8 Big trends in big data analytics

8 Big trends in big data analytics
The world is moving fast and all technologies are improving, big data disciplines and analytics are evolving so quickly that businesses have the risk of being left behind so waiting is not an option. In this blog i present the top emerging technologies and trends that should be on your watch list according to IT leaders, consultants and industry analysts.
1. Big data analytics in the cloud                                                                                                      Hadoop is a group of tools used for processing very large data sets and was first designed to work on clusters of physical machine. Nowdays there is a big number of technologies that help us to process data in the cloud. Amazon redshift is a company that collects online and brick-and-mortar retail sales and cusstomer demogrphic data as well as behavioral data and analyze the information helping the retailers to create a  targeted messaging in response to the shoppers. Amazon redshift is innovative and easy to use; it is cheaper to expand on virtual machines than buy physical machines. Itwill soon get to the point where it will be cost prohibitive to move all the data to the cloud.                                                                                                                        2. Hadoop: The new enterprise data operating system                                                              Distributed analytics framework are evolving into resource managers that are turning Hadoop into a general-purpose data operating system. With these you can manipulate and analyze the data by plugging them into Hadoop as the file storage system. This ability of running many different queries and data operations will make it low cost. The strategy is a long-term strategy to enable all types of interactions with people and products.
3. Big data lakes
Traditional database theory dictates that you design the data set before entering the data. Data lakes do not do that, they take all the data sources and put them in the Hadoop repository, Instead it provides tools to analyze the data and a high definition of it. People who use it has to be highly skilled. Building a big data lake in Hadoop is risky because the platform is not enterprise ready.

4. More predictive analytics
Analysts need not only data but processing power to handle large numbers with many attributes. The combination of big data and compute power lets the analyst explore new behavioral data called sparse data. It is called like that because tin order to find something of interest you must go through a lot of not important data. Sometime ago it was computationally impossible to use machine-learning with this type of data, now is possible and cheap.

5. SQL on Hadoop: Faster, better
It is used to put the data into a format and language people are familiar with. Tools that support SQL querying let business users that understand SQL apply similar techniques to the data. It is not going to replace data warehouses but offers an alternative to more costly softwares and appliances for certain types of analytics.

6. More, better NoSQL
NoSQL stands for Not only SQL. There is estimated 15 to 20 open source NoSQL each with its own specialization, they are being used more because of the kind of analyses people need. A NoSQL key-value database has a special purpose: high performance and lightweight.

7. Deep learning
Set of machine-learning techniques based on neural networking shows potential for coming up with business problems solutions. It enables computers to recognize items of interest in big quantities of unstructured and binary data and deduce relationships without the need of specific models or programming instructions. It could be used to recognize many different kinds of data and indeed needed for the future.

8. In-memory analytics                                                                                                                  
The use of in-memory databases is getting more common and very beneficial, many business are using hybrid transaction/analytical processing (HTAP) letting the information stay in the same inside memory database. One of the problems  where users get confused is that most of them are putting transactions from many systems together when you still have to integrate the data.
However,  an in-memory database means there is another product to manage, secure, and figure out how to integrate and scale.

Staying one step ahead

With so many new trends around big data and analytics, I believe IT organizations need to create conditions that will allow analysts and data scientists to experiment.


Comments

  1. Erick LoboJune 8, 2020 at 12:33 AM

    This is a very interesting and useful article that I could learn a lot by reading it. By reading a lot of Big Data related articles, I could learn that most people think that Big Data is extremely important for companies nowadays and that if you do not pay attention to it and fail to use and implement it in your business you can get left behind by your competitors and this is not something anyone that owns a business wants. That is why in my opinion reading an article like this, that talks about the trends in big data, are very interesting and helpful for a person that is starting to learn about Big Data. It is good to learn about Big Data in the cloud, since cloud services are more and more present in everyone's life nowadays, so it is possible that Big Data in the clouds will become a big thing inside the industry. It is also good to read a little bit about Hadoop, that is the new enterprise data operating system.
    The part I liked the most is when we could learn a little bit about "deep learning". Deep learning is about a set of machine-learning techniques based on neural networking that can show you potential problems inside of your business. With that, computers can recognize large amount of data that is of the companies interest. It can also be used to identify different kinds of data and will be very important in the future.

    In general, I really liked to read about these Big Data trends and learn a little bit about it.

    ReplyDelete

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