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🏆 Top solvers in Data

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

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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Redis: eviction

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PostgreSQL: partial index

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Kafka: acks

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Pandas: value_counts

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Airflow: sensor

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Spark: write mode

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DBMS: rollback

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Elasticsearch: bm25

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Cassandra: gossip

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DataViz: legend

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DynamoDB: single digit latency

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Hadoop: application master

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MySQL: innodb transactions

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Neo4j: fraud detection

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Transaction ACID

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DataEng: Kafka

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MongoDB: unique index

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Redis: maxmemory

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PostgreSQL: explain analyze

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Kafka: acks 0

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Pandas: sort_values

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Airflow: poke

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Spark: schema inference

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DBMS: dirty read

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Elasticsearch: aggregation

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Cassandra: hinted handoff

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DataViz: title viz

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DynamoDB: dax

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Hadoop: container yarn

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MySQL: myisam

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Neo4j: acid neo4j

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Rollback

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DataEng: Kafka topic

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MongoDB: text index

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Redis: persistence RDB

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PostgreSQL: seq scan

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Kafka: idempotent producer

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Pandas: sort_index

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Airflow: reschedule mode

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Spark: select

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DBMS: non repeatable read

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Elasticsearch: bucket agg

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Cassandra: read repair

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DataViz: gridlines

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DynamoDB: eventual read

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Hadoop: hive

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MySQL: transaction mysql

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Neo4j: property type

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Isolation dirty read

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DataEng: Kafka partition

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MongoDB: index tradeoff

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Redis: persistence AOF

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