Apache Spark vs Dremio: What are the differences?
Apache Spark: Fast and general engine for large-scale data processing. 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; Dremio: Self-service data for everyone. It is a data-as-a-service platform that empowers users to discover, curate, accelerate, and share any data at any time, regardless of location, volume, or structure. Modern data is managed by a wide range of technologies, including relational databases, NoSQL datastores, file systems, Hadoop, and others.
Apache Spark and Dremio can be categorized as "Big Data" tools.
Some of the features offered by Apache Spark are:
- 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
On the other hand, Dremio provides the following key features:
- Democratize all your data
- Make your data engineers more productive
- Accelerate your favorite tools
Apache Spark is an open source tool with 23K GitHub stars and 19.8K GitHub forks. Here's a link to Apache Spark's open source repository on GitHub.
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