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  5. Apache Parquet vs AtScale

Apache Parquet vs AtScale

OverviewComparisonAlternatives

Overview

Apache Parquet
Apache Parquet
Stacks97
Followers190
Votes0
AtScale
AtScale
Stacks25
Followers83
Votes0

Apache Parquet vs AtScale: What are the differences?

<Apache Parquet vs. AtScale>

1. **File Format**: Apache Parquet is a columnar storage format, optimized for reading and writing large datasets efficiently, while AtScale is not a file format itself, but a platform that enables enterprises to work with multi-structure data.
   
2. **Data Processing**: Apache Parquet is suitable for running analytical queries on large volumes of data due to its optimized indexing and compression techniques, whereas AtScale focuses on providing a unified view of data across various sources, enabling users to query and analyze data without the need for data movement or transformation.

3. **Compatibility**: Apache Parquet is compatible with a wide range of data processing frameworks and tools like Apache Spark, Hive, Impala, etc., while AtScale integrates with business intelligence tools such as Tableau, Power BI, and Excel for interactive analysis and visualization.

4. **Storage Optimization**: Apache Parquet offers significant storage savings by utilizing techniques like dictionary encoding and run-length encoding, reducing the overall storage requirements for the data, whereas AtScale focuses more on data virtualization and providing a logical abstraction layer over underlying data sources.

5. **Data Governance**: Apache Parquet does not provide built-in data governance features, as it primarily focuses on performance and efficiency, while AtScale includes data governance capabilities, such as data lineage tracking and access control, to ensure data security and compliance.

6. **Deployment**: Apache Parquet is typically deployed as part of a data lake or data warehouse environment, where it serves as a storage format for structured data, whereas AtScale is deployed as a layer on top of existing data infrastructure to provide a semantic layer for unified data access and analysis.

In Summary, Apache Parquet and AtScale differ in terms of file format, data processing capabilities, compatibility with tools, storage optimization, data governance features, and deployment scenarios.

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Detailed Comparison

Apache Parquet
Apache Parquet
AtScale
AtScale

It is a columnar storage format available to any project in the Hadoop ecosystem, regardless of the choice of data processing framework, data model or programming language.

Its Virtual Data Warehouse delivers performance, security and agility to exceed the demands of modern-day operational analytics.

Columnar storage format;Type-specific encoding; Pig integration; Cascading integration; Crunch integration; Apache Arrow integration; Apache Scrooge integration;Adaptive dictionary encoding; Predicate pushdown; Column stats
Multiple SQL-on-Hadoop Engine Support; Access Data Where it Lays; Built-in Support for Complex Data Types; Single Drop-in Gateway Node Deployment
Statistics
Stacks
97
Stacks
25
Followers
190
Followers
83
Votes
0
Votes
0
Integrations
Hadoop
Hadoop
Java
Java
Apache Impala
Apache Impala
Apache Thrift
Apache Thrift
Apache Hive
Apache Hive
Pig
Pig
Python
Python
Amazon S3
Amazon S3
Tableau
Tableau
Power BI
Power BI
Qlik Sense
Qlik Sense
Azure Database for PostgreSQL
Azure Database for PostgreSQL

What are some alternatives to Apache Parquet, AtScale?

MongoDB

MongoDB

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

MySQL

MySQL

The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.

PostgreSQL

PostgreSQL

PostgreSQL is an advanced object-relational database management system that supports an extended subset of the SQL standard, including transactions, foreign keys, subqueries, triggers, user-defined types and functions.

Microsoft SQL Server

Microsoft SQL Server

Microsoft® SQL Server is a database management and analysis system for e-commerce, line-of-business, and data warehousing solutions.

SQLite

SQLite

SQLite is an embedded SQL database engine. Unlike most other SQL databases, SQLite does not have a separate server process. SQLite reads and writes directly to ordinary disk files. A complete SQL database with multiple tables, indices, triggers, and views, is contained in a single disk file.

Cassandra

Cassandra

Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.

Memcached

Memcached

Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.

MariaDB

MariaDB

Started by core members of the original MySQL team, MariaDB actively works with outside developers to deliver the most featureful, stable, and sanely licensed open SQL server in the industry. MariaDB is designed as a drop-in replacement of MySQL(R) with more features, new storage engines, fewer bugs, and better performance.

RethinkDB

RethinkDB

RethinkDB is built to store JSON documents, and scale to multiple machines with very little effort. It has a pleasant query language that supports really useful queries like table joins and group by, and is easy to setup and learn.

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

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