What is MapD?
Interactively query and visualize massive datasets with the parallel power of GPUs.
MapD is a tool in the Databases category of a tech stack.
MapD is an open source tool with 2.1K GitHub stars and 288 GitHub forks. Here’s a link to MapD's open source repository on GitHub
MySQL, PostgreSQL, Amazon S3, Kafka, and Microsoft SQL Server are some of the popular tools that integrate with MapD. Here's a list of all 11 tools that integrate with MapD.
Why developers like MapD?
Here’s a list of reasons why companies and developers use MapD
- column store
- interactive visualization
- machine learning
MapD Alternatives & Comparisons
What are some alternatives to MapD?
See all alternatives
Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
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 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.
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.