Existing files of the same name will be overwritten. The file name will be the feature type name. parquet file to the folder for each feature type. The writer operates on a folder and will write a single. The Parquet writer writes all the attributes of a feature to a Parquet dataset. If FME detects latitude and longitude column names (for example, Latitude/Longitude or Lat/Long), the source coordinate system will be set to LL-WGS84. If a schema scan is performed and field labels contain variations of x/y, east/north, or easting/northing, FME will create the point geometry. This data may not necessarily have a spatial component, but columns can be identified as x, y, or z coordinates to create point geometries. Latitude/Longitude and x, y, z coordinatesįME automatically recognizes some common attribute names as potential x,y,z coordinates and sets their types. The writer will write a single file per feature type, but data can be partitioned using feature type fanout.Ī dataset has only one reader feature type. The reader operates on a single *.parquet file, but multiple files making up a partitioned dataset can be selected. However, Bulk Mode bridges the gap between columnar storage and row-based access in FME. While Parquet is a columnar format, the Parquet reader produces a feature for each row in the dataset. To write to this dataset, you would select the "customers" folder as the format dataset. For example, a Parquet dataset of customer information, partitioned by account type, might look like this: More about FME Desktop Editions and Licenses.Ī Parquet dataset consists of multiple *.parquet files in a folder, potentially nested into partitions by attribute.More about FME Licenses and Subscriptions.Parquet Product and System RequirementsĪvailable in FME Professional Edition and higher This format, the Apache Parquet Writer, is released with FME, and has full bulk-mode functionality. The package format is an easy way to add the Parquet format to an existing FME installation. Apache Parquet Reader/Writer (Technology Preview) (FME Desktop Package).Note that this format differs from the Apache Parquet FME package format: Parquet is additionally supported by several large-scale query providers, such as Amazon AWS Athena, Google Cloud BigQuery, and Microsoft Azure Data Lake Analytics. It is supported by many Apache big data frameworks, such as Drill, Hive, and Spark. It can be queried efficiently, is highly compressed, supports null values, and is non-spatial. (if you want to follow along I used a sample file from GitHub: ) import pandas as pd #import the pandas library parquet_file = 'location\to\file\example_pa.parquet' pd.Apache Parquet is a columnar, file-based storage format, originating in the Apache Hadoop ecosystem. Now we can write a few lines of Python code to read Parquet. Now we have all the prerequisites required to read the Parquet format in Python. It will be the engine used by Pandas to read the Parquet file. It is a development platform for in-memory analytics. We are then going to install Apache Arrow with pip. Within your virtual environment in Python, in either terminal or command line: pip install pandas This version of Python that was used for me is Python 3.6.įirst, we are going to need to install the ‘Pandas’ library in Python. To follow along all you need is a base version of Python to be installed. It can easily be done on a single desktop computer or laptop if you have Python installed without the need for Spark and Hadoop. This walkthrough will cover how to read Parquet data in Python without then need to spin up a cloud computing cluster. If you want to keep up in the data world, you're going to want to learn how to read with Python. Many cloud computing services already support Parquet such as AWS Athena, Amazon Redshift Spectrum, Google BigQuery and Google Dataproc. Parquet is growing in popularity as a format in the big data world as it allows for faster query run time, it is smaller in size and requires fewer data to be scanned compared to formats such as CSV. Parquet is an open-sourced columnar storage format created by the Apache software foundation.
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