Data analysis always gives the end result in some defined terms. Different techniques, tools and procedures can help in the dissection of data, transforming it into actionable information. If we look to the future of data analytics, we can predict some of the latest trends in technologies and tools used to dominate the analytics space:

1. Model implementation systems

2. Display systems

3. Data analysis systems

1. Models of implementation systems:

Several service providers want to replicate the SaaS model on premises, especially the following:

– OpenCPU

– Yhat

– Domino Data Labs

In addition, by requiring the implementation of models, there is also an increasing requirement to document the code. At the same time, one might expect to see a version control system, however, that is suitable for data science, providing the ability to track multiple versions of data sets.

2. Display systems:

Visualizations are on the verge of being dominated by the use of web techniques such as JavaScript systems. Basically everyone wants to make dynamic visualizations, however, not everyone is a web developer, or not everyone has the time to spend writing JavaScript code. Naturally, some systems have been rapidly gaining popularity:

Bokeh:

This library may be limited to Python only, however it also provides a strong possibility for rapid adoption in the future.

Plotly:

By providing APIs in Matlab, R, and Python, this data visualization tool has made a name for itself and seems headed for rapid mainstream adoption.

Also, these 2 examples are just the beginning. We should expect to see JavaScript-based systems providing APIs in Python and constant R to evolve as they see rapid adoption.

3. Data analysis systems:

Open source systems like R, with its fast maturing ecosystem, and Python, with its scikit-learn and pandas libraries; they seem to be in favor of continuing their control over the analytic space. In particular, some projects in the Python ecosystem seem ripe for rapid adoption:

Bcolz:

By providing the ability to perform processing on disk rather than in memory, this exciting project aims to find a middle ground between using local devices for in-memory calculations and using Hadoop for cluster processing, providing thus a ready-made solution when the data size is very small need a Hadoop cluster but not as small as managed within memory.

Radiance:

These days, data scientists work with many data sources, ranging from SQL databases and CSV files to Apache Hadoop clusters. Blaze’s expression engine helps data scientists use a constant API to work with a full range of data sources, improving the cognitive load required by using different systems.

Of course, the Python and R ecosystems are just the beginning, as the Apache Spark system is also increasing in adoption, mostly because it provides APIs in R and Python as well.

By establishing a common trend of using open source ecosystems, we can also predict a move towards distribution-based approaches. For example, Anaconda provides distributions for both R and Python, and Canopy provides only one Python distribution suitable for data science. And no one will be surprised if you see the integration of analysis software like Python or R into a common database.

Beyond open source systems, a set of tools under development also helps business users communicate with data directly, while helping them form guided data analysis. These tools attempt to abstract the data science procedure from the user. Although this approach is still immature, it provides what appears to be a very potential system for data analysis.

Moving forward, we expect data and analytics tools to see rapid application in core business procedures, and we anticipate this use to guide companies toward a data-driven approach to decision making. For now, we need to keep our eyes on the tools above, as we don’t want to miss out on seeing how they reshape the world of data.

So, discover the strength of Apache Spark in an integrated growth environment for data science. Plus, experience data science by joining the data science certification training course to explore how both R and Spark can be used to build your own data science applications. So this was the full overview of the top tools and technologies dominating the analytics space in 2016.