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🏆 Top solvers in Data

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

PostgreSQL: vacuum

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Kafka: batching

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Pandas: merge

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Airflow: retries

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Spark: filter spark

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DBMS: phantom read

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Elasticsearch: metric agg

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Cassandra: anti entropy

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DataViz: annotation viz

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DynamoDB: strong read

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Hadoop: hiveql

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MySQL: commit mysql

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Neo4j: multiple labels

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Subquery

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DataEng: Kafka consumer group

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MongoDB: covered query

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Redis: durability tradeoff

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PostgreSQL: autovacuum

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Kafka: compression

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Pandas: concat

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Airflow: retry delay

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Spark: groupby spark

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DBMS: serializable

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Elasticsearch: rest api es

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Cassandra: write path

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DataViz: dashboard viz

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DynamoDB: partition throttle

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Hadoop: pig

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MySQL: rollback mysql

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Neo4j: relationship type

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View

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DataEng: Kafka retention

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MongoDB: explain

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Redis: pub/sub

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PostgreSQL: analyze

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Kafka: exactly once

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Pandas: join type

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Airflow: sla

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Spark: join spark

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DBMS: read committed

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Elasticsearch: index doc

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Cassandra: commit log

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DataViz: interactive

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DynamoDB: key design

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Hadoop: hbase

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MySQL: acid mysql

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Neo4j: variable length

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Window function

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DataEng: Airflow

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MongoDB: collscan

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Redis: pubsub delivery

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PostgreSQL: MVCC

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Kafka: at least once

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Pandas: pivot_table

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Airflow: trigger rule

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Spark: withcolumn

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DBMS: locking

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Elasticsearch: get doc

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Cassandra: memtable

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DataViz: tableau

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