What are the best ways to ensure your work meets the deadline while maintaining data security?

Achieving deadlines in data engineering while maintaining security involves meticulous planning. Implement encryption and access controls, conduct regular security audits, and promptly address vulnerabilities. Clearly communicate within the team, prioritize tasks based on impact, and stay agile to unforeseen challenges. Striking a balance between efficiency and robust security measures ensures timely project completion without compromising data integrity.

In my view, Data security is very important aspect of all Data Driven initiative in an Enterprise. From the ideation of a project, the Program manager must include Information Security team. The Information Security team needs to assess the data security need and help the project team ensure right data, right access, required encryption, testing mechanism and the Program manager need to consider all these above for scope, schedule and cost.This will ensure data security is taken care without impacting the timeline.

Clearly define project objectives and scope. Evaluate the complexity of tasks and set deadlines. Create a framework that allows you to meet deadlines efficiently while prioritizing and maintaining data security throughout the data engineering process. Regularly assess and update your approach based on project dynamics and emerging security threats.

In my perspective, data security is a crucial element in data-driven initiatives, given its vital role and the necessity for assurance in each deliverable. Inadequate planning can be a potential showstopper. To address challenge of meeting deadlines, key strategies include clearly defining project goals, conducting a risk assessment, establishing a practical timeline, using project management tools, assigning responsibilities, implementing robust data security measures, providing team training, fostering regular communication, scheduling adequate time for testing, remaining adaptable, documenting processes, consistently reviewing and adjusting, and actively seeking feedback for continuous improvement.

In my perspective all enterprise initiatives should have data/cyber security representation from project conception. To many organizations bring in security throughout the project for check points and some time the results are catastrophic.

Lembre-se de que o seu trabalho geralmente n?o é para você consumir, mas sim para outras pessoas. Muitas vezes, o que queremos ou gostaríamos de fazer se distancia um pouco de como as áreas de negócios est?o acostumadas ou desejam trabalhar. Com isso, é nossa fun??o traduzir o lado do negócio já existente para os dados. Isso n?o significa que n?o podemos fornecer insights e tentar modificar alguns processos; na verdade, essa também é uma das nossas fun??es. No entanto, precisamos primeiro mudar o pensamento da área de negócios para depois conseguir alterar processos.

Encourage open communication within the team and with stakeholders. Regular updates and meetings ensure everyone is aligned of project progress and challenges. Open Communication Channels: Accessible, Diverse, Interactive Regular Meetings: Scheduled, Concise, Collaborative Stakeholder Engagement: Informative, Responsive, Inclusive Transparency: Honest, Open, Trust-building Feedback Mechanisms: Encouraging, Constructive, Continuous Conflict Resolution: Timely, Fair, Effective Cross-Functional Collaboration: Interdisciplinary, Integrative, Synergistic Documentation and Record Keeping: Accurate, Comprehensive, Referential Cultural Sensitivity: Respectful, Adaptive, Considerate Celebrating Milestones: Acknowledging, Motivational, Unifying

Use automation to speed up repetitive tasks, reduce human error, and free up valuable resources for more complex tasks. Automation is needed for: Efficiency: Streamlining Reduction of Human Error: Precision Resource Optimization: Allocation Scalability: Expansion Consistency and Standardization: Uniformity Faster Turnaround Times: Acceleration Continuous Operation: Uninterrupted Cost-Effectiveness: Economical Enhanced Data Analysis: Insightful Improved Employee Satisfaction: Motivating

- Utilize Data Discovery Software: Use data discovery software to identify where personal data exists within your organization's information systems, some of the tools like Alation or Collibra or Azure Purview etc can be chosen based on the client dynamics.

A documenta??o é a parte menos glamorosa do trabalho, porém é necessária. é na documenta??o que você garante que seu trabalho poderá ser consultado por você mesmo no futuro ou por outras pessoas. Enquanto constrói a documenta??o, você também está refletindo sobre seu trabalho e muitas vezes encontra maneiras diferentes de ter resolvido alguns problemas, o que gera aprendizado para trabalhos futuros ou novos incrementos.

In data engineering, comprehensive documentation is vital for clarity in processes, specificity in system configurations, and structure in data models. It ensures consistent operations, secure protocols, and preparedness for emergencies. Additionally, it provides instructive, educational, and analytical resources for effective system management. Detailed Process Descriptions: Clarity System Configurations and Settings: Specificity Data Models and Structures: Structured Code Documentation: Instructive Standard Operating Procedures (SOPs): Consistent Change Management: Traceable Security Protocols: Secure Training Materials: Educational Disaster Recovery Plans: Preparedness Performance Metrics and Reports: Analytical

?? Be careful about task delegation: the point is learn about solutions and implementation from outsourcing team to maintain it over time by data engineering team. ?? Check that the knowledge transfering and training can meet the deadline project.

The best way to ensure you hit your deadlines is to not pick up work that will flow over your deadlines. Yes, everything may be important and management, users or whoever may be pushing all their stuff to be done like yesterday, but at the end of the day, you can't do all of them at once. Take a list, prioritise together with your stakeholders, figure out dependencies and readjust accordingly. Be very realistic and factor for unforeseen aspects and circumstances

One critical observation I've made across various data-driven initiatives is the frequent omission of data security as a fundamental element in enterprise data management strategies. When data security is not inherently integrated into the core of your data strategy, it tends to be treated as an optional component by development teams and business users. This approach often leads to compliance being addressed as a last-minute consideration, jeopardizing project objectives and deadlines. Ideally, data security should be an integral part of every stage of the design and development process, prioritized as the foremost aspect to be tested for each deliverable for team to meet their deadlines.

To ensure that our work meets the deadline while maintaining data security, we can consider additional strategies like: - Conduct Risk Assessments: Perform regular risk assessments to identify and address potential security vulnerabilities. - Implement Encryption: Even though encryption at rest is provided as a default feature in most of the services but it doesn't harm to encrypt sensitive information to protect it from unauthorized access.(for the cases where this feature is not available) - Limit Data Access: Monitor and restrict access to sensitive information to authorized personnel only. The simple formula of unless needed explicitly, always provision minimum level of access as a default can do wonders.

Meeting the deadlines with ensuring security is quite important in all fields not only bound to data engineering. To do so i prefer to have my own plan mentioned in my sticky notes. I make sure to prioritise the tasks and achieve those with security measures. Even agile methodologies helps in meeting deadlines.

As per Cockroach Labs: Netflix had challenges in handling large-scale data, necessitating a revamp of their data engineering tech stack. They chose scalable tools like Apache Kafka and Cassandra, integrated these with their existing systems using a microservices architecture, and moved to a cloud-based solution with AWS for cost-effectiveness and scalability. Additionally, they focused on security, compliance, and team training. The outcome was a more efficient, , and cost-effective system, leading to improved data processing, personalized user experiences, and increased team productivity. This example illustrates the importance of strategic tool selection and tech stack preparation in managing large-scale data engineering projects.