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Delta Lake vs Apache Spark: What are the differences?
What is Delta Lake? Reliable Data Lakes at Scale. An open-source storage layer that brings ACID transactions to Apache Sparkâ„¢ and big data workloads.
What is 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.
Delta Lake and Apache Spark can be categorized as "Big Data" tools.
Some of the features offered by Delta Lake are:
- ACID Transactions
- Scalable Metadata Handling
- Time Travel (data versioning)
On the other hand, Apache Spark provides the following key features:
- 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
Delta Lake and Apache Spark are both open source tools. Apache Spark with 22.5K GitHub stars and 19.4K forks on GitHub appears to be more popular than Delta Lake with 1.26K GitHub stars and 210 GitHub forks.
Pros of Delta Lake
Pros of Apache Spark
- Open-source58
- Fast and Flexible47
- One platform for every big data problem7
- Easy to install and to use6
- Great for distributed SQL like applications6
- Works well for most Datascience usecases3
- Machine learning libratimery, Streaming in real2
- In memory Computation2
- Interactive Query0
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Cons of Delta Lake
Cons of Apache Spark
- Speed2
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What is Delta Lake?
What is Apache Spark?
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What tools integrate with Apache Spark?
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