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Delta Lake

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181
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0
Apache Spark

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2.3K
+ 1
131
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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 Spark™ 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
    Be the first to leave a pro
    • 58
      Open-source
    • 47
      Fast and Flexible
    • 7
      One platform for every big data problem
    • 6
      Easy to install and to use
    • 6
      Great for distributed SQL like applications
    • 3
      Works well for most Datascience usecases
    • 2
      Machine learning libratimery, Streaming in real
    • 2
      In memory Computation
    • 0
      Interactive Query

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    Cons of Delta Lake
    Cons of Apache Spark
      Be the first to leave a con
      • 2
        Speed

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      What is Delta Lake?

      An open-source storage layer that brings ACID transactions to Apache Spark™ and big data workloads.

      What is Apache Spark?

      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.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use Delta Lake?
      What companies use Apache Spark?

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      What tools integrate with Delta Lake?
      What tools integrate with Apache Spark?

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      What are some alternatives to Delta Lake and Apache Spark?
      Snowflake
      Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.
      Splunk
      It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.
      Apache Flink
      Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.
      Amazon Athena
      Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
      Apache Hive
      Hive facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage.
      See all alternatives
      Interest over time