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🏆 Top solvers in Data

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

MongoDB: projection

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Redis: Set type

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PostgreSQL: join

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Kafka: consumer group

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Pandas: boolean index

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Airflow: worker

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Spark: dag

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DBMS: index

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Elasticsearch: analyzer

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Cassandra: clustering key

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DataViz: correlation viz

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DynamoDB: scan cost

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Hadoop: map phase

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MySQL: order by

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Neo4j: where neo4j

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Count

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DataEng: Backfill

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MongoDB: sort

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Redis: Sorted set

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PostgreSQL: left join

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Kafka: group rebalance

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Pandas: isnull

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Airflow: schedule interval

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Spark: stage

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DBMS: index tradeoff

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Elasticsearch: tokenizer

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Cassandra: primary key cassandra

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DataViz: pie caution

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DynamoDB: getitem

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Hadoop: reduce phase

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MySQL: group by mysql

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Neo4j: merge

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Group by

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DataEng: Data quality

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MongoDB: limit

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Redis: ZADD

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PostgreSQL: group by

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Kafka: one consumer per partition

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Pandas: fillna

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Airflow: cron airflow

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Spark: shuffle

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DBMS: btree

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Elasticsearch: mapping

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Cassandra: denormalize cassandra

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DataViz: color meaning

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DynamoDB: putitem

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Hadoop: shuffle

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MySQL: having mysql

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Neo4j: set

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Having vs where

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DataEng: Schema evolution

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MongoDB: $set

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Redis: leaderboard

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PostgreSQL: having

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Kafka: commit offset

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Pandas: dropna

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Airflow: start date

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Spark: narrow

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DBMS: clustered index

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Elasticsearch: dynamic mapping

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