Why Hadoop still make sense

 

Reasons Hadoop still make sense

Hadoop has moved to the cloud now. As the widespread benefits are for everyone to see, we will see how it enhances the power of Hadoop.

• Lowering the cost of innovation

Running Hadoop on the cloud makes sense for relevant reasons as performing any other software contributing on the cloud. 

Companies are still testing the waters with Hadoop, the less capacity investment in the cloud is a no-brainer. 

The cloud plays an important role in making sense for a quick, one-time help case including big data computing.

• Procuring large-scale resources quickly

The point above of quick resource procurement needs some elaboration. 

Hadoop platform was inspired by the vision of linear storage and compute using commodity hardware a reality. 

On internet google, who always managed at web-scale, they know that there will be a demand for running on more hardware resources. 

Hardware had to be acquired on their own.

• Handling Batch Workloads Efficiently

Hadoop is a batch-oriented system, where you can schedule jobs and have an incoming data feed. 

Enterprises are collecting activity data from web server logs and consume this collected data into an analytic function on the Hadoop.

The capacity to compute resources of a Hadoop cluster varies based on the timings of these scheduled runs or rate of incoming data.  

A fixed capacity Hadoop cluster built on physical machines is always on whether it is used or not – consuming power, leased space, etc. and incurring a cost.

• Handling Variable Resource Requirements

Not all Hadoop jobs are created equal. 

Some Hadoop jobs require more computer resources, some of them needs more memory, and some others demand a lot of I/O bandwidth. 

Normally, a physical Hadoop cluster is built of homogeneous machines, usually, they are large enough to handle the biggest work.

• Running Closer to the Data

Today businesses are moving their services to the cloud, it follows that data starts living on the cloud. 

• Simplifying Hadoop Operations

As the cluster merger happens in the business, there is one thing which gets lost is the segregation of resources for different sets of customers. 

As all of the client's jobs get grouped up in a shared cluster, the authority of the cluster gets started to deal with multi-tenancy problems such as clients jobs interfering with each other, varied security constraints etc.

Conclusion:

It is said that the next decade will be going to be dominated by Big-data wherein all the companies will be using the data available to them to learn about their company’s ecosystem and improving fallback.

Learn Big Data with ETLHive.

How Netflix Uses Big Data to Drive Success

Netflix has over 100 million subscribers in the past nine years since its founding; the online streaming platform Netflix has turned the film and television industry on its head. With that comes a huge amount of data they can analyze to improve the user experience. Big Data has helped Netflix to become the king in online streams.

If you have a Netflix subscription understands the process of finding a movie or TV show to watch. For Netflix user it has unique sets of options with Netflix, putting a lot of effort into ensuring you find something to watch quickly. This differentiates Netflix from other online streams.

Let’s see how Big Data helps Netflix

Big data helps Netflix to decide which programs will be of interest to the user and the endorsement of the system actually influences 80% of the content that user watches on Netflix.

The Netflix even gave away a $1 million prize since it has been founded to the group who came up with the best algorithm for anticipating how customers would like a movie based on previous ratings.

Algorithms help Netflix to save $1 billion a year in value from customer retention it is an eye-opening figure in the entertainment industry.

Finding the next smash-hit series with Big Data

Netflix has arranged itself as a content designer, not just a distributor for movie studios and other networks.

Big Data enabled Netflix to spend $100 million on 26 episodes of the House of Cards, as they were confident the show could be marketed successfully to their audience.

They know it would appeal to the fans of the originals British “House of cards” and the built-in fan bases for director David Fincher and actor Kevin Spacey.

The ultimate strategy which Netflix hopes to improve in the number of hours those customers spends using its service.

Big Data in Personalized video ranker

Netflix collects information for each member profile in a personalized way.

The same genre now for each member has an entirely different selection of videos.

These rows are based on genre, with headers like “Suspenseful Movies”.

Big Data algorithm orders Netflix’s catalog according to the user tastes and the resulting order is used to select the order of videos in a particular genre and in the other rows as well.

Big Data algorithm in Top-N video ranker

This algorithm of Big Data analyzes the best few personalized recommendations in the entire catalog for each member.

It focuses on the top of the rankings, unlike PVR which looks at the ranking for the entire catalog.

Big data in Trending Now

In “Trending Now” Big Data hones in on short-term trends, spanning anywhere from a few minutes to a few days.

It takes into account unique personalized metrics and teases out two types of trends – those that repeat at regular intervals, such as an uptick of romantic films around Valentine’s Day, and one-off events, like a hurricane, that drive sudden interest in documentaries about natural disasters.

Big Data in Continue Watching

“Continue Watching” is the list of shows the user has not completed watching.

Big Data sorts the recently viewed titles based on Netflix’s best estimate of whether you intend to resume watching, or if you’re just going to abandon the title.

Big Data considers factors such as time elapsed since viewing, whether you stopped watching in the middle, beginning, or end, or whether different titles have been viewed since.

Big Data algorithm in Video-video similarity

Finally, the “Because You Watched” row anchors its recommendations to a single video. This video-video similarity Big Data algorithm, as it is called, helps drive this list.

Big Data algorithm that generates a ranked list of videos for every video in Netflix’s entire catalog.

But even though the ranking itself is not personalized, the anchor video which makes it to the homepage is, as is the subset of BYW videos recommended.

Conclusion

Netflix is just one example of how Big Data helps us make simple viewing choices and how Big Data has influenced the entertainment industry in a big way. The applications of Big Data are many fold.

Want to know how you can reap the benefits of Big Data and Data Analytics?

Contact us at ETLHive today to know how it can drive your business!

Big Data & healthcare

Do you know Big Data helps in healthcare

The healthcare industry may be going through a seemingly endless period of flux, but there are a few unchanging truths about big data analytics that can help guide executive leaders through troubled times.

What is big data in Healthcare?

Big data analytics is not new in healthcare industry and it has lots of positive and life saving results.
Big data analytics prefers to the wide quantity of data created by the digitization of everything, that gets merged and analyzed by some specific technologies.
useful to healthcare, it will use precise health information of  people (or of a specific individual) and can sure that it helps to prevent epidemics, cure disease, cut down costs, etc.

Why there is a Need of Big Data Analytics in Healthcare?

There are big opportunities for big data in healthcare industry, because of increasing costs of hospitality in nations like United States. According to the report, healthcare costs currently are 17.6 percent of  Gross Domestic Product(GDP) —almost $600 billion  much ahead than the expected measure for a nation of the United States’s size and wealth.
In the earlier strategy, healthcare contributors was not having any source or motive to communicate patient information with expert and consult them, which made it tougher to apply the power of analytics.
Now many of them are getting paid based on patient end results, they have a proper financial motive to communicate information which will help to improve the lives of patients whereas the cutting costs for insurance companies.

1) Patients predictions for an improved Staffing

 In one hospital manager faces so many things like: If you put on too many workers, you run the risk of having unnecessary labor costs add up. If suppose you put very less workers, you will experience poor customer service result – which leads to a problem for patients in that industry.
Big data helps to resolve this problem, some hospitals using data from a variety of sources to come up with daily and hourly predications of how many patients are excepted to be a each hospital.

2) Electronic Health Records (EHRs)

It’s the broadest application of big data in hospital and medicine. Every patient that comes to hospital has its own digital report which includes populations, medical record, allergies, laboratory test results etc.
Through secured data systems records are shared to each other and are opened for providers from both private and public sector.
 Every report is collectively of one updatable file, which means changes can be implemented over time whenever doctor wants to make changes and can reduce paperwork and no danger of data replication.

3. Big Data helps patients come up with a treatment plan
 

Big data in healthcare will help you for strategic planning and to superior insights into public motivations.
Healthcare managers can inspect body check-up outcomes among people in various demographic groups and can identify that what are the things that discourage people from taking up treatment.

4. Big Data can Cure Cancer

Large amounts of data can be used by medical researchers on therapy plans and improvement rates of cancer patients. This will help to discover trends and treatments which have the maximum rates of success in the real world.
For e.g, researchers can study tumor samples in biobanks that are merged up with patient treatment records.
 Using this data, researchers can see things like how certain mutations and cancer proteins interact with different treatments and find trends that will lead to better patient outcomes.

Conclusion:

Bigdata Analytics in healthcare is coming with great results and promising this field for providing insight from very large data sets and doing best practices with positive results while reducing costs.
In the current day and age, we cannot ignore the potential of Big Data and there are many companies and industries that need Big Data professionals.
Contact us at https://www.etlhive.com and learn Big Data today.
https://www.etlhive.com/contact-us/