Automatically tagging, captioning and categorising locally stored images using the Azure Computer Vision API

It’s easy in the digital age to amass tens of thousands of photos (or more!). Categorising these can be a challenging task, let alone searching through them to find that one happy snap from 10 years ago. Significant advances in machine learning over the past decade have made it possible to automatically tag and categorise…

Useful queries for the Hive metastore

The Hive metastore stores metadata about objects within Hive.  Usually this metastore sits within a relational database such as MySQL. Sometimes it’s useful to query the Hive metastore directly to find out what databases, tables and views exist in Hive and how they’re defined. For example, say we want to expose a report to users…

Python + JDBC = Dynamic Hive scripting

Working with Hive can be challenging without the benefit of a procedural language (such as T-SQL or PL/SQL) in order to do things with data in between Hive statements or run dynamic hive statements in bulk.  For example – we may want to do a rowcount of all tables in one of our Hive databases,…

Useful date formulas for Hive

Hive comes with some handy functions for transforming dates.  These can be helpful when working with date dimension tables and performing time-based comparisons and aggregations. e.g. Convert a native Hive date formatted date string: date_format(myDate,’dd-MM-yyyy’) Return the week number (within the year) of a particular date – i.e. first week of the year is 1,…

Finding a sequence of events in Hive using analytic functions

Hadoop Hive features several useful functions for efficiently performing analytics over ordered sets of rows — these are known as the windowing and analytics functions.  For example, lead and lag functions can be used to produce rolling averages or cumulative sums over a window of time (e.g. hourly averages for some metric over the preceeding…

Using HDFS path and filename as columns in a Hive table

A handy feature of Hadoop Hive is the ability to use the filename and path of underlying files as columns in a view or table using the virtual Hive column INPUT__FILE__NAME.  This is particularly handy in the case of external tables where some metadata about files is embedded in the location on HDFS or the…

Creating a custom Hive input format and record reader to read fixed format flat files

Apache Hive is great for enabling SQL-like queryability over flat files.  This is trivial in the case of tabular formatted files such as CSV files where we can set custom row and field delimiters out-of-the-box (e.g. Hive’s inbuilt CSV serde).  Even more complex files can be read and converted to a desired row and column…