登录查看更多内容

What are the best tools for data engineers to manage deadlines?

由人工智能和领英社区提供技术支持

Data engineering is a demanding and complex field that requires constant attention to data quality, performance, scalability, and reliability. As a data engineer, you need to deliver high-quality data pipelines and solutions within tight deadlines, often with changing requirements and priorities. How can you manage your time and tasks effectively and efficiently? In this article, we will explore some of the best tools for data engineers to manage deadlines, from planning and tracking to testing and deploying.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

1 Project management tools

One of the first steps to manage deadlines is to have a clear and realistic plan for your data engineering projects. You need to define the scope, objectives, deliverables, milestones, dependencies, and risks of each project, and communicate them to your stakeholders and team members. Project management tools can help you create, organize, and update your project plans, as well as assign tasks, monitor progress, and collaborate with others. Some popular project management tools for data engineers are Jira, Trello, Asana, and Airtable.

添加您的观点

Yashkumar Makwana

Building Bridges Between Data and Business Goals | Transforming Raw Data into Actionable Insights
举报内容
One of the best project management tools I've used is Asana. - The user interface is rich and friendly, you can customize Asana's background, theme etc. - It's seamless collaboration allows users to create shared workspaces, projects, and tasks. Real-time communication features, such as commenting, file sharing, and task updates, enhance teamwork, especially in remote work scenarios. - Managers can use it's visualization features to track the progress real-time. - It allows integration to third party tools like Github. It excels in prioritizing your tasks Best thing I liked about Asana: It lets you map out your Quarter vision in a flow map visual.

已翻译

赞

2 Version control tools

Another essential tool for data engineers to manage deadlines is version control. Version control allows you to track and manage changes to your code, data, and configurations, as well as collaborate with other developers and engineers. Version control tools can help you avoid conflicts, errors, and data loss, as well as rollback to previous versions if needed. Version control tools can also facilitate code reviews, testing, and deployment. Some common version control tools for data engineers are Git, GitHub, Bitbucket, and GitLab.

添加您的观点

3 Data pipeline tools

Data pipeline tools are the core of data engineering, as they enable you to build, run, and monitor data flows from various sources to destinations. Data pipeline tools can help you automate, orchestrate, and schedule your data processing tasks, as well as handle failures, retries, and alerts. Data pipeline tools can also improve your data quality, performance, and scalability, as well as reduce your development and maintenance costs. Some popular data pipeline tools for data engineers are Apache Airflow, Luigi, Prefect, and Dagster.

添加您的观点

Santhosh Kumar Ravindran

Senior Engineering Product Manager at Microsoft | Microsoft Fabric | Advisory Board Member
举报内容
Azure data factory has been my go to tool when it comes to building a data ingestion or transformation pipelines at scale. You get ~120 connectors out of the box and has integrations with all transactional and SaaS platforms. Users can use these as a PaaS offering or also use the Data Factory workload available as part of Microsoft Fabric. (This could be my biased choice, but don’t think any pipeline orchestration that matches the support broad set of connectors (including multi cloud) and scale offered by Data Factory)

已翻译

赞

4 Testing tools

Testing is a crucial step to ensure the quality and reliability of your data engineering solutions. Testing tools can help you verify the functionality, accuracy, and performance of your code, data, and pipelines, as well as identify and fix bugs, errors, and anomalies. Testing tools can also help you adhere to best practices, standards, and compliance requirements. Some useful testing tools for data engineers are PyTest, unittest, Great Expectations, and dbt.

添加您的观点

Nilay Parikh

AI in AlgoTrading, Risk, Portfolio & Quantitative Finance | Augmented AI for Structured Scientific and Arithmetic Data | Realtime Data | AI & Forecasting for Timeseries AIOps | MLOps | DataOps | Data&AI Platforms
举报内容
Great Expectations has been invaluable for managing deadlines and ensuring data quality. By codifying data quality checks and data validation as pipelines are built, issues can be caught early. This avoids the last minute scramble when defects are found right before launch. Great Expectations makes it easy to re-run tests when updates are made, giving confidence that changes didn't break things. The validation reports also provide assurance to stakeholders that quality is baked in from the start. Testing early and often is crucial for on-time delivery in data engineering.

已翻译

赞

5 Deployment tools

Deployment is the final step to deliver your data engineering solutions to your end-users or customers. Deployment tools can help you automate, streamline, and optimize the process of moving your code, data, and configurations from development to production environments. Deployment tools can also help you manage your infrastructure, resources, and security, as well as monitor and troubleshoot your deployments. Some common deployment tools for data engineers are Docker, Kubernetes, Terraform, and Ansible.

添加您的观点

Nilay Parikh

AI in AlgoTrading, Risk, Portfolio & Quantitative Finance | Augmented AI for Structured Scientific and Arithmetic Data | Realtime Data | AI & Forecasting for Timeseries AIOps | MLOps | DataOps | Data&AI Platforms
举报内容
Using Docker containers for deployment can be cumbersome for data pipelines. Though it packages code nicely, orchestrating data and configurations across environments poses challenges. I've found using a workflow automation tool like Prefect to be simpler for deployment. With Prefect, you build your flows and define parameters once. Then deployments to dev, test, prod happen in one click. The control plane handles permissions, secrets, and resource allocation across environments. Failures trigger alerts, and robust logging helps troubleshoot issues quickly. Automated flow testing catches problems early too.

已翻译

赞

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Data Engineering

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

查看全部

What are the best tools for data engineers to manage deadlines?

1

2

3

4

5

6

1 Project management tools

2 Version control tools

3 Data pipeline tools

4 Testing tools

5 Deployment tools

6 Here’s what else to consider

Data Engineering

给文章评分

感谢您的反馈

更多Data Engineering相关文章

更多相关阅读内容

What are the best tools for data engineers to manage deadlines?

1

2

3

4

5

6

1 Project management tools

2 Version control tools

3 Data pipeline tools

4 Testing tools

5 Deployment tools

6 Here’s what else to consider

Data Engineering

给文章评分

感谢您的反馈

查看其他技能