilizes commodity hardware along with open source…

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  • Data is a key asset of any company, particularly transactional data which holds business secrets such as financial or health records. Data is most vulnerable in transit between the server that stores it and that client that requests it.

    The standard approach to ensuring security is to encrypt data on the server and use the SSL-enabled HTTPS protocol to secure data in transport. However, what if we could increase the level of security even further, by using HTTPS and sending data in an encrypted format over the communication line, only to decrypt data on clients who have valid certificates? That approach would make a traditional man-in-the-middle (MITM) attack much more difficult.…

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  • Top Hadoop Big Data Interview Questions and Answers

    Find below the list of Hadoop interview questions and answers jotted down to help job seekers

    Question: What is Hadoop and its workings?

    Answer: When “Big Data” appeared as problematic, Apache Hadoop changed as an answer to it. Apache Hadoop is a context which offers us numerous facilities or tools to store and development of Big Data. It benefits in analysing Big Data and creation business decisions out of it, which can’t be done professionally and successfully using old-style systems. 

    Question: What is the usage of Hadoop?

    Answer: With Hadoop, the employer can run requests on the systems that have thousands of bulges scattering through countless terabytes. Rapid data dispensation and assignment among nodes helps continuous operation even when a node fails averting system let-down.

    Question: On what idea, the Hadoop framework runs?

    Answer: Hadoop Framework acts upon the subsequent two core…

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  • Guest blog post by Bill Vorhies

    Summary:  The shortage of data scientists is driving a growing number of developers to fully Automated Predictive Analytic platforms.  Some of these offer true One-Click Data-In-Model-Out capability, playing to Citizen Data Scientists with limited or no data science expertise.  Who are these players and what does it mean for the profession of data science?

     

    In a recent poll the question was raised “Will Data Scientists be replaced by software, and if so, when?”  The consensus answer:

    Data Scientists automated and unemployed by 2025.

    Are we really just grist…

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  • Ten top languages for crunching Big Data

    Guest blog post by Bernard Marr

    With an ever-growing number of businesses turning to Big Data and analytics to generate insights, there is a greater need than ever for people with the technical skills to apply analytics to real-world problems.

    Computer programming is still at the core of the skillset needed to create algorithms that can crunch through whatever structured or unstructured data is thrown at them. Certain languages have proven themselves better at this task than others. Here’s a brief overview of 10 of the most popular and widely used.…

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  • Recent technological advancement has seen a lot of sectors improve and the analytics sector has not been left behind. The latest advances in the Apache Hadoop system is a major improvement to the Hadoop’s viability as far as data storage is concerned. With these advances, it is clear that SQL software on Hadoop is the best way to access big data which confirms the authenticity of the concept of analytics in Hadoop system. Of course, this is no doubt a big deal, and it has shown how technology has evolved with the intention of fulfilling the potentials of big data analytics.

    That said, there are a few reasons why in-cluster analytics is being classified as the big deal today. We highlight some of the top three behind this buzz.

    1.     …

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  • PhalconPHP: A Solution for High-load RESTful APIs

    BY Andrew Belousoff

    Suppose you need to create a high-load project based on a PHP MVC framework. You would probably use caching wherever possible. Maybe you would build the project in a single file, or maybe even write your own MVC framework with minimal functionality, or rewrite some parts of another framework. While, yes, this works, it’s a little bit tricky, isn’t it? Fortunately, there is one more solution that makes most of these manipulations unnecessary (save for the cache, perhaps), and this solution is called the PhalconPHP framework.

    What Is PhalconPHP?

    PhalconPHP is an MVC framework for PHP written in C and supplied as a compiled PHP extension. This is what makes it one of the fastest frameworks available (to be completely honest the fastest one is Yaf, but it is a micro framework and…

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  • Data Engineering

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    With the rise of  and data science, many engineering roles are being challenged and expanded. One new-age role is .

    Originally, the purpose of data engineering was the loading of external data sources and the designing of databases (designing and developing pipelines to collect, manipulate, store, and analyze data).

    It has since grown to support the volume and complexity of big data. So data engineering now encapsulates a wide range of skills, from web-crawling, data cleansing, distributed computing, and data storage and retrieval.

    For data engineering and data engineers, data storage and retrieval is the critical component of the pipeline together with how the data can be used and analyzed.

    In recent times, many new and…

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  • Boost Your Data Munging with R

    The R language is often perceived as a language for statisticians and data scientists. Quite a long time ago, this was mostly true. However, over the years the flexibility R provides via packages has made R into a more general purpose language. R was open sourced in 1995, and since that time repositories of R packages ar constantly growing. Still, compared to languages like Python, R is strongly based around the data.

    Speaking about data, tabular data deserves particular attention, as it’s one of the most commonly used data types. It is a data type which corresponds to a table structure known in databases, where each column can be of a different type, and processing performance of that particular data type is the crucial factor for many applications.

    R can be used for very efficient data munging of tabular data

    R can be used for very efficient data munging of tabular data

    In this article, we are going to present how to…

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  • Guest blog post by Alessandro Piva

    The proliferation of data and the huge potentialities for companies to turn data into valuable insights are increasing more and more the demand of Data Scientists.

    But what skills and educational background must a Data Scientist have? What is its role within the organization? What tools and programming languages does he/she mostly use? These are some of the questions that the Observatory for Big Data Analytics of Politecnico di Milano is investigating through an international survey submitted to Data Scientists: if you work with data in your company, please support us in our…

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  • Switching careers from Java to Big data Hadoop

    There is this point in all our lives where we think of switching careers or apprising our skill sets to improve our career growth or even just to stay updated with the growing trends. But careful analysis of the current trend and observing the requirements serves as a good method to choose which skill set get updated with. Looking at the current market, Hadoop and Big Data technology are growing extremely fast and has lots of market demands as well. A surge in interest in “Big Data” is prompting many Development Team Managers to consider Hadoop technology as it’s increasingly becoming a significant component of Big Data applications. In doing so, taking inventory of the skills sets required when dealing with Hadoop is vital. According to Helena Schwenk, analyst at MWD Advisors, quoted to SearchSOA.com that a well-rounded Hadoop implementation team’s skills should include experience in large-scale distributed systems and knowledge of languages such as Java, C++, Pig Latin and…

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  • Cloudfare and GitHub Pages

    BY AMIN SHAH GILANI 

    I have a secret that saves my clients a ton of money, keeps their website secure, and has built-in backups.

    The secret: I make their website static. Then, I store and host it with GitHub, and use Cloudflare to serve it over HTTPS, and make it fast. My clients only ever pay for their domain name, yet they get a lot more than they ever bargained for.

    Why Static Content?

    Static sites are wonderfully fast since there’s no server processing time involved. Also, by committing a code base of static assets in a git repository, rolling back changes simply becomes a matter of reverting to a previous commit. Backups are a git push away, and you essentially serve…

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  • Originally posted on Data Science Central

    Recently, in a previous post, we reviewed a path to leverage legacy Excel data and import CSV files thru MySQL into Spark 2.0.1. This may apply frequently in businesses where data retention did not always take the database route… However, we demonstrate here that the same result can be achieved…

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  • 8 Hadoop articles that you should read

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