Big Data has been on the tongue of every Data & Analytics expert in the past couple of years. The use and processing of Big Data for business decisions has taken the world by storm. Right from consumer marketing to political campaigns to finance and science, everyone is lapping up on the immense power of Big Data & Predictive Analytics. Taking advantage of this surge, many players have come into the market with their platforms that can quantify Big Data and produce valuable information through it. The most well-known ones include Apache Hadoop, Apache Spark, SAP HANA, Google BigQuery, and Oracle Big Data Appliance. Let’s take a sneak peak at what each of these is.
Apache Hadoop is by far the most popular and widely used Big Data platform. It is a distribution data process framework of the Map-Reduce method and is equipped to handle large-datasets on computer clusters easily. Hadoop splits files into large blocks and distributes them amongst the nodes in the cluster. It takes advantage of the data locality for quick and efficient data processing and works better than a supercomputer architecture relying on a parallel file system in this regard.
SAP HANA is a memory-based storage made from SAP. Its characteristic is to organize a system optimized to analysis tasks, such as OLAP. If all data is inside system memory, maximizing CPU utilization is crucial and the key point is to reduce bottlenecks between memory and CPU cache. In order to minimize Cache miss, consecutive data for processing within the given time is more advantageous; meaning that configuration of column-oriented tables could be favorable when analyzing many OLAP.
Apache Spark is the new kid on the block, offering lightning fast Big Data computing. Seemingly, Spark’s multi-stage in-memory primitives provide performance up to 100 times faster for certain applications. It is well suited for machine learning algorithms owing to its feature of allowing data to be loaded into a cluster’s memory & repeated querying. It runs on top of existing Hadoop cluster and can access Hadoop data store (HDFS), can also process structured data in Hive and Streaming data from HDFS, Flume etc.
Google BigQuery is a web service from the big daddy of data. It is an IaaS (infrastructure as a Service) working in conjunction with Google Storage to interactively analyze extremely huge datasets. It can query massive datasets fast, without being too heavy on the pocket. It enables super-fast, SQL queries against append-only tables, using the processing power of Google’s infrastructure.
Oracle Big Data Appliance is a high-performance, secure platform for running diverse workloads on Hadoop and NoSQL systems. With Oracle Big Data SQL, Oracle Big Data Appliance extends Oracle’s industry-leading implementation of SQL to Hadoop and NoSQL systems. It is a new architecture for SQL on Hadoop, seamlessly integrating data in Hadoop and NoSQL with data in Oracle Database. It radically simplifies integrating and operating in the big data domain through two powerful features: newly expanded External Tables and Smart Scan functionality on Hadoop.
Intrigued and fascinated yet? The adoption and evolution of Big Data processing & analytics is growing at a massive rate. This would be the absolute perfect time for Data enthusiasts & professionals to get their hands dirty and equip themselves with the proficiency in a Big Data platform. Come checkout our lot of the Big Data courses and we assure you this will be the best learning you will ever receive. Happy learning!
Apache Spark has been the new kid on the block that is now being touted as the next big thing in Big Data. It is the largest open source project in data processing and comes equipped with features that make it fast, easy to use and make it a unified engine. From the point of inception, Spark has taken followers in big companies such as Yahoo, Amazon, eBay, Groupon etc. on a massive scale. It has in a short span of time become the largest open source community in Big Data, with over 750 contributors from 200+ organizations.
Spark is a framework that enables parallel, distributed data processing. It offers a simple programming abstraction that provides powerful cache and persistence capabilities. Its framework can be deployed through Apache Mesos, Apache Hadoop via Yarn, or Spark’s own cluster manager. It also serves as a foundation for additional data processing frameworks such as Shark, which provides SQL functionality for Hadoop.
Spark is an excellent tool for iterative processing of large datasets. One way Spark is suited for this type of processing is through its Resilient Distributed Dataset (RDD). By using RDDs, programmers can pin their large data sets to memory, thereby supporting high-performance, iterative processing. Compared to reading a large data set from disk for each iteration of processing the, in-memory solution is obviously much faster. Spark enables applications in Hadoop clusters to run up to 100x faster in memory, and 10x faster even when running on disk. Spark makes it possible by reducing number of read/write to disc.
Developers can use the Spark framework via several programming languages including Java, Scala, and Python, enabling them to create and run their applications on their familiar programming languages and making it easy to build parallel apps. It comes with a built-in set of over 80 high-level operators. Spark supports SQL queries, streaming data, and complex analytics such as machine learning and graph algorithms out-of-the-box. Not only that, users can combine all these capabilities seamlessly in a single workflow.
Spark has seen implementation in standalone use cases over Hadoop such as in iterative algorithms in machine learning, interactive data mining and data processing, stream processing and sensor data processing. Spark is very easy to get started writing powerful Big Data applications. So go on, take your turn and become a master Spark developer with our all-inclusive live online course Big Data with Apache Spark using Scala.
The use of internet has gone beyond its initial expectations and parameters. Interaction through social media has become the widest use of this technology and how! The rise of increased users and the need for online presence has given way to social networking sites, blogs, forums and many more tools. Since the whole world is online, so are the services and products on offer by various companies. This entire online sphere of existence has given way to a smart analysis tool, what we popularly call social media analytics.
Social media analytics refers to the gathering and analysis of data from online resources and map the trends of consumers. We express our entire lives on various online platforms like Facebook, WordPress, YouTube, Flickr, Twitter, Instagram among others. This data is used to measure how the product is prevailing with its users and what is the general consensus on the abilities of the product. This gives the business an insight into the consumers’ minds and can be used to determine a future course of action. Social media analytics helps companies take business decisions based on how its product is trending in terms of social media presence in the form of reviews, complaints, satisfaction levels etc. This enables a more real time picture than just the sales and revenue numbers. Having access to customer opinions at such a base level is beneficial for a company, and by properly leveraging this information gathered from millions of netizens, a company can take a step closer to its business goals of increased revenue, reduced customer service costs, feedback through customer opinions and improving the product overall. Social media is used to mine something called ‘customer sentiment’. I know there is a ‘say whaaat’ expression on your face right now, questioning how an emotion or sentiment can be gauged through an online medium. Let me quell your curiosity, when I tell you it is done through the language and content a consumer used to express their opinions. Certain keywords are parsed, the general tone of the message, status, tweet etc. is analyzed and a report is made on what is the overall opinion of the product in the market.
This might seem irrelevant to some, stating that as long as there is a revenue stream, there isn’t a need for anything else. But you do want that revenue stream to grow don’t you? Social media analytics helps realize how the product will grow based on current user trends and opinions, and how it can be improved further as per the consumer needs. It is always necessary for a business to have an eye on the future of the product and know how to extend its lifecycle for maximized profits.
Social media statistics throws up countless bundles of data, what we have come to call big data, and analyzing such huge amounts of data and making relevant and cognizant reports out of them is no mean task. Social media analytics employs various big data tools and techniques for getting the precise information needed by a business about its product and customers. Big Data gives analysts ample opportunities to explore the various aspects of trends and usage and gives a holistic view of the penetration of the product or service into the everyday lives of the consumers. From this emerged various software technologies and programming languages such as Tableau, Python, Apache Spark, Business Intelligence, and Predictive Analytics among the major ones. The demand and utilization of software to employ Big Data strategies is at its peak now and finds use in elections, marketing strategy, media, retail, science etc. As the data grows, so does the need to analyze and assess it.
Social media analytics has become an integral part of business solutions and strategies rather than just being a supplementary tool. An individual with these analytic skills is highly sought after and this is where we @LearnSocial have captured the opportunity. Our courses on various Big Data technologies and implementation have helped numerous learners get on board with the trend and give them a competitive edge, so why should you be any different. Log on right now and explore your options!!
What is Big Data?
Big Data is a term used to describe the availability of data & exponential growth; structured, unstructured & multi structured. It’s playing a vital role for all kinds of businesses irrespective of their size. More accurate data may lead to more accurate analyses, more accurate analysis may lead to more accurate decision making & more accurate decision making may help in improvising operational efficiencies, cost reductions, risk mitigation & so on. Earlier times, decision was taken by an organization on the basis of Gut Feeling but now organizations rely on the historical data to make better decisions. This has given more importance to big data.
In 2001 research report by Gartner, analyst Doug Laney articulated the definition of Big Data as 3vs which is still used to define Big Data –
- Volume (increasing amount of data)
- Velocity (increasing speed of data in & out)
- Variety (different formats of data, range of data types & sources)
Why Big Data Matters to You?
Organizations now have various mediums & sources to collect the data but they don’t know what to do with that data. Even if they know what to do with that data, they don’t know what technology to use for big data processing & analyzing. It is rightly said, that earlier times organization’s problem was how to collect data, then a time came when the problem was how to store that collected data & currently the problem is what to do with the collected and stored data. How to make every bit of data collected count? This is the major question that organizations ask themselves.
Talent pool in India!!!
When we talk about the required talent pool that organizations are looking for, we don’t have a huge no. of people who know Big Data Analytics & the required knowledge of advanced technologies for Big Data. Since, Big Data space is evolving & more and more organizations are practicing it, we expect more requirement will be needed in the near future in this domain.
Source – Analytics India Magazine
The right mix of a professional with excellent analytical skills & hands on experience with advanced technology like Hadoop, R, MongoDB & so on is what organizations are looking for. According to latest McKinsey report, more than 2,00,000 data scientists will be needed by the industry (2014-2016). Also, according to a report published in 2011 by McKinsey & Co., U.S. could face a shortage by 2018 of 140,000 to 190,000 people with “deep analytical talent” and of 1.5 million people capable of analyzing data in ways that enable business decisions. Almost same is the requirement across the globe for this era of Big Data.
This Big is Big Data!!!!!