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🏆 Top solvers in Data

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

Spark: columnar processing

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DBMS: cap theorem

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Elasticsearch: snapshot es

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Cassandra: snitch

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DataViz: diverging palette

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DynamoDB: capacity exceeded

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Hadoop: secondary namenode

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MySQL: slow query log

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Neo4j: deep traversal

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Drop table

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DataEng: cost of shuffle

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MongoDB: distinct

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Redis: HGET HSET

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PostgreSQL: uuid

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Kafka: under replicated

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Pandas: duplicated

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Airflow: deferrable

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Spark: predicate pushdown

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DBMS: sharding

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Elasticsearch: scroll

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Cassandra: scylla

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DataViz: qualitative palette

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DynamoDB: partition split

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Hadoop: hdfs command

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MySQL: optimizer mysql

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Neo4j: node degree

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Constraint NOT NULL

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DataEng: partition skew

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MongoDB: capped collection

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Redis: atomic counter

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PostgreSQL: numeric type

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Kafka: backpressure

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Pandas: replace

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Airflow: smart sensor

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Spark: count vs collect

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DBMS: replication

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Elasticsearch: search after

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Cassandra: cql insert

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DataViz: exploratory

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DynamoDB: adaptive capacity

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Hadoop: append

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MySQL: constraint mysql

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Neo4j: cypher create rel

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Check constraint

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DataEng: orchestrator retry

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MongoDB: TTL index

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Redis: write-through

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PostgreSQL: coalesce

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Kafka: event sourcing

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Pandas: at iat

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Airflow: parallelism config

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Spark: persist levels

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DBMS: wal

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Elasticsearch: highlighting

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Cassandra: allow filtering

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DataViz: explanatory

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DynamoDB: nosql tradeoff

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Hadoop: throughput

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MySQL: cascade mysql

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Neo4j: knowledge graph

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