Data analytics has manifested itself as pivotal in business, taking into consideration the fact of treating data as an asset. Data scientists claim that the digital universe will add fuel to the fire by a huge amount of information: 5200 gigabytes for every person by 2020. The challenge is that only 33 % of data can become valuable if being analyzed and managed in an appropriate way.
The growing popularity of implementing DataOps is a representative contemporary occurrence nowadays. Evidently, the mountains of data, produced every single minute, require professional evaluation and orchestration. To handle this problem numerous enterprises have already put DataOps strategies into practice. This approach responds to many challenges of evolving market needs, optimizes the efficiency of data analytics and improves the unity between developers, operators and other members of data team. DataOps takes the best of Agile and DevOps, hence it incorporates the speed, the automation, the reliability, the guidance and the quality of methods mentioned above.
The core principles of ML, data science and predictive analytics are impossible without large information datasets that need to be requested, transformed and visualized. When Google MapReduce and Apache Hadoop became widely popular, they represented the idea that data locations had lost such great importance. With the common implementation of cloud computing, the major companies faced to cluster platforms.
Apparently, the underlying problems in data science can be outlined as data collection, visualization, modeling, testing and then deploying. In fact, working with small segments of the code simplifies the whole process, that’s why it is typical for data specialists to break pipelines into the minor components. This enables to handle them separately and store in clusters. For instance, the container platform Docker is commonly popular. This tool is widely used by Kubernetes — an open-source container-orchestration platform that serves for automatic application deployment, management and scaling.
Kubernetes is considered to be one of the most popular systems for managing containerized applications through multi-tenant hosts. Initially designed by Google, Kubernetes is now supported by the Cloud Native Computing Foundation (CNCF). The platform is responsible for provisioning specific mechanisms that conduct container orchestration. Though k8s is relatively young (presented in 2015), it is one of the most frequently discussed cloud-native projects in recent years. According to MARP infographics, the world’s most innovative companies, like AWS, Google, OpenStack, Microsoft, IBM, etc., if running containers, have chosen Kubernetes for their management.
The Kubernetes’ popularity is determined by its features and a number of benefits.
- Service deployment and adjustment — k8s tends to label containers with individual names and addresses and is able to accommodate and align them. It enables consumers to avoid extra application modifying.
- Storage Alternatives — Kubernetes makes it possible to allocate container applications in accordance to your choice: within a public/hybrid cloud or on-premises on local hardware.
- Automatic Selection — The Kubernetes solutions are selective, i.e. respond to the needs of applications running at the moment. The system can spin up and down the specific nodes providing consistent apps operation and availability.
- Recovery Mechanisms — The platform is highly self-sustainable in managing node failures and providing self-healing. If any disruption occurs, containers recover from a backup.
- Constant Monitoring — Orchestrating large sets of data requires a continuous control of diverse processes within the container itself, in applications, logs and also the network. Kubernetes is reliable in analyzing reported files, detecting bottlenecks, tracking the network streams and automatically responding to any changes.
Let us now review some other positive aspects one can significantly leverage from while implementing Kubernetes into practical usage.
cloud-oriented open source
As it was mentioned above, Kubernetes solutions give you numerous advantages of cloud-native: it works out of container engines and easily manages to move massive workloads. K8s is fully integrated, no matter the location, and enables the multi-tenancy with the help of namespaces. Multiple users and teams not only have personalized access to clusters but their actions there are constantly observed and controlled.
simple scaling and provisioning;
flexible and available;
easy, fast and extensible;
reliability and performance.
Kubernetes environment is persistently becoming the leading platform for cloud computing. Consumers looking for dynamically-scheduled container-oriented DataOps solutions should consider Kubernetes-as-a-Service.
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