SQL and Databases
Structuring data and querying it, from single-table reads to architecture and standards work.
Working with SQL and databases means deciding how data gets stored and how you get it back out. You start with single-table lookups, then move to queries that join several tables. Next come schemas drawn from real requirements, tuning that starts with an execution plan, and architecture calls that split transaction processing from analytics. What separates the levels isn't fluency with one product's features. It's whether you can explain why you chose that structure and that storage technology.
Level Overview
| Level | Stage | Hashtags |
|---|---|---|
| Lv.1 | Query Reader | #Select#Where#TableStructure#PrimaryKey |
| Lv.2 | Data Retriever | #Join#GroupBy#Subquery#DataTypes |
| Lv.3 | Schema Builder | #Normalization#IndexBasics#Transaction#DataModeling |
| Lv.4 | Performance Tuner | #QueryOptimization#ExecutionPlan#LockContention#Partitioning |
| Lv.5 | Data Architect | #SchemaEvolution#Replication#WorkloadSeparation#RetentionPolicy |
| Lv.6 | Platform Governance Lead | #DataGovernance#PlatformStrategy#StorageEngine#TeamEnablement |
| Lv.7 | Field Shaper | #StandardsAuthorship#DatabaseResearch#DataLegacy |
Levels
Level 1
#Select#Where#TableStructure#PrimaryKeyPulling rows that match a condition from a single table.
You've just started working with databases. You understand that a table is rows and columns, and that every column carries a data type. Conditions and sorting get you the rows you want. You also know a missing value won't turn up under a normal comparison. Working across several tables at once is still out of reach.
What Comes Next
If you've checked off most of this list, you're ready for the Data Retriever stage, writing queries that link tables together. A relational database gets its power from storing data in separate tables and stitching them back together on demand.
Level 2
#Join#GroupBy#Subquery#DataTypesJoining tables and aggregating them into reportable results.
You link two or more tables, pull only the columns you need, and group rows to produce sums and counts by category. You can reproduce the gap between an inner join and an outer join with real data and explain it. You change data too, checking the affected row count as you go. You can build the result someone asked for, but not the structure behind it.
What Comes Next
If you've checked off most of this list, you're ready for the Schema Builder stage, turning requirements into tables nobody handed you. The biggest jump in autonomy sits between querying someone else's structure and deciding the structure.
Level 3
#Normalization#IndexBasics#Transaction#DataModelingDesigning schemas from requirements and handling transactions.
You listen to business requirements, sort out entities and relationships, and design the schema yourself. You normalize, but you can point to the spots where denormalizing pays off and say why. You wrap several changes in one transaction, so a failure rolls all of them back. You add indexes and measure how query time shifts. Your backup and recovery procedure is written down.
What Comes Next
If you've checked off most of this list, you're ready for the Performance Tuner stage, making the queries you already write survive real load. The query processing and optimization chapters laid out by Silberschatz et al.(2019) are the theoretical starting point for this shift.
Level 4
#QueryOptimization#ExecutionPlan#LockContention#PartitioningReading execution plans to clear slow queries and contention.
You read an execution plan, find the wasteful step, and rewrite the query around it. You reproduce lock contention and deadlocks, then clear them by adjusting isolation levels. For large tables, you partition or apply a retention policy to cut what they cost to keep. When something breaks, you trace the cause through logs and metrics and lead the recovery.
What Comes Next
If you've checked off most of this list, you're ready for the Data Architect stage, choosing which system each workload belongs on. In the SFIA 9 Database design definition, this is the boundary where your scope widens from tuning single queries to advising on platform choice.
Level 5
#SchemaEvolution#Replication#WorkloadSeparation#RetentionPolicySplitting transactional and analytical work, designing failover.
You separate transaction processing from analytical demand and design a structure for each. You set recovery time and recovery point targets as numbers, then build replication and failover to hit them. Your criteria for choosing storage technology are documented, and you advise the wider organization on them. Schema changes ship without downtime. You coach developers, lifting query quality across the whole team.
What Comes Next
If you've checked off most of this list, you're ready for the Platform Governance Lead stage, writing rules that bind teams you never sit on. Designing one system well is one job. Defining the criteria many teams work to is another.
Level 6
#DataGovernance#PlatformStrategy#StorageEngine#TeamEnablementSetting the data standards and governance an organization runs on.
You set the data architecture standard for the whole organization and roll it out across teams. You design and run governance that ties quality metrics to ownership and change approval. In public, you argue technology choices from how storage engines actually work. Your training programs and open source contributions reach past your own organization.
What Comes Next
If you've checked off most of this list, you're ready for the Field Shaper stage, authoring the standards and methods the field runs on. When rules that hold inside your organization start getting cited outside it, your reach widens to the whole industry.
Level 7
#StandardsAuthorship#DatabaseResearch#DataLegacyLeaving a legacy in data design methodology and standards.
You redefine how data gets designed and handled from the ground up. The modeling methodology you created is adopted by outside organizations, and your writing and standards documents get cited in both practice and research. Your work on certification systems or standards bodies leaves a record behind. The architects you trained now work on their own.
What Comes Next
If you've checked off most of this list, you're standing at the front edge of how data gets handled. You shape the design criteria the next generation will work from. The task here is writing new standards, not restating the textbook ones Silberschatz et al.(2019) already settled.
References
The ladder from SQLD (two subjects covering data modeling concepts plus SQL basics and application) to SQLP (three subjects adding advanced application and tuning, with 2 practical questions), together with the 40% per-subject failing threshold, grounds the level boundaries in this guide.
Describes observable behavior at each responsibility level, from Level 2 (assisting with design under supervision) to Level 5 (advising on DBMS selection and tuning transactional and analytical platforms together). Direct evidence behind the checklist items at every level.
The standard textbook for university database courses. The official author site publishes the table of contents, online chapters, and a practice SQL tool for free, which sets an authoritative learning order running from the relational model through transactions and query optimization.
Sorts 18 families of SQL error found in over 33,000 queries submitted by 237 students into syntax, semantic and logical classes. The novice error types it names, comparison with NULL, omitted joins, and illegal or insufficient grouping, are direct evidence for the Lv.1 and Lv.2 checklist items.