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🏆 Top solvers in Data

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

Hadoop: large files

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MySQL: like

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Neo4j: schema optional

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N+1 problem

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DataEng: slowly changing dim

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MongoDB: accumulator

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Redis: single-threaded

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PostgreSQL: schema

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Kafka: avro

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Pandas: unique

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Airflow: hook

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Spark: pyspark

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DBMS: trigger

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Elasticsearch: logstash

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Cassandra: write heavy

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DataViz: trend line

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DynamoDB: conditional write

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Hadoop: small file problem

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MySQL: wildcard mysql

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Neo4j: graph vs relational

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Truncate vs delete

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DataEng: grain

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MongoDB: replica set

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Redis: replication

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PostgreSQL: role

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Kafka: dead letter

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Pandas: mean col

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Airflow: templating

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Spark: spark scala

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DBMS: query optimizer

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Elasticsearch: beats

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Cassandra: time series cassandra

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DataViz: outlier viz

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DynamoDB: optimistic locking dynamo

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Hadoop: parquet hadoop

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MySQL: subquery

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Neo4j: bolt

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Any single char

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DataEng: idempotent load

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MongoDB: primary

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Redis: Sentinel

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PostgreSQL: grant

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Kafka: throughput scaling

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Pandas: sum col

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Airflow: ds macro

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Spark: coalesce

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DBMS: execution plan

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Elasticsearch: elk

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Cassandra: secondary index

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DataViz: distribution viz

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DynamoDB: batch dynamo

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Hadoop: avro hadoop

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MySQL: view mysql

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Neo4j: browser

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Aggregate + non-agg

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DataEng: orchestration

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MongoDB: secondary reads

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Redis: Cluster

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PostgreSQL: row level security

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Kafka: partition count tradeoff

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