A few things I've learned that help me to be more productive, happier, and more successful:
- Mermaid Diagrams - a nice tool for making diagrams in markdown.
- https://mermaid.js.org/intro/
- Good for putting diagrams directly in markdown but doesn't translate easily when Markdown is converted to another format
- Bad for converting markdown into other formats. My diagrams couldn't convert from Markdown to HTML b/c of some vulnerabilities in the conversion tools so I ended up using PUML & DrawIO instead.
- Back stretches and strengthening -
- An alternative approach to ADRs - https://martinfowler.com/articles/scaling-architecture-conversationally.html
- Diving in a little more on Capability Mapping. I've done this before but revisited the capabilities of my current team.
- Key
- identifies capabilities that the business needs in order to execute and operate on strategy
- Thee keys to capability mapping
- easy to use in practice
- static, doesn't change often
- is to understand by both business and technology
- Highlights
- not an organizational structure
- it's not a process
- capability cannot be duplicated
- focuses on what an organization does, NOT how it does it
- start small if it's new; starting with the whole org is very hard
- don't go too deep; going past level 4 is usually not useful
- Steps
- Map Capabilities
- Rate Capabilities in how well they support the organization
- Identity Capability Uplifts - Red/Yellow/Green to call out the capabilities' maturity
- Prioritize Capabilities
- Create Business Roadmap
- Create Technology Roadmap
- Benefits
- identifies silos
- helps to prioritize where budgeting is needed
- Diving in on different data stores to better understand the nuanced differences between databases, data warehouses, and data lakes. I've worked with all of these data storage
- https://www.youtube.com/watch?v=-bSkREem8dM
- Database
- designed to capture and record data (OLTP)
- data is live, real-time
- data is generally in columns and rows with a lot of detail
- has a flexible schema
- designed for fast reads and writes
- Data warehouse
- designed for analytical processing (OLAP)
- data is refreshed regularly from sources (ETL) and kept for a long time
- data is summarized
- rigid schema so it needs more planning
- only as fresh as the ETL frequency
- fast reads
- Data lake
- designed to capture raw data (structured, semi-structured, unstructured)
- made for large amounts of data
- used for ML and AI
- can clean up the data for use in EDW or databases
- Also, a Data Lakehouse is a technology that support both data lake and data warehouse functionality
- Decentralized Architecture Thru Advice
- https://martinfowler.com/articles/scaling-architecture-conversationally.html
- New Architecture Podcast: Thoughtworks Technology Podcast:
- https://www.thoughtworks.com/en-us/insights/podcasts
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