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

Airflow: local executor

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Spark: spark ml

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DBMS: sql dml

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Elasticsearch: replica read

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Cassandra: any consistency

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DataViz: simplicity

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DynamoDB: encryption dynamo

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Hadoop: input split

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MySQL: deadlock mysql

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Neo4j: bidirectional

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Count matching

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DataEng: compression

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MongoDB: transactions

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Redis: SETNX

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PostgreSQL: point in time

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Kafka: leader election

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Pandas: copy vs view

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Airflow: sequential executor

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Spark: explain

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DBMS: aggregate

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Elasticsearch: shard sizing

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Cassandra: all consistency

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DataViz: too many colors

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DynamoDB: iam dynamo

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Hadoop: key value model

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MySQL: isolation level

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Neo4j: optional match

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Distinct count

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DataEng: incremental load

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MongoDB: atomic single doc

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Redis: APPEND

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PostgreSQL: logical replication

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Kafka: poll

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Pandas: inplace

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Airflow: ui graph

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Spark: speculative

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DBMS: group by db

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Elasticsearch: index template

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Cassandra: partition size

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DataViz: 3d charts

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DynamoDB: adjacency list

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Hadoop: scalability limit

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MySQL: row locking

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Neo4j: unwind

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Group sum ordered

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DataEng: watermark

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MongoDB: data types

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Redis: EXISTS

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PostgreSQL: connection pool

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Kafka: flush

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Pandas: axis 0

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Airflow: ui grid

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Spark: adaptive query

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DBMS: having

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Elasticsearch: alias es

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Cassandra: hot partition

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DataViz: label direct

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DynamoDB: overloaded gsi

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Hadoop: data lake

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MySQL: charset

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