MPP databases Insights

What is MPP databases?

MPP (Massively Parallel Processing) databases are a type of parallel processing architecture where the entire database runs across multiple processors. MPP systems typically employ a "shared nothing" architecture, where each processor has its own dedicated memory, storage, and operating system. MPP databases are commonly used for data warehousing and business intelligence applications, where large volumes of data need to be processed and analyzed quickly.

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What other technologies are related to MPP databases?

MPP databases Competitor Technologies

AWS Redshift

AWS Redshift is a fully managed, petabyte-scale data warehouse service in the cloud, directly competing with other MPP databases like Snowflake.

mentioned alongside MPP databases in 0% (53) of relevant job posts

Snowflake

Snowflake is a cloud-based data warehouse built on MPP architecture, making it a direct competitor to other MPP databases such as AWS Redshift.

mentioned alongside MPP databases in 0% (72) of relevant job posts

MPP databases Complementary Technologies

Spark

Spark can be used for data processing and transformation before loading data into an MPP database or for querying data in object storage accessed by MPP databases.

mentioned alongside MPP databases in 0% (112) of relevant job posts

Databricks

Databricks, powered by Spark, provides a platform for data engineering and analytics that complements MPP databases. It can be used for ETL and feature engineering tasks before loading data into MPP systems.

mentioned alongside MPP databases in 0% (73) of relevant job posts

Hadoop

Hadoop, specifically HDFS and MapReduce, can serve as a data source for MPP databases, providing a cost-effective storage layer for large datasets that can be ingested into MPP systems for analysis. However, its use is diminishing in favor of cloud object stores.

mentioned alongside MPP databases in 0% (85) of relevant job posts