Greenplum Database VS Hadoop

Greenplum Database Reviews

Hadoop Reviews

Greenplum Database mentions (4)

• Ask HN: It's 2023, how do you choose between MySQL and Postgres?

  Friends don't let their friends choose Mysql :) A super long time ago (decades) when I was using Oracle regularly I had to make a decision on which way to go. Although Mysql then had the mindshare I thought that Postgres was more similar to Oracle, more standards compliant, and more of a real enterprise type of DB. The rumor was also that Postgres was heavier than MySQL. Too many horror stories of lost data... - Source: Hacker News / about 1 year ago

• Amazon Aurora's Read/Write Capability Enhancement with Apache ShardingSphere-Proxy

  A database solution architect at AWS, with over 10 years of experience in the database industry. Lili has been involved in the R&D of the Hadoop/Hive NoSQL database, enterprise-level database DB2, distributed data warehouse Greenplum/Apache HAWQ and Amazon’s cloud native database. - Source: dev.to / almost 2 years ago

• What’s the Database Plus concept and what challenges can it solve?

  Today, it is normal for enterprises to leverage diversified databases. In my market of expertise, China, in the Internet industry, MySQL together with data sharding middleware is the go to architecture, with GreenPlum, HBase, Elasticsearch, Clickhouse and other big data ecosystems being auxiliary computing engine for analytical data. At the same time, some legacy systems (such as SQLServer legacy from .NET... - Source: dev.to / about 2 years ago

• Inspecting joins in PostgreSQL

  PostgreSQL is a free and advanced database system with the capacity to handle a lot of data. It’s available for very large data in several forms like Greenplum and Redshift on Amazon. It is open source and is managed by an organized and very principled community. - Source: dev.to / over 2 years ago

Hadoop mentions (15)

• Getting thousands of files of output back from a container

  Did you check out tools like https://hadoop.apache.org/ ? Source: about 1 year ago

• 5 Best Practices For Data Integration To Boost ROI And Efficiency

  There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago

• Data Engineering and DataOps: A Beginner's Guide to Building Data Solutions and Solving Real-World Challenges

  There are several frameworks available for batch processing, such as Hadoop, Apache Storm, and DataTorrent RTS. - Source: dev.to / over 1 year ago

• Effortlessly Set Up a Hadoop Multi-Node Cluster on Windows Machines with Our Step-by-Step Guide

  A copy of Hadoop installed on each of these machines. You can download Hadoop from the Apache website, or you can use a distribution like Cloudera or Hortonworks. - Source: dev.to / over 1 year ago

• In One Minute : Hadoop

  The Apache™ Hadoop™ project develops open-source software for reliable, scalable, distributed computing. - Source: dev.to / over 1 year ago