Data analysis always gives ultimate result in some definite terms. Different techniques, tools, and procedures can help in data dissection, forming it into actionable insights. If we look towards the future of data analytics, we can predict some latest trends in technologies and tools which are used for dominating the space of analytics:
1. Model deployment systems
2. Visualization systems
3. Data analysis systems
1. Model deployment systems:
Several service providers want to replicate the SaaS model on the premises, especially the following:
– Domino Data Labs
In addition, requiring for deploying models, a growing requirement for documenting code is also seen. At the same time, it might be expected for seeing a version control system however that is suited for data science, providing the capacity of tracking various versions of data sets.
2. Visualization systems:
This library may be limited to Python only, however, it also provides a solid possibility for rapid adoption in future.
Providing APIs in Matlab, R, and Python, this tool of data visualization has been creating a name for it and appears on track for rapid broad adoption.
3. Data analysis systems:
Open source systems like R, with its rapid mature ecosystem and Python, with its scikit-learn libraries and pandas; appear stand for continuing their control over the analytics space. Particularly, some projects in the Python ecosystem appear mature for fast adoption:
By giving the capacity for doing processing on disk rather than in memory, this exciting project targets for finding a middle field between utilizing local devices for in-memory computations and utilizing Hadoop for cluster processing, thus giving a prepared solution while data size is very small to need a Hadoop cluster yet not really small as being managed within memory.
These days, data scientists work with lots of data sources, ranging from SQL databases and CSV files to Apache Hadoop clusters. The expression engine of blaze helps data scientists utilize a constant API for working with a complete range of data sources, brightening the cognitive load needed by utilization of different systems.
Of course, Python and R ecosystems are just the beginning, for the Apache Spark system is also appearing increasing adoption – not least as it provides APIs in R and also in Python.
Establishing on a usual trend of utilizing open source ecosystems, we can also predict for seeing a move towards the approaches based on distribution. For instance, Anaconda provides distributions for both R and Python, and Canopy provides only a Python distribution suited for data science. And nobody will be shocked if they see the integration of analytics software like Python or R in a common database.
Beyond open source systems, a developing body of tools also helps business users communicate with data directly while helps them form guided data analysis. These tools attempt for abstracting the data science procedure away from the user. Though this approach is still immature, it provides what seems for being a very potential system for data analysis.
Going forward, we expect that tools of data and analytics will see the rapid application in mainstream business procedures, and we anticipate this use for guiding companies towards a data-driven approach for making decisions. For now, we need to keep our eyes on the previous tools, as we don’t want to miss seeing how they reshape the data’s world.
So, encounter the strength of Apache Spark in an integrated growth ambiance for data science. Also, experience the data science by joining data science certification training course for exploring how both R and Spark can be used for building the applications of your own data science. So, this was the complete overview on the top tools and technologies which dominate the analytics space in 2016.