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Apache Parquet has proven to be immensely valuable for users in solving various business problems related to handling and analyzing large …
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What is Apache Parquet?
Apache Parquet is an open source and free, column-oriented data file format designed for efficient data storage and retrieval. It provides data compression and encoding schemes with enhanced performance to handle complex data in bulk. Parquet is available in multiple…
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What is Apache Parquet?
Apache Parquet is an open source and free, column-oriented data file format designed for efficient data storage and retrieval. It provides data compression and encoding schemes with enhanced performance to handle complex data in bulk. Parquet is available in multiple languages including Java, C++,…
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Apache Parquet has proven to be immensely valuable for users in solving various business problems related to handling and analyzing large volumes of data. Users have been able to efficiently store and compress massive amounts of data using Apache Parquet, allowing them to handle big data workloads seamlessly. This capability has been particularly beneficial for organizations dealing with data-intensive tasks such as statistical analysis, machine learning, and ETL processes.
The combination of Apache Parquet with Apache Spark has provided users with faster querying capabilities on large datasets. This integration has enabled users to perform complex analytics tasks on their consolidated raw data, leading to more informed decision-making. Furthermore, reviewers highly recommend Apache Parquet as a columnar-store data solution, highlighting its effectiveness as an intermediate storage format in applications.
Users have successfully stored terabytes of data with Apache Parquet, including training data for production models. This has empowered them to run sophisticated statistical analysis and machine learning algorithms on their consolidated raw data, unlocking valuable insights. By simplifying the process of gathering and consolidating raw data, Apache Parquet has significantly accelerated project completion and allowed companies to keep up with the demands of big data analysis.
Additionally, Apache Parquet has been praised by users for its ability to build reliable and efficient Java resources for their data processing needs. This has streamlined the development process and ensured that critical business operations can be performed smoothly.
Overall, Apache Parquet addresses key pain points in the realm of big data analysis, providing robust support for storing and querying large datasets. Its exceptional compression capabilities and seamless integration with tools like Apache Spark make it an invaluable resource for organizations seeking powerful solutions for their data-driven challenges.
Efficient Compression and Predictive Push Down Capabilities: Users have praised the Parquet file format for its efficient compression and predictive push down capabilities, allowing them to select only the necessary columns. Several reviewers have mentioned that this feature significantly improves processing efficiency when working with large datasets in an ETL pipeline for Hadoop.
Easy Connectivity with Other Platforms: Reviewers appreciate that Parquet files are easy to connect with other platforms, simplifying data integration tasks. Compared to formats like JSON or CSV, users find it easier to load and work with data in Parquet files. This benefit has been highlighted by multiple customers who found it useful for their data handling needs.
User-Friendly Column-Store Data Structure: The user-friendly nature of the column-store data structure in Parquet is highly regarded by several reviewers. They consider it as the best columnar storage option for their Hadoop system. This characteristic not only enhances performance but also offers numerous benefits in terms of query execution speed and overall usability.
Inflexible Schema: Some users have expressed frustration with Apache Parquet's lack of support for frequently changing dataset schemas, leading them to seek alternative file formats. They have mentioned the need for more schemas available for different business solutions.
Steep Learning Curve and Limited Documentation: Multiple reviewers have mentioned that it took them a while to understand Apache Parquet due to its steep learning curve. Although they eventually found it great once they figured it out, they also noted that finding libraries supporting Parquet can be challenging.
Difficulties with Large Files: According to some users, loading from S3 becomes problematic when dealing with large files in Apache Parquet. This difficulty may hinder the smooth processing of data and impact overall performance.