You’re looking to build a data engineering strategy. What’s the best way to start?

1. Define Objectives: Clearly outline goals aligned with business objectives. 2. Evaluate Current Setup: Assess existing infrastructure for strengths and weaknesses. 3. Understand Data Needs: Collaborate with stakeholders to define data requirements. 4. Choose Tools and Architecture: Select appropriate tools and design a scalable architecture. 5. Implement and Iterate: Develop integration pipelines, enable accessibility, and continuously improve processes based on feedback.

Just think in a simple way- 1. Analyze data properly. Yes, do it multiple times until you start visualizing that data by your own mind and brain. 2. Be clear about the stakeholder's expectation about how they want to consume/display data. 3. Yes, you are 50%-70% done!! Now everything would become easier to take decision and implement rest of the steps of your "data journey" including ETL, pipeline, systems architecture, tools and technology etc.

In my experience, a well-defined data vision acts as a compass, guiding the entire team through the complex landscape of data engineering. It's not a theoretical exercise, it's a practical roadmap that directs our technical expertise towards meaningful, impactful outcomes. Also, make sure to know your stakeholders' expectations and define data governance policies.

In my view, comprehending the business objectives and the challenges faced by clients provides a foundational understanding of what data infrastructure is required to address their needs effectively. This approach lays the groundwork for developing robust data solutions that align with the client's goals and ultimately lead to customer satisfaction.

I think managing data well and resourcefully is the key to the success of any organization. All industries today related to consumer, retail, banking, finance, insurance, etc. want to make most out of their consumer and client base. Data Engineering has I think is therefore become quintessential for all such industry types. To have effective data engineering in place, architects & engineers must, 1) Understand the data sources. 2) Talk to Analysts and Management to understand Business goals. 3) Look at security and scalability aspect. 4) Choose the right technology and platform to build effective, highly available data stores and solutions. 5) Review the data requirements with Business Users and stakeholders, Improve / Innovate.

Do a thorough assessment of your data sources. Understand the volume, variety and velocity of your data sources. Identify gaps with existing architecture, the data quality checks required, the intended velocity of use. This will help streamline which tools you need to utilize to bring in those data sources into your architecture.

To build a data strategy for data engineering, begin by assessing your data landscape. This involves - cataloging available data sources, - understanding their formats, quality, - and how they're currently utilized. I know that it's important to identify gaps in data collection, integration points, and any inefficiencies in existing data pipelines. Analyze - data storage, - processing capabilities, and - security measures in place. Understanding the current state of your data ecosystem is crucial. It helps - to identify opportunities for improvement and - to plan strategic investments in data infrastructure and tools to support business objectives.

Assessing our data landscape involves understanding the entirety of our data ecosystem. We analyze data sources, storage systems, processing mechanisms, and accessibility. Utilizing tools like data catalogs and lineage diagrams, we identify strengths, weaknesses, and gaps in our infrastructure. This assessment enables us to pinpoint challenges and prioritize improvements. Our goal is to obtain a clear view of data assets, capabilities, and requirements, guiding strategic decisions for enhancing our data landscape efficiently

I think this is the most apt starting point when designing a data engineering strategy, vision and architecture come later. Most of the architecture points for data engineering are pretty much defined in terms of the data stack and technologies. Assessing your data landscape helps one to identify data quality, definitions , integration methods and even bottlenecks in sourcing respective data. Below are the points to consider while assessing your data landscape before starting a data engineering project: - Identify source definitions, systems and how to integrate that data. - Highlight potential bottlenecks and data quality issues. This will help you to create a picture of the data architecture also keeping in mind the cost.

To begin building a data engineering strategy, start by assessing the data landscape. First, define the scope of data sources including databases, files, APIs, and streams. Identify data types (structured, unstructured) and volumes. Evaluate data quality, consistency, and security. Assess existing infrastructure, tools, and technologies. Consider data governance, compliance, and regulatory requirements. Engage stakeholders to understand business goals and data needs. Document findings to inform strategy development.

Planning your data architecture is pivotal in developing a comprehensive data strategy for data engineering. This process includes designing a framework that is: - scalable, - flexible, and - secure. This framework aligns with the business objectives to support: - data collection, - storage, - processing, and - analysis needs It involves - choosing the right technologies and platforms, - establishing data governance and quality standards, and - ensuring interoperability among systems. A well-planned data architecture facilitates: - efficient data flow, - drives insights, and - enables innovation while maintaining data integrity and compliance.

To plan the data architecture for an organization, one should look at the three V’s of data -Velocity -Variety -Volume These three indicators could help us to decide whether we should opt for a DWH, data lake or delta lake. Depending on how often the data is being used we could dump our lesser used or archived data to glacier for even more optimisation on storage costs. Our data architecture should be able to support upcoming data projects and be able to scale to that level.

You need to know The 3 V's (volume, velocity and variety) of your data. It will help you to plan, architect, design and selecting appropriate tools and technologies fit for your organisation and data goal.

A well-designed data architecture can help data engineers to get a lot of clarity: Improve the performance and scalability of data systems. It helps to ensure that data is stored and accessed in a way that maximizes performance and scalability. Improve the quality and reliability of data

Get an agreed version of High level conceptual Current and Target level data architecture. Understand and define gaps between current and desired state. Initially focus is not desired to be at tools and technology level but on what is to be achieved as per the required deliveries. Other things are similar as suggested by others.

Steps that can be used to implement data solutions: -Assess data needs and objectives, identifying key data sources and insights desired. -Design a scalable and flexible data architecture to accommodate current and future requirements. -Implement data quality-focused solutions for robust ingestion, storage, and processing. -Automate data workflows for efficiency and apply governance policies for quality and compliance. -Ensure system scalability to handle growing data volumes. -Continuously monitor, test, and iterate solutions to maximize data asset value. -Adopt best practices for data management and security to support evolving business needs.

Strategic Prioritization: Align data solutions with organizational goals. Prioritize projects for maximum impact and feasibility. Resource Allocation: Optimize resource and budget allocation. Utilize internal expertise and external partnerships effectively. Performance Measurement: Implement metrics to track progress and outcomes. Evaluate against predefined benchmarks regularly. Collaborative Execution: Embrace agile, scrum, devops, or mlops methodologies. Foster collaboration among teams for quality and adaptability.

Implementing your data solutions is a critical step in operationalizing your data strategy for data engineering. This phase involves developing and deploying - data pipelines, - databases, - analytics tools, and - other infrastructure components According to the planned data architecture. To do: 1. Focus on automating data workflows for efficiency, applying data governance policies for quality and compliance, and 2. ensuring scalability to handle growing data volumes. 3. Continuous monitoring, testing, and iteration of these solutions are essential to maximise the value derived from data assets.

The implementation phase is too early to go with. Before that we need to understand and Stakeholders approval on budget and financial flow to have delivery phases defined. Analyze alternatives available to implement the desired state of data architecture and iterations defined to absorb changes identified during the implementation phase. Rest is same as we do in usual SDLC.

Implementing the data solutions is what gives the value to data. The solutions would include 3 steps 1. Get the data from the source and store the data 2. Archive the historical data and process the required data 3. Send the data to target (reporting/storage). Implementation process involves effectively executing above 3 steps. More budget and technical resources are required in 2nd step (mostly in the processing) An optimized coding standards and storage solutions would help in reduced the budged and increased efficiency of the jobs. The approaches would include memory optimized processes, storage optimized solutions and even effective handling of size of clusters by data volume forecast.

Evaluating your data impact is essential for refining and validating your data strategy. Do: Measure how you are achieving business objectives, such as - improved decision-making, - increased operational efficiency, and - enhanced customer experiences. Use metrics and KPIs to assess: - data quality, - usability, and - the ROI of data projects. Identify areas to improve by: - Gathering feedback from stakeholders and - analyzing usage patterns. Continuously monitoring these impacts helps to align data initiatives with strategic goals, to have long-term success.

Always tie business metric(s) to the usage of data and evaluate how data, insights & inferences are helping the business see uplift in these metrics. Sometimes through quick POCs and experimentation, one can help bring out the value of data and insights. Also the data engineering strategy in isolation may not be able to achieve the business outcomes, so partnership with Data Science/AI and/or with business teams will help unlock the true potential

Key metrics for a successful data framework: Clear Data Schema & Governance: Ensuring versatile data usability and robust governance. Infrastructure Investment: Adequate funding for a scalable and efficient framework. Data Protection: Rigorous measures to prevent data breaches and ensure confidentiality. Data Quality Assurance: Implementing systems for real-time, error-free data integrity. These metrics gauge framework efficacy, aligning investments with outcomes, safeguarding data integrity, and optimizing usability. Strengthen your data framework to drive organizational success.

To start evaluating data impact for a data engineering strategy, assess current data sources, formats, and quality. Identify key business metrics impacted by data, prioritizing based on strategic goals. Analyze data usage patterns and stakeholder requirements. Conduct a gap analysis to identify areas for improvement. Consider scalability, security, and compliance needs. Develop a roadmap with clear milestones and measurable outcomes. Iterate based on feedback and evolving business needs. Engage stakeholders throughout the process for alignment and buy-in.

Agora é hora de medir os resultados do que foi implementado, gerar relatórios , aferir KPIs, coletar evidências e calcular os benefícios gerados, todo projeto deve gerar impacto e medir os resultados é essencial para que a área de TI/Dados seja visto como uma área de entrega de valor.

From my work with Databricks ... you should also consider the following points: Data governance framework: Create clear data governance policies and procedures to ensure data quality, security and compliance. Scalability and performance: Evaluate scalability requirements and choose appropriate technologies to process large amounts of data efficiently. Data integration and pipelines: Design robust data integration pipelines to optimize data ingestion, processing and delivery. Focus on establishing data governance, ensuring scalability and developing efficient data integration pipelines to build a solid foundation for your data engineering strategy.

1. Understand Business Objectives 2. Deep dive into Use cases and their impact on the business 3. Technology and Data roadmap for the organization 4. Data Compliance and Governance 5. Scalability and Flexibility by Use Case 6. Integration and Orchestration 7. Data Architecture and Ecosystem 8. Data Quality and Reliability 9. Talent and Team Structure 10. Cultural Change and Adoption 11. Data maturity and literacy 12. Security and Privacy 13. Budget (capex and opex) 14. Monitoring and Maintenance 15. Innovation and Experimentation 16. Analytics requirement and maturity 17. AI requirements and maturity 18. Industry standards 19. Stakeholder Engagement 20. Diversity and Volume of Data Sources, Products, Contracts etc

Most important is understanding how you want to use the data and what use case you want to achieve Once we know the end use we can build much efficient data model which will prevent wastage of storage and processing time post deployment.

While planning the data architecture, 1.define the project objective clearly 2.consider the technology limitations-budget for the tech stack proposed based on what type of storage, orchestration, cloud integrations 3.data - how data will be retained in the system and at what level, data storage platforms we will use (data warehouse, data lake, delta lake house) 4.Practice - Prioritize tasks and set milestones, make sure all stakeholders are data informed, define proper role definitions & responsibilities. 5.Follow best practices, ensure data quality, data privacy regulations and compliance standards.