Apache Phoenix vs. Apache Spark vs. ThinkSQL vs. dbForge Data Compare for PostgreSQL Comparison Chart

Apache Spark™ is a unified analytics engine for large-scale data processing. Apache Spark achieves high performance for both batch and streaming data, using a state-of-the-art DAG scheduler, a query optimizer, and a physical execution engine. Spark offers over 80 high-level operators that make it easy to build parallel apps. And you can use it interactively from the Scala, Python, R, and SQL shells. Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application. Spark runs on Hadoop, Apache Mesos, Kubernetes, standalone, or in the cloud. It can access diverse data sources. You can run Spark using its standalone cluster mode, on EC2, on Hadoop YARN, on Mesos, or on Kubernetes. Access data in HDFS, Alluxio, Apache Cassandra, Apache HBase, Apache Hive, and hundreds of other data sources.

dbForge Data Compare for PostgreSQL is a GUI tool for table data comparison and synchronization. It helps to custom-tailor the detection of data differences between two PostgreSQL tables, establish smart management of inconsistencies and safe deployment of changes. This PostgreSQL diff tool also delivers the generation of the PostgreSQL data synchronization scripts with multiple options to keep your PostgreSQL data in sync. Key Features: Accelerate routine tasks with integrated AI Assistant Compare, analyze and synchronize data in PostgreSQL databases Generate SQL scripts to migrate content between databases Copy lookup data from development databases to staging or production Keep an accurate history of all previous PostgreSQL database records Restore missing or damaged data to a single row Automate routine synchronization tasks from the command line Operate with the latest versions of PostgreSQL and Amazon Redshift Connect to IBM Cloud (via SSL) and Amazon Aurora