Evaluation and selection of database and data platform technologies to meet analytical and operational needs. Covers assessment of relational, non relational, columnar, and specialized systems such as time series and search engines; data warehouse platforms and cloud analytics platforms; query patterns and workload characteristics; consistency and transactional guarantees; partitioning and clustering strategies; storage formats and compression; performance and scalability trade offs; operational complexity and administration overhead; data ingestion and incremental loading patterns; pricing and cloud platform considerations; and how to choose the right solution based on data volume, concurrency, latency requirements, and total cost of ownership.
EasyTechnical
0 practiced
As SRE, explain when to choose a search engine (Elasticsearch/OpenSearch) versus relying on a database's built-in full-text search. Cover aspects such as relevance/ranking, fuzzy matching, indexing cost, scaling, operational complexity, and how search clusters change monitoring and alerting requirements.
EasyTechnical
0 practiced
As an SRE, explain basic indexing types (B-tree, hash, inverted) and their operational trade-offs: index maintenance cost during writes, disk/IO implications, selectivity, and when an index can cause more harm than good in a production OLTP workload.
EasyTechnical
0 practiced
As the SRE responsible for multiple services, explain the operational trade-offs between relational databases (e.g., PostgreSQL) and NoSQL systems (key-value, document, wide-column). Discuss scaling strategies, consistency models, schema evolution, backup/recovery, operational tooling, typical failure modes, and when one is preferable to the other for production services.
HardTechnical
0 practiced
Create a cost model comparing Redshift (reserved instances), BigQuery (on-demand), and Snowflake (credits) for an organization with 5 PB raw data and 10 TB daily scanned. Describe key assumptions, unit costs to model, sensitivity to query patterns, storage vs compute split, and how to present an apples-to-apples comparison to finance.
MediumTechnical
0 practiced
As SRE, design an approach to benchmark a candidate database for your workload. Describe workload generation (reads, writes, transaction mix), metrics to collect (latency percentiles, CPU, IO, queue depth), result reproducibility, and how you'd present findings to engineering and product stakeholders.
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