Background Handling Hive/CSV interaction is a common reality of many analytical and data environments. The question on exporting data from Hive to CSV and other formats is frequently raised on online forums with answers frequently suggesting making use of sed that combined with nifty regular expressions pipes Hive output into a flat CSV files as an exporting solution. Import of large amounts of data is best handled by suitable tools like Apache Flume....

Recently, I cam across an interesting discussion on StackOverflow^[SO discussion on: Fuzzy Join with Partial String Match in R] pertaining to approach to fuzzy matching tables in R. Good answer contributed by one of the most resilient and excellent contributors to whom I owe a lot of thanks for help suggested relying on regular expression, combining this with basic sting removal and transformations like toupper to deterministically match the tables. The solution solved the problem and was accepted....

Problem Solving challenges on project Euler or HackerRank is a good past time. For folks working in the wider analaytical / data science field, places like project Euler provide an excellent opportunity to work with academic programming concepts that do not frequently appear in real-life. I was looking at common problem: You are given an unordered array consisting of consecutive integers [1, 2, 3, …, n] without any duplicates. You are allowed to swap any two elements....

Challenge File manipulation is a frequent task unavoidable in almost every IT business process. Traditionally, file manipulation tasks are accomplished within the ramifications of specific tools native to a given system. As such, the one may consider writing and scheduling shell script to undertake frequent file operations or using more specific purpose-built tools like logrotate in order to archive logs or tools like Kafka are used to build streaming-data pipelines. R is usually though of as a statistical programming language or as an environment for a statistical analysis....

Rationale Maintaining partitioned Hive tables is a frequent practice in a business. Properly structured tables are conducive to achieving robust performance through speeding up query execution (see Costa, Costa, and Santos 2019). Frequent use cases pertain to creating tables with hierarchical partition structure. In context of a data that is refreshed daily, the frequently utilised partition structure reflects years, months and dates. Creating partitioned table In HiveQL we would create the table with the following structure using the syntax below....