Big Data Trends For 2016
Big Data Trends For 2016
Today we will talk about three big data trends that 2016 brought out.
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The process by which huge data sets are examined so as to uncover hidden patterns, customer preferences, unknown correlations, market trends as well as other useful business information is what is known as big data analytics. Big data analytics can be very useful in that it can help organizations to reduce cost, facilitate faster and better decision-making, as well as bring forth new products and services. Today we will talk about three big data trends that 2016 brought out.
Originally developed in 2009 at UC Berkeley, Apache Spark is a great open-source processing engine that has been built for sophisticated analytics, speed and ease of use. It offers programmers with an application programming interface concentrated on a data structure which is known as the resilient distributed dataset, a read- only multiset of data items spread over a cluster of machines, maintained in a fault tolerant manner.
The resilient distributed dataset (RDD) helps in the implementation of interactive algorithms, which visit their dataset several number of times, and interactive or explanatory data analysis. The latency of these applications could be cut down by various orders of magnitude. The training algorithms for machine learning systems found in the class of interactive algorithms formed the initial impetus used in the development of Apache Spark. Here is a simple data analysis carried out using Apache Spark.
Let us look at some of the features making Apache Spark to cause ripples in the big data world.
Lightning Fast Processing
With big data processing speed is always a vital aspect. Apache Spark facilitates applications in Hadoop clusters to operate up to 100 times faster in memory and ten times faster on disk. Spark makes this possible by cutting down the number of read or write to disk. The intermediate processing data is stored in-memory.
Easy to Use as It Supports Multiple Languages
Sparks allows one to quickly write applications in Java, Scala or even Python. With this developer can not only create but also run their applications on their familiar programming languages. Sparks comes with a built-in set of more than 80 high-level operators.
Supports Sophisticated Analytics
Apache Sparks supports complex analytics, streaming data as well as SQL queries. In addition to this, users can put all these capabilities together in one workflow.
Real-Time Stream Processing
Apache Sparks can without a problem handle real-time streaming. It can manipulate data in real time while using Spark Streaming.
Ability to Integrate With Hadoop and Existing Hadoop Data
Sparks can operate independently as well as on Hadoop 2’s YARN cluster manager and also read any Hadoop data. This amazing feature makes Sparks suitable for migration of existing pure Hadoop applications.
Hadoop-based Multi-core Servers
Organizations are slowly shifting from expensive mainframe and enterprise data warehouse platforms to Hadoop-based multi-core servers. Hadoop is an open source Java-based programming framework which supports the processing and storage of extremely large data sets in a distributed computing environment. Companies have Hadoop as their big data platform for a couple of uses.
Low-Cost Storage and Data Archive
Hadoop is useful for storage and combination of data such as clickstream, transactional, scientific, machine, social media, sensor etc. due to the modest cost of commodity hardware. This low-cost storage enables one to keep information that is not viewed currently critical, but which you might want to analyze later.
Sandbox for Discovery and Analysis
Hadoop can run analytical algorithms as it was designed to work with volumes of data in a number of shapes and forms. Big data analytics on Hadoop can enable companies to operate more efficiently, discover new opportunities and come up with next level competitive advantage. The sandbox approach provides an opportunity to come up with minimal investment.
With data lakes, storage of data can be done in its original or exact format. The aim is to provide a raw or unrefined view of data to data scientists and analysts for discovery and analytics. This enables them to ask new or difficult questions without many constraints.
Complement Data Warehouse
Hadoop sits beside data warehouse environment and some data sets being offloaded from the data warehouse into Hadoop or new kinds of data going directly to Hadoop. The main goal of each organization is to have a good platform for storing as well as processing data of various schema, formats, etc. to support different use cases which can be integrated at different levels.
IoT and Hadoop
At the center of IoT is a streaming and on torrent of data. Hadoop is normally used as the data storage for several transactions. Huge storage and processing capabilities allow one to use Hadoop as a sandbox for discovery and definition of patterns to be monitored for prescriptive instruction.
Predictive Analytics and Internet of Things (IOT)
The use of data, statistical algorithms and machine learning techniques to point out the likelihood of future outcomes based on historical data is known as predictive analytics. The aim is to go past knowing what has occurred to providing a better assessment of what will happen in the future. Predictive analytics is used for detecting fraud, optimizing marketing campaigns, improving operations and reducing risk.
Internet of Things (IOT) is the concept of connecting devices with an on/off switch to the internet or to each through the internet. The market for IOT is rapidly growing at an incredible rate. It is predicted that over the next 20 years the Internet of Things will add about $10 to $15 trillion to global GDP.
The examination of huge data sets is extremely vital for the purposes of uncovering hidden patterns, understanding market trends as well as other useful information. The above mentioned big data trends have been proved in 2016 to help reduce risk, improve operations and detecting fraud. With for single software environment and real-time analytics, Hadoop is the way to go for becoming a leader in the market of best websites. For the combination of real-time sources of data and together with huge data to create more insights, predictive analytics is the way to go. The three big data trends have huge benefits as shared here today.
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