Data analytics is a buzzword, and companies are inclined towards data-driven planning to be ahead of the competition in their respective industries. Whether mid-size or a conglomerate, every business these days is rooting for big data. However, complex and big data are useless unless they are synthesized and simplified into meaningful data.
The biggest challenge for organizations is building links between their data and business intelligence. Making data suitable for business intelligence requires data mining using advanced analytical techniques. The data analytics stack makes the process of extracting insights easier.
What is the Data Analytics Stack?
Data analytics is a set of tools that allow the analyst to extract meaningful data using one platform. The data analytics stack is essential and advantageous because it saves time and effort and increases accuracy. This stack comprises three fundamental steps:
1. Data Integration
In this step, data from many sources is gathered, combined, and transformed into a storage-compatible format. The sources could range from a database (like MySQL), an organization’s log files, or event data from mobile apps or websites, such as clicks, logins, bookmarks, etc. You may use all of this data, combined with a data analytics stack, to conduct insightful analytics.
2. Data Warehousing
In this step, data is consolidated in one place to make it feasible to search for the required information simultaneously. The only purpose of warehousing data is to curb the complexity of data from different sources.
3. Data Analytics
In this final step, we load the data from the warehouse into a visualization tool and use it to extract significant patterns and insights from the data in the form of charts, graphs, and reports.
How to Choose an Open Source Data Analytics Stack?
You can find open-source analytics libraries easily but ensuring uninterrupted services is suspicious. Thus, you need to be aware of the best and safest stack. There are specific parameters for choosing an open-source data analytics library. Those are:
1. Security
One of the critical parameters of choosing an open-source stack is security. To ensure that the required safeguards are in place to protect your information, you must analyze your analytics provider’s and vendor’s security. Establish standardized security controls and procedures at all levels, including the process, system, and data levels, to accurately restrict which users or groups can access what data. Additionally, it’s critical to comprehend the consequences of mobile BI, given that users can access data outside of company boundaries and from any location.
2. Sources of Data
Multiple complicated data sources can be combined using modern analytics technologies to evaluate structured, semi-structured, and unstructured data. Choosing products that don’t need your IT department’s support is crucial. You may get a complete picture of your company’s success if you can collect and merge data from several systems onto a single dashboard.
3. Advanced Analytics
Ensure you choose an open-source tool that helps predict future trends, events, and outcomes. It must produce contextualized insights and go beyond straightforward mathematical calculations so that you can construct sophisticated statistical models and secure the future of your company.
4. Cost
There are several free, open-source libraries for data analysis. However, some of them can have hidden costs. Ensure you do not fall prey to such strategies. Different analytics solutions have different cost structures, and it is essential to understand them before investing.
The Advantages of Open-Source Analytics Tools
1. Flexibility
There will always be limitations on how proprietary SaaS analytics packages can be used, particularly true for the free trial or lite versions of the tools. For instance, specific tools don’t support complete SQL, making it challenging to mix and query internal and external data.
On the contrary, you have total freedom with open-source software, including how you utilize your tools, combine them to create a stack, and even use your data. You can adjust your requirements without having to pay extra for customized solutions if your needs change, which, let’s face it, they surely will.
2. There Is No Vendor Lock-in
The term “proprietary lock-in” refers to the situation in which a client relies on the vendor for all of its goods and services. The expense of moving to a different provider would be too high for the customer.
Open-source tools nearly never experience this. The norm is constant innovation and change. The community can continue and maintain the project even if the person or group managing the tool departs. With open-source, you don’t have to depend heavily on anyone for your tools to be updated.
3. Using Open-Source Is the Modern-Day Solution
When it comes to innovation, open source puts you in the lead. With it, you’ll be able to take advantage of the strength of a thriving developer community to create better products faster. Major tech companies are investing in open-source libraries, increasing their future scope.
The Bottom Line
Open-source libraries are cost-effective and provide you with the desired flexibility. Whether an individual or an organization, you can use an open-source data analytics tool to get insightful data. Even if you are a beginner, this data analytics tool will help you practice without spending an excessive amount. Libraries like Theano, Chainer, Apache Spark, etc., offer the best quality data analytics stack. You can begin by searching on kandi, where you can find the best code libraries in data analytics and numerous other trending topics. The more you explore data analytics libraries, the better options you will get.
