Big Data and Hadoop Development 2016
What is Big Data?
Let’s demystify this concept. Simply put, Big Data can be defined as the compilation of huge datasets that can’t be processed making use of conventional computing methodologies. It is neither one single tool nor a procedure. It rather engages multiple areas of technology and business.
Big Data Management & It’s Significance
For companies of every size, data management has gained an added dimension over the years. From being considered as a core competency today it is viewed upon as a crucial differentiator that holds the key to verify market winners. In the recent times, the government bodies as well as Fortune 1000 organizations have immensely benefited from the enhancements brought upon by the web pioneers. Furthermore, these companies are also outlining fresh and revolutionary practices and re-estimating the present strategies to review the methods in which they can convert their businesses with the usage of Big Data. This attempt has made them realize the basic nature and importance of Big Data. Forward thinking organizations today have understood that Big Data indicates a harmonious blend of innovative initiatives and technologies that involves data that is fast-changing, extremely varied and colossal.
However, in this highly developed and high-tech era, that makes it possible for companies to realize the real utility and value that Big Data management entails. For instance, modern day retailers have the choice to keep a track of user web clicks in order to recognize and outline the customer behavioural trends. This in turn allows them to ideate better and enhance their existing marketing and promotional campaigns, pricing structure as well as the stockage. The utilities are able to track the levels of household energy usage and forecast the energy outages and also emphasize on effective consumption of energy. Even government bodies and brands like Google using Big Data management can track and spot the surfacing of disease outbreaks through the social media signs. Furthermore, leading oil and gas corporations can derive the result of sensors in drilling devices in order to arrive at secure and effective drilling decisions.
The Big Data Benefits
Therefore, some of the visible core benefits of Big Data across multiple industry verticals usually include:
Making use of important data in social media platforms like the preferences and product awareness of customers, product organizations and how the retail companies are planning production
Marketing agencies are efficiently utilizing the vital data that is stored in social networking sites like Facebook and constantly updating their know-how about their campaign responses, promotions as well as the advertising channels
Hospitals make use of big data concerning the previous medical history of chosen patients in order to offer fast and improved service
Components That Falls under Big Data
It is a known fact that Big Data comprises of the data that is generated by multiple applications and devices. However, there are certain components that fall under the huge gamut of Big Data. They are:
Social Media Data – Social media sites like Twitter, StumbleUpon, Pinterest and Twitter stores vital information and interesting perspectives shared by millions of individuals globally.
Black Box Data – It can be described as a constituent of airplanes, jets, helicopters and the like. It helps to store the flight crew voices, the microphone and earphone recordings and the aircraft performance data.
Power Grid Data – This stores data that is used up by a certain node with regards to a base station.
Stock Exchange Data- This stores vital data about the sell and buy directions that gets created on the shares of multiple organizations.
Search Engine Data- This refers to the huge chunk of data that the search engines successfully regain from multiple databases.
Transport Data – This comprises of capacity, model, accessibility and distance of the vehicle
What Are The Big Data Challenges?
In the last few years, the huge overflow Big Data has made companies encounter a series of functional challenges. With ongoing integration, exchange and broadcast of Big Data, the Big Data challenges have been expanding. According to recent research updates, the Global Internet traffic has expanded from a data storage capacity of approximately 8.6 million Gigabytes to a massive count of 1 billion Gigabytes. And that is not all. Latest statistic figures reflect than above 183 billion emails are exchanged every day. To add on to that, the connectivity of IoT (Internet of Things), devices as well as the systems that function apart from the machine-to-machine open communications gets covered by a wide selection of protocols, applications and domains.
Therefore, to list down the core challenges of Big Data are:
Collecting data effectively
In order to effectively and adequately address and management these above-mentioned challenges, companies usually take assistance from enterprises.
The 4 Core V’s of Managing Big Data Challenges
Addressing and managing the challenges faced in Big Data management is usually classified in the context of the following 4 V’s that stands for:
Volume: A conventional PC device perhaps had an in-built storage capacity of 10 gigabytes back in 2000. However, in our new age high-tech era social networking sites like Facebook consume approximately 500 tetra bytes of fresh data regularly. Similarly, a Boeing 737 can produce up to 240 tetra bytes of flight data whilst taking a flight across US. In addition to this, the increasing use of Smartphone devices and the data it consumes and generates, the sensors that are in-built to daily devices is going to lead to billions of new, constantly upgraded data that includes, location, environmental data and even video. Simply put, it refers to the process of consuming huge data chunks that is produced inside and outside a company.
Velocity: This refers to the speed management in which the Big Data gets generated and is made to move across multiple business functions. This includes the ad impressions and the click streams store behaviour of users at a rate of millions of events every second. It also comprises of the high-frequency algorithms for stock trading that indicate the market changes in micro-seconds, the machines that processes huge exchange data between several devices and many more.
Variety: It is crucial here to understand that Big Data does not indicate dates, strings and numbers. It is also inclusive of the 3D data, geospatial data, video, audio as well as unorganized texts that comprises of social media and log files. The conventional database systems were arranged in a manner to address volumes of lesser structured data, minimal updates and constant data formating. In addition to that, it was also engineered in such a way to function on one server, making maximized capacity limited and costly. As the applications are transforming to cater to an increased number of users and the application development initiatives are becoming agile, the conventional usage of relational database had become an unavoidable responsibility for several organizations than a catalyst for business development.
Veracity: This indicates the determination or verification of the data authenticity that is being both processed and managed.
Big Data’s Significance in Today’s Growing Economy
Our economy is an expanding one and data forms a core aspect of it being generated in multiple ways every day. In times of concurrent usage, Big Data has proven instrumental for making precise analysis, understanding consumer behaviour or other crucial patterns and also arrive at well-informed decision making. These three activities collated together can make a company grow exponentially and attain a competitive advantage over others. It further improves productivity and develops vital value for the entire economy simply by maximizing the product and service quality. Simultaneously it also curbs down the percentage of wasting valuable resources.
The present investment and IT innovation market trends, in addition to their profound influence of the competitiveness and profitability concerning the propagation of Big Data technology, are resulting in productive management enhancements and new age analytical capacities for small and huge sized business firms. Keeping in mind, the present day situation, all organizations are required to have a clear understanding of the potential that Big Data holds and work with it to generate favourable value, especially if companies want to strive, survive, perform well and stand out in a competitive market. Latest research highlights that there are retailers who are making apt use of Big Data technology and are making plans to expand their functional margins beyond 60 percent. That is an ambitious vision and if used well and strategically, Big Data technology can fulfil this objective.
Big Data Solutions
The Conventional Enterprise Way towards Big Data Solutions
This customary approach entails that a company will possess a computing device for storing and processing Big Data. When it comes to storage activities, it is expected that the programmers are going to depend on their traditional selection of database vendors like IBM and Oracle. This conventional medium requires that the user communicates with applications that in turn can manage data analysis and storage.
This approach however has proved to be a limited one. As in it seems to work perfectly well for the applications that are required to process a limited data volume and can be stored by typical database servers. However, when it is to do with a huge, bulk and scalable data, processing data through a single database seems to be a hurdle. Hence, an advanced and innovative solution needs to be implemented.
Need for Revolutionary Big Data Solutions
Considering the potential that Big Data has for progressive companies, it is crucial to look at the factors that pose a challenge on Big Data management with an optimistic eye than a limiting mind set. This would enable in the emergence of revolutionary solutions that will allow companies to understand the ways in which the same can be used to cultivate growth and increased profit.
Solution provided by Google
Google made its attempt to solve the limitation posed by the traditional solution by implementing an algorithm named MapReduce. This helps to divide the task in smaller chunks and then allocate them to disparate computers and collate outputs from them, that when assembled forms a dataset.
Today, new technologies like Hadoop have emerged to address and effectively solve the Big Data challenges and enable businesses to perform better.
The Emergence of Hadoop
Making use of the solution offered by Google, it was Doug Cutting along with his team that developed HADOOP, which is an Open Source Project. Simply put, Hadoop operates applications making use of MapReduce algorithm, where the data gets processed corresponding to the others. To state in a nutshell, Hadoop is being used to come up with applications that has the potential conduct complete statistical review on bulk data.
Brief History of Hadoop
Things started rolling with the advent of World Wide Web. So as this web started expanding back 1900’s stretching over to early 2000’s, the indexes and search engines were established in order to assist identifying and locating vital data amongst every text-oriented content. Back in those years, the search outputs were actually returned by the humans. However, as the World Wide Web expanded from a couple of dozens to millions of pages, there was a need for automation. Responding to this requirement the web crawlers got created. This apart, the search engine start-ups along with the university-driven research projects too started off.
Amidst all these projects, there was one named Nutch, which was an open-source web search engine. This was pioneered by none other than Mike Cafarella and Doug Cutting. Their objective was to invent a solution to be able to circle back to web results quicker by disseminating data and circulations all over multiple computers in order to perform multiple tasks simultaneously. During this same phase, there was yet another search engine project that was on the progress known as Google. It too was modelled on a similar concept of processing and storing data in an automated and distributed manner for the significant web outputs to return faster.
It was in 2006, Doug Cutting joined hands with Yahoo and started working on the Nutch project along with focusing on the scopes of work with Google’s early initiatives concerning an automated and distributed data processing and storing. What followed was a division of the Nutch project and the web crawler part stayed at Nutch. And the processing and distributing part came to be named as Hadoop that derived its name from a toy elephant that belonged to Doug Cutting’s son. Back in 2008, Yahoo successfully released Hadoop as an open-source project.
In the recent times, Hadoop’s technology ecosystem and structure are maintained and well-managed by none other than the non-profit ASF (Apache Software Foundation), which happens to be a global community of expert contributors and software developers.
What Are the Components of Hadoop?
Presently, there are 4 core modules that are built-in the fundamental structure from Apache Foundation. They are:
Hadoop Common: This comprises of the utilities and libraries by other modules of Hadoop.
MapReduce: This is a programming software model used to process huge data sets in parallel.
Hadoop Distributed File System (HDFS): This is Java-oriented scalable system that can store data all over various devices and does not require any prior organization.
YARN- This is an acronym for “Yet Another Resource Negotiator”. It is a resource management structure that is used for managing and scheduling resource requests from the distributed applications.
Important Advantages of Hadoop
One of the primary reasons why companies are resorting to Hadoop is its capacity to process and store mammoth data fast, i.e. any type of data. As the data volumes and is variants increase at an ongoing basis, this is one key consideration that every company is making. Few core benefits of this technology include the following:
Computing capacity- Hadoop’s distributed computing model is quick to process the bulk data. The increased number of computing nodes that gets used, the greater is the processing power.
Robust- Here applications and data processing are completely secured against any hardware failure. This means that if any node underperforms or stops performing, then the job gets disseminated to other nodes automatically to ensure that the distributed computing suffers no failure. This helps it to store several data copies automatically.
Flexibility- In contrast to the conventional relational databases, here there is no need to pre-process the data prior to storing the same. Hadoop allows you to store as much data as you prefer and also make your decision how you would want to utilize it. This comprises of unorganized data such as the videos, images as well as texts.
Scalability- This enables you to simple expand the system by adding extra nodes with minor administration.
Top Utilities of Hadoop
Other than the core utility of searching multiple web pages with significant results, today leading companies are gearing to use Hadoop as their big data platform. Keeping this in mind, the various uses that Hadoop can be put to include:
Reasonably priced storage and proactive data archive: The affordable pricing structure of commodity hardware is what makes Hadoop instrumental for combing and storing data like social media, transactional, sensor, click streams, scientific, machine and many more. This affordably priced storage allows you to keep data that isn’t deemed critical and that you may want to evaluate later.
The data lake: It has been observed that Hadoop is frequently used for storing huge data chunks without the restraints that are present in schemes usually present in SQL-oriented world. It gets utilized as a reasonably priced compute-cycle platform, which assists data quality jobs and ETL processing simultaneously making use of commercial or hand-coded data management techniques. The processed outputs are circled around in other systems as required.
Platform for analytics store and data warehousing: A common utility of Hadoop is to be the platform for huge and raw data chunks for it to be loaded to a EDW i.e. Enterprise Data Warehouse. Alternatively, the data chunks can also be loaded to an analytical store used for various activities like query, reporting and new age analytics. Companies today are aiming to implement Hadoop to monitor various kinds of data and offload historical data directly from EDW’s.
Recommendation systems: A prominent analytical use done by few Hadoop’s biggest adopters is targeted at web-oriented recommendation systems. For instance, LinkedIn – the career scopes you might be keen on, Facebook- the contacts you might know better, eBay, Netflix, Hulu – which stands for the items that you could be interested in. Such systems evaluate big data chunks in real-time fast for forecasting the preferences prior to their customer leaving a web page.
Sandbox for assessment and discovery: Since Hadoop was devised to manage data volumes in multiple forms and shapes, it can also operate systematic algorithms. The Big Data Analytics on Hadoop is effective in supporting your company to function proactively, discover new scopes and obtain a competitive edge over other market players. This sandbox approach offers a scope to revolutionize operations with minor investments.
The Challenges of Hadoop Implementation
Hadoop is not bereft of its execution and functional challenges.
MapReduce programming isn’t suitable for every concern: MapReduce is effective for easy data requests and concerns that can be categorized into separate units. However, it isn’t effective to manage interactive and iterative analytic tasks, as its file-intensive. The nodes aren’t capable of intercommunicating except via shuffles and sorts and the iterative algorithms need various sort-reduce/map shuffle stages to be complete. This generates various files between the MapReduce stages and is considered ineffective for high-end analytical computing.
Data safety: The other challenge that Hadoop faces revolves around the issue of fragmented data safety, even though advanced technologies and tools are emerging. In the recent times the Kerberos authentication protocol has proven to be an advanced step towards securing Hadoop platforms.
There exists a talent gap: At times it becomes a tad bit difficult to locate entry-level programmers with adequate Java expertise who can excellently manage MapReduce. This is a major reason as to why the distribution providers are in a hurry to place the SQL technology instead of Hadoop. It is simpler to find SQL programmers. Furthermore, Hadoop management appears to be a mix of science and art, needing lesser know-how of the OS, Hadoop kernel settings and hardware.
Total governance and data management: It has been observed that Hadoop does not possess complete and easy-to-use tools for appropriate data cleansing, data management, metadata and governance. The tools that lack to a great extent are for data standardization and data quality.
How to Overcome the Hadoop Challenges?
The answer is SAS. Industry guidelines, company requirements and governance that place specific limitations on the deployment of analytics model in a productive platform is not new to SAS. Hence, SAS has established a wide selection of solutions that can help clients monitor their models through a predictive analysis life cycle. In addition to that, SAS has also adopted Hadoop as a new age Big Data technology for distributed storage and is capable of performing simultaneously with open source analytics products in order to rise over the challenges.
Recently, there has been an emergence of multiple SAS solutions and products focusing on Hadoop that enables the clients to:
Have access to data from any Hadoop cluster for the purpose of modelling as well as writing prognostic scores to Hadoop
Conduct data management along with data quality functions that is crucial to produce analytic base tables to model in a Hadoop group
Discover the data in a visual and interactive way by placing them to the shared memory of a distributed platform
Create perceptive models in a consolidated way, by making effective use of in-memory technology to conduct model training with high-end algorithms running parallel and that leverages the distributed computing resources
Publish and authenticate such models to productions, utilizing scoring acceleration to fasten up the scoring procedure by imitating scoring job implementation across Hadoop cluster nodes
Manage the model performance over a span of time and commence the set up of a new model for a new life cycle phase, that is either generated through alerts or takes place in an automated manner
Effectively monitor all the needed resources that comprises of users, servers, models, workflows, life cycle templates, reporting content and many more utilizing the SAS meta data
Solving the Hadoop challenges and shortcomings with SAS will allow you to make the most of Big Data and use it as a catalyst to bring about positive outcomes of organizational growth, profit and development.
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