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.
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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!!!!!