Overview

A new technology that has been gaining popularity amongst app programmers is known as container, or Docker containers. This technology is designed to expedite the process of creating an app, and is conveniently in a digital package that contains all requirements to run the software. This package contains everything that is needed to run software by bundling all the required softwares, libraries, and dependencies in a transportable package. Since a container is an executive package that contains its own environment, it has increased ease of collaboration. This is because teams on different stages of an app’s development, staging, and testing processes will be able to approach the software in the same manner, no matter the differences in their systems. Also, these containers are located in the cloud, allowing infinite access from multiple systems and real time capabilities.

If it were only one person working on an application, a container may not be needed because the software would be subjected to the same system its creator always uses. When multiple programmers or teams interact with many bits of software, the Docker container technology is useful in keeping things together in a succinct package that runs the same on any system.

In this article, I will be focusing on how containers can be used with cloud-based storage and data analytics. To find out more basic information, visit this article on Docker Containers.

Connection to Data Science

When considering the uses that containers could have in data sciences, there are some issues that emerge. For one, containers are currently incapable of processing the large amounts of data that big data analytics requires. It is challenging to find scalable and reliable data analytics that utilize containers. However, there are many integrable applications and types of software that have been created to cross the bridge between containerized data and data analytics. When these two technologies are used together, cost and time efficiencies are created that are hard to ignore.

In order to effectively make use of a Docker container while performing data science, there are applications that have been developed that are recommended add-ons. They are specialized applications that work within the containerized data, adding extensive data analytics technology and integrated tools. These add-ons share the same advantages that are inherent to containers, they are flexible and are transported in a universal package. Therefore any friction between transporting data to other teams during different stages of a process is minimized.

With data science integrated software add-ons, running a cloud-based container analytics process become not only plausible, but affordable and efficient. The effectiveness in which the Docker container systems have increased collaboration amongst separate teams in an applications development process is hard to ignore when developing apps. However, cloud-based container analytics can also be just as effective when deployed throughout the multiple staged of big data processing, especially analytics and data science.

Cloud Storage released this article on containerized data analytics that goes into greater depth on a possible example of how cloud storage technology can be integrated with containers. In the article, the author discusses two programs called Pachyderm and Minio that can be paired together. Minio is a cloud storage program that divides the data it’s holding into buckets. Pachyderm is a program that runs based off of Minio’s buckets. Pachyderm automatically assigns data to containers via data sources.

There are many other examples of how Docker technology can aid in a data science environment; Data Science named many of these in this article. Containerization is effective at an enterprise level because it reduces the workload for IT. Rather than having to reproduce and construct custom environments for every application or instance, containerization completely simplifies the process.

The two programs work together to simplify the process of using both cloud storage and containerized data. Usually, with large data sets, multiple containers are required to process and contain the data. It is difficult to keep track and properly analyze all of these sets of containerized data without software that is compatible. By utilizing the pairing of Pachyderm and Minio that is outlined in this article, an effective and proven process is available for those who desire the results.

Corporate Adoption

There have been multiple big corporations that have entered this industry in hopes of capitalizing on the opportunities that containerized data analytics brings. There are also small companies that have been created to provide container technology. For an example of a large corporation innovation, Capital One released its own platform, designed to increase the adoption of containerized data across the corporate world. Capital One uses its platform to conduct its services in a unique fashion. By utilizing container technology, Capital One has reduced the cost of it batch processing methods of credit card transaction processing. Capital One claims that the institution of containerized data has modernized their information technology infrastructure and has provided benefits of increased automation and flexibility.

IBM has also released an article that discusses wide-spread corporate adoption of containers. IBM points out that with the introduction and expansion of the Internet of Things and increases in corporate data collection, disruptive and innovative technologies such as Docker containers have the ability to transform the industry and revolutionize what is possible with cloud-based computing of big data. IBM has produced its own containerized analytics service known as IBM Softlayer that promises to provide high efficiency on big data workloads.

Closing Thoughts

In any industry, disruptive innovation is a direct result of Capitalism. Docker containers is such a flexible and useful technology that it is capable of transforming multiple industries, creating tremendous innovation and progress. In this article, I have discussed how containers have affected application development processes, data analytics, and batch processing heavily.

In my opinion, as digital transactions and online payment-to-payment methods are increasing in popularity, the cloud-based containerized data process that Capital One has created will increase in popularity. By utilizing batch processing, large corporations will reduce much of the cost of processing transactions and will increase their transaction margin. Outside of the finance world, I see containers becoming more popular in the data analytics sphere. As more and more data is created and processed, there needs to be innovation in how the data is compartmentalized and dealt with.

Bibliography

“What is a Container.” Docker, Docker Inc., 11 Dec. 2017, www.docker.com.

Tiwari, Nitish. “Containerized data analytics at scale, with Minio and Pachyderm.” Cloud Storage, Cloud Storage, 29 Mar. 2017, blog.minio.io.

“Containerized Cloud Analytics – SAS Analytics for Containers.” SAS, SAS Institute Inc., www.sas.com.

“Capital One Launches Critical Stack Enterprise Container Orchestration Platform Beta.” NASDAQ, NASDAQ, 21 Nov. 2017, www.nasdaq.com.

Raj, Chelliah Pethuru, and Skylab Vanga. “Use big data and fast data analytics to achieve analytics as a service (AaaS).” IBM, IBM Inc. , 24 Sept. 2015, www.ibm.com.

“Docker Containers.” Aqua Sec, Aqua Security, www.aquasec.com.

Swanson, Brittany-Marie. “Using Docker Containers For Data Science Environments.” DataScience, Data Science, www.datascience.com.