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Introduction
Now that organizations are dealing with big data on a day-to-day basis to generate useful insights, we require more efficient software/data development lifecycles.
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.
Why are DataOps Tools Important
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.
9 Great DataOps Tools
Now that we understand what DataOps Tools are and why they are important, let’s discuss some most popular tools:
Data Pipeline 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:
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Genie
Website Link: https://netflix.github.io/genie/
Developed by Netflix, the DataOps tool is an open-source engine that offers distributed job orchestration services. This tool provides RESTful APIs for developers who wish to run a wide range of jobs with Big Data, such as Hive, Hadoop, Presto, and Spark. Genie also provides APIs for metadata management in distributed processing clusters.
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Piper
Website Link: https://www.piperr.io/
Piper is a package of Machine Learning based DataOps tools that enable organizations to read data more smoothly and efficiently. This solution exposes data through a set of APIs which integrate easily with digital assets of the organization. Furthermore, it merges batch and real-time to offer the best of data technologies along with detailed support. With a focus on AI, Pipper allows companies to minimize turnaround time of data operations and manages a complete software development lifecycle through its prepackaged data apps.
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Airflow
Website Link: https://airflow.apache.org/
Apache Airflow is an open-source DataOps platform that manages complex workflows in any organization by considering data processes as DAG (Directed Acyclic Graphs). This took was first designed by Airbnb to schedule and monitor their workflows. Now organizations can utilize this open-source tool to manage their data process on macOS, Linux, and Windows.
Automated Testing Tools
The second category of DataOps tools covers automated testing. Simply put, automated testing tools test and compare the actual outcomes of a software technique, versus the expected outcome. These tests are applied to repetitive tasks to identify the best methods.
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Naveego
Website Link: https://www.naveego.com/
Naveego is a cloud data integration platform that allows businesses to reach accurate business decisions by integrating all company data in a regular business-centric format. This tool cleans stored data and makes it analytics-ready for data scientists. With Naveego, you can conveniently monitor and validate all your company’s stored data with security.
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FirstEigen
Website Link: http://firsteigen.com/
FirstEigen is a platform including Machine Learning tools that provide big data quality validation and data matching on the basis of self-learning. This platform learns about data quality behaviors and models using advanced ML techniques and then tests big data with just three clicks. With FirstEigen, organizations can ensure accuracy, completeness, and sanctity of their data as it moves via multiple IT platforms.
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RightData
Website Link: https://www.getrightdata.com/
RightData is a self-service group of applications designed for achieving data quality assurance, integrity audit and continuous control along with automated validation. This suite is best suited for organizations seeking tools with automated testing and reconciliation capabilities. With RightData, you can achieve testing for Data Migration, Database Upgrades, DAP, BI, reports, and much more.
Data Science Model Deployment Tools
Model deployment is basically a method in which you integrate the AI or ML data model into any existing production environment so as to make business decisions based on the data sets. This is usually the last step in the model lifecycle and therefore, it is very crucial.
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Badook
Website Link: https://badook.ai/index.html
Badook is a popular tool among data scientists since it allows them to write automated tests for datasets used in training/testing data models. This tool not only allows them to validate data automatically but also reduces turnaround time for generating insights.
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DataKitchen
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.
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Lentiq
Website Link: https://lentiq.com/
This data model deployment tool works in a service environment for smaller teams. With Lentiq, you can run data science and data analysis at the scale of your choice in the clouds so your team can ingest real-time data, process it, and share useful insights. With Lentiq, your team can train, build, and share models within the environment and innovate without restrictions. It is suggested to use Jupyter Notebooks for training models on Lentiq.
Conclusion
Data Ops, or data operations, is an agile DevOps based methodology for design, implementation, and maintenance of data in a distributed architecture. The main goal of this approach is to offer efficient and accurate results on big data – received by organizations on a daily basis – and extract useful analytics.
All in all, DataOps are an entrance to the world of smarter products. Now organizations can utilize fully managed platforms to created autonomous data pipelines which not only fuel analytics but also ML applications. It is essential for companies to leverage DataOps platforms so that their teams can adopt and collaborate conveniently while working with enormous datasets on routine basis.
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