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🏆 Top solvers in Data

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

Kafka: log

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Pandas: apply

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Airflow: task instance

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Spark: tungsten

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DBMS: isolation

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Elasticsearch: term query

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Cassandra: consistency level

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DataViz: chartjunk

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DynamoDB: wcu

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Hadoop: fault tolerant

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MySQL: index mysql

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Neo4j: index free adjacency

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MongoDB: referencing

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Redis: caching pattern

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Kafka: immutable

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Pandas: map series

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Airflow: dag run

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Spark: spark sql

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DBMS: durability

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Elasticsearch: bool query

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Cassandra: quorum

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DataViz: data ink

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DynamoDB: on demand

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Hadoop: batch

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MySQL: unique index

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Neo4j: relationship performance

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Index tradeoff

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DataEng: Spark lazy

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Redis: cache invalidation

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PostgreSQL: btree

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Kafka: zookeeper

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Pandas: groupby

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Airflow: xcom

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Spark: read parquet

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DBMS: transaction

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Elasticsearch: filter context

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Cassandra: tunable consistency

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DataViz: encoding position

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DynamoDB: provisioned

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Hadoop: resource manager

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MySQL: explain

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Neo4j: use case social

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Normalization

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DataEng: Spark action

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PostgreSQL: gin index

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Kafka: kraft

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Pandas: agg

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Airflow: xcom size

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Spark: parquet benefit

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DBMS: commit

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Elasticsearch: relevance

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Cassandra: eventual consistency cassandra

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DataViz: color accuracy

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DynamoDB: auto scaling dynamo

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Hadoop: node manager

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MySQL: innodb

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Neo4j: recommendation

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Denormalization

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DataEng: shuffle

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MongoDB: compound index

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