Is ACID maintained in Greenplum?
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Data Engineer
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Data Vault is a convenient thing, but there are quite a few objects. Can you explain the difference between hubs, links, and satellites?
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What processes are triggered when deleting or changing records in ClickHouse?
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You work with Data Science and big data processing — is there a replacement in this library written in Rust, multithreaded, unlike Pandas, very fast. Do you know such?
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How do you break down a query into stages? What do we do for that?
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How does CTE differ from a subquery?
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Parquet, you uploaded data — what technologies were used to receive and upload the data, and what mechanisms were employed?
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With dependencies between jobs and DAGs — did you use sensors to run several jobs one after another?
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You uploaded data from Spark to S3 in Parquet format, and then from Parquet to Greenplum — how does the loading process occur?
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Have you worked with Git? What did you store in Git?
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Did you usually design the database schema manually in the database, or did you store scripts somewhere, or did you use Liquibase at all, what was the process?
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How was data from an external table loaded into target tables?
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Did you write Airflow DAGs and jobs manually or using templates?
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Have you heard of Iceberg working with streaming? It's a trashy file storage.
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If CTE is used only once in a query, does it make sense to use it?
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This is real-time, how to implement some kind of connection between Kafka and ClickHouse Spark?
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Have you worked directly with Spark? What is its disadvantage?
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We will parse JSON — who will parse JSON? As far as I know, ClickHouse does not have functions for this.
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We need a pipeline engine — a mechanism that allows very fast stream creation by providing contracts and parameters as input. How would you implement this at a high level?
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Iceberg is an engine that allows you to turn S3 into a database, and it even complies with ACID. What is its downside?
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