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

Delta Lake

78
281
+ 1
0
Apache Spark

2.8K
3.2K
+ 1
139
Add tool

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.

Advice on Delta Lake and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 363.4K 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.

See more
Replies (2)
Recommends
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.

See more
Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 235.5K views
Recommends
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"

See more
Get Advice from developers at your company using StackShare Enterprise. Sign up for StackShare Enterprise.
Learn More
Pros of Delta Lake
Pros of Apache Spark
    Be the first to leave a pro
    • 60
      Open-source
    • 48
      Fast and Flexible
    • 8
      Great for distributed SQL like applications
    • 8
      One platform for every big data problem
    • 6
      Easy to install and to use
    • 3
      Works well for most Datascience usecases
    • 2
      Interactive Query
    • 2
      In memory Computation
    • 2
      Machine learning libratimery, Streaming in real

    Sign up to add or upvote prosMake informed product decisions

    Cons of Delta Lake
    Cons of Apache Spark
      Be the first to leave a con
      • 3
        Speed

      Sign up to add or upvote consMake informed product decisions

      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!

      Jobs that mention Delta Lake and Apache Spark as a desired skillset
      CBRE
      United Kingdom of Great Britain and Northern Ireland England Feltham
      CBRE
      United States of America Texas Richardson
      CBRE
      Philippines National Capital Region Makati City
      CBRE
      United States of America Texas Richardson
      What companies use Delta Lake?
      What companies use Apache Spark?
      See which teams inside your own company are using Delta Lake or Apache Spark.
      Sign up for StackShare EnterpriseLearn More

      Sign up to get full access to all the companiesMake informed product decisions

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

      Sign up to get full access to all the tool integrationsMake informed product decisions

      Blog Posts

      Mar 24 2021 at 12:57PM

      Pinterest

      GitJenkinsKafka+7
      3
      1847
      MySQLKafkaApache Spark+6
      2
      1809
      Aug 28 2019 at 3:10AM

      Segment

      PythonJavaAmazon S3+16
      7
      2341
      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.
      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 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.
      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