How can you make data engineering projects agile and adaptable?

Agile is good, but the team needs a clear vision of what the platform will look like in the next, say 1,2, or 3 years, and how each project contribute to the ideal state of the data engineering ecosystem. So we end up not doing daily firefighting.

Clear and aligned metrics are indeed the key. It can be difficult to measure the impact of data engineering work but well worth the investment as you change from a cost center to a value delivery center.

Simple approach is to plan ahead and understand what we need to solve to limit blast radius of key pain points to add more value through multiple iterations of product features. "one step at a time to connect the dots to deliver something through agile principles"

Sprints and Iterations (Backbone of Agile and Adaptability): -> Break down your data engineering work into iterations or sprints, typically 2-4 weeks in length. Focus on delivering valuable increments of functionality in each sprint such as versioning of usability according to the customer requirements. Feedback mystification: Collect feedback from data consumers and stakeholders continuously. This feedback should guide iterations and improvements in your data engineering processes. Standardization of certain aspects of the project: Implement data lineage and metadata management to track the flow and transformations of data. This enhances transparency and traceability, which is crucial for data governance.

PART 1 On my view below details can help us achieving it : ? Agile development methodology : project break down into smaller unit or manageable part that can be delivered in sprint cycle and focus on MVP ? CI/CD : to automate testing & deployment, use GIT to manage code & track changes ? Modular Design : Architecture to be modular with which logical unit can be developed, tested & deployed independently ? TDD (Test-Driven development) : Write tests for new features before writing the code that implements the features (TDD) ? Flexible Data Modeling: Use data storage solutions that allow for schema evolution and flexibility, such as NoSQL databases or schema-on-read systems like data lakes.

When upgrading legacy systems or applications to an Agile development approach, it's essential to choose the right tools and platforms that support Agile methodologies and practices. Scalability: Consider the scalability of the tools and platforms. As Agile projects grow, the tools should be able to accommodate an increasing number of users and the complexity of the projects. Ease of Use: Choose tools and platforms that are user-friendly and intuitive. A steep learning curve can hinder Agile adoption and slow down a team's productivity. Visibility and Reporting: Ensure that the tools provide visibility into project progress and team performance. Robust reporting and analytics capabilities can help teams make data-driven decisions.

You can have all the ceremonies, sprints, iterations, objectives, planning and whatever buzz words your organisation prefers to use. However, the most important aspect of delivering an Agile project is the people executing the delivery of said project. All the ceremonies in the world won't compensate for a lack of quality staff. Agile will only ever be as good as the people executing it.

You can imagine your data project is like a DIY home improvement adventure. Step 2: Tool time! It's just like picking the right tools for a job. You want ones that are as adaptable as those jeans you've been wearing since high school (scalability). And, just like avoiding that one puzzle piece that doesn't quite fit, you need tools that play nicely with your existing stuff (compatibility). Think of them as the smartphone with the super-easy interface you wish your grandpa could use (ease of use). And the best part? Some of these tools are like having a little robot buddy to handle the boring stuff. So, when you're choosing your data project tools, think of it as your personal DIY adventure with a side of laughs and a lot less frustration.

Tools always play a vital role in Data engineering implementations, once you have identified the size of the data, next will be identifying tools like ETL.

This is a challenge for a number of reasons, but businesses can make simple decisions that enable an agile data engineering methodology to blossom. I'll speak from the perspective of a business deciding between multiple 3rd party tools. Typically, during the procurement process, a business will define use cases and zero-in on features and capabilities needed. What is often neglected is whether or not the technology supports specific workflows. Don't assume all do. Not all technologies support agile workflows, especially within my field of expertise which is MDM. Simply adding that question to the review process can be enough to identify if the technology will enable agile workflows and ultimately reveal time-to-value.

Projects can be made agile and adaptable by: Having the ability to code using functions/frameworks which can be reused by merely calling them; this can be really helpful in standardizing the code and also make it more robust. For example, we can have framework for validating the date/security which can be called whenever needed for any new process/extract rather than reinventing the wheel.

Modular Components: Break down data pipelines into modular components, such as data ingestion, transformation, and loading (ETL) modules. To achieve reusability each module should perform a specific function. e.g. in C# MVC with the help of APIs and Databases The data ingestion module can retrieve data from external sources. Data Transformation Module: This module takes the ingested data and processes it according to business logic. Data Loading Module: This is responsible for storing the transformed data in a database or another storage system. Controller: In the MVC controller can orchestrate the data processing by utilizing the modular components. The controller handles requests and invokes the appropriate data-processing modules.

Data Engineering projects can be made agile and adaptable by enhancing usability: Implement Data Versioning: A data versioning system can help track changes to data. It enables reusability and data consistency. Centralize Configuration Management: Configuration settings should be stored separately from code. Use environment variables, configuration files, or configuration management tools to make it easy to configure and adapt modules for different environments like prod, testing, qa.

Define and communicate clear goals to the development team: Ensure goals are SMART (Specific, Measurable, Achievable, Relevant, Time-bound). Establish key performance indicators (KPIs) and data quality metrics. Utilize data governance tools for visualization and reporting. Foster understanding among the team regarding goals and metrics. Share results and feedback through dashboards and reports on regular bases.

Implement a CI/CD pipeline using tools like Jenkins or GitLab CI. This automates the testing and deployment of code changes and reduces manual tasks. Template Workflows: Develop template workflows using tools like Apache NiFi, where you can configure the flow of data processes. Templates save time when setting up new data pipelines. Testing Frameworks: Create reusable testing frameworks using testing libraries like PyTest. These frameworks include test cases for common data quality checks, ensuring consistency across projects. Data Quality Framework: Develop a data quality framework that can be applied to different data sources and pipelines. It should include checks for missing values, data outliers, and data consistency

Continuous Integration (CI): This is like having a chef's team in your kitchen. They're always checking the ingredients, making sure the sauce is perfect, and the cheese is just right. In your data project, CI is about regularly checking your code (the recipe for your data) to make sure it's top-notch. Continuous Delivery (CD): Now, think of CD as the pizza delivery part. In your data project, CD is like a well-oiled delivery system. It takes the amazing recipe (your code) and ensures it gets to the right place without getting cold or messy. So, these CI/CD processes are like having quality control for your data project. It's like having a super organized kitchen team and a reliable pizza delivery service!

* To establish a data governance framework we need to first evaluate the present state of the data maturity. * Establish the goal in terms of scalability ,usability and the definitive rules and regulations. * This would lead towards understanding the gaps, risks and areas of opportunities

This final step is generally always taken for granted, people put their more than 100 % in all preceding steps but hardly take the time to review to those steps. Sprint retrospective is one of the most important and last step in agile development which should help to make sure the team has a safe space (possibly without the involvement of management) for the developers, QA resources and other team members of sprint can discuss success and improvement points as internal feedback. Sprint delivery is usually a spiral but we should make conscious efforts to identify the pain points to avoid running in circles and keep on marching towards the goal.

As you have projects and new uses for data, the objectives should be defined early, ensuring data stakeholders are aware of necessary work as projects are being defined. This means data models are architected first, reporting and dashboards are defined first, not as an afterthought. This helps preventing making quick decisions that don't align with your data and governance strategy. Data teams should also be involved in retro processes and other project ceremonies.

Data is tricky, really tricky. You can have roadmaps and plans but none of that will help you actually realise much more than a shifting milestone. Time box and aiming for goals is likely going to be more fruitful, you can do most of that using SCRUM but a shift to Kanban and moving closer to something consumable that can be improved on daily is much more focussed. In my unbias opinion. ??

Forming cross-functional teams that include data engineers, data scientists, analysts, and other relevant roles. This promotes collaboration and knowledge sharing, allowing team members to collectively address various aspects of the project.

"agile and adaptable" should not be adopted for its own sake. Designing adaptable and agile projects should be considered a strategy. A strategy for what? For creating value, increasing profits, reducing costs, etc. Taking on adaptability and agility as an arbitrary ideal rather than understanding its purpose within your business is a major mistake and ultimately creates a rigid, inflexible mindset that will become an obstacle in and of itself. Do not neglect the "Why" and stay skeptical of the "should" mentality (we should be doing this or that). Keep a radical focus on the aim of all your systems and methodologies. They must support the objectives of the business. And if agile does that best, all power to you.

Organize the data engineering function around data products. Products are a powerful constructs that ensures all the technical work is tried to value. Data products derive most of their value by enabling AI applications, analytics and dashboards, in decreasing order. Organizing in product teams also brings the data engineers much closer to the problem which enables them to be more effective problem solvers, not just coders!