Best Columnar Databases for Mac of 2025

Sadas Engine is the fastest columnar database management system in cloud and on-premise. Sadas Engine is the solution that you are looking for. * Store * Manage * Analyze It takes a lot of data to find the right solution. * BI * DWH * Data Analytics The fastest columnar Database Management System can turn data into information. It is 100 times faster than transactional DBMSs, and can perform searches on large amounts of data for a period that lasts longer than 10 years.

When seeking a database that ensures both scalability and high availability without sacrificing performance, Apache Cassandra stands out as an ideal option. Its linear scalability paired with proven fault tolerance on standard hardware or cloud services positions it as an excellent choice for handling mission-critical data effectively. Additionally, Cassandra's superior capability to replicate data across several datacenters not only enhances user experience by reducing latency but also offers reassurance in the event of regional failures. This combination of features makes it a robust solution for organizations that prioritize data resilience and efficiency.

Greenplum Database® stands out as a sophisticated, comprehensive, and open-source data warehouse solution. It excels in providing swift and robust analytics on data volumes that reach petabyte scales. Designed specifically for big data analytics, Greenplum Database is driven by a highly advanced cost-based query optimizer that ensures exceptional performance for analytical queries on extensive data sets. This project operates under the Apache 2 license, and we extend our gratitude to all current contributors while inviting new ones to join our efforts. In the Greenplum Database community, every contribution is valued, regardless of its size, and we actively encourage diverse forms of involvement. This platform serves as an open-source, massively parallel data environment tailored for analytics, machine learning, and artificial intelligence applications. Users can swiftly develop and implement models aimed at tackling complex challenges in fields such as cybersecurity, predictive maintenance, risk management, and fraud detection, among others. Dive into the experience of a fully integrated, feature-rich open-source analytics platform that empowers innovation.

A Kudu cluster comprises tables that resemble those found in traditional relational (SQL) databases. These tables can range from a straightforward binary key and value structure to intricate designs featuring hundreds of strongly-typed attributes. Similar to SQL tables, each Kudu table is defined by a primary key, which consists of one or more columns; this could be a single unique user identifier or a composite key such as a (host, metric, timestamp) combination tailored for time-series data from machines. The primary key allows for quick reading, updating, or deletion of rows. The straightforward data model of Kudu facilitates the migration of legacy applications as well as the development of new ones, eliminating concerns about encoding data into binary formats or navigating through cumbersome JSON databases. Additionally, tables in Kudu are self-describing, enabling the use of standard analysis tools like SQL engines or Spark. With user-friendly APIs, Kudu ensures that developers can easily integrate and manipulate their data. This approach not only streamlines data management but also enhances overall efficiency in data processing tasks.

Parquet was developed to provide the benefits of efficient, compressed columnar data representation to all projects within the Hadoop ecosystem. Designed with a focus on accommodating complex nested data structures, Parquet employs the record shredding and assembly technique outlined in the Dremel paper, which we consider to be a more effective strategy than merely flattening nested namespaces. This format supports highly efficient compression and encoding methods, and various projects have shown the significant performance improvements that arise from utilizing appropriate compression and encoding strategies for their datasets. Furthermore, Parquet enables the specification of compression schemes at the column level, ensuring its adaptability for future developments in encoding technologies. It is crafted to be accessible for any user, as the Hadoop ecosystem comprises a diverse range of data processing frameworks, and we aim to remain neutral in our support for these different initiatives. Ultimately, our goal is to empower users with a flexible and robust tool that enhances their data management capabilities across various applications.