What are the trends for next year's big data industry?

In the coming 2016, big data technology continues to evolve, and new PA is expected to adopt big data and Internet of things in many mainstream companies by next year. New PA finds that the prevalence of self-service data analytics, combined with the widespread adoption of cloud computing and Hadoop, is now changing across the industry, with more and more companies seizing the situation, or ignoring change, and thus facing danger. In fact, the tools are still there, and the Hadoop platform promises not to reach the point where the company lacks it.

Deep learning

Deep learning is a neural network-based machine learning technology, and deep learning is still evolving, but it shows great potential in solving business problems. Deep learning allows a computer to find interesting content from a large amount of unstructured data and binary data, and to derive relationships without the need for specific models or programming instructions.

A key concept of the deep learning approach is the distributed representation of the data, which allows for a large number of combinations of the abstract features of the input data, which can be used to represent each sample in a compact manner, resulting in a richer generalization. The source power of these algorithms is mainly from the field of artificial intelligence, the overall goal of AI is to simulate the human brain's ability to observe, analyze, learn and make decisions, especially to deal with extremely complex problems.

Deep learning is primarily used to learn from a large number of unlabeled/unsupervised data, which makes it attractive to extract meaningful representations and patterns from big data. For example, it can be used to identify many different types of data, such as shapes, colors, and objects in the video, or even the cat in the image.

As a result, businesses may see more attention being directed to semi-supervised or unsupervised training algorithms to process large amounts of data entering.

Cloud computing

Hybrid cloud and public cloud services are becoming increasingly popular. The key to big data success is running on a resilient infrastructure (HADOOP) platform.

New PA found that the company wanted to expand its platform, by investing heavily in the final rigid data center is impossible to do this. For example, the Human Genome Project started out as a gigabyte program, but quickly reached terabytes and petabytes. Some leading companies have started to split workloads in a dual-mode (bi-modal) fashion, running some data workloads in the cloud. Many people expect this trend to accelerate as the solution grows in the adoption cycle.

More and more companies are running APIs in the cloud to provide resiliency to better respond to demand spikes and create efficient connections that allow them to adapt and innovate faster than their competitors.

Apache Spark

New PA noticed that spark was lighting up big data. Spark is now the largest open source project for big data, providing significantly faster data processing than Hadoop, so it's extremely natural, extremely precise, and extremely convenient for programmers. Stream large chunks of data, splitting big data into smaller packets, then converting them, thus speeding up the creation of elastic distributed Datasets (RDD). This is useful in the present, and today data analysis often requires a set of resources for a machine that works together.

Internet of Things

The Internet of things and big data are two sides of the same coin, and billions of of the "objects" connected to it will produce a lot of data. However, this in itself does not trigger another industrial revolution, does not change the daily digital life, nor does it provide an early warning system for saving the Earth. Data from the outside of the device is what makes the business unique. Combining context to capture and analyze this type of data presents a new development for the company.

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What are the trends for next year's big data industry?