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🏆 Top solvers in Data

🥇 @malathi-shetty 11 solved
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Default value

Data Easy Free +10 XP
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DataEng: SLA

Data Medium Free +15 XP
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MongoDB: change streams

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Redis: cold start

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PostgreSQL: cast

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Kafka: cdc

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Pandas: where

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Airflow: dag concurrency

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Spark: partition pruning

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DBMS: backup

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Elasticsearch: fuzzy

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Cassandra: no rollback

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DataViz: data type drives

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DynamoDB: managed benefit

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Hadoop: yarn scheduler

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MySQL: json mysql

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Neo4j: cypher case sensitive

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Auto increment

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DataEng: reprocessing

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MongoDB: schema validation

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Redis: memory efficiency

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PostgreSQL: limit offset

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Kafka: fanout

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Pandas: assign

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Airflow: failure callback

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Spark: bucketing

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DBMS: connection pool

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Elasticsearch: wildcard

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Cassandra: availability tradeoff

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DataViz: accuracy over beauty

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DynamoDB: key value speed

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Hadoop: ecosystem breadth

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MySQL: full text

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Neo4j: remove clause

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CTE

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DataEng: streaming vs batch

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MongoDB: Atlas

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Redis: client library

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PostgreSQL: distinct on

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Kafka: mirror maker

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Pandas: memory usage

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Airflow: email on failure

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Spark: spark ui

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DBMS: n plus one

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Elasticsearch: range query es

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Cassandra: cql select

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DataViz: line vs bar

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DynamoDB: updateitem

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Hadoop: write pipeline

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MySQL: prepared statement mysql

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Neo4j: distinct neo4j

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Recursive CTE

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DataEng: source of truth

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MongoDB: driver

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Redis: DECR

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PostgreSQL: json operators

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Kafka: rack awareness

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Pandas: vectorization pandas

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Airflow: data pipeline

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Spark: lazy benefit

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