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🥇 @malathi-shetty 11 solved
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Pandas: cumsum

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Airflow: taskflow

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Spark: repartition

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DBMS: full table scan

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Elasticsearch: log analytics

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Cassandra: materialized view cassandra

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DataViz: geographic

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DynamoDB: query efficient

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Hadoop: orc

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MySQL: stored procedure mysql

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Neo4j: apoc

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Correlated subquery

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DataEng: data catalog

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MongoDB: election

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Redis: hash slots

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PostgreSQL: extension

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Kafka: log compaction

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Pandas: rolling

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Airflow: dynamic tasks

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Spark: skew

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DBMS: cardinality

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Elasticsearch: full text use

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Cassandra: ttl

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DataViz: bubble chart

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DynamoDB: projection

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Hadoop: compression hadoop

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MySQL: trigger mysql

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Neo4j: gds

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Primary vs unique

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DataEng: lineage

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MongoDB: write concern

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Redis: key naming

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PostgreSQL: postgis

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Kafka: tombstone

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Pandas: datetime

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Airflow: subdag deprecated

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Spark: spill

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DBMS: null

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Elasticsearch: scaling es

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Cassandra: lightweight transaction

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DataViz: stacked bar

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DynamoDB: item size limit

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Hadoop: heartbeat hadoop

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MySQL: normalization mysql

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Neo4j: pagerank

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Sum a column

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DataEng: governance

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MongoDB: read concern

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Redis: big keys

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PostgreSQL: full text

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Kafka: lag

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Pandas: resample

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Airflow: taskgroup

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Spark: collect danger

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DBMS: constraint

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Elasticsearch: rebalancing

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Cassandra: batch cassandra

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DataViz: grouped bar

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DynamoDB: global tables

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Hadoop: rack awareness

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