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

Pandas: explode

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Airflow: config file

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Spark: window function

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DBMS: data warehouse

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Elasticsearch: green yellow red

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Cassandra: data replication benefit

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DataViz: jitter

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DynamoDB: number set

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Hadoop: yarn job submit

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MySQL: alter table

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Neo4j: label filter

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DataEng: kafka offset

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MongoDB: replace vs update

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Redis: blocking pop

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PostgreSQL: offset perf

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Kafka: scale limit

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Pandas: idxmax

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Airflow: zombie task

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Spark: cores config

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DBMS: oltp vs olap

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Elasticsearch: yellow meaning

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Cassandra: partitioner default

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DataViz: aggregation viz

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DynamoDB: map type dynamo

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Hadoop: container allocation

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MySQL: drop table

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Neo4j: graph algorithm centrality

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DataEng: kafka replication

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MongoDB: bulkWrite

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Redis: queue pattern

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PostgreSQL: keyset pagination

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Kafka: delivery decouple

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Pandas: clip

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Airflow: scheduler ha

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Spark: memory config

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DBMS: materialized view

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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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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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