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🏆 Top solvers in Data

🥇 @malathi-shetty 11 solved
🥈 @arun-kumar 1 solved

Elasticsearch: query vs filter cache

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Cassandra: cluster expansion

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DataViz: white space

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DynamoDB: atomic counter

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Hadoop: mapreduce output

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MySQL: binlog

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Neo4j: node similarity

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Date now

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DataEng: idempotent producer

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MongoDB: index direction

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Redis: cache stampede

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PostgreSQL: concurrent index

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Kafka: linger ms

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Pandas: groupby size

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Airflow: dag versioning

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Spark: shuffle partitions

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DBMS: schema

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Elasticsearch: doc values

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Cassandra: consistency vs latency

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DataViz: font legibility

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DynamoDB: return values

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Hadoop: key sort

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MySQL: buffer pool

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Neo4j: cypher return alias

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Deadlock

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DataEng: orchestration vs scheduling

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MongoDB: sparse index

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Redis: lock expiry

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PostgreSQL: bloat

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Kafka: max poll records

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Pandas: transform group

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Airflow: max active tasks

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Spark: caching when

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DBMS: acid vs base

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Elasticsearch: painless

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Cassandra: local quorum

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DataViz: context data

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DynamoDB: cost model

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Hadoop: scaling storage

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MySQL: autocommit

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Neo4j: foreach neo4j

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Index on low cardinality

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DataEng: great expectations

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MongoDB: read preference

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Redis: atomic INCR

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PostgreSQL: lateral join

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Kafka: heartbeat

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Pandas: to dict

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Airflow: provider packages

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Spark: dataframe api vs rdd

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DBMS: bcnf

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Elasticsearch: search relevance tuning

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Cassandra: read path

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DataViz: color consistency

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DynamoDB: table class

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Hadoop: cheap scale

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MySQL: right join mysql

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Neo4j: load csv

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Sum where filter

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DataEng: columnar vs row

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