Querybook vs Apache Spark
Overview
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CLI (Node.js)
Manual
Detailed Comparison
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. | It is a Big Data IDE that allows you to discover, create, and share data analyses, queries, and tables. It's core focus is to make composing queries, creating analyses, and collaborating with others as simple as possible. |
Run programs up to 100x faster than Hadoop MapReduce in memory, or 10x faster on disk;Write applications quickly in Java, Scala or Python;Combine SQL, streaming, and complex analytics;Spark runs on Hadoop, Mesos, standalone, or in the cloud. It can access diverse data sources including HDFS, Cassandra, HBase, S3 | Organize analyses with rich text, queries, and charts;
Compose queries with autocompletion and hovering tooltip;
Use scheduling + charting in DataDocs to build dashboards;
Live query collaborations with others;
Add additional documentation to your tables;
Get lineage, sample queries, frequent user, search ranking based on past query runs |
Statistics | |
GitHub Stars 42.2K | GitHub Stars 2.2K |
GitHub Forks 28.9K | GitHub Forks 277 |
Stacks 3.1K | Stacks 4 |
Followers 3.5K | Followers 19 |
Votes 140 | Votes 0 |
Pros & Cons | |
Pros
Cons
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Integrations | |
| No integrations available | |
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