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Apache Spark vs Kylo: What are the differences?

Developers describe Apache Spark as "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. On the other hand, Kylo is detailed as "Open-source data lake management software platform". It is an open source enterprise-ready data lake management software platform for self-service data ingest and data preparation with integrated metadata management, governance, security and best practices inspired by Think Big's 150+ big data implementation projects.

Apache Spark and Kylo 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, Kylo provides the following key features:

  • Self-service data ingest with data cleansing, validation, and automatic profiling
  • Wrangle data with visual sql and an interactive transform through a simple user interface
  • Search and explore data and metadata, view lineage, and profile statistics

Apache Spark and Kylo are both open source tools. It seems that Apache Spark with 24.2K GitHub stars and 20.5K forks on GitHub has more adoption than Kylo with 744 GitHub stars and 358 GitHub forks.

Advice on Kylo and Apache Spark
Nilesh Akhade
Technical Architect at Self Employed · | 5 upvotes · 299.3K 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)
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.

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Akshaya Rawat
Senior Specialist Platform at Publicis Sapient · | 3 upvotes · 190.2K 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"

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Pros of Kylo
Pros of Apache Spark
    Be the first to leave a pro
    • 59
      Open-source
    • 48
      Fast and Flexible
    • 8
      One platform for every big data problem
    • 7
      Great for distributed SQL like applications
    • 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 Kylo
    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 Kylo?

      It is an open source enterprise-ready data lake management software platform for self-service data ingest and data preparation with integrated metadata management, governance, security and best practices inspired by Think Big's 150+ big data implementation projects.

      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 Kylo?
      What companies use Apache Spark?
      See which teams inside your own company are using Kylo or Apache Spark.
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      What tools integrate with Kylo?
      What tools integrate with Apache Spark?

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      Mar 24 2021 at 12:57PM

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      What are some alternatives to Kylo and Apache Spark?
      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.
      Presto
      Distributed SQL Query Engine for Big Data
      See all alternatives