Apache Parquet vs Event Store

Overview

Apache Parquet

Stacks97

Followers190

Votes0

Event Store

Stacks69

Followers82

Votes1

Apache Parquet vs Event Store: What are the differences?

Data Organization: Apache Parquet is columnar storage that focuses on efficient data compression and encoding, making it ideal for analytics workloads. On the other hand, Event Store is a time series database that stores events in an append-only log, providing efficient writes and reads for event sourcing scenarios.
Query Performance: Apache Parquet provides excellent read performance due to its columnar storage format, enabling efficient data scanning and retrieval. In contrast, Event Store is optimized for event-based queries and time-series data analysis, offering fast access to specific events and historical data.
Data Model: Apache Parquet follows a traditional table-based structure with columns and rows, making it suitable for SQL-like queries and analysis. In contrast, Event Store stores data as streams of events, capturing the sequence and context of changes over time, making it ideal for event sourcing and event-driven architectures.
Use Cases: Apache Parquet is commonly used for data warehousing, analytics, and big data processing tasks where efficient data storage and retrieval are essential. Event Store, on the other hand, is preferred for event-driven architectures, time-series data analysis, IoT applications, and event sourcing scenarios.
Scalability: Apache Parquet provides scalability through distributed processing frameworks like Apache Spark, enabling parallel processing of data across large clusters. Event Store offers horizontal scalability by partitioning data across multiple nodes to handle high volumes of event streams and time-series data efficiently.
Storage Format: Apache Parquet stores data in a columnar format, which minimizes I/O operations and improves query performance by reading only the necessary columns. Event Store uses an append-only log structure, ensuring data integrity and immutability while enabling efficient writes and sequential reads.

In Summary, Apache Parquet excels in columnar storage and analytics workloads, while Event Store is optimized for event sourcing, time-series data, and event-driven architectures.

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Detailed Comparison

Apache Parquet	Event Store
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.	It stores your data as a series of immutable events over time, making it easy to build event-sourced applications. It can run as a cluster of nodes containing the same data, which remains available for writes provided at least half the nodes are alive and connected.
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	Guaranteed writes; High availability; Projections; Multiple client interfaces; Optimistic concurrency checks; Subscribe to streams with competing consumers; Great performance that scales; Multiple hosting options; Commercial support plans; Immutable data store; Atom subscriptions
Statistics
Stacks 97	Stacks 69
Followers 190	Followers 82
Votes 0	Votes 1
Pros & Cons
No community feedback yet	Pros 1 Trail Log
Integrations
Hadoop Java Apache Impala Apache Thrift Apache Hive Pig	.NET SQLite MySQL

What are some alternatives to Apache Parquet, Event Store?

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

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

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

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 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

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 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.

ArangoDB

A distributed free and open-source database with a flexible data model for documents, graphs, and key-values. Build high performance applications using a convenient SQL-like query language or JavaScript extensions.

Related Comparisons

Apache Parquet vs Event Store: What are the differences?

Data Organization: Apache Parquet is columnar storage that focuses on efficient data compression and encoding, making it ideal for analytics workloads. On the other hand, Event Store is a time series database that stores events in an append-only log, providing efficient writes and reads for event sourcing scenarios.
Query Performance: Apache Parquet provides excellent read performance due to its columnar storage format, enabling efficient data scanning and retrieval. In contrast, Event Store is optimized for event-based queries and time-series data analysis, offering fast access to specific events and historical data.
Data Model: Apache Parquet follows a traditional table-based structure with columns and rows, making it suitable for SQL-like queries and analysis. In contrast, Event Store stores data as streams of events, capturing the sequence and context of changes over time, making it ideal for event sourcing and event-driven architectures.
Use Cases: Apache Parquet is commonly used for data warehousing, analytics, and big data processing tasks where efficient data storage and retrieval are essential. Event Store, on the other hand, is preferred for event-driven architectures, time-series data analysis, IoT applications, and event sourcing scenarios.
Scalability: Apache Parquet provides scalability through distributed processing frameworks like Apache Spark, enabling parallel processing of data across large clusters. Event Store offers horizontal scalability by partitioning data across multiple nodes to handle high volumes of event streams and time-series data efficiently.
Storage Format: Apache Parquet stores data in a columnar format, which minimizes I/O operations and improves query performance by reading only the necessary columns. Event Store uses an append-only log structure, ensuring data integrity and immutability while enabling efficient writes and sequential reads.

In Summary, Apache Parquet excels in columnar storage and analytics workloads, while Event Store is optimized for event sourcing, time-series data, and event-driven architectures.

Apache Parquet vs Event Store

Overview