Interzoid's Cloud Data Connect Matching Wizard and our Full Dataset APIs now support Parquet files. In a few simple clicks (or an API call) you can discover data inconsistency issues, data matching issues, and other data quality anomalies present in a Parquet file, the same way you can with CSV text files and Excel spreadsheets, either with local files or those stored in the Cloud.
A Parquet file is a type of columnar storage file format extensively utilized in analytics, data lakes, AI, and other various data-intensive operations. Unlike traditional row-based storage, it organizes data by columns. This organization allows for reading only the specific columns required for a query, thereby bypassing unnecessary data scanning. Such column-focused querying significantly enhances performance, particularly with large datasets. Furthermore, Parquet supports parallel processing, enabling multiple threads to concurrently read column data, which can lead to substantial improvements in performance. Additionally, columnar storage is highly efficient in terms of compression. It achieves this by storing common values in a column once, rather than repeating them for each row, optimizing space usage.
A Parquet file is, at its core, a standalone file. This means it isn't tied to any specific database engine. As a result of this independence, it offers versatile storage options. You can store it in a local directory or utilize various Cloud storage services. This includes popular platforms like Amazon S3, Microsoft Azure Blob Storage, Google Cloud Storage, and other Cloud-based data storage solutions. This flexibility makes Parquet files adaptable for different storage scenarios, processing use cases, and application needs.
Parquet files make use of data types, including various traditional string and integer types, boolean, and floating point types, and more complex types such as structs, lists, and maps. These logical types allow Parquet to efficiently handle complex, real-world data in a way that is both space-efficient (due to its columnar nature) and expressive (due to the variety of data types supported). A schema is attached at the foot of each Parquet file that is accessible and provides the column data type mapping.
These advantages are why the use of Parquet files is growing rapidly across many industries and use cases.
For example, Parquet has become the de-facto file format in Spark for its speed and efficiency. Most organizations using HDFS, Hive, Impala, Spark etc for big data workloads now use Parquet extensively. Cloud data warehouses like Amazon Redshift, Google BigQuery, Databricks, and Snowflake leverage columnar formats like Parquet to optimize query performance and reduce cost. When pre-processing and storing raw, semi-structured data in Cloud data lakes (e.g. AWS S3), Parquet helps compress storage needs and speed up downstream processing. Streaming systems like Apache Kafka and Amazon Kinesis often use the Parquet format for persistence after running real-time computation. Also, Machine Learning pipeline workflows store training datasets and model output in Parquet given its wide language support. Its row groups allow partial file reads. Newer applications of AI, IoT, digital marketing, Web analytics etc find Parquet's flexibility and performance useful. Its adoption is thus expected to grow further alongside data analytics usage growth in general. Most modern data platforms have good integration support for the Parquet format.
However, Parquet provides nothing to help with the usability, consistency, redundancy, and efficiency of data content. All the speed and flexibility in the world is of little use if data quality is poor and suspect.
Sign up for a free-tier of Interzoid today for our Cloud Data Connect Wizard. Perform an analysis of a sample Parquet file we provide (or bring your own), and quickly and easily see if you have good data on your hands.
Want to learn more about Parquet? Click here for the Apache Parquet home page.
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