Table of Contents
Difficulty In Choosing A Technology Stack
There are many technologies for working with Big Data. Because of this, companies often find it challenging to choose the best tools – for example; they use a stack whose functionality far exceeds the tasks of the enterprise. This leads to high investments at the project implementation stage and increased maintenance costs. When selecting tools, evaluating the possible costs of operating and developing the solution is essential.
According to the study, 11% of respondents face difficulty choosing the right technology stack.
There are two ways to solve this problem:
- Engage consultants or integrators to select a stack for the company’s requirements.
- Use cloud tools. Providers provide a ready-made stack of tools for the entire cycle of working with big data – for each stage of data preparation and processing. Taking advantage of this, you can deploy the complete solution natively in the cloud or use the cloud to test individual tools.
IT Infrastructure Readiness
To implement Big Data projects, you need a reliable, productive IT infrastructure that allows company departments to work with many data sources. In addition, the infrastructure must be easily scalable to cope with the ever-increasing amount of data.
Infrastructure for Big Data is essential not just to create – it needs to be regularly upgraded, maintained, and expanded. This involves the need to attract a separate team of specialists and constant investment. The study showed that 12% of Russian companies are experiencing difficulties due to the unpreparedness of their IT infrastructure for Big Data projects.
The solution to this situation may be to move to the cloud. Deploying projects in the cloud allows you to reduce infrastructure costs and provides fast and flexible scaling of available resources. In addition, the cloud enables using a set of pre-configured tools for working with Big Data.
According to our research, 46% of companies in Russia already use cloud solutions for working with Big Data projects, and 29% plan to start working with clouds shortly.
Heavy Expenses
Despite the emergence of available technologies and tools for working with big data, implementing Big Data projects requires infrastructure investments, business process revisions, project team creation, or third-party data scientists and other prominent data specialists. The project’s cost is the main deterrent for 12% of the respondents.
Such a position may be influenced by the complexity of assessing the economic effect of implementing projects for working with big data: 43% of respondents could not answer the question about the payback of Big Data projects.
One of the ways to reduce costs is a complete migration to the cloud or testing hypotheses for working with Big Data in the cloud. In this case, investing in your infrastructure and configuring and maintaining tools for working with data is not needed. The provider provides everything according to the Pay-as-you-go model, with a fee only for the resources used. At the same time, capital expenditures are replaced by operating ones, reducing the company’s financial burden.
In the study, we talk in more detail about the problems and the features of working with big data: payback, the culture of working with data, and the use of cloud technologies. You can get the full text of the study at the link.
Briefly About The Main
- 62% of Russian companies are already working with Big Data, and 34% of companies have been using Big Data solutions for over 3 years.
- Many companies that have implemented projects to work with Big Data face difficulties: a lack of team competencies, improper data organization, the problem of choosing a technology stack and preparing an IT infrastructure, and the need for capital investments.
- Migration to the cloud helps solve the technical difficulties of working with big data. Deploying projects in the cloud allows you to use the resources of your DevOps team efficiently. Big Data Scientists and other specialists reduce infrastructure costs, quickly create pilot projects to test hypotheses, and access convenient Big Data tools.
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