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Apache Spark vs Delta Lake: What are the differences?

Introduction

Apache Spark and Delta Lake are two popular big data technologies used for processing and analyzing large datasets. While both technologies are often used together, they have some key differences that set them apart.

  1. Data Storage and Processing: The key difference between Apache Spark and Delta Lake lies in their approach to data storage and processing. Apache Spark is primarily a distributed computing system that provides an interface for processing data in parallel. It supports various data formats and can process both batch and streaming data. On the other hand, Delta Lake is an open-source storage layer that operates on top of existing data lakes and provides ACID (Atomicity, Consistency, Isolation, Durability) transactions. It allows data engineers and data scientists to handle large-scale datasets with reliability and consistency.

  2. Data Consistency and Reliability: Delta Lake provides atomic writes and reads, as well as schema enforcement and schema evolution capabilities. This ensures that data is written and read in an all-or-nothing manner, making it easier to maintain data consistency and reliability. Apache Spark, on the other hand, does not provide built-in support for data consistency and reliability. Data engineers and developers need to implement custom logic to ensure data consistency and reliability.

  3. Data Versioning and Time Travel: Delta Lake is designed to provide built-in versioning and time travel capabilities. It allows users to query and access older versions of data, making it easier to track changes over time. In contrast, Apache Spark does not provide built-in support for data versioning and time travel. Data engineers need to implement custom logic or use additional tools to achieve similar functionality.

  4. Data Quality Management: Delta Lake includes features like schema validation and data quality checks, which help ensure that data remains consistent and of high quality. It provides mechanisms to enforce schema evolution rules and perform data validation during write operations. Apache Spark does not have built-in support for data quality management. Data engineers need to implement custom logic to validate and enforce data quality rules.

  5. Optimized Performance: Delta Lake optimizes data reads and writes by using various techniques like Z-ordering, data skipping, and caching. These optimizations improve query performance and reduce the amount of data scanned during read operations. Apache Spark also provides optimizations for data processing, but it does not have the same level of built-in optimization capabilities as Delta Lake.

  6. Ecosystem Integration: Apache Spark has a vibrant ecosystem and supports integration with various data processing and analytics tools. It provides connectors for different data sources and supports integration with popular frameworks like Apache Hadoop, Apache Kafka, and Apache Hive. Delta Lake, being a storage layer built on top of data lakes, can seamlessly integrate with Apache Spark and leverage its ecosystem.

In summary, Apache Spark is a distributed computing system primarily focused on data processing, while Delta Lake is a storage layer that provides reliability, consistency, and versioning capabilities on top of existing data lakes. Delta Lake's built-in support for data consistency, reliability, versioning, and data quality management sets it apart from Apache Spark.

Advice on Delta Lake and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 535K views

We have a Kafka topic having events of type A and type B. We need to perform an inner join on both type of events using some common field (primary-key). The joined events to be inserted in Elasticsearch.

In usual cases, type A and type B events (with same key) observed to be close upto 15 minutes. But in some cases they may be far from each other, lets say 6 hours. Sometimes event of either of the types never come.

In all cases, we should be able to find joined events instantly after they are joined and not-joined events within 15 minutes.

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Replies (2)
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ElasticsearchElasticsearch

The first solution that came to me is to use upsert to update ElasticSearch:

  1. Use the primary-key as ES document id
  2. Upsert the records to ES as soon as you receive them. As you are using upsert, the 2nd record of the same primary-key will not overwrite the 1st one, but will be merged with it.

Cons: The load on ES will be higher, due to upsert.

To use Flink:

  1. Create a KeyedDataStream by the primary-key
  2. In the ProcessFunction, save the first record in a State. At the same time, create a Timer for 15 minutes in the future
  3. When the 2nd record comes, read the 1st record from the State, merge those two, and send out the result, and clear the State and the Timer if it has not fired
  4. When the Timer fires, read the 1st record from the State and send out as the output record.
  5. Have a 2nd Timer of 6 hours (or more) if you are not using Windowing to clean up the State

Pro: if you have already having Flink ingesting this stream. Otherwise, I would just go with the 1st solution.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 376.4K views
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Apache SparkApache Spark

Please refer "Structured Streaming" feature of Spark. Refer "Stream - Stream Join" at https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#stream-stream-joins . In short you need to specify "Define watermark delays on both inputs" and "Define a constraint on time across the two inputs"

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Pros of Delta Lake
Pros of Apache Spark
    Be the first to leave a pro
    • 61
      Open-source
    • 48
      Fast and Flexible
    • 8
      One platform for every big data problem
    • 8
      Great for distributed SQL like applications
    • 6
      Easy to install and to use
    • 3
      Works well for most Datascience usecases
    • 2
      Interactive Query
    • 2
      Machine learning libratimery, Streaming in real
    • 2
      In memory Computation

    Sign up to add or upvote prosMake informed product decisions

    Cons of Delta Lake
    Cons of Apache Spark
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      • 4
        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.

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      What companies use Delta Lake?
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