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Large-Scale Data Engineering in Cloud

Performance Tuning, Cost Optimization / Internals, Research by Dmitry Tolpeko

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  • ETL,  Hive,  Presto

    Presto vs Hive – SLA Risks for Long Running ETL – Failures and Retries Due to Node Loss

    December 4, 2019

    Presto is an extremely powerful distributed SQL query engine, so at some point you may consider using it to replace SQL-based ETL processes that you currently run on Apache Hive.

    Although it is completely possible, you should be aware of some limitations that may affect your SLAs.

    Read More
    dmtolpeko
  • ETL,  Snowflake

    Snowflake – Reloading Data from Stage – TRUNCATE, DELETE, COPY and Transactions

    May 6, 2019

    Sometimes you need to reload the entire data set from the source storage into Snowflake. For example, you may want to fully refresh a quite large lookup table (2 GB compressed) without keeping the history. Let’s see how to do this in Snowflake and what issues you need to take into account.

    Read More
    dmtolpeko

Recent Posts

  • Nov 26, 2023 ORDER BY in Spark – How Global Sort Is Implemented, Sampling, Range Rartitioning and Skew
  • Oct 25, 2023 Reading JSON in Spark – Full Read for Inferring Schema and Sampling, SamplingRatio Option Implementation and Issues
  • Oct 15, 2023 Distributed COUNT DISTINCT – How it Works in Spark, Multiple COUNT DISTINCT, Transform to COUNT with Expand, Exploded Shuffle, Partial Aggregations
  • Oct 10, 2023 Spark – Reading Parquet – Pushed Filters, SUBSTR(timestamp, 1, 10), LIKE and StringStartsWith
  • Oct 06, 2023 Spark Stage Restarts – Partial Restarts, Multiple Retry Attempts with Different Task Sets, Accepted Late Results from Failed Stages, Cost of Restarts

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