The era of big data calls for some powerful data operation tools which can automate processes and reduce the cycle time of the data analytics for enormous datasets. For this purpose, the concept of DataOps is what works as the solution. It is a process-oriented methodology that monitors and controls the data analytics pipeline by using Statistical Process Controls.
In this article, we will discuss the role of some great data ops tools. So without further ado, let’s start.
DataOps is not just about managing data pieces, it is more about delivering business value. This methodology is a combination of data related elements and softwares which together run the business operations. It is built with the use of DevOps – a widely accepted practice for accelerating software development – in a more sophisticated manner.
With DataOps Tools, you can deliver new and existing data services more quickly despite the changing semantics and infrastructures of data environments. The DataOps tools also help applications in interacting more easily while working with dynamic technologies. Furthermore, the tools transform stodgy BI into democratized and real-time analytics capability which obviously unlocks a bigger potential.
Now that we understand what DataOps Tools are and why they are important, let’s discuss some most popular tools:
Simply put, data pipelines provide organizations access to well-structured, reliable datasets so as to extract useful analytics and insights. This helps get data from operational and application systems into data warehouses analytical systems. Some of the most popular data pipelining tools include
Website Link: https://www.datakitchen.io/
One of the most popular DataOps tools, DataKitchen is best for automating and coordinating people, environments, and tools in data analytics of the entire organization. DataKitchen handles it all – from testing to orchestration, to development, and deployment. Using this platform, your organization can achieve virtually zero errors and deploy new features faster than your business. DataKitchen lets organizations spin up repetitive work environments in a matter of minutes so teams can experiment without breaking production cycles. The Quality pipeline of DataKitchen is based on three core sections; data, production, and value. It is essential to understand that with this tool, you can access pipeline with Python Code, transform it via SQL coding, design model in R, visualize in Workbook, and gain reports in form of Tableau.