#456: Data Architectures with AWS Hero Elliott Cordo
Jun 27, 2021
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Topics covered include data architectures with Amazon Redshift, trends in data, integrating ML in data environments, and the value of being part of a technical community. The conversation also explores selecting the right tools for specific business requirements, aligning technological solutions with business needs, the enduring value of SQL, enhancing data sharing with Redshift, and embracing community and building expertise in data architectures.
Balance data lake and warehouse-centric architectures based on business needs and data types.
Design data systems tailored to user queries, accessibility, and flexibility for efficient utilization.
Develop data infrastructure for adaptability, cost-effectiveness, and managing large datasets in dynamic environments.
Leverage Redshift's new features like data sharing, Aqua architecture, and Data API for enhanced data processing and accessibility.
Deep dives
Data Architecture Decisions: Data Lake vs. Data Warehouse Centric Approach
When structuring data environments, the choice between a data lake and data warehouse centric architecture is critical. Companies with massive semi-structured data opt for data lakes supported by storage solutions like S3. Alternatively, smaller companies with varied data requirements gravitate towards data warehouse centric models. The popular 'lake house' approach integrates data warehousing technologies with data lakes, aiming to optimize architecture based on business needs and stakeholder requirements.
Data System Design Philosophy: Goal-Oriented Approach
Crafting an effective data system must align with business needs and stakeholder goals. Successful system design hinges on understanding the desired data outcomes, user interactions, and required service levels. By prioritizing user queries, system accessibility, and operational flexibility, data architects can tailor platforms to meet specific business needs, fostering efficient data utilization and enhancing user experiences.
Data Infrastructure Flexibility: Adaptation to Unpredictability
In dynamic operational environments, data infrastructure must anticipate and accommodate unpredictability. Companies like Capsule focus on extensibility, cost-effectiveness, and managing large data sets to meet evolving customer demands. Embracing flexibility over scale enables nimble adjustments to business needs, ensuring agile responses to market shifts while enhancing customer experiences.
Enhanced Data Sharing within Redshift: ACWA Features and Benefits
Redshift's new capabilities, such as data sharing within accounts and cross-accounts, revolutionize data accessibility and collaboration. These features enable seamless data sharing within organizational boundaries and across subsidiaries, enhancing data utilization and streamlined operations. Additionally, Redshift's Aqua architecture accelerates query performance by optimizing scans, joins, and aggregations, offering up to 10 times faster data processing compared to traditional methods, propelling data-driven insights and decision-making.
Advances in Data Access: Redshift Data API and Semi-Structured Data Support
Redshift's Data API simplifies data access and egress from diverse programming languages, fostering familiar interaction models for users. The Super Data Type empowers users to efficiently handle semi-structured data like JSON within Redshift. These advancements streamline data accessibility and processing, elevating user experience and enabling seamless integration of varied data formats for enhanced analytics and decision-making.
Future Data Infrastructure Vision: Dynamic Instance Decoupling
The future of data infrastructure envisions a shift towards instance-less storage models, akin to serverless paradigms. Dynamic storage allocation, as seen in Redshift's RA3 instances, minimizes resource complexities, optimizing compute and storage balance. Progression towards instance-agnostic storage solutions can enhance scalability, resource efficiency, and operational simplicity, aligning Redshift with agile and cost-effective modern data architecture paradigms.
Community Engagement and Professional Growth: AWS Data Heroes Program Impact
Participation in community programs like AWS Data Heroes fosters collaboration, knowledge sharing, and peer-to-peer learning across diverse industries and global contexts. The program serves as a valuable platform for exchanging ideas, troubleshooting technical challenges, and exploring innovative technology solutions. By engaging with a network of peers, professionals can gain insights, expand skill sets, and stay abreast of industry trends, enhancing career development and fostering continuous learning.
Career Development Advice: Technical Mastery and Hands-On Experience
An essential career development tip, besides mastering SQL, emphasizes becoming proficient in technical skills and practical experience. Cultivate expertise through hands-on project involvement, experimentation, and continual learning. Transitioning from technician to engineer, focus on crafting solutions, making mistakes, and honing problem-solving abilities. By investing in skill development and embracing challenges, aspiring professionals can advance towards leadership roles within data-focused domains.
AWS Data Hero and Head of Data at Capsule, Elliott Cordo, has built many ground-up data architectures over the years. Simon speaks to Elliott about his eight years of experience with Amazon Redshift, including recent innovations he's excited about and what's on his Amazon Redshift wishlist. Simon and Elliott also discuss making sense of trends in data, integrating ML in your data environment, and the value of being part of a technical community. https://aws.amazon.com/developer/community/heroes/
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