Friday, March 29, 2024

Simple Steps to Implement Successful DataOps Practices

DataOps aims at code development, testing, and operations focusing on agility in the end-to-end delivery of the data pipeline. It also helps in improving communication among data stakeholders.

DataOps caters to detailed data identification that helps deliver value and guarantee data privacy and security. Here are a few steps businesses need to take, to implement an effective DataOps practice.

Orchestrate Data

Modern analytics platforms contain a considerable volume of data in batches to process and stream in real-time. Manual management of complex data pipelines is time-consuming and error-prone, leading to stale data and minimal productivity.

Automated data orchestration allows the scheduled execution of data engineering with the right tools. DataOps can streamline complex interdependent data pipelines and offer scalability to manage hundreds of flows.

Simultaneously, it supports a variety of pipeline triggers, data dependencies, sensors, & inter-pipeline dependencies. This results in the flexible configuration of jobs for high availability and disaster recovery.

Monitor Data

Data monitoring is a crucial step and a precursor to data quality. This allows the organizations to observe data profiles over time and helps detect anomalies. Successful data monitoring can be implemented by gathering numerous metrics of datasets and individual data columns like the number of process records, values for numeric or date columns, and the number of null values.

Therefore, this information allows the companies to observe a substantially different data item or dataset that helps the businesses to learn more about the data to validate the hypotheses readily.

An understandable way of data monitoring can be amplified by AI-driven anomaly detection. Modern anomaly detection algorithms can detect periodic data patterns, utilize correlations between various metrics, and minimize the number of false positive alerts.

Also Read: The role of real-time data analytics in improving and guiding business decisions 

Qualitative Data

While data monitoring helps data engineers learn additional details and anomalies of the data, data quality capabilities improve data trustworthiness & veracity. The critical goal of data quality is to detect data corruption in the pipeline and prevent it from spreading.

One of the primary techniques to ensure solid data quality is deploying business rules that continuously run in the production data pipeline to check if data complies with pre-defined requirements. This ensures data integrity and quality.

Additionally, anomaly detection implemented for data monitoring can be reused for data quality enforcement by setting a significant threshold to balance precision & recall. A comparison of data in the lake with data sources can be carried out for the occasional validation of data.

Governance of Data

Data governance consists of people and process techniques focusing on the technology and the tooling aspect of it. The crucial elements of data governance tools for any modern analytics platform are data catalog and data lineage.

These aspects enable data scientists, analysts, and engineers to quickly find required datasets and learn how they were created. Incorporating data catalog, data glossary, and data lineage capabilities accelerates the productivity of the analytics team, improving the speed of insights.

Also Read: How can enterprises manage data complexity with business intelligence?

Streamlined Delivery

DataOps ensures secure integration & a continuous delivery process, reducing time-to-market when new data pipelines or data analytics use cases are implemented. The ongoing delivery process for data needs to follow microservices best practices in cases with normal application development.

These means more freedom for businesses to scale, crunch time to implement & deploy new data or ML pipelines, and improve the overall quality and stability of the system. Organizations need to pay attention to unit and functional testing with generated data.

At the same time, an immense focus is required on data quality & monitoring need for production and testing for data outputs. DataOps has become a vital methodology for a modern data analytics organization. DataOps can recognize value quickly and achieve business goals more strongly.

For a successful implementation of DataOps, companies must adhere to new skills and adapt the culture extending their data lakes with unique technical capabilities and tools.

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Apoorva Kasam
Apoorva Kasamhttps://enterprisetalk.com/
Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in Blockchain, data governance, and supply chain management. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.

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