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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.

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++, Python, etc.

MongoDB is an open source document-oriented database system. It is part of the NoSQL family of database systems. Instead of storing data in tables as is done in a "classical" relational database, MongoDB stores structured data as JSON-like documents with dynamic schemas (MongoDB calls the format BSON), making the integration of data in certain types of applications easier and faster.

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MarkLogic Server is a multi-model database that has both NoSQL and trusted enterprise data management capabilities. The vendor states it is the most secure multi-model database, and it’s deployable in any environment. They state it is an ideal database to power a data hub.

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Apache Parquet

Some cloud platforms offer options for using a database as a service, without physically launching a virtual machine instance for the database. In this configuration, application owners do not have to install and maintain the database on their own. Instead, the database service provider takes responsibility for installing and maintaining the database, and application owners pay according to their usage. For example, Amazon Web Services provides three database services as part of its cloud offering, SimpleDB, a NoSQL key-value store, Amazon Relational Database Service, an SQL-based database service with a MySQL interface, and DynamoDB.

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Apache Parquet demo

The Apache Software Foundation provides support for the Apache community of open-source software projects. The Apache projects are characterized by a collaborative, consensus based development process, an open and pragmatic software license, and a desire to create high quality software that leads the way in its field.

We consider ourselves not simply a group of projects sharing a server, but rather a community of developers and users.

Apache Parquet: Parquet file internals and inspecting Parquet file structure

In this video we will look at the inernal structure of the Apache Parquet storage format and will use the Parquet-tool to inspect the contents of the file. Apache Parquet is a columnar storage format available in the Hadoop ecosystem

Related videos:
Creating Parquet files using Apache Spark: https://youtu.be/-ra0pGUw7fo
Parquet vs Avro: https://youtu.be/sLuHzdMGFNA

Apache Parquet  Explained in 5 minutes

#Data #ApacheParquet #GCP #PySpark #Dataproc
What is Apache Parquet ? When it can be used ? and how to convert CSV to Parquet using PySpark job with cloud Dataproc on GCP

Github link for pyspark job :https://github.com/gitrekm/ParquetConverter
Experiments source: https://dzone.com/articles/how-to-be-a-hero-with-powerful-parquet-google-and

NoSQL databases are designed to be used across large distrusted systems. They are notably much more scalable and much faster and handling very large data loads than traditional relational databases.

Performance

How fast the database performs under data load

Availability

Availability is the probability that the NoSQL database will be available to preform its function when called upon.

Concurrency

Concurrency is the ability for multiple processes to access or change shared data simultaneously. The greater the number of concurrent user processes that can execute without blocking each other, the greater the concurrency of the database system.

Security features include authentication against external security mechanisms liker LDAP, Windows Active Directory, and authorization or privilege management. Some NoSQL databases also support encryption.

Scalability

NoSQL databases are inherently more stable than relational databases and have built-in support for replication and partitioning of data to support scalability.

Data model flexibility

NoSQL databases do not rely on rely on tables, columns, rows, or schemas to organize and retrieve data,  but use use more flexible data models to accommodate the large volume and variety of data being generated by modern applications.

Deployment model flexibility

Can be deployed on-premise or in the cloud.

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Apache Parquet

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What is Apache Parquet?