What changes will the big data industry usher in in 2017?

In my opinion, the changes in big data in 2017 will be mainly in the following points:

1. Internet of Things (IoT)

Companies increasingly expect to benefit from all data To gain value, organizations will have to adapt their technology to connect with IoT data. This brings countless new challenges and opportunities in areas such as data governance, standards, health assurance, security and supply chain.

The Internet of Things and big data are two sides of the same coin. Billions of "things" connected to the Internet will produce a large amount of data. However, this in itself will not trigger another industrial revolution, transform daily digital life, or provide an early warning system that saves the planet. Data coming from outside the device is what sets companies apart, and capturing and analyzing this type of data in context opens up new possibilities for companies.

2. Deep learning

Deep learning is mainly used to learn from large amounts of unlabeled/unsupervised data, so it is very attractive for extracting meaningful signs and patterns from big data. force. For example, it can be used to recognize many different types of data, such as shapes, colors and objects in videos, or even cats in images, as a neural network developed by Google did in 2012. As a result, enterprises may see more attention directed to semi-supervised or unsupervised training algorithms to handle the large amounts of data coming in.

3. In-memory analysis

Unlike conventional business intelligence (BI) software that runs queries on data stored on the server's hard drive, in-memory technology queries are loaded into memory information, which can significantly improve analytical performance by reducing or even eliminating disk I/O bottlenecks. As far as big data is concerned, it is precisely because of terabyte-scale systems and massive parallel processing that in-memory analysis technology is more interesting.

At this stage, the core of big data analysis is actually discovering data. Without millisecond latencies, running iterations to find correlations between data points would not be possible across millions/billions of iterations. Processing in memory is three orders of magnitude faster than processing on disk.

4. Cloud computing

Hybrid cloud and public cloud services are becoming more and more popular. The key to big data success is running a (Hadoop) platform on elastic infrastructure. We will see data storage and analytics converge, leading to new, smarter storage systems that are optimized for storing, managing and sorting massive petabyte-scale data sets. Going forward, we can expect to see the cloud-based big data ecosystem continue to grow beyond just “early adopters.”

5.Apache Spark

Apache Spark is lighting up big data. The popular Apache Spark project provides Spark Streaming technology to process data streams in near real-time by primarily using an in-memory micro-batch processing method. It has gone from being part of the Hadoop ecosystem to a big data platform favored by many enterprises.