Data analysis always gives the final result in some certain conditions. A variety of methods, tools and procedures can help in data dissection, shapes them into actionable insights. When we look at the future of data analytics, we can predict some of the latest trends in technologies and tools that are used to dominate the analytics space:
1. The deployment model of the system
2. Visualization system
3. Data Analysis Systems
1. Model deployment:
A few of the services that the SaaS model you want to replicate on the spot, in particular, the following:
Domino Data Labs
In addition, required to provide models, increasing demand for documentation from the code seen also. At the same time, it is expected to view in the version control system however it fits the data for science, the possibility of tracking different versions of a record.
2. Visualization system:
This library may be restricted only in Python, but it also provides a solid opportunity for fast acquisition in the future.
Providing the API in Matlab, R, Python, this data visualization tool was a name and appears on track for a quick and broad introduction.
3. Data Analysis Systems:
Open-source systems like R, with its fast maturity of the ecosystem and Python, with scikit-learn and pandas libraries; appear, are in favor of continued monitoring Analytics space. In particular, some projects in the Python ecosystem appear ripe for rapid adoption:
By selecting the capacity to do the processing on the disk and not in memory, this interesting project-for the purposes of finding the average between the use of local devices for in-memory computing and Hadoop processing cluster, resulting in the prepared solution, while the data size is very small, you need a Hadoop cluster managed actually no less than in memory.
These days, data scientists are working with a large number of data sources ranging from SQL database clusters and CSV files to Apache Hadoop. Expression engine blaze helps data scientists use the permanent API for working with the full range of data sources, lightening the cognitive load required when using different systems.
Of course, Python and R ecosystems only the beginning, because Apache Spark-the system also appears to increase the adoption last but not least it provides an API in R and Python.
Establishing on the conventional trend of using open-source ecosystem, we can as well say the prognosis for vision in the same direction, approaches based on sales. For example, the Anaconda distributions for R and Python, the cab and the hood ensures only one Python distribution fits the data for science. And no one will be shocked if you see that the integration of the video Analytics software, such as Python or R in a common database.
About open-source systems, developing organism helps business users to interact with the data directly while will help you drive data analysis. These tools try to abstract the data science method from the user. Although this approach is still not Mature enough, it offers what seems a very potential system for data analysis.
Going forward, we expect that the tools, data and analysis, see rapid application in the primary business process, and we expect to use this guide for companies a data-Driven approach to solutions. For now, we must keep our eyes on the previous tools that we don’t want to miss to see you data to make the world.
So, the meeting, the power of Apache Spark in the complex environment of growth for data science. Study also experience in data science, joining the data science certification training, as both R and the spark can be used by applications for building their data science. Thus, it was a full overview of the top tools and technologies dominate the analytics space in 2016.