Every business now recognizes the power of Big Data Analytics in developing deep actionable insights to enjoy business advantages. However, unlike before when businesses were required to deal with gigabytes of data, the present scenario requires to store and process huge piles of data that is measured in petabytes and terabytes as it is produced by rapidly growing internet population, systems and enterprises. But, as we all have learnt all the years growing up, no problem lasts forever in the technology world. Likewise, Hadoop Analytics is one such tech solution that brings an end to all your big data analytics concerns.
Hadoop is an open-source framework that lets an organization to process huge data sets parallely. Hadoop was designed keeping in mind that system failures is a common phenomenon, therefore it is capable of handling most failures. Besides, Hadoop’s architecture is scalable, which allows a business to add more machines in the event of sudden rise in processing-capacity demands.
As said earlier that the amount of data available today is humongous, the role of Hadoop in big data analytics becomes very important. Hadoop works by filtering and breaking large amounts of data into pieces, and then distributing each piece of data to several nodes of a specific cluster for processing. It’s very important to understand the core of Hadoop if you want to give your business a competitive edge using big data analytics. Many big organisations working on blockchain technology like ethereum gold are also making use of big data analytics to better take care of their data.
Basic Components of Hadoop Architecture
Hadoop Distributed File System (HDFS) : HDFS is the distributed storage system that is designed to provide high-performance access to data across multiple nodes in a cluster. HDFS is capable of storing huge amounts of data that is 100+ terabytes in size and streaming it at high bandwidth to big data analytics applications.
MapReduce: MapReduce is a programming model that enables distributed processing of large data sets on compute clusters of commodity hardware. Hadoop MapReduce first performs mapping which involves splitting a large file into pieces to make another set of data.
After mapping comes the reducing task, which takes the output from mapping and assemble the results into a consumable solution. Hadoop can run MapReduce programs written in many languages, like Java, Ruby, Python, and C++. Owing to parallel nature of MapReduce programs, Hadoop easily facilitates large-scale data analysis using multiple machines in the cluster.
YARN: Yet Another Resource Negotiator or YARN is a large-scale, distributed operating system for big data applications. YARN is considered to be the next generation of Hadoop’s compute platform. It brings on the table a clustering platform that helps manage resources and schedule tasks. YARN was designed to set up both global and application-specific resource management components. YARN improves utilization over more static MapReduce rules, that were rendered in early versions of Hadoop, through dynamic allocation of cluster resources.
Every business has different data analytics requirements, which is why Hadoop ecosystem offers various open-source frameworks to fit your special data analytics needs. Let’s check out below!
Apache Hadoop Frameworks
1. Hive
Hive is an open-source data warehousing framework that structures and queries data using a SQL-like language called HiveQL. Hadoop allows developers to write complex MapReduce applications over structured data in a distributed system. If a developer can’t express a logic using HiveQL, Hadoop allows to choose traditional map/reduce programmers to plug in their custom mappers and reducers. Hive is a very good relational-database framework and can accelerate queries using indexing feature.
2. Ambari
Ambari was designed to remove complexities of Hadoop management by providing a simple web interface that can provision, manage and monitor Apache Hadoop clusters. Ambari, which is an open-source platform, makes it simple to automate cluster operations via an intuitive Web UI as well as a robust REST API.
Ambari’s Core Benefits:
3. HBase
HBase is an open-source, distributed, versioned, non-relational database model that provides random, realtime read/write access to your big data. Hbase is a NoSQL Database for Hadoop. It’s a great framework for businesses that have to deal with multi-structured or sparse data. HBase makes it possible to push the boundaries of Hadoop that runs processes in batch and doesn’t allow for modification. With HBase, you can modify data in real-time without leaving the HDFS environment.
HBase is a perfect fit for the type of data that fall into a big table. HBase first performs the task of storing and searching billions of rows and millions of columns. It then shares the table across multiple nodes, paving the way for MapReduce jobs to run locally.
4. Pig
Pig is an open-source technology that enables cost-effective storage and processing of large data sets, without requiring any specific formats. Pig is a high-level platform and uses Pig Latin language for expressing data analysis programs. Pig also features a compiler that creates sequences of MapReduce programs.
The framework processes very large data sets across hundreds to thousands of computing nodes, which makes it amenable to substantial parallelization. In simple words, we can consider Pig as a high-level mechanism that is suitable for executing MapReduce jobs on Hadoop clusters using parallel programming.
5. ZooKeeper
ZooKeeper is an open-source platform that offers a centralized infrastructure for maintaining configuration information, naming, providing distributed synchronization, and providing group services. The need of a centralized management arises when a Hadoop cluster spans 500 or more commodity servers, which is why Zookeeper has become so popular.
ZooKeeper also avoid single point of failure situation as it replicates data over a set of hosts, and the servers are in sync with each other. Although Java and C are currently used for ZooKeeper applications, Python, Perl, and REST interfaces could also be used someday for ZooKeeper applications.
Apache Hadoop is a great platform for big data analytics and there are various other technologies, like NOSQL, Avro, Oozie, and Sqoop, available in the tech market that makes Hadoop ecosystem very versatile. You must carefully assess big data analytics needs of your business before jumping on a Hadoop technology, so that you get the best results. Hadoop big data analytics has already helped many businesses to touch new heights, you can also help your business grow by using Hadoop analytics for data science.
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‘चलो StartUp’is a 10-episode video series that present inspiring lessons and start-up stories on how to be a successful entrepreneur & jobs creator in India.
Why am I doing this?
When I re-engaged with startups as a mentor & angel investor in late 2016 after a 2-year gap, I was flooded with requests. I realised that most of them had similar questions and concerns. The 10 videos will hopefully provide answers to the most common questions usually asked. What business to start? Do it alone or with partners? How easy or difficult is it? Is language, gender or education a barrier? Is funding necessary? Can technology help?
Episode.1 DON’T STRESS. STARTUP KARO! – How to turn your dream into StartUp success
Sujit Panigrahi saw China top the medals tally in 2008 Olympics. His passion made him a sports entrepreneur. He was determined to make India a sporty, fit nation
Sujit’s startup Fitness365 (http://fitness365.me/) has taken over sports & physical education at schools, and completely professionalized it. Over 1 lac school kids will get detailed physical fitness report cards this year. His aim is 1 crore children. He has struggled to get here, but his passion is finally tasting success.
Passion is main reason why people become entrepreneurs (“Felt restless. Wanted to do something different or big”)
Have a dream? Don’t waste it. STARTUP KARO!
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From health and banking to education and shopping – big data has completely changed the way we experience the world around us. But besides its benefits, most people don’t really understand the dangers of this phenomenon.
Namely, this can jeopardize the traditional notion of privacy in the digital world. The problem is so acute that there were already a lot of official appeals to highlight the need for stronger privacy safeguards. Bearing in mind the importance of this topic, we decided to explain to you how big data expansion can significantly damage our privacy in the future.
6 ways big data jeopardize personal privacy
A big number of IT specialists already pointed out that information in the digital age need careful monitoring because the very essence of big data may influence our privacy.According to digital security experts at Aussie Writings, the risk of being deprived of private life is constantly increasing.
They noted that the development of technology, Internet, social media, and automation make it much more likely to happen than a decade ago. But what are the exact fears of privacy issues in this field? Let’s take a closer look.
1. Big time data breach
We’ve already seen how Yahoo experienced two major data breaches, affecting more than a billion accounts altogether. This is not the only example as other companies faced similar problems in the last few years: JP Morgan Chase, eBay, Target Stores, etc. Big data brings huge risk of large-scale information leakage and this is not something that we can always prevent from happening. The only question is who and how will get exposed in future cases like this.
2. Individuals don’t control personal information
More than 90% of U.S. citizens believe consumers have lost control of how companies are using their personal information. This is not too far away from the truth and it’s something that can frighten every single person in the world. We can hardly even guess who owns information about us, our families, jobs, leisure activities, and hundreds of other details. We are poorly protected and we can do almost nothing about it at the moment.
3. Weak data protection
National governments and international organizations still don’t have strict and uniform laws against big data misuse. According to the recent study, unauthorized use of IT systems is endemic across Europe as almost half of CIOs claim it’s a widespread problem. The situation is not much different in other parts of the world, which means that people can barely ever feel safe when it comes to big data exposure.
4. Data sales
The fact that you revealed some details about yourself online doesn’t mean that you want to see a third party obtaining this information eventually. However, cases like this are not rare at all. As companies gather huge volumes of data about everything and everybody, they can sell it to other businesses that search for this kind of information. They don’t ask for your permission and there are no direct regulations which could prevent this scenario from happening.
5. Discrimination
Data science has the power to analyze and predict things that even you don’t know about yourself. This is extremely dangerous because it can automate discrimination and let it go by without anyone taking responsibility for it. For instance, algorithms can learn that a job candidate has a chronic disease and eliminate this person from the hiring process. There are countless similar examples how big data can boost discrimination in modern societies.
6. Nobody is anonymous
Today, it is almost impossible to stay anonymous. Whatever you do, say, watch, read, click, or comment will be recorded and analyzed through big data algorithms. The only escape from it is to isolate completely from the outside world, which of course is not an option. Therefore, you should bear in mind that your favorite retail store probably knows all about your vegetarian diet. And the bank knows even the smallest transaction you’ve ever made.
Not to mention the Government and its institutions like FBI or other intelligence agencies. They have your fingerprints, bank account information, photos, everything. This can have serious political consequences but also implications on the state of human rights in the long run.
Conclusion
Big data changed the way we live and allowed us to enjoy benefits unknown to previous generations. It also brought us new privacy issues that we still cannot figure out completely, but we will probably have to find the solution somewhere in between. In this article, we showed you 6 ways how big data jeopardize our privacy – feel free to write us a comment and tell us what you think is the biggest threat in this field.
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While online privacy is well recognized as a matter of paramount consideration, global internet users surprisingly, aren’t doing enough to protect it.
In a statista survey, over 2.14 billion people worldwide are forecast to shop online in 2021. There were reportedly 1.66 billion global digital buyers in 2016.
The global economic story has all to take encouragement from this fact; however it also leaves open avenues for exposed privacy.
Users aren’t naive to modern day online practices where every website tracks its visitors’ behaviour to target them later, based on their history.
Growing social trends aren’t healthy either. Today, an average person has 5 social media accounts and spend over 1.5 hours on the net which accounts for nearly 30% of his daily internet time.
With so much dependence on internet, it’s understandably difficult to imagine a life without web. However, a change to our internet habits is imperative if we are to expect a modest online experience.
See this infographic for some more useful tips to protect online privacy.
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For years the retail sector has been making use of customer data to propel their own marketing campaigns forward. What remains to be seen in this regard is, how the future of better data collection and technology, coupled with an enhanced need for customer personalization shakes up this procedure in the future.
For the retail sector the future promises amplified feasibility and a better distribution network. We even have data centric innovations that use up data to point out towards what the customers want. On the other side of the coin, customers love every little bit of personalization that is thrown at them. Personalization feels like a brand is communicating with them, and they love every bit of it. But, the only drawback here is that customers are beginning to question what happens to data collected by or through them? Simply put, customers want the best of both worlds; a complete personalized experience, with the perks of enhanced privacy. The onus now lies on protagonists within the retail sector, and how they are able to meet these enhanced needs from customers.
Up Close with Customers
The recent wave towards providing a better customer experience has meant that retailers now value customers more than they ever did before. This has meant that retailers are bidding to go up close with customers and find out exactly what they are looking for. A recent study conducted at a mass level across the globe has garnered sufficient insight into what the customers want from this digital transformation. Are they okay with the huge chunks of data that is collected through them? Or, are they just indifferent to the whole issue? Let’s have a look at some of the most interesting facts and figures found out through this authentic research.
The key here is to get all the basics right. The journey for better customer satisfaction begins with understanding what customers want, because if this is not achieved organizations actually risk losing customers.
Providing Personalization
Providing personalization is not as simple as it seems. With over millions of customers at times, ensuring a personalized experience requires the power of smart analytics. Data algorithms lead the way in this regard and provide a simple yet proficient solution. Data from multiple sources can be fed to these algorithms, which can in turn give retailers the right tools for targeting the right customers through a seamless form of personalization across all channels.
Several fashion brands have already implemented the right mix of personalization. Fashion brands have realized the importance of personalization in their offerings and have come up with ways to predict products that will appeal to a certain customer. Another subtle approach to the conundrum of personalization will be for retailers to give customers the leverage of shaping their loyalty cards according to their own preferences. This would mean that customers could tailor the offers they get on their loyalty cards based on their preferences. But, despite the benefits of personalization it often collides with interests of customers who want their data to be safe and secure. To understand this better, we need to take a look at GDPR regulations.
GDPR
What the new General Data Protection Regulation Law does is it shifts data control from businesses to clients. Using this control, clients would be able to specifically decide the companies they want to store their data and the companies they would rather pass. Moreover, they can specify the manner they want their data to be used by such organizations. As per the GDPR, clients can exercise the following rights:
Ask the Readers
Every single customer can give their insight to propose a solution to this conundrum. This is why we ask all our readers to suggest whether customers need a solution to control their data? And should consumers communicate with companies anonymously, by sharing some basic information or by providing personal data? Below you can find an example how you can (temporarily) provide access to the personal information you would like to share so the retailer can personalize its offers.
We look forward to seeing what you think and how retailers can flex their offerings based on the opinion of all customers.
Originally appeared here. Published with permission.
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Big data has proven to be more than just a buzzword floating around the business-world. The industry has continued to grow at a rapid pace, and the global big data market is expected to hit over $40 billion in 2018. More and more companies in multiple sectors and industries are getting in on this valuable technology.
So why are some small-to-medium sized businesses (SMBs) and startups still holding out?
The truth is that many smaller companies feel that there are just too many challenges in their way when it comes to using this big data. This relatively new and swiftly changing form of technology can be intimidating, but the rewards far outweigh the risks. In fact, IDC’s 2017 survey found that 87% of SMBs saw better results than they anticipated once they invested in data technology.
Big data has shown to be especially effective when applied to project management strategies. If your business is considering adding big data to their project management methods, then they will need to be aware of the challenges they will face and the tactics to use to overcome them.
Let’s discuss.
Multiple Team Coordination
One of the greatest qualities of big data is that it is not limited to any single branch. It can help marketing teams develop better strategies, accounting departments budget more efficiently, and make sure IT departments have the best information at their fingertips.
However, one of the biggest challenges that businesses face is coordinating this type of technology between various departments. Project management with big data requires leaders to manage cross-functionally across multiple teams and departments. For example, big data can bring tremendous results for sales teams, but only if they are able to coordinate their findings with marketing and web development departments for a unified strategy. Project leaders are responsible to make sure this happens.
If a business is ready to start using big data for development and project planning, they must implement the best tools to encourage team coordination and organization. Many teams have found that web based project management is especially effective for keeping teams virtually connected through every step. Businesses that started using project management programs saw significant spikes in productivity, communication, and overall project quality, with 75% of teams successfully reaching their goals.
Coordination and communication are vital to any team project, especially when big data systems are involved.
Knowing How to Apply it
Last year, 16.1 billion zettabytes (one ZB = trillion gigabytes) of data was generated on a daily basis. While that number is certainly remarkable, by 2025, that number is expected to increase to 163 ZB every single day. Due to the sheer volume and complexity, many struggle to know what exactly certain datasets mean and how they can apply new advancements to their organization.
Project leaders must be creative and find new ways to implement big data resources into their business. Sadly, poor application of data can cost businesses big time, with estimates between 6% to up to 30% of revenue lost. In order to avoid this catastrophic mistake and properly use the information provided through data analysis, businesses need to be open-minded to change and innovation.
Once teams know how relevant datasets will benefit and guide their efforts, they will be able to apply it more accurately. Project managers must push teams to find new ways to use their data findings for improved results.
Staying on Top of New Developments
The industry of big data is constantly changing, evolving, and expanding, causing many small businesses to question whether or not they can keep up with latest developments. As more businesses begin to rely on big data, industries are predicting a major shortage in 2018 of up to 190,000 workers who can fulfill the necessary roles for proper data analytics. Becoming a data-driven organization will require teams to stay educated on the latest developments in the field.
Project managers should encourage everyone involved to stay educated and up-to-date with big data developments. There are lots of courses offered online for education and certification in data science that could be beneficial to project leaders and managers. Leaders should set an example for their team members by familiarizing themselves with big data trends and terms while sharing their findings with others. There are lots of intricacies and misunderstandings when it comes to big data, so managers must stay on their toes and informed in order to lead their organization to success.
Businesses of all sizes should be proactive when it comes to training their employees so that they don’t fall behind or use outdated practices. By pushing for continued education, startups and SMBs can pave the way in their industry and surpass their competitors who fail to adapt.
Conclusion
Big data has the potential to revolutionize traditional project management practices. From planning strategies based on reliable metrics, to accurately measuring results, companies seem to only gain from implementing big data to their systems.
It is important for small businesses and startups to understand the incredible impact that big data can have on their success. This realization can help fuel project managers and team members alike to come up with creative strategies and innovative ways to collect and apply big data metrics.
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